MMA869Individual.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Wangmiaobeng/MMA_Share/blob/master/MMA869Individual.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "# MMA 869\n",
        "* **Machine Learning and AI**\n",
        "* **Dr. Stephen W. Thomas**\n",
        "* **Individual Assignment 1**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjFJVCNCCFQw",
        "colab_type": "text"
      },
      "source": [
        "# 1.\tHOW LOVELY!\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTznUTFVq8Zf",
        "colab_type": "text"
      },
      "source": [
        "[For this question, please answer the parts below in English, and also turn in your code. Your code should be submitted as a notebook – either a Python Jupyter Notebook (.ipynb) or an R Markdown file (.Rmd), along with a PDF rendering of the notebook. Make your code completely reproducible. E.g., set random number seeds.]\n",
        "You work at a local jewelry store. You’ve recently been promoted and the store owner asked you to better understand your customers. Using some sneaky magic (and the help of Environics!), you’ve managed to collect some useful features for a subset of your customers: age, income, spending score, and savings. Use these to segment your customers and create customer personas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uyvTH12HGek",
        "colab_type": "text"
      },
      "source": [
        "## 1.1 Coding Part (You can skip this and check the answers below)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxCIODszDgD6",
        "colab_type": "text"
      },
      "source": [
        "#### Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXTVW1RKBKHJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics import silhouette_score, silhouette_samples\n",
        "import sklearn.metrics\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
        "from sklearn.mixture import GaussianMixture\n",
        "\n",
        "from yellowbrick.cluster import (\n",
        "    SilhouetteVisualizer,\n",
        "    InterclusterDistance,\n",
        "    KElbowVisualizer,\n",
        ")\n",
        "\n",
        "import itertools\n",
        "\n",
        "import scipy\n",
        "\n",
        "import pandas_profiling\n",
        "\n",
        "# The following is just to change the display settings.\n",
        "InteractiveShell.ast_node_interactivity = \"all\"\n",
        "pd.set_option(\"display.max_colwidth\", 200)\n",
        "pd.options.display.max_columns = None\n",
        "\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjbJBCgBpXsc",
        "colab_type": "code",
        "outputId": "2676df49-2469-4152-e1f6-8831b5f593b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_t1gF0wpEYlk",
        "colab_type": "text"
      },
      "source": [
        "#### Load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rwdWl4UBUM3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/MMA/MMA869/jewelry_customers.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK01fk9pBbyM",
        "colab_type": "code",
        "outputId": "8f95b760-f54a-4f6a-bc44-e111bf679aa8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.head()\n",
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>77769</td>\n",
              "      <td>0.791329</td>\n",
              "      <td>6559.829923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>59</td>\n",
              "      <td>81799</td>\n",
              "      <td>0.791082</td>\n",
              "      <td>5417.661426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>74751</td>\n",
              "      <td>0.702657</td>\n",
              "      <td>9258.992965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>59</td>\n",
              "      <td>74373</td>\n",
              "      <td>0.765680</td>\n",
              "      <td>7346.334504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>87</td>\n",
              "      <td>17760</td>\n",
              "      <td>0.348778</td>\n",
              "      <td>16869.507130</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   Age  Income  SpendingScore       Savings\n",
              "0   58   77769       0.791329   6559.829923\n",
              "1   59   81799       0.791082   5417.661426\n",
              "2   62   74751       0.702657   9258.992965\n",
              "3   59   74373       0.765680   7346.334504\n",
              "4   87   17760       0.348778  16869.507130"
            ]
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          "metadata": {
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          "execution_count": 4
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          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 505 entries, 0 to 504\n",
            "Data columns (total 4 columns):\n",
            "Age              505 non-null int64\n",
            "Income           505 non-null int64\n",
            "SpendingScore    505 non-null float64\n",
            "Savings          505 non-null float64\n",
            "dtypes: float64(2), int64(2)\n",
            "memory usage: 15.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrvpihmcBn1k",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X = df.copy()\n",
        "scaler = StandardScaler()\n",
        "X[list(X)] = scaler.fit_transform(X[list(X)])\n",
        "X.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>-0.042287</td>\n",
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              "      <td>1.103593</td>\n",
              "      <td>-1.072467</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.000821</td>\n",
              "      <td>0.174811</td>\n",
              "      <td>1.102641</td>\n",
              "      <td>-1.303473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.123577</td>\n",
              "      <td>-0.021200</td>\n",
              "      <td>0.761727</td>\n",
              "      <td>-0.526556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.000821</td>\n",
              "      <td>-0.031712</td>\n",
              "      <td>1.004705</td>\n",
              "      <td>-0.913395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.160228</td>\n",
              "      <td>-1.606165</td>\n",
              "      <td>-0.602619</td>\n",
              "      <td>1.012686</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "        Age    Income  SpendingScore   Savings\n",
              "0 -0.042287  0.062733       1.103593 -1.072467\n",
              "1 -0.000821  0.174811       1.102641 -1.303473\n",
              "2  0.123577 -0.021200       0.761727 -0.526556\n",
              "3 -0.000821 -0.031712       1.004705 -0.913395\n",
              "4  1.160228 -1.606165      -0.602619  1.012686"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XvegYU-B3vR",
        "colab_type": "text"
      },
      "source": [
        "#### Normalize data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIzFXXrHEjrc",
        "colab_type": "code",
        "outputId": "f61d6772-a9c4-4e5d-926f-3ddceef721ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X = df.copy()\n",
        "scaler = StandardScaler()\n",
        "X[list(X)] = scaler.fit_transform(X[list(X)])\n",
        "X.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.042287</td>\n",
              "      <td>0.062733</td>\n",
              "      <td>1.103593</td>\n",
              "      <td>-1.072467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.000821</td>\n",
              "      <td>0.174811</td>\n",
              "      <td>1.102641</td>\n",
              "      <td>-1.303473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.123577</td>\n",
              "      <td>-0.021200</td>\n",
              "      <td>0.761727</td>\n",
              "      <td>-0.526556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.000821</td>\n",
              "      <td>-0.031712</td>\n",
              "      <td>1.004705</td>\n",
              "      <td>-0.913395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.160228</td>\n",
              "      <td>-1.606165</td>\n",
              "      <td>-0.602619</td>\n",
              "      <td>1.012686</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "        Age    Income  SpendingScore   Savings\n",
              "0 -0.042287  0.062733       1.103593 -1.072467\n",
              "1 -0.000821  0.174811       1.102641 -1.303473\n",
              "2  0.123577 -0.021200       0.761727 -0.526556\n",
              "3 -0.000821 -0.031712       1.004705 -0.913395\n",
              "4  1.160228 -1.606165      -0.602619  1.012686"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQGiVm-REnN9",
        "colab_type": "text"
      },
      "source": [
        "#### Elbow Method\n",
        "##### find the bestfi k for k-means\n",
        "I first use Elbow method to find the best k (the number of clusters). According to the plots below, 5 is the best cluster number in this case. Here is the reason:\n",
        "\n",
        "In Silhouette score elbow for Kmeans Clustering, when k is 5, we got the highest Silhouette score, which means that, at this point, the cluster is farthest away from the closest neighbouring cluster. So, 5 is the best k number."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmupUvrpEtd9",
        "colab_type": "code",
        "outputId": "5238e334-11f3-4f7c-af09-56b3f5830fb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = KMeans(init=\"k-means++\", random_state=23)\n",
        "KElbowVisualizer(model, k=(2, 11), metric=\"silhouette\", timings=False).fit(X).poof()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ARCQKp1fRJPfxm1R1ffHjlw3r9mmKIyrKx5NDnc9MD3y1yOM0xhTMl5NT9LR2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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4L20ng-KEygU",
        "colab_type": "text"
      },
      "source": [
        "***According to the plos above, 5 is the best cluster number***\n",
        "#### Try differnet k with Internal Validation Metrics\n",
        "I use a for loop to try different k value (from 2 to 10)\n",
        "Based on InterclusterDistance plot, we can find that 5 is the best clustering number. There is no overlapping between each two circles. Besides the distance between each others looks good."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zczBjroeE288",
        "colab_type": "code",
        "outputId": "6059ea4a-7c7b-4538-8aed-3549d745eaff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for i in range(2, 11):\n",
        "    kmeans = KMeans(init=\"k-means++\", n_clusters=i, random_state=23)\n",
        "    kmeans.fit(X)\n",
        "\n",
        "    visualizer = SilhouetteVisualizer(kmeans)\n",
        "    visualizer.fit(X)\n",
        "    visualizer.poof()\n",
        "\n",
        "    # Instantiate the clustering model and visualizer\n",
        "    visualizer = InterclusterDistance(kmeans, random_state=40)\n",
        "    visualizer.fit(X)  # Fit the training data to the visualizer\n",
        "    visualizer.poof()  # Draw/show/poof the data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97de65b00>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pw3DW3Y8rFIeIyOED5Po9A3Qt+RbaGbgDZatwfba5/r+TgRt9t0Yr8G1coSh2SuTNwGIR\nedQvbz4DbGh8y3MvXGF/wk/7Udype48UTP5dYJqIPIo7aH69v30l7jSzh0XkYeBU3HEd/DTXiMhx\nwDdwH7yHcRuZebjXY6QuwfVlP+6XfQ1DfKiGeG8Um+9lP98l/n36A9zZZE/69/2hwDf8524HXNcJ\nuI3mK8Bf/F8/bo+pcPnX4j5vN/rP7+O4YrePz3wurj97lV/28bgN5AeK5R7AvbiN0Gr//9aC+2/C\n9cc/6J+f9wC3+z2N24AHROQvuOf6Y8Nc92A+AWwnIn/N+zutcCL/PC/DHbdS/1xciusGusa/hocC\nn8r7DN+Z13U3mF8Au4vIA75P/OO412EV7iDqtYPMdwnwn35df8GdpXVnsRXFbDx1Y4oTkViuIIvI\n14FkCS2+uiQlfDnPlJcNE2BMEeJOKXzAdye04s5eGuisCWMiYbO/smtMjfsZ7rzhVbjjGD9lQ5eQ\nMZFj3S/GGFNDRr2lvmLFijTunOfncUeLjTHGDC2BO2X2gcWLF68dauLBlKP7ZVs2PYXJGGNMaZay\nGWdslaOoPw8wZ84cUqnUUNOW1cqVK1m4sNThXWo3Q1RyRCFDVHIMmmFnfxr6PZtzFuYo5KigKGSI\nQo5169bxyCOPwMZDKA9bOYp6BiCVSpFOp8uw+OGxDBtEIUcUMkA0cgyYYc2a3J3h5qiwKGSAyOTY\nrG5rO6XRGGNqiJ3SaEyU7LM5IxoYU+NF/fev3cszjz0daoZnXn869AxRyRGFDFHJMWiGL/ii/thN\nm97nZbL9bN+3AxPbJpUpnalmNV3U3wre5p/rhxqSobzezoafISo5opAhKjlGkiEIAmKxGO/qeRcT\nWjd74EJTo2q6qBtTbeZd6kYMXvWxwh8Lgo50B0snLaUj3bnJfcbk2IFSYyJk9tV3MPvqOza5PQgC\n+lr7rKCbIVlRN6YKZIL1TG+fMfSEpu5ZUTcm4jLZDIvHbUt3U3fYUUwVsKJuTIQFQcCktkksGBv+\nNy5NdbCibkyE9Qf9bNkz1G+6G7OBnf1iTIT0N238NfVUPEVXY9cgUxuzKSvqxkTIz39y9kbXW1Ot\nxGO2Q21KZ+8WYyKsMdkYdgRTZayoGxMhYx56jDEPPfbO9baGthDTmGpk3S/GRMiS4y8G4JY7LiAb\nZOm0/nQzTNZSNyaiEvEEszpmhR3DVBkr6sZEVFOykWTCdqbN8FhRNyaiWhpaw45gqpAVdWMiKp2I\nxE+rmSpjRd2YiNpi7JZhRzBVyDrsjImQuy/8LABT26faN0nNiFhRNyZCXl4wnUw2w6zmcWFHMVXK\nul+MiZgMGSa0TAg7hqlS1lI3JkIO2Od4iMVoWX1k2FFMlbKibkyExPozJGKJsGOYKmbdL8ZETCwW\nCzuCqWJW1I2JmFQiFXYEU8WsqBsTIYlYnDjWUjcjZ0XdmAixrhezuexAqTERkQ0yvPyJ5fS1jg87\niqliVtSNiYjmZAt9J50VdgxT5az7xZiIGN9iLXSz+ayoGxMBmaCfGR0z4GMfc3/GjJB1vxgTAcl4\nA+Nb++DOO8OOYqqctdSNiYCp7dPCjmBqhBV1Y0IWA7Ycu0XYMUyNsKJuTMjGNvfQmmoLO4apEVbU\njQlRNsjaMLtmVNmBUmNCNrd73oYrW28dXhBTE6yoGxOSbJBlWsc0EvG8oXZvuCG8QKYmWPeLMSGJ\nxeBdPVuFHcPUGCvqxoQgGwTs2LcTranWje+4/HL3Z8wIWfeLMRUWBAFjkmOY0Tlz0ztPPdX9X768\nkpFMDbGWujEVlgkyTG6cHHYMU6OsqBtTYR3pDroS3WHHMDXKiroxFTa+Zbz9GIYpGyvqxlRQNsjS\nke4MO4apYVbUjamgeCzG7M7ZYccwNczOfjGmQoIgYGLbhI2/bFRo5crKBTI1yYq6MRUSj8fZYfyO\nxSdqbS1+vzFDsO4XYyogCALmd8+nsaGp+IRPPOH+jBkha6kbUwH9QT/TOqYPPeHuu7v/q1eXNY+p\nXdZSN6bMgiBgTGM3HemOsKOYOmBF3ZgyywQZdpu8e9gxTJ2wom5MmbWl22gpHLjLmDKxom5MmaXi\nqbAjmDpiRd2YMgqCgE77BqmpIDv7xZgySsQTbDt+u9Jn+Na3yhfG1AUr6saUUXu6nVRiGN0v++5b\nvjCmLlj3izFlEgQBE1snhR3D1Bkr6saUSTwWQ7pkeDPtvLP7M2aErPvFmDIIgoDelnE0JhuHN+Mz\nz5QnkKkbkW+pP/LII+y5555ceeWVYUcxpmTrs+tZPG6bsGOYOhTpov7mm29y2mmnseOOQ4xsZ0zE\nJOIJ2lPtYccwdSjSRT2VSvHd736X3t7esKMYMyxjm8YWHzfdmDKJdJ96MpkkmYx0RGM2EQQB3Y32\nw9ImHFYxjRl1AVv1bj2yWQ87bHSjmLpjRd2YUZSMJ9mhb4fhfeEo35lnjm4gU3ci3aduTDUJgoDp\nHdOZ0j417CimjkW6pb5y5UrOPvtsnn32WZLJJLfffjsXXXQRnZ02QJKJnoCAhWMWbt5C/uM/3H9r\nsZsRinRRX7hwIVdccUXYMYwZUjqRZlHvIpoamjdvQVdf7f5bUTcjZN0vxmymbJBlbvdcZnXODjuK\nMVbUjdkc2SDD9PZpzBszP+woxgAR734xJsqyQZatehezYOyCsKMY8w5rqRszAtkgw6S2Scztnht2\nFGM2Yi11Y0agPd3Bssm7jf6CJ9n462bzWFE3ZpiCIDv8cdJLdc895VmuqRtW1I0ZhlSigZ0n7sL4\nlvFhRzFmQIMWdRHZvdiMqvqr0Y9jTHQFBGwxdlF5C/qtt7r/9lulZoSKtdRPLnJfAFhRN3UhCAIC\nssztno90l6nbJeeTn3T/V68u73pMzRq0qKvqRkeBRCSmqkH5IxkTHdkgw5wuYcueRcP/aTpjQjBk\nn7qILAIuBVqBuSJyMvALVf1DucMZE6YgCGhPtbNd3/ZhRzGmZKWcp34x8FHgeX/9WuC8siUyJgL6\ns/1s0bMFe03dO+woxgxLKUV9var+KXdFVR8B+ssXyZhwZIMsmSBDb3Mv75n5XrbsGYUBuoypsFJO\naewXkem4g6OIyL5ArKypjKmgIAjoD9bTlmpnv+n7k06mw45kzIiVUtSPB24GREReA54EPlLWVMZU\nQDbI0ppqZVr7dCa1TaK7sZt4LOSRM35lJ5WZzTNkUVfVPwNbikgPsFZVXyt/LGPKqyGeZP6YBcwb\nMz/8Qp5vxoywE5gqV8rZL/OBU4EFQCAifwZOVVUtczZjyiITZFk8fltmdESwgL7xhvvf2hpuDlO1\nSmmi/AC4DXg/8AHcl46uLGcoY8olG2RpTbQwvX162FEGtnCh+zNmhErpU39DVS/Lu75KRN5frkDG\nlE/A7M5ZxN9MEovZsX5Tm4qN/ZJrxd8hIgcBdwBZYA/gtxXIZsyoCYKAvtY+tp+wIyueXxF2HGPK\nplhLvR93GuNATZp+4IyyJDKmDBLxOIvHLQ47hjFlV2zsl0H720XEfmHXVJWdJiyhI90Zdgxjyq6U\ns18SwLuBsf6mNPBlYFr5Yhkzeia1TWZK+9SwYxhTEaUcKL0S6AIWAfcAOwCnlDOUMaMlk80wpW1K\n2DFKd+qpYScwVa6UUxonqeo+gKrqB4CdgW3LG8uY0RAwq2sWk9snhx2kdMuXuz9jRmg4X6VLikij\nqj6F+yKSMZEVBAHSPY8dJ+xEIpYIO44xFVNK98uvROQE4CbgQRF5kuFtDIypuIAs88fMDzvG8B10\nkPt/ww3h5jBVq5SxX04RkYSqZkTkXmAc8IvyRzNm5NLJRpqSTWHHGL4HHww7galyxb589NGC6/lX\nDwEuw5gICoKA2R121q2pT8Va6kuL3BdgRd1EVCwWY8FYGz/F1KdiXz46spJBjBkN8VicrXu3piHR\nEHYUY0JRyoFSY6pCLBZjzyl7MbZ57NATG1OjrKibqpcNsqQTKbYZv231F/Q99gg7galypQwTsK+q\n3lqJMMYMVzbIMq6llz2n7F0bw+leemnYCUyVK+V88+NExFr0JpIaE421U9CNGQWlFOtXgL+IyIPA\nutyNqvrhsqUypgSZbIaZY2bVVkG/4AL3/3OfCzeHqVqlFPWf+j9jIqUhkWRhrZ26aEXdbKYhu19U\n9fvACuBVf/lm/9+Y0MRjcZZMXEpD3E5dNCbfkEVdRD6P+6LRV/1NJ4vISWVNZUwR2SDLzM5ZTG6r\notEXjamQUg6UHoYbQ/0lf/2LwP5lS2TMEOKxONI1J+wYxkRSKUX9dVXN5q74y9ki0xtTNtkgS19L\nH+3pjrCjGBNJpRwofVxETgG6ROQg3GBeq8oby5hNBUFAe7qdXSbtGnaU8mmwYwRm85RS1I8FPgs8\nCxyB+0m7i8sZyphCmWyGaR1T2Wb8diTiNfyjF48+GnYCU+VKKuqqeg5wTu4GEfkq9julpoI60h3s\nNGHn2i7oxoyCYuOp7wbsDhwhIt15dzUAR2JF3VRQX0tffRT0FSvc/8WLw81hqlaxlvpfgT5/OZN3\n+3rg0LIlMqZAPBZjdled/OjF+9/v/q9eHWoMU72Kjaf+PHCViNyrqqsBRCQN9Krq0xXKZ+pcJsgw\np2s+nY1dYUcxpiqU0qd+mIi8AXwP983S10XkF6p6cnmjmXqXDTJsM25b5o2ZF3YUY6pGKeepH4A7\n2+WDwE9UdXtgSVlTmbrXH6xnyYQlVtCNGaZSivp6VQ2AfYGb/G11cMTKhCUej7P3lHczvXNm2FGM\nqTolDb0rIj8DJqnqfSKyP/aNUlMmyXiCnScsZXxr39ATG2M2UUpR/xCwF/A7f30t8JGyJTJ1Kwiy\nLB63AxPaJoYdJTzXXBN2AlPlSinqh/j/B4hI7rbJuJEbjRkV6USK3SbvQXdT99AT17Iddgg7galy\npRT1pXmXU8D2uFa7FXWz2YIgICDL3O55VtCNGQVDFnVVPTL/uog0A/+vbIlM3cgEGRriSfab8V7a\nUm1hx4mGBQvc/4cfDjeHqVrD/kFpVX1TRGaVI4ypD5mgn/HNfczonMHk1imkkqmwI0XHP/8ZdgJT\n5YYs6iJyNxDk3TQR+FPZEpma5bpaAuZ0zWX7vu3DjmNMTSqlpZ7/03UB8BrwUHnimFqUyWboaGxn\nfGo875t1IC0NLWFHMqZmFRulcfdB7uoGdgN+VZZEpmYEQUAsBjM6Z7DThCU8+MqDVtCNKbNiLfVi\nY7sEWFE3g8gGWfqz/bSlWtlr6rtpT7eHHcmYulFslMbdcpdFpENVX/WXx6vq/1UinKkuudMT53QJ\nC8YspLmhmVgsFnas6nLMMWEnMFWulAOlxwB7A+/zN10lIjeoqv2knXmnkHc2djGmsZstehbR2tAa\ndqzqdcIJYScwVa6UA6X/ysZfQNob+C32O6V1JQgC1mfXE4/FSCcaaW5opiPVTmdjF7M7Z9PU0Bx2\nRGMMpRX1hKr2510PANunrhPZIMu45nGMb+1jXHMvXeluGhL2i/dlc/TR7v8ll4Sbw1StUor6LSJy\nL3A3bqjePYAflzWVCU08FqO7sZt0opFUIkU6kWbBmIWkk+mwo9WH224LO4GpcqUME3C6iNyFG/Ml\nAI5R1d+XO5gpnyAIyAZZMoH76dlELE5zQwttqTZmds5ipo1jbkzVKmmYAFW9B7inzFlG3fT0NGaN\nCXdEg4aXGpgzJvwfTd6QI0YqmaYp0URbqo2mZBPpRJpE3H73xJhaMOyxX6rJtMYZLOrdKtQM/U9n\nQ88QpRzGmPIq5efsjDHGVImabqkbU3UWLgw7galyNV3Udc06Mo+/GGqGR18IP0NUckQhQ1RyDJrh\nwsvd/wrli/RzUQHZABZP66YhWTudFjVd1B/7x3r+mX4l1AzPv7SeN/8Wboao5IhChqjkiEKGqOQI\nM0NLOsn2M8eEsu5yqZ3NkzE1YOadtzDzzlvCjlEXMpksM3tba258oppuqRtTbbb93nkAPL7He0JO\nUtuCIGDymBa2q7FWOlhL3RhTZ4IgoKctzS7zemuulQ5W1I0xdSaThT0X9tHWWJtjGFlRN8bUlWk9\nLbQ11WZBByvqxpg6ks0GTOpuCjtGWVlRN8bUjWwQMGd8bf+8op39YkyE3PCdm8OOUNMaGxKkG2p7\n8Dor6sZEyLq2jrAj1LTejsawI5Sddb8YEyEta56nZc3zYceoSdlslvkTan+jaS11YyLkgM8cBsA1\nV98VbpAaNL6zmem9tf+j6NZsL7L7AAASAUlEQVRSN8bUvHgsxlLpCTtGRVhRN8bUtCAImDehne7W\n+vidXSvqxpia1p8JWDCp9vvSc6yoG2NqWltTko7mVNgxKsaKujGmpvW21/5pjPns7BdjIuS+T50U\ndoSa0p/JMm9CbX+DtJAVdWMi5Kkle4Ydoaa0NzUweUxL2DEqyrpfjDE1KQgC5k5or8kx04uxom5M\nhOx33BHsd9wRYceoCf2ZgPEdtT0i40Cs+8WYCGl7/pmwI9SMeAzG1cFYL4WspW6MqUkdzSmSifor\ncfX3iI0xdaE5VdtD7A7Giroxpia1NNZn77IVdWNMzQmCgJ62+utPBztQakykPLnLPmFHqAn92YCu\nlvoZGiBfpIv6GWecwUMPPUQsFuPEE09kyy23DDuSMWX1h09+KewINSEIAhrq8CApRLio33///Tz1\n1FNce+21PP7445x44olce+21YccyxlSBGDG6WhrCjhGKyG7K7rvvPvbc031leubMmbz66qu88cYb\nIacypry2vvxCtr78wrBjVL3GhgSNqci2WcsqskX9xRdfpKur653r3d3drFmzJsRExpTfnNtvZM7t\nN4Ydo+p11mkrHSJc1AsFQRB2BGNMFchkA2R8fY3MmC+yRb23t5cXX3zxnesvvPACPT318RuDxpiR\ni8dg3sT6+aWjQpEt6kuWLOH2228H4OGHH6a3t5fW1tr/JXBjzOZpbUwSj9fXyIz5InskYeutt2bB\nggUceuihxGIxTjnllLAjGWOqQD2OzJgvskUd4Atf+ELYEYypqLc6x4Qdoeq11unwADn1/eiNiZib\nv/XjsCNUtXX92bpvqUe2T90YY4arIRFj8pjmsGOEyoq6MREyYcXvmLDid2HHqFqdzam6+/m6Qtb9\nYkyE7HLOlwG45uq7wg1ShbJBwFbTuoaesMZZS90YUxOmjW1hdh1/6SjHiroxpuoFQcCErvruS8+x\nom6MqXr9mYCJXfV91kuOFXVjTNXr62ykuzUddoxIsKJujKlqQRBYQc9jZ78YEyG3nn1p2BGqShAE\npJJxFk3pDDtKZFhRNyZCXp0yM+wIVSWbDTh42ym0NtXv+OmFrPvFmAiJr19HfP26sGNUhSAI6GxJ\nWUEvYC11YyLkgx/eG7AvHw0l149+wFYTw44SOdZSN8ZUoRj7LZpAuiERdpDIsaJujKkq6/uzHLjN\nJJrS1tEwECvqxpiq0Z/JMq6jkZ72xrCjRJZt6owxkZfJZEk1JNhycjfbzbQfEinGiroxJpLW92dJ\nJeN0taYY397IjrN76vq3R0tlRd2YCPmfwz8ZdoRQre/P0pCI0d7cwI6ze5jU1WyFfJisqBsTIbr/\nIWFHCE08BnstHM/McW1WyDeDFXVjTGiCICATQE9bmndN7WJaT2vYkaqeFXVjImT30z4HwK9OviDk\nJKMvkw3IZLK0NDbQ2dxAZ3OKjuYGZqdfZrdtJocdr2ZYUTcmQnr/8r+jvswgCDZcLriQDQKy2WDD\n7QPMkz9LEARkA4jhuksgRjwWIx6HRDxGMh4nHo+RiEMqEac5naQplaQlnaC7JU1fZyPN6eRGvyO6\n4h/2BaLRZEXdhCKbDcgEAZls4IpCDFLJOE2pBI0N7q+wXzVW+N8XhljBBBuuxwqmd9dibySZNb5t\nwGUWLmTjeQdf12BZyLs/f13Nb69hzpSuTZadSrqvjmw9rYvYoFmKP+5YwcpjMf+Xmzd3GVjFP5g/\nfxwxYi5H3n28M1/snftTyTjpZJxkIk4yESMRj5OMx6wPPEKsqJuyC4KA/kxAZ2OCraZ20ZCM05iM\n05RO0pJK0phKkErEaUhW5rtw7W89w+J54yqyrsHEX3mKxQOdb+2fg8XTK3Mu9hvPNzCnz37Xs5ZY\nUTejIpsN6M9mCQJoTicZ05qmrTFJczpBcyrJlLEtPL7qdRbPsC+OGFNOVtTNiMVjMLevnaZUkqZ0\ngs6mFO1NDTSnExv1mRpjKseKuhm2/v4szY1J3jWlk0VTu8OOU1uWLg07galyVtRNyYIgoKslxZI5\nPfR1NllrvByuuCLsBKbKWVE3Q+rPZEnEYswa38ZS6aEhaaegGRNVVtTNRrL+NMPulhS97U10NCXp\naknR19VEU8reLmX3jW+4/8ceG24OU7XsU1rH+jNZWhuTdLekaUwlaE4laG1MMrO3lea0/e5jKL7+\ndfffiroZISvqVSbXks5kA+Js+CZf4bf5EjF3m7seZ+0rcSZ1NxGPxUgm4rQ1JulpTzNlTCsJ++KI\nMTWjpov6nJ4Us6d2hZpB165BRilDIuG+zdeSTtLamKSxIUHKf7tvKCv4O4sX2Y/0GlPrarqozx7T\nEP6XXV5OhZ/BGFM37DdKjTGmhpSjpZ4AWLduXRkWPXxr164NO0IkMkA0ckQhA0Qjx4AZenpyd4ab\no8KikAHCzZFXMzfrnOHYQENsbo4VK1bsDNw9qgs1xpj6sXTx4sX3jHTmcrTUHwCWAs8DmTIs3xhj\nalEC6MPV0BEb9Za6McaY8NiBUmOMqSFW1I0xpoZYUTfGmBpiRd0YY2qIFXVjjKkho3JKo4g0AJcD\nU3GnMR6pqk8UTNMFXA28oaoHj8Z685Z9PrADEACfVdUH8u7bEzjD5/q5qp42museRo5G4NvAAlXd\nJqQMuwFn4p4LBY5S1WyFM3wc+JjP8BBwrKqW5RSsYjnypjkT2FFVl1U6g4isBp5mw6m/h6vqsyHk\nmIz7bKaAB1X16EpmEJGJwA/zJp0BfElVr6pkDn/fscARuNfkj6r6uRAyvBc4CVgLXKOqFw9n2aPV\nUv8Q8Iqq7gz8J65wFLoEGPEJ9YMRkV2B2aq6I65Y/HfBJP8NvB9YAuwtIvNHO0OJOb4O/G851j2M\nDN8BDlbVJUAbsE8lM4hIM3AosNRnmAvsONoZhsqRN818YJdyrL/UDMC+qrrM/5WroA+V41zgXFXd\nDsiIyJRKZlDVZ3PPAbAn8DfgltHOMFQOEWkHvoh7f+4MzBeRHSqcIQ5cDPwL7r15gIhMGs7yR6uo\n7wHc6C/fgSughY6iDEXdr/smAFVdBXT5FwcRmQG8pKpP+xbpz/305TBoDu9ENjxH5TJUhsWq+oy/\nvAYox0hjg2ZQ1TdVdQ9VXe8LfAfwf2XIUDRHnnOBL5dp/aVmqIRin5E47suCt/j7j1XVv1UyQ4Hl\nwI9V9Y0yZBgqxzr/1yoiSaAZeKnCGcbiGshrfM26E7ehK9loFfXxuCKBDxKISCp/AlV9fZTWNei6\nvTX+toHuewH3ja1K5yjn4x9OhtcARKQP2Bu3katoBr/+LwGPAz8q7KarVA4RWQ78BlhdpvUPmcG7\nRETuEZGzRKRcA9sXy9EDvA6c73MMtJdd7gz5jgIuLVOGojlU9W3gq8ATwFPAH1T1kUpm8JfbRGS2\n79beDRg3nIUPu6iLyFEi8vv8P2CvgsnC/NWFYuuuZK4o/PLEJhlEpBf4CXCMqv4jjAyqehau33Qf\nERlor66sOUSkGzgS11KvpMLn4ivAccAyYCGum7DSOWLAROBCYFdgKxHZr8IZABCRHYG/5hofFZL/\nvmjH7VHPAaYD24vIokpm8MeXPgJchtuzf5Jh1pJhHyhV1e8B38u/TUQux21pHvJbl5iqVmqYxufY\neIs/ATfuzED3TfS3VTpHpRTN4N+0twJfVtVfVDqDL6YLVfW3qvqWiNyK66r7XSVzALvjWqh3A2lg\npoicr6qfr2AGVPUHucsi8nNgC+D6Uc4wVI4XgadU9XGf405gAfCzCmbI2R/XfVtOxXLMA55Q1RcB\nRORuYDHugH6lMqCqv8F1ieUO5K8ezsJHq/vlF8AH/OUDgF+P0nJLXffBACKyNfBcrqtDVVcD7SIy\nzfeR7e+nr2iOChoqw7nA+ap6W0gZGoDLRaTVX98OdxZORXOo6vWqOl9VdwAOxJ3xMdoFvWgGEekQ\nkdvzuil3BVaWIUPRHKraDzwhIrP9tIspz2tSyudjW0a/gA4nx2pgnog0+evbAI9WOAMicquI9IpI\nC66eDmtDNyoDeolIAtd6n407DWe5qj7t+05/A9yP6/DvxLWWHwa+pqq/2uyVu/WfhTtSnAWOBbYC\nXlXVG0VkF+BsP+mPVfWc0VjnCHJcB0zGtYJWAN8pxylbg2UAbgdeBu7Lm/wqVf1OpTL452G5v60f\n9wH+ZBlPaRw0R94004DLy3hKY7Hn4rO4Xe23gP8BPh3GcyEis3CnJMeBP+Nek3Kc6lr09RCRPwN7\nqurfR3vdpeYQkU/guub6gXtV9YQQMhyE65oLgHNU9YeDL2lTNkqjMcbUEPtGqTHG1BAr6sYYU0Os\nqBtjTA2xom6MMTXEiroxxtQQK+p1TEQuEJHFIrJMRO7xt90lbmTLcq73iLzLH/Ljj1SUiJwjIitF\nZBsR+ayIPCIi+4vINX7UwMHmK3r/EOvc7McqIstF5MrNWYapbaMy9K6pTrlhRUVkWaXW6b/T8BUg\nV5i+CvwId75uJR0I7K+qq0TkbNzwp7cCPy02k6oeuhnrDOuxmjpiRb0OiMgE3HjVMaAJ+LaqXiYi\ndwGn475okW8PEfk8bgyMr6rqlSIyDjfQUivuq/X/5b8ocSqQVNWT/LpW475A8piInIEbBqAJ9yW0\nE3BjWkwVkV/gvgg1C7hTRA4EFgGn+JzrgY+r6pMFj2V74ALcaHovAR8G3vS3LcZ9YeNXqnqyn/7T\nwAdx7/W/AsfgRmaciPt268/8fGf5IS7+Gzcq3hP+cm7s+3NV9boSHt+uwJeAZ3BfNFuPG+L43/Mf\nq6q+5PN9HXhZVc/w10/CDYt8HnCFz90BXJg/rMAAz/Uy4HRV3Vnc0LnfxI0y2AqcqKp3iMghwBeA\nf/rneJPfPTDVz7pf6sMhuIGSluGKTvMQ08dUdT/cN+v+3d/2NeA3fhnvBb4lIm2DLUBEPgBMVNVd\n/Tjds3DDNJwCrFHVvVX1FD/5HsDbuDH3D1LVXYGLgIG+/Xslrtjviiuk++GK9nRcgd0FN27+riKy\nHa5Fvosfu/oV3A+DnIQb8vdwVf0abpz741U1fwzvw4FxfiiBfYDlfi9jqMcHboz4E/06M8C78x9r\nrqB7P8R/Zdw7BFfMJwAXq+rufrnnDfZcD+BbuI3Q7sB7gO/5YTJOBD7lX8MTcBs2U2OspV4fbgWO\n8QOv/Qz3C0zF3OX/P4Mb2gFge1yxQFVfEJFnACmyjN2AHf3eALjW5nTc19AHshA3LPINIgKQwLW6\n3yEiY4FOVV3pc1zgb78AuMN/xT7jB2LaFtdomQX82i+zBddyLsX2+OdBVV/BbTzwyyn2+P4ErFLV\nF/ztTwHdg61EVf9XRNLixv5vBPpVdaXfMzpBRE7AbRiGM/b9brjhW3MbkvVAL24ogMtF5MfADar6\nh2Es01QJK+p1QFX/Ku5XfnbFDbz2OQb+IZOc/O6Y3LCfheNJxPxthbfnBqhaixvfZqPWth9rZSBr\ngb8NMQZLwMB7l4NlWwvcoqqfKrLM4a4rZ7DHt4xNu7OGGjr1KlxrvYUNxxpOBx5V1cP8AGgDDQ6X\n/7jzf79gLW6P58WC6c8Xkatwex7fFpHvqepQG3hTZaz7pQ6IyIeAbVX1Dlyf8hS/Oz4cvwfe7Zc3\nAdeqVuA13EBliMgCXIsQ3K9cHZRbj4h8xY8EmMWN1pgT+OuPAGNFZKGffhcR+bf8AH789xdFZFs/\nzfEicozPtpeIxPz6dvW3/Q7Y1xdFROQYcWN2l+Je/M/9iUi7iPxBNv7hl8EeXzG5x1roKtxofAf4\ny+B+GOFhf/lDQFZE0gXzvfPc44YTzs/2QZ9rrD/LKeEHkXpVVb8PnIr7jUxTY6yo14e/AOeJyG9w\nwyKf7YdcHY5TgJ19d8MNwL/5nxy7DvfDCnfjfrUmV4huwBXVe0XkPlyRegI3lvT/icgKcUOL3gb8\nEdeHfARwqc95Gq7PvNC/Ahf6aXbBtWyvAx7DFbN7gJtU9Xeq+kfgG8Bd/pTNZZQ+tOuPgCdF5F7g\nl8B5Bb8RMNjjK+Y24I8iMjP/Rn8wOMAda8iNq30x8DUR+SWulX4nGwp+zrm45+s23MHPnM8AB/rX\n5Oe4A8cZ3Njp94obM/04Bj5mYaqcjdJojDE1xFrqxhhTQ6yoG2NMDbGibowxNcSKujHG1BAr6sYY\nU0OsqBtjTA2xom6MMTXk/wMtbIl6KjWNNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97ddfa7b8>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zB9hbWsHm/cV8s/sAHp+f9KQE0hLjyUpJICMpwbLRL4FgkMraespqXFTUuqis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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97dcf7a20>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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416Gc2ClcoVgmIgcWyOsuCnQt+RbaSbgDZQ/j+myz/X/HAVf5bo123Dm9PxGRYqdEXgPM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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97dcdb7b8>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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66yyuuOIK7r33Xu655x6GDRvGSy+9xEsvvcS5557L9u3b+eijj0hOTuZnP/sZ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\ncf8ffZLh8N9i77EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97dc4f780>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ga20Lu41bYsNJA7ns0sap6t6AqupHcZc0ltzljMaY0vTMAYt55oDFBY3RUNVg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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/manifold/mds.py:421: UserWarning: The MDS API has changed. ``fit`` now constructs an dissimilarity matrix from data. To use a custom dissimilarity matrix, set ``dissimilarity='precomputed'``.\n",
            "  warnings.warn(\"The MDS API has changed. ``fit`` now constructs an\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97dc44630>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2SqazYPOR4rEYP/veN3lp13aybHbe+K/v4erNW5JtVtqgl3Sim36KE4nF6B8d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\nXAgh0tz/AdEIBwbwykmJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97db3de80>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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25HHDjF1tccOMXa3rvGnHKgB+/8zv854vHaRoaxjFpJbJTGyeWKz0ylausV+6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\nWFE3xpgKYkXdGGMqiBV1Y4ypIP8P+JELo5gpEr0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97dbbaac8>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RVVU5XtdGUXoyV5cXRdqceSfeFMd7rt2IXidxtLaVQDAYaZMWLULII0huSgJG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\nJ4JcEAQhxP0fB81qT+U3cOkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=6, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97da9ae10>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yurq6+MxnPsNee+1VirDGDFnBsGFhp1AyQTpNcocd7FDGHvRb1EVkBnAmsAUQ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\nWFE3xpgKYkXdGGMqiBV1Y4ypIP8PNgyOqi1qCEMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97da05550>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ihDzOGA16Vuc5eeHUhXibcsWMV0msLs7VxDwJ68msFCKRKIfqW6nMz+KtIkNl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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=7, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97d987b70>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s8BNARGQd8DTwgbJmtY08z2P18rU8uWFFxWO/8tS6QOIGGbuW4kYv+F8ABu4b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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97d8f51d0>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m1nOkYIRcm+jUyCaljqU14ZlOK9y1/SB6nZSVcMpEfOEoD+5qo8Jl58r1LcSS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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=8, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97d876f28>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xEcmUNq2xzSNwowoOKAeJZndyTjThLqgcTbiine6Gju3DQa0SNrysMaboCqkq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\n/wEA5Hbg8KHSSAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97d8eb320>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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joRl5y3WuDEYjcRoDJQVrL53NMRSKsb9JF/IV5VTXEP/26MvUl3nntcJBW4L/\n8Kut1Jd5KPcWJ4f2RmWqdf5iaw9HWrpRlOKUXKsr87Kjrpz/fOIYXSMTAOyoCzA8EZ21ryAI7KwN\nMBCMzNqms/GZiCWpKyucW+XC0Dg768qn5TDaqKwJIVcUlUdPtPKTF89wTXPtgqu6hiaiPPJqK5sC\nXkqLuJJxozNpnfeMhPjVa21zZicsBNU+N7vrK/jWk8foHA7SVOEjGEvOGYa4qcJHOJEmPUeCLZ2N\nSzKdQVHVgv2eVVWlayTENc21BWlvrbPqQp5MZ/nB4RMcb+/ndTsbFwwZHAnHePS1NjZV+PAVMeXs\nlYJoNLCjrhxFUXjw6HmC0URR+qkscbG3sZJvP/Uqo+E4DQEvA+OzLW+LaGJzhY8+3Sq/ohjOXxOF\nSpQ1FIritpmpK+DE6VpmVYV8LBLnX3/1EuFEkpu2N2JdIGB/LBLnkVdbaSz3zruCUOfyMAjQVOEj\n4LbzP6+cp3t0oij9VHhd7K4vv5hFsXNk7n621ZQxGopp1ZZ0NjyqqjISitFcWbhcKB1DQW7Y1nDF\nzJutWhx571iIbz1xnIaAly1VC3+B0USKX73aQl2Zp+B5GHQ0Kkpc2Cxmnjp1gYNN1eyoK3wS/iqf\nG0VVOXy2k0xWIRxPzopS8LvseJ1WxiIxynTX2YYnGEtiM4sEPIX5ruOpDKFYkl315QVpbz2wKhZ5\n71iI/3j8GNvrAosS8Uw2x6Mn2ihzO/UfdpHx2C3sbijPl74bLEofNX4PjWUlRBNJ5L65o2Z21pXT\nNxbRQxGvAAaCEXbUBQpmPcv9IxzaUr2hl+TPZFWE/IEjZ9jbUEH1Iqp1qKrKM2c7MQgCNX49zelK\nYBVN7K6v4ETXAK929Bdl8dCWKj81fi9HWnpIZ2fngmkoK8FiMuoLhDY4yfyagqYCJbSKpdIMBqPc\nvL2xIO2tF1ZFyHfUBqhYZO7p1y4MMhKKsaVyeTUjdS4Pi2hkd10FZ3qGea0IlrkgCFy7tRazycjj\nJ9pnbTcYBA5tqaF7JKT7yjcwA8EIzZX+gtWkPdM9zI3bi5eGYq2yKkK+2BCjzuEgJ7sH2V5btmIV\n5XV+jVk0squunNPdg0VxsxgNBt6wdzMnuga5MDQ7a2KN343XYWVoQo9g2YgoispIKI5UXVaQ9kKx\nJBOxBNdv8ARZc7Hq4YeXYjya4PDZTrZVl2JegQryOnNjFo3srKvgtc4BzvYMF7z9hkAJDWUlPHy8\nlbFIfNo2QRA4tLmG3rEwim6Wbzj6xiNUljgLlqX0dM8wt+1uuiJzLa1JIc/kcjxxoo26Us8V94i0\nFrHmLfOX2/ouLrUvFIIgcNPXC2yOAAAgAElEQVSOBgwGeOxEG5nc9BWm5V4nlSUuesdmL+nXWb9k\nc1pVq4ObC1O1ZyQcI53JbugqQPOxJoX8eHs/RqNRX3q/hrCaTTRX+3nqdAcTscLmQqkqcVFX6iWW\nSvNKe++s7QebqhkIRsjmipNGQGfl6R0L01hWUpD1IKqqcqZnmDv3NyMar8yn9zUn5EMTUc73jbK5\nonDJc3QKg9dupbrEzWMnWgtadUgQtIlNAwLnekcZnOETL3Ha2Fzpp2Nodr1PnfWHlswqyr4CJbO6\nMBzEbhbZU19RkPbWI2tKyDO5HM+cuUBjubdgs9g6haXS58Iqmnj6dEdB/dZlbge1ZV6cFpFnznTO\ncrEc2lxNPJVhPFKcFAI6K0f3SIitVWW4C+A2jacyyH2j/NZ1O6/ogIg1JeTH2/sxGY36op81zqYK\nP6F4ilfa+wra7sGmKhLpDAaBWS4Wi2jipu0NtA+Nky1Spkad4pNIZwhGE+xpXL71rKoqxzr6uXlH\nw6LDmTcqa0bIdZfK+sEgwLbqMuS+EdoHxwrWrtdhQ6oqRUBA7hub5WKp9rvZVF5Cx6DuYlmPqKpK\n28A4ezdVFiSy5MJwEIvJyE1X2OKfuVgTQp5TFA7rLpV1hWgyIFWX8cL5buIzyrkthwNN1eRUFb/T\nyjNnOsnNcrHU6C6WdUp/MIpFNLGzdvk5UKa6VPSqYGtEyFv6R1FBd6msM1w2M363g2fPdxVsGb9o\nMnLz9gbGY0lUVaVlRgUj3cWyPkmmM/SNhblpe8Oyfdm6S2U2qy7kmWyO4+39NATmLyahszapL/Uy\nGo7TNlA4F0tFiYvmSv/FH2wmO73IxKSLpW1gTE+qtQ5QVZXWgXH2baosyOKftoEx3aUyg1UX8rO9\nw9gsor7wZ51iMMCWSj8vtvQQK6CL5WBTNQaDgZyicGaOFaVXN9cBAj2jhV2gpFN4+oNRrKKJnXXL\nd6kMTUS5MBzkXTft0V0qU1jVTyKZznCic1C3xtc5ky6W584V3sWSySkc7+gnkZ5ehk40GrhjTxMj\n4Rhjur98zRJP5V0qOxqWXf0nmkxzrKOfd920V69JMINVFfKTXUN4HdYrMjfCRqO+1MtopPAulgOb\nqogm07za0T9ru9Nq4fY9TbQPjhNLFafeqM7SyeYUzvWOcG1zLV7H8lwqmVyOI3I3d+3bQlNFYVLe\nbiRWTcijyTTneoevmJp6G51JF8tLrb2zfNrLYW9jJc2Vfl4430M4Pjs1QIXXxbXNtZzrHZ61iEhn\n9VBVlXO9I2yu8NFcvbwSbqqqcrS1l521gSummPLlsmpCfrprkFKX/Yqq4rHRcdnM2C0iZ3tGCtam\nIAjctrsJp93MY3PkLQdori5lc6Wf870jeu7yNULHUBCXzcJVW5YvvKe7h3FYzNxzaJtek+ASrIqQ\nZ7M5WgbGqPIvXCFIZ33RUOblRNcAyXThXB2iycjbrtlBS/8o3SNzT25evbkGp82iR7KsAQaDEeKp\nDLfs2rTsUMMLQ+OMRmK8++a9+uTmPKzKJ9M1OoHdImLVrfENh80i4rFbOVHgQhRlbgf7NlXx1Ol2\nwonUrO0Gg4Hbdm0CFTqGxnUxXyVC8SS9Y2Hu2LN52b/vrpEJ2gfH+fDtB3FazQUa4cZkVYS8tX+M\nSj2Qf8NSX+blXO8I0eRswV0O+xorcdksPH+ui+gcoY4W0cQb9m8hnVPoHJ4oaN86CxNNpJD7xrhl\n16Zlx4v3jIZo6RvlI3cc0hcKLoJVEfJkJkuJw7oaXeusABbRSJnHwfGOgYK2G/A4cNks7Gus5Llz\nXcTniFSxiibu2reFRCrNhaEJ3TJfIWLJNGd7R7h5RwM1/uUFMPSMhjjXO8wHbz+g1yRYJKsi5H6X\nXZ+02ODUlXroGAwSKmARCkEQqC/1Ek9nuX13E8+e7ZxTzG1mkbsPSiQzGd3NsgLEkmnO9Ixww7Z6\nGgLLS3rXNTLB+b4RPnz7Qap9+hzaYlkVIQ949GD+jY7JaKDUbedcX2HrfDaWl3Cud5jdDRXctruJ\nw2c753Sz2Mwidx9oJptTaBvUJ0CLRTSR4kzPMDdsq6Opwr+sti4MjdPaP8pH7zhElS7il8WqCLnR\noGc4vBKo9rto6R8raHy3aDIScDs41TnIDdvquXPvFp471zlnjLlVFLlrfzNGg4EzPcN6kq0CE4on\nOds7ys07Gpcl4qqqcrJzkK6REL/zhqt0d8oS0ON5dIqGVTRhM4t0Dhc2f3iV383J7iEArpFqefOh\nbTx/vpv+8dkFmi2iiTv3bqGyxMWJC4Mk9BWgBWEgGKGlf4zbdm9aljslk8vx/PluQOV3775Gn9hc\nIrqQ6xSVyhIXp7oGC5aDBaDC46RvLHQxSde+TVV88LYDnO0Z5lzfyKy+jEYD12+t4+DmKk52DxOM\nFrZ49JWEqqq0D44zGo5zz8Gty5rYjCZSPH36Ak0VPj54mx5iuBz0QG6douJz2rgwrP3wyzyFsbaM\nRgM+p522wTH2NFQCWsjj7959Dd975jWOtvZysKka45QFJIIgsLU6gMdu4/GTbVSkXNRcIQvSVFWL\nFEtlsiQzWdKZHMn862Q6SzqbRVFUVFQUFQRAEEBAwGgwYDEbsYoioklgKBjDbbdy0/YGrOaly8fg\nRITjHQO86UAzhzbX6MEPy0QXcp2iIggQcDs42zPMzZ7C5Y8u9zo53T18UchBKxX3O6+/ip8cOcPT\nZy5wnVSHzTI9IVtliYs3H9rG4yfaaElm2Fzp21BFe1UV4ukMkUSKcCxFMJYglEiBqlV1MhkMmIwC\nBoMBs9GI3WLCbTdjQACBi4KqqiqqCoqqksnliCZS9AcjeB1WTEaBw2c7SWayWqipy07A68TvsuN3\n2md95tPHp15cofuBW/bTWK6XdiwEupDrFJ3KEjfHO/q5JlNbsNw61T4XT57qIJPLIRp/PXluNhl5\nxw27OHymkydOd7CzNkBd2fQ0yW67lXsObeO5810cv9DPlsrlRVusFqqqZRiMJtOMRWIMhWJauKeg\nTfRaTEYcNpEmt3faZ3R5faiMRTNEkimu2lxNzZQkd6qqksxkiSRS9IyGkPtGiacymE1GKn0uGspK\nqPa5MOe/81gyzbH2PuwWMx+/+xp8BSgyoaOhC7lO0RFNBhxWMwPByLLjjCexmkXsVjNdIxNsnhEx\nIQgCN+9sZFOFjx+9cIq+8TD7GiuxTkmXLJqM3LJzE53DQZ4710U2GaeqSl1T1vmkSyQSTxFLZ0hm\nMiTTWZLpDOF4mnAySTKVIato+dnNJiOiyYBBEEhnc6gqqBMKKlzcJhpNWEQjosl4cTLaIhqZ66xT\nmSx9wQhmk5FrpbpZPmxBELCZRWxmkYBncsyauI9HExxr7+PpMynKPU7MJiORZJq79zdzw7Z6jAZ9\neq6Q6EKusyKUOGx0Dk8UTMhBc9mc7xuZJeST1JZ6+OQbr+WJkx08efrCnNZ5Q6CECq+TB587dtE6\n99hXftXxVNEOJZIEY0nCsSSKqmI1mzSLWlWJJDOE4wkEQcBpMVPhcWI1i3MK8SSKopJTFLL5f8l0\nllgyTSankMlpKYcdFjNOqxm71YxNNOWt/DhbKn00BEoW7cOeFPdqn0i1z00smeZE5yDpXI5yj5Pj\nHf2YTUb2NFRin8cFsxFQVZVgLEk8lcYgCHgdtqKdsy7kOiuC323nVNcgiqJgKJA15nfZ6Rqev9Sb\naDRy574t7KgN8KMXTtE7FmbfpsppxUysZpF9NT7M3jKeO9eFx25hU3nxfeeKAhOxBMPhGIMTEVKZ\nHDazCYvJhM1iwl/uxWg0Eo4nGQ7FCMdS2K0iVT43ZtPiXSUGg4DBYERk7mOySo5UJkc0mWYkHCcU\nT2AyGthaXYbbbkVVtbmOy0FVoX88Qu94mEOba9hZF0AQBEbCMV5q7eXh463sa6zg6ubaDbX4R1VV\nOoaCvNjSoxUOF7QwXEVViSczeOwW9m6q5FBTTUHql06iC7nOimDNP84Ph2IFq3zuc9l4ua0XRVnY\nJTJpnT91qoOnTnVQ4/ewraYMcYogTlrnR+QejnX0U+N3U+51UUg9z2QVxqJxhiaiDIdiGA0GHBYz\nFV4ntimWdSanMBpOMByKoKgqLpuF6lI3xiJEd5gMRjAJJNIZFFVBqvTjddgIJ9Oc7Boik81R5nFQ\nkZ/QXOgmMh5N0DU8gcNq5s2Htk4TrIDHScDjJJnO0DEU5BuPvUyZ28H1W+vZURdYsi9/LTAWifPT\nF88wNBGlPlDCLTsbp7nzVFVlIpakfWCc5852cfOORm7e0ViQ9LxCIeN7F+LYsWMNwIVzYcjoS6av\nOC4MTVDqtnPVlpqCtfnYiTY+esehy1oNOBFL8PjJDk52DtBYXkJzZSnt7W00Nzdf3GdoIsrLbb1M\nxJLUlXkodTku2yqdRFUhlEjSPTLBwHgUq9mEy2rG7bBgniFcOUVlMBRlcDyKRTTitluKmu5ZUVSC\nsQTxVIYyt4PKEue0mxtoi3bC8RSRZIZEOk25x0ldmZcSh3WayyWSSNM5HEQFrt5SQ32Zd0GXjKKq\nDIxH6BgaJ6so3LmvmT31FWtqrmIxtA2O8f1nTtBQ5kWqLl3wvBPpDMfa+7FbRN5/y/4FXS6pVIrT\np08DNB44cKBz5nZdyHVWjEgizYWhcX7r+l0Fixt+qbWXm7Y3sK+xcuGdZzAcivLIa620D4xjycW5\n+eCeaQWCVVWlbzzM0ZZeUtkcDQEv3svI2plVFIaCMTqHgyTSGbwOKz6nbU4LTFFhNByjdzyM2Wik\nxGktqnWqqiqheIpIIkWJ00a1z7WoiKKcojAeTTARSyGaDDSUefE4bPSNhUimsxzYXM2WCv+0GP7F\nMhKKcbpnSMtgub8ZqWphQVwLdI1M8M0njnFwc/VlrUxVVZXXLgygAB+94+C837cu5DprBlWFV9r7\nuPfQtssSxPk41ztMqdvBPQe3LrmNntEQ//nQU2BzUVfqZVN5ybRHYkVRuTA8zsttfRgNBmpK3Xhs\n1kta6IlUhs7RCXpHw1hMRnwuGy6bZc4JSQUIRhP0joZAEPA5rEUtf5jNKYTiSeKpDC6bhRq/e0kT\ncKqqMh5N0j2qrbDdWRfg9j2bl13dXlVV+oMRzvVo3+td+5upnzFBvZZIZbJ86aEX2Fzhp3oJC8xU\nVeWI3MOO2gBv2Lfl0v0sIOS6j1xnxRAEcFrNjEViBRNyn9N+yfJvi6W21MM9u2qpaNzC0ZYenjx9\nAZ/TRlOFj1KXHYNBoKnCT0NZCS0Do5zsGiKXCxLwOCkvcWDKT96mMjk6hsbpHg3htVtoDHjn9SfH\n01kuDI6Tzin4nNZpE7CFRFVVEmkt3judy1HmctAQ8C6pPwUIx5IEYwkyOYVtNWVU+VyMhGM89Mp5\nNlf42bupEodlacvtBUGg2uemqsRF53CQbz15DKmqjHsPbcWxBpfwP3P2AjazaUkiDtr57m+q4qlT\nHexvqlpyrhnjZz/72SUduBQGBga8wKdGU9oFoXPlEU9lUFWWXXxgEoto5LULA9y8o3GaW+RyGRgY\nQGpqZFtNgKu31GIyGjje0U/b4DiZrILdImIRTZS5HeyoDeB32xkNx5H7Rokm0wxNRDndPYyiqtSW\nevDYrZeMlVZUGJiI0DE0gdNqptRtL4obJZPNEU6kGIskyCkKlSUuGgNeSpy2y+4vk1MYDscZGIsg\nCNBU7mdnXTllbm3ys8RhI+B1MDwR5ZX2frK5HH6XfckTeYIgUOK00RgooXs0xBOn2il12ylzO9aM\nuyWTy3H/s6fYt6lq3qeosyde5dMfeQ+gsm3X3lnbTUYDiXSGUDyJVFU6Zxu5XI7h4WGAL1dVVc0q\nf6Vb5DorittmZWgiWrD2RKMRk9FIJJHC6yhMOJfdInKtVMc1zbV0DAV5pb2Pw2cvYDYZKXM7qPZ7\nqCpxUeFxcqJzgMNnO4kkU3jtNqxmE8I8Ud3xdJaOwXFyqkJliaOgAq6qKqlMjlgqTTKTBbT4/eoq\nNw6LeNkCqKgQTiQJx1PEUlmqfS62VftxXyLOXjQa2VTho8qfpXtkgrO9I+xtrNSig5Z4nkajgb2N\nlYyGY/zkyBlOVg6tGeu8YzCoTVzbLJfcR8nl+MG/fw2Ha/5IraZyH8+d6+JNB6Ql3ah0IddZUZw2\nMy39o4sKGVwsFtFINJkumJBPIggCTRU+mip85BQlX4JshFNdgwyH4wxNRLCYTOzfVI3PaWUkEqd/\nPELrwBgW0YjDasZjs2I1G1FULVFU/3gUr92Cyz63z/xyUFWVTE7RkmGltSRYZtGIz2mjzu7BvgTx\nzuQUQrEUkWSKZDqD12GjrtRDZYkb0bQ469oqmmiuKiWeytDaP8a53hFu3t6wrLDTUreD23Y1cap7\niC/8z/O89Zrt7KgtX3J7haB3LLTg4rEnf/UQ/rIATtf8rheH1YySD09cSny5LuQ6K4poNGA0CoQT\nyYIJr8VkIpKYXSWokBgNBhoCJVT73RgM8MTJDrZWl6GoKr3jIdqHxnFYRNx2CxVeJ4qiMBFL0jsW\nIpNTCCeSGAUD5V4nFvHyrVNFVcnmRTuVyZFRcqQzOcwm7YZR5nHgsVsue6JUUbVQuHAiTTyVJptT\nKPM4aK7y43c5MC9SvOfCbhHZXlvGaDjOI6+1IVX5ObC5uiDW+QMvnKF90zhvPCCt2nL/4VAMj/3S\n1ng0Eubn93+fP/77L/Dtr31xwfacVjOjkbgu5DrrA4fFzFg4XjghFzXXSrHpHQvz4xdOoagqd+9v\nnhbZkkhnGI8mGA3HGA7FGIskiKUzGI0GhkKaK0kRtOgXo9GAaJjMfWLEbDJejIBRVS1sUVVVsopy\ncXm9CpiNmmj7nFbsVjN2s3hZPuhJ0U6ktHwtyWyOdDaLwyJS6razucJHicNa8BjuUrcdj8NC+0CQ\nn754tiDW+a27NvFiay/DoeO884bdq+JqyakK5nmqnf30B9/hmptvpbKmdlHtGQwCirK0cL4lC7kk\nSd+TZfk9Sz1e58rFbhEZjcRpKlDWQbPJVFQhz+YUnjzdznPnutleUzZnvpip+UUm6RoO8sSpDq5q\nrsFjs5LOZkmmcxfD/2LpNPFkhnAiicGQTzFrMGAWtfzfVrMJm2jCZhExi8Z5V3WqQC6npZzN5HJk\nsgrpnEIulyOnKGRy6kXR9jqslHo8uO0WXFYLxhVYfCMajWytKS2YdS6ajFy/tY5TXYN89eGXeP8t\n+1a8RJzDYp73ujvx8ksEx0d58pc/J5vN0nL2NAajkTff9+45909ntRQNS2HeoyRJmu92X7jleTpX\nFHaLyESscFV6bGYTE3PU7CwEkUSKHxw+QTSZ4tYZS64vhaqqvNY5yInOQXbWBnDN8/gNWs6VWCpN\nIpUhlc2Sygt+Mp0hmkozGo2TyWrJrQRBmDaZqqLm21ARTZp1bzWL2EQjJVYrVtGIWTTlV5OujGjP\nx6R13jYwTv/L53n9ns0455ksnA+DILCnoZLO4SD/+shR7rt+F9tqygo84ktT7XNztLXnkts/87ef\nI5fVJp2/8cX/j03NW7n1znvm3FdRVaKJ9JJvRgvJfxaYy9YXLvG+js6CWE0mRsPxgrVns4iE44W3\nyPvGw3z36VcpzecCWczEYS6ncPhcJwPBKHsbKhblDzcYwGUz47Jd2j2gKJpoq6pWxWcyk5UACAYB\nA6ybZe2i0cjW6lJ6xyI8ePQct+/ZvCxruiFQgstm4f7nTnLHns3csK2+gKOdr18vvzguo6jqnKGv\n5ZVVF19bLBZcbjcuz9xht4PBCJUli1tdOxcLHfUV4DVZlr89c4MkSU8tqUedeXn5yYd5/hc/xWA0\ncvtvvpfth65b7SEVHLNoIl7AIsg2s6itjCwgp7qGeODIaXbUBagrXdzKwmxO4alTHUzEk+ypLy+o\nsGrzeUL+3/pHEARqS904LCZ+9WoL1zTXIlUv3Zr2u+zcvKORJ0+1k87muGVnY9HjzQMeJ5VeF72j\noVnpkWfyx3//hXm3dwwFuXXXpiWPZaGZkj8EDkiSNNftcnzJverMyUhfD0888H3u+cDH2XvDrRx7\n+pGCFi1eK4gmI6lMFkUpzLIwoyCQzRWmLUVRefREKz958QzXNNdelog/cbKNcCLJ9prAurGOVxuf\ny87OunKOtvVxRO4mt4xrwm4RuWl7A8+f7+Kxk20r8tu5fU8TZ3qGL+Z1Xwq9YyEUVWVX3dLDKee1\nyGVZzgCfuMS2ty25V505aTv9Kl5/GU0799K0c/YKsI2CQQCT0UgyncVegGgDwSCgFOBHm8nl+NHz\np+gZDfO6nY2Lzjo4KeKxVIatNYGCpr29ErBbRPY2VHCud4SJWJLbdzfNysC4WKxmTcwPn+1EQOD2\n3U1Ftcw3V/jZ21DBK219XNNce9l9RZNpTnUN8YFb9y/5nGEBi1ySJIMkSX8mSZJxyntbJUn60yX3\nuAHpajnLf3/jS8tuJzQ2gtEk8qU/+Chf+5NPcPyZxy5uG+rp5Nn/eWDZfawVRJOReLow7hUDWvrX\n5ZDNKfzgmRMMTkS5cXv9okU8l3enRJNppOoyXcSXiMloYGddgFQmyyOvtZLOLt3CtYgmbtzewBG5\nh6fPXCjgKOfmTQe34rSaeam197KeDCdiCZ4928kbDzQvu3LWQq6VvwAOAFOnlfuBPZIkfXJZPevM\nQgDCwVE8/lIatu7g8R99l1hY8/2W1zZw4z1vX90BFhDRaCCRzhakLUEQUNSlP5KnszkeOdfPRCzB\ntVLdoheYqKrKs+e6mIgn2VqtW+LLRRAEmqv85BSVX73aQiqz9OvDKpq4cXs9h890ckTuLuAoZyOa\njLzvlv1U+9w8frKd/vHwvG6ddDbH6e4hXmrp5a3XbOeqLYuLM5+PhcyONwHXy7J8MSRAluWwJEnv\nAx5HmwxdV5w88gw9reeIRyOM9vdy85vv4+zLLzA62Me9H/g4A10dnH35BQRBYMveg1x9+xt59qEH\nSEQjBEeGmBgd5qZ7f4uTLzxNaGyE3/r4ZwAYHxrgcx9/D0bRxDWvv4fr734rowO9PHr/txEEAbPV\nyhvf+zGSiTgPfetriBYrB173ekb7e5FfexlBEBDNFrz+AGarDQEBRcnx/c//Fe/4vT9hYnSY408/\nyls++im++kcfR9p3FQNd7bi8Pu794O9iNK2vtV2i0XAxH8hyUVUVg7C01X2ZXI7vPf0aiYzC7c21\nl5V460TnIP3BcL4QwpK615mBIAhsqfTRPhjkkddauXNf82WVtZuKzSxy/bZ6Hj7eSsDjpKnCV+DR\n/hqzychvXreTXX0j/OrVVk53D1PqtuNz2rCZxYv53ydiCUYjcXbWBvjUm67DU6AsoAtdfompIj6J\nLMsJ1nECw+DwIG//X5/m2jvfzJFHHuStH/t9rn3DvbzwqweRXz3Kuz/9l7zrD/6CllePEhofBSAZ\ni3HfJ/6Irfuv5vSLhy++bj15nMGuDkYHevGWBiivaeDE80+TiEV57Iff4c53fYh3fupPady2+6Kr\nZKini3s/8HE279rPS4//gvd8+rO85w//isqGJsaG+kknE0TDE5hEkaade2l59eVp44+Ggmw/dB3v\n/cxfo6oq7WdeW/HPcLkIglCwyU4FlhQfnc0p/ODwCSKJJFvLXZcl4t0jE5zoHNQnNouAluOmBFWF\nR19rvRhDvxScVjMHmqr4weHXGIsULuT1UmytLuP33ngtH7h1PztqA2RzCgPBCMOhKG67heu21vGZ\n37iR+27YXTARh4UtcqckSQ5ZlmNT35QkqQRY8hrbwcFBEpmlfznLITgexOkL0NfXTzKTw1FSSn//\nAIl0loGuDlRF4Vv/8OcApBNx2s6fIxyOYCsppbe3j6wiYPP46e3tI5OD6NAgTq+f8vomTGYL6WQC\nZ0kpLWdO03ehjZ/959cAUHJZ/JW1+AYGcXhLGJsIw0SYmuYdfPtzf0njjn3U7zpEOpvj1acexjJm\n56o730omlWJkaBDB6iCRSNDb24dJNKOa7fT29uH0B7jQch67//Ir5Kwm4xMxetQkQiy47LaiqQwj\nYzGOHTu26GNUVeWJlkHGYil2VHgQBIGWlpZFHRtJZXihY4Q6r4ORocGlDltnAWyqSu9YnPufHOdQ\nvX9ZaYrFdJzP3f8wv7G7dskW/uXiBLY5AedkfwkIJWgJDRW8r4WE/HvAf0uS9HFZllsBJEnaA3wN\n+PxSO62oqFi1CkHjPW3kkjFqaqpJBocYdbouvs6mkmw7eC13vuvD0455dnwIe36/oTYPZtFw8XVc\nNFC3eTODHecwmkwIqoJoNlNeHsBssfCBP/qbaTPZE2MjWK02amqqAaj5yCcZG+zj3LGXeOaH/8n7\n/s/fMNLdwevvez9l1bUce/oR4qKBsrJSemxTjsv/3+d2T/t7vZAwjlFV4ae5NrDstsYicbLWcQ4c\nOLDoY5481Y5iifHm/bswGgy0tLRMq9l5KZLpDA8ePc/OpjoqVnhJ+JVITY3K6e5hJrBxTXPdkttp\nBo6199EaF3n3zXvX3VPUlApBczKva0WW5S8APwOekCQpJElSFPgp8C1Zlr9X0JGuAcrrGulqOUsm\nnUJVVR770XfIpBeXVW+opxNFUVAVhbHBfrxl5QRq6uk4cwKAsy+/QOf56V9EMhHnuV/8FH9FNTe8\n8a1Y7Q5SycSCfWUzaQa7OgDou9CGv3L9ZUsQoGBxvol0Zt6c0DM50zPE4bOdXCvVXlbmPEVRefL0\nBRxWURfxFUIQBLbVlNE2OI7cN7KstvY1VtEfDPP4qbYCjW7tsFCuFTfQCJwAjgJflmU5vBIDWw3c\nvlK27r+aH3z+rxEMBpr3HEQ0Ly7Ouby2gaGeTlKJONff9Rasdge3/+Z7+dUP/oMXH/05JtHMvR/8\n3WlCbbXZiUfDfOcf/gzRYqVmUzM2x8ICYXM4OX30eR7/8fdwerxs2r57yee8WqgUbkl5Ip2lZJH+\nxsFghB+/cJqrttQuKhNYBnwAACAASURBVG/KVE53DxGJJ9lVX7GUYeosEZPRwLaaMl5s6cHrsC15\nOb/BIHCNVMdTpzporixddsjfWmLe4suSJH0fLdzwMPBWoFeW5b9YamcbtfhyKpkgHg7xyH99k1Qy\nwb0f+DhOr2/RN4HL5cuf/ii/90/fKErbK0VL/xhSdSlbl7Ese5KTnYNsqfRzywJLnKPJNF/95Ys0\nlpfMKui7kGtlIpbgZ0fPs6e+HOsSM9TpLI/xSJzO4QnefNW2JSfaAm0lZcdQkE+96boV85cvl4WK\nLy/0XNkgy/JnZFl+CPgIcFPhh7j+kV89yr/95e/Tef40A53t/Ntf/j79nRvv8a2gqGAoUN6QdDaL\ne4EMg1qEymv4nLbLrsquKCrPnOn8f+29eXQj93Xn+6nCvoMASXDfupvoXb1KLbUWS7Zly5K8Jh7Z\nz7GdiTPOTBJPnJPjxInnnTzPeScn8T/O5Hkmc+KZJLbjk+R5Evslzsi2YkuyVrd7X8luNvcF+74W\nqur9ARLdLfVCAiAJgPU5R6dFEFX4kQS+dev+7v1e+jxOTcQ3EY/DSofLzo/OTdTUEt/ndWHU6/jh\n2at1XN3mcq93ZaX1bmxsTPb7/S0UR9eP/Q8+xv4HH9uw12v2aBzKpvz6GqbP3EyhJGM3313IX740\nRTJbqMoZ78JMgHxRYked/NM1qqfP6+DyfJiT1xY45q++kebQcDcvnL/O3n5fS6RY7iXkbxXuLSHk\nqqoyHYozGYyTK0g8tLOfsfkIc5EEiWwBURTQiSJtNjO7+zsYbHejq3Ja+FZFkmWsa8xR34liScZ+\nFwvYpViKly5O8tietTvixTM5Tk0uct+gj3U209NYBSsNQ6cnlxjyuelyV1cFbTTo2T/o4+9fu9BU\nKZY7cS8hf8jv99/c39q5/LUAqGNjY9XXAzUIqloepTUXSXJmaomZUOJtBkz+3nZeH3+7gXwkleXa\nUpQPHN3J6clFZEVBFET8PV529XVo4n4XpJKy5s3GO5EvlnDcwXyrJCv8/WsX8Pe2YzWt7fVUVeXl\nS1pKpdEw6HSMdLbx8sUpPnRsd9VThvq8LuYiSV44d433HfLXeZUby73enc39092FRDbPSxemmA7H\nOTbaz5nJRTLLHtkGnYjTamKksw1/Xzsui4nPve8YoihQlGXyBYlMQSKeyRNKZrCZDeSlEsFEuW9q\nNpLghfPX6XBaecfe4VvGf2mUqWWs1c0UpBIq3LH88OVLkxRKMsNV3D5Ph+Kkc0W2d2kplUaj3Wkl\nnMzWnGI5MNzNj89NcGx0AE8VQ48bhXvZ2E5v1EI2AlVVuTwf4pXLM5XBBqIooBcFPvTALmxmI5Z7\nRIlGnQ6jVYfTaqa7zcEuylUXH39kP/mixIWZIOMLEULJDKFkln944xLPHd+H0aDDaTGtu9l9M1BS\nFASButzORtM5ej2O2/5eF2MpXro4VVVKRZYVfnZ1jsFOt5ZSaVC2dbfVnGIxG/T0d7j513MT/OJD\ne+u8wo1jS9wvqqpKtiDxypVpLs+VvVNEQWBHj5d37hvGWCfDKbPRwJHtvRzZ3ouiKMyEk+SKEq+N\nzTAZjCMA/e0u3rl/BJe1fj4LzUZBKufH63FRi6Vzt61CURSV/7fKlArA1aUIQFNHaa3OzSmWDx/b\ng77KVObO3nZ+dGaCR3YN0tVWtfPIptLySdzrgSh/9ZPTzEYSjC9E6HDaeM+BbXzu6WM8dXBH3UT8\nrYiiyFCnm119HTyxb4TtPg8qMBNO8Jc/Ps1rV2bqMgyhGSlKclXiejsS2Tw9t0ldnZ5cIFuQqkqp\nSLLCqYmFlqhmaHXanVZ0osiluWDV5zDodGzrauMHZ5q3ZLhlI/K8VOL5U1eZCsUB0Iki//aJQ9jq\nMJFmrTgsJp456iedL/Kd1y8QzxT42bV5ri5G+OjxvfdM57QaBUmqqaHjZpK5Aj1viaIkWeYHZ66y\nf7C7qqj/8mwQk0F312HIGo3DYKeLs5OL+Hvaqx5evL3by4/OTjAVjDXlBbwlI/LrS1G+/qOTFRF/\nbNcg27s8myLiN2M3G/n044d4x+5BbCYDynLKp17zJpuFdL6I12Gt+TwFqYSsqG8715vjc5gMBtqd\na3+NglTizNQig034Yd6q2ExGHBYT56erdxXUieVqs+dPN2eTUMtF5MWSzPdPjSMrKgadyAeO7qSv\n3bXZy7qFAyM97B3soiTLfOvlcwhC2QT/vQd3bImcbLYo1UXIb7fRmStK/Ov5CY6NVlfJMLEUxW42\nYqtT6kdjYxjsdHNuKsCu/g5spuoCtsHONn545irz0WTTVZq1TESezBZI5Qp8/2RZxEd8bfzae442\nnIivoNeJmI0Gntg3QipXJJjI8Dcvn2V6+S6iVVFVyOQlvHW4YIWSmbflwF+5PI3HbqlqM1lVVS7O\nBulu0g2vrYzZoMfrsHBmcrHqc4iCwEC7mzdu0zPS6LSEkEfTOf721fP845uXGWx38e7923j/0Z1r\nsijdLEZ8bXzg6E6gPED4uz+7zLXFyCavav3IFiUsJkNdmoHCySz+m0y3krkCr1yeZneVHueLsTTF\nUmlLVxQ1MwMdLq4uRkhk81WfY6TLw9nJxUp5crPQ+Ep3D0LJDH//6gWyBYlcUWL/kI89A7UPK9hI\nhn1tfPShPcse3fDPJ8eZWIpu9rLWhVSuQIfTVvN5MoUiUkmm33vjjuvE1Tk6Xbaqb60vzQbxuRxa\n3XiTYtDp6HTZuDRbfQVLObK3cnpyoY4rW3+aWshj6Rz/6/VLlSG+7zs0ir7Kdt3Npsfj5GMP78Nr\nt+Cymuj1NleObrWkcwU6XbUL+Xwkye7+G/MyS7LCa2MzbK/S2CovycxGEvjaal+bxubR3eZgfCFC\nsYY5nyNdHl69PF23wScbQVNudpYUhXSuyPNnrlVEvN/rZCGWZjIYQ1ZUVFWt/CEEQUAQykZXZoMe\ns1Ff/tegx2TQYzLqsJuN6Dc5FdPptvMLD+1BVlRevTzDw7sGiGXyLTWNpl4bnYFEmqdv8se4Mh/C\nqNdVnRaZjWfw2G2b/h7QqA2zQY/dbOT6UpSdfdV53bc7rEiywrWlaNM4Xja8kJcUhVS2SCpXIJ7J\nEcvmyRUkjHodilwWarfNzEiXB4NOxG01Ioplp+uVW2RVLds2yopCSVaQSgqxYg5FUZBklZIsUyyV\nm1TcNgtumxmnxYTdsvHibjEamA7FOT8TYCIQpViSee74vqpK6RqNkqyQLUh01phakUoyiUyebV2e\nymOvXpmuqvkHypucs9Esh3Zqk39age42OxdnA/h726vqIxAEgT6vizOTi5qQ10ImLxFKZQjE0sQz\neYwGHSa9DotRj89lI57J47GbuboQwWzQ89juwVVubN457aKokJcksgWJuXCCfElGkmTctrKnSrvT\nWrduxHsx2OGmu83OYiyNXhT50dlr/Jvj+5puYOxbiaZydLc5MNTosbIYSzHs81SaPwLxNAvRFPsG\nqhPieCaPpKjYN7nPQKM+uG1mrgdiBBOZqsfC9bU7eeXSNIqiNsXnriGEXFXLrdbBeIbFeIqCJGM3\nG3BYTHS6behuuqqGU1neGJ9jxNdGf4eLbre9LtUpogBWo+EWj2xZVUnlCsyGE1yeD2E1lofudrht\nuCzmdd0Ue/+RnXz9hZOUFIVAIsPPJ+a5f0fzDVm+mUg6y54qK0puZj6avGVAxJtXZ+nzuqr+wM1G\nEthNOm2Ts0UQBIFOl51Lc8GqhdxmMmLQ65gJx5ui03NThLxYKiHo9EglhcVYkqlQnGJJxmE20emy\nYTUZbjsErCQrnLq+iEEnMh2M8+77tq1rlKwTBNxWM26rGRXIFopE0lmmw3FMBj3DnW10ue1Vm/Xc\nDYupXGP+o3MT6HXi8sXL07QpFmX5Yt3nra2uP1+UiKXz7B3wAeV2/JMTCzy2Z7jqc04GYjjNWgNQ\nK9HdZuPkxCIFqVR1236H08bYfFgT8jsxE06QLsjMR1NYTXq8dit2i/GeExwvzYVI54sAFcHfKATK\nV2mbyYgKpHJFrgeiXJ4L0edx0t/hqvut+Z6BTmbCCcYWwnjslqaOGBOZPC6ruWabhImlKAdHuiv+\nNFPBOBaToer3Qq4oEU3n6LFoQt5K6HXlAob5aJIRn+feB9yGXq+Tc9NLPHlge8PbT2+KkF+cDeJ1\nOtjW5V71dI/w8jQevShSUhS2dW3eVVIAnBYjTouRoiwTSeZ47coMbpuF0V4v7jo2lLz34HY6XFb2\n9nfWbaLOZhBJZar+QK2gqCqzkQRPHbox7f7KfLCmzdO5SBKnxYQoFGtam0bj0WY3Mx2MV/2+a7OZ\nSReKRFJZ2uvQ+7CebEqtVb/XicWoX9OIJqNOR4fTSklRsBj1dFdpJF9vjDod3W12RnvbMegEfnZ1\nntPXF8nk69MZJggCgx1u/vnkOKFEhh+evUYsnavLuTcKVYV4tkB/jbXxC9EkHU5bxbZWVVXOTwdq\nqrmfDsbx2LVOzlbE67AyE04gK9WZ0gmCQIfDxthCuM4rqz+bIuR97S5imbWJkdNqqtQIj/Y0XkmQ\nSPmNs6PbQ0lRePXKNBdmgpU691ow6fXMRZI8f/oqV+bCnJ1eqn3BG0gil8eo1+GpsX58Khi/ZZNz\nKZ6mJCs4q7TEVVWVpXgKt631jcq2IiaDHpNBRyCervocXoeVqWDj+x9tipCvFNyvVuSuB2Kk80Wy\nBQmDTmToNhNhGgWdIOBz2dje7SGTL/DyxWmuB2JUGRQA5YtYu9NKJJ2jx+Pg0mwIqYbOtY1mMZpi\nb7+vpjxjKlcgW5Bu8VG5Mheiw2mr+ry5YomSrFS9GabR+LisZmZCiaqP9zoszIarP36j2BQhFwWR\nwQ434VT2ns9N54vl6fbhBD0eB3v6O5vCDEsvinS3ORjxuZkNx3ljfIZUrvo87LEdZVvWbEGiWJKb\n4nYPytOAktkC27pry4+PL4Q5Ntp3SzruwkzgttOBVksklcVmNjb1JrLG3elwWpkKxaput3dYTJUg\nopHZNEUc7HCTyUsU7hGVXw/EEIRy7XAsnWPE1/ilQDdj1OsY6mzDYjTw+ths1dH5tq42THod0XQO\nj93C2amlpvCCWIyn2N7twVxD1JvOFwkmMxzfeSOtki+WWIqn6ajBtyWaym656UxbDavJQEGSyeSr\nC6IEQcBlM7EUT9V5ZfVl04TcqNcx4msjEM/c8TklWWE6FKfDYSWVK2IyNKchlkD5Fu3m6Hytm6GC\nIPCgvxyVe+wWfG571Zs4G4WiQjCRYVdvdZ4XK1ycCfCO3cO3lC4uxVM4LCbEGsLppXhaG+fW4giC\ngM1sIFJDgYDdbGI+kqzjqurPpuYoBjvcFEqlO962zEWTSLJSqRGWmnwk2kp0bjYYeH1shlDyzhex\n23FguJtdve2M+Np41/5tDe/0GE1lcVnNeGso3YqlcyRzBR7aOXDL4wuxVE0irKoqoWSm6o1SjebB\nYjQQXuNn7WbabBZmGjxPvqlCrteJ7Oj2Ekjc/peczhVpd1hvNAE1eC3nahCAdqeFXo+D09eXmAzE\nWUuG5OFdg3S3OZAVhVAy09DzPhdjKfbW2JJ/YSbAu/dve9uG5EwoTlsNU4YKUglJ2+jcEjgtRoJ3\n0JjV4LFbWIhqEfld6fO6UCl7mryVvQOdPLp7sLJJ6La1Tr2vzWxkpMvN9UCU89NLyMrq1Pwf37zM\nTy5MMrEU5W9ePrfmqH6jiGfzyIpa0xDjQDyNoqoc2f52j5nZcKKm+abZgoRJr/mrbAUcFhOhZKbq\nPSWryUAim2/oPalNF3JRFPD3tBNM3PkXbTbqMep1LRc9GXU6RrraSOYKvDk+S166d0mhw2JkJpyg\ntFx+WEuN7HqhquWa76Pbe6v2oVFVlUuzQZ46OPq2c+SLJeKZPM4aOmhzRalmF0aN5sBk0C/Piq1u\nw9Og16GoUFjF53Oz2HQhB+hy2zEZ9UTSN2btzYQT/PDsBLmiRElWaK/DMIJGRCcI9Le70Ot0vDk+\nQ6549yqeleneoVQWi1Ff0y3jehFJZzHqxJpa8icDMewWY8Uc62aW4imc1to2OrPFEoZ1MDvTaExq\n3fC0GPWk82/PGjQKDfFOFgSB/QM+wslMZURTLJ2riPj923vZ219b5UMjIwBdbhs2s5E3r87etWZ1\npR19MZbG57I3nJCrKswE4xzd3le1rWymUOTKfIiPPnR7D/ZoOldz2WCuKK2La6VGY2LS6yt7bVUd\nb6jt+PWmYd7JdouJ7d0e5qIpVFUllsnjtpq5thTl1bFZ7FuguqDTacNhNvHm1bk7RuYrVRbJXIFO\nt41IOktJbpxbvkA8hdNmor+9OrtaVVU5ObHAE/u23dFLOpUrYKoxLZLJFTHqWytVp3FnjHqxpoja\nqNeRvM0+XqPQMEIOMNTZhlEnEk7lSGTzuG1miiUZVVUb+mpYTzqcVhwWEz+7OntbCwOTQU+f14kA\n7Ozt4ANHdzWMxaaiqMxGkhzd3lf1miYDMSxGPY/sHrzjcxKZfM1OkOlC8/YlaKwdk0Ffk5GdUa9r\naA1qqJBEFAT2D/r4yYVJZEXFbTMzE04gyQrpfHHLjOLqdFoJqionrs5zzN9/Sy5XrxM5NNJNPFPA\nY7fUVLlRb2YjCbrcDrqqdKZcSan8+lPH7mrDEM/msZhqe+vmi9KWeT+tBkVR+N/f+gvGT/8Mk9XG\n4x/+OLsOH9vsZdUNo15XU47cqNdVvVm6ETRURA7lFMuQz43HbsFlNVXK8mrZ2GpGOl02dKLAuakl\nbq5MLFf2CGzztaGqKlcXIjU1O9SLTF4imMjw0M7+qo5fTUplhWS2UHOOXFFomDuZRuDCmz/lysk3\n+OTvfpndRx7kX77531EaKGVXKyaDnmwNEblOFBq6k7rhhBxgb7+P7d0eirJSEfBG/iWuFz1eJ8lc\ngWuLkcpjJUXh/ztxhQuzQQC+f2qc8Zu+vxkoatnU6oEdfdjN1e1ljC+E75lSWSGZy9cs5LKqNOab\nf5PYtucA//YP/ghvVy8ubwdSoUAx31y+93fDqNeRLUpV14ILgkCpgTWooVIrN3NgqIvXx+dod1gJ\np7IoDVyMv16IwEC7i+uBGA6zkW6PA3m5k/PM1CLHdw6g14mb3t05G47T5rAw2tNe1fGLsRQz4QS/\ncY+UCpQj91SuiMVY21tXVVVaqRvo3Osv8S/f+O+Vr21OF7/5x/9t1cfbnC5sThe5dIo3fvhPdPYN\nYrZVN7i4ESlXKKlIJRljFf0oAgKK3Lga1JBCPrEU5fsnx3nmiJ9Ly5GnssrOx1bDoBMZaHdxfiaI\n1WxkpRpPJ5QFTxSETf3drKRUPnxsd1WpimSuwJnJRX75iUOrarlfiYpqtTIW7jkhtvlwetr5zH/6\nEwCEKko/C/kcf/f//DHZdJIP/up/rPfyNh2dKCIpCtXujFTzO90oGlLIdaKISrk1dldfB3mpRFdb\n60QHa8Vi1NPltnHq+kKlrM+8HJHKioJuk95gtaZUpJLMG2MzPHPEv+pJ5Yqi1iW3LYhCQ7dcV4vR\nXF23q6qq/NP//Bqx4BLPfe736R4cqfPKNh8BUKsMehRVRdfAd3ANKeQrjRolWaEkK8xHy053u3o7\ntuwGldtmJpUrcmmmfIfS5bajqiqyom5aY8t0sPqUiqKqvHF1jgPD3Ry9jZfKnZAVtS4b36IgoNJa\nQl7IZvjrP/5PFPM57n/X09x3/PFVHzt2+gTXzp/iA5/5HB09/RTzefRGI2ITDHFZLYIgUG0SUlXV\nhh5o05BCrlsWJllR6FyuYJiPpHBZzZUW9a1Ij8dBZHmqUndbucTvYw/vq9j8biTBRIZ4NscH768u\npXJ+eok2m5n3HfKv6ThVVeuS2taLwqqNypqBtg4fOw8f4+Aj72TszAme//b/YGjXPlye1V1kJy6c\nAuB7X/8vlcc+9vkvMTi6e13Wu1lUG5HLioJBrwn5mrAtVySkckUGO9zoRIFUvkgqVySUzNLhbE3f\nlXuhEwX6250UZZlerxNBEO5ZqrcepPNFpoJxnj48WtVFZHwhTDJb4D+894E1302IorAm2987YTUb\nK3YQrUD/9p30b98JgMFk4vXnv0cssLhqIX/6k7/G05/8tfVc4qajUn2eu1iSq67I2gga8hLjtJq4\nb6iLNrsFQRAqTS89HgepXIFwqnXKotZCsSQTTmbx2CwsxdIsxdOcmw5saIu+VFK4PBfi4V0DtFfh\nDz+xGGE+muQz7zpyy8Sf1SIKQl0qmGwm4z3HDDYTP/2n7/Dn/+fnSURCjJ0+gajT4e1efcpqS6Cq\nVQteQZJxNLBNSEMKuSAIPL53mL5lg6iVAbuxdI77d/QRz+SI1tCl1azE0jkuzoXQ6QQWYynOTwd4\n6eLkhu0bKApcmguyu6+TbV3eNR8/FYgxHY7z7959tOqhEGKdNiltZiNSC0XkRx5/D57OLr7+5S9w\n9tWf8PQnP4vD3VzzbdebWiPyRh4L2JCpFaBinOWymjg03M18NMFkMMbDuwZ5YEcfb16dQwW8DdSi\nvt4sxsoDYCPJLCNdHk5dX8TrsG7YJsy1pQgeu5VDIz1rPvb6UpTJYIxfffcRvDVYEutFEUEQkGWl\nspdSDRajvulHB96Mxe7go7/xu5u9jIamJCsYqxyPmJdKDW3p0JAROcDVxSjfePEMkVQWl83MgaFu\nDHo9eamEzWzkgdFyZB5e3vxrdUqywtTy3MBujwO3zUw6X9ywmZMz4QRSSeYde4bXbE97bTHCVCjG\nZ588Sqertpy+IAg4zEZyxerbrQGsRkNLCbnG3SnJCqIoVDVMRFVVilJJy5FXQ6ernH8NxMs+Ih1O\nGy6rqbI5ZjMZeWBHP8lsgcBdpgu1CrORBIqiYtTr6HY7yBYkFFWlWJJZ7+KLuXCSeDrP+w6NYlyD\nY6CqqlyaCzEfTfLZJ++vKqd+O5xW0z0HcNwLs9HQUqkVjbtTKJWwVmnrUJBKmAz6hvav35SVrUZ3\nXFYTJoOuMjhBkhXGFyJcnAlW7F2tJgPHRvuRSjIzkWRLd38ms2Uv5BFfG6IoVLyRTQY9i9HUur3u\nQiRFOJ3lfYdHsa7h1lKWFd68OkcmV+DXnry/pnTKW3HZzDVH5LblqpUGts/QqCNFSa66TDeVL9Y0\n6Hsj2BQhn1gM3/MDJAgC3W0OpoIxFFWl1+OgzWbmxYtTnJtaqjzPbNTzwGgfTouJ68FYS5WU3Yzb\nZkYUBIY73QD0tDl45vAoQx0uxhbC63IRm4+kCCTTPH1odE079rmCxIsXJ/G57Hz2PffjqvPQbJe1\ndiE36EQcFhOZQuNak2rUj4JUwmaqLscdTeUYqHJQykaxKUJuN5u4NBe8p/js7e8ktVyzLAgCB4a7\nATgzuXjLsTpRZP+gj6FON9cDsZb7cGYLEoMdbt53aAcWo6GSRjLqdTgsJnQiLNV5CPNsOEE4leGZ\nw/41DTkOJ7O8eHGSh3cO8tzD+zCuw4Bjt9Vcc2oFyhOZVu50NFqbQkmuespYIpuveuLVRrEpQv6Q\nfwCP3cKF2eBdnftGfB6eOTzKYEf5l7iztx2dKJAtljg/E7jluYIgMOLzcHCkm7lIsmXKE+OZPM+f\nucbVxUhFFMcXI7xyZaZi7euxW5kKxurSKKOqMBmME8/keebI2kR8MhDj5xPzPPfwfh7bO7xuZZF2\ni6kud16dblvLXfQ1bo9UkrFVm1rJFehqq25YykaxKUIu6kQe3ztMv9fFmaklcncYNiyKAtu7vZXy\nOpNBz6GRHtw2M69cniaRzb/tmA6njQf9A8Szeeaiyab2MVcUlRMT8wDklyNQVVWZDMRv8X5wWk1k\nClLNMwUVReXKfAhJlnnmyM5V79JLsszJiQVmIwn+/XvuZ1ff+g7K9totdRFgj91ac4pGoznIS6Wq\nGnpkWSFbkCrFF43Kpm3DiqLIg/5+jm7v4fxM8K4R9Ilr85y4Vha04zsHKpap06H4bZ9vNxs5vnMA\np8XEtaUYqQYemno3xhbCpHJFBKjc2i3F02SLEiM3uQUKsDwW7/a/j9WQl2TOTC7htVt5+pB/1RtD\ngXiafz13HZ/bzm88dWxDLAO62hykc8Wa9wU8dku5+qd5r/Uaq0BVVTIFCY9j7RuWsUyOTpcNQ5X1\n5xvFpjYECYLAzt5O3FYLL5ybIJuXlj1Ebn1eMJFhJhzn4HAXep0Op8XEI7sH7zpDz6DTsXfAh89t\n5/x0gESuQLfb3tAOZjcTz+S5PB8GwN/bjnt5w/B6IIbZoK+YZq3gsVuYWIqys6djzeY+8Wye8fkI\nh0a62TvgW1VKRJJlzk8FiGdzfPyR/VUPlagGo16Hx2Ehns3XNLPUsLzHoKVXWpuCJKMXxarKDyOp\nHIMd7nVYVX1pCFXranPwgft3kcoXGL9NBcb+QR8FSebyXLjyWDiR4Y3xOc5OLd42xbJCh9PGI7sH\ncZibKzpP5goIgNNiZOeySKbzRQKJDEOd7rc15Rh0IlaTkflYck2vsxhNcW0xwjv3j7BvsGtVIr4S\nhXe67fzWM8c3VMRXGGh3E61DM1ivx0ksvTWayrYqyVyBdqetqj2bSCrDiM+zDquqLw0h5AAOi4ln\nl/OyZ6eXyNw0KLXP66TLbefVKzOVKPz4rkGcFhMvXpji+dNX75oLN+h07Bv0cWC4i0Aiw1wk2fDN\nIFajAaNex5FtvRXRthoN7OptvyWtcjNum4nF6OqEvCQrjM9HCKeyPHtkF33ee+/K54sSP782z8XZ\nAB9/ZD+/8OCeTbHQhfIIvHjmzhfw1TLYUZ/zaDQu6XwRn3vtOW5ZVoimc2zvXruv0EbTMEIO5Vvd\nd+4f4dBINxdng8yE4ihqOQXz5IHtSLLMixcngfLt9bvv24YKLMbS/O9TV+/pircSnXscViYCMRZj\n6YbbDJ1YinJ1MUK708p7D26vpFRUVUUUBXb1dVSmA70Vu9lEMlukIN39IhVN5Tg9uYjXaeED9++q\nvMadkEoy56aX5rKeWgAAGetJREFU+PH56wz53JsWhd9Md5uDVL72uyuf20GuWGrowboatZEtFGl3\nrF3Il+Jp+tvdmxasrIWGEnK4kTf/8LHdyIrK2alydO6xW3jX/m23TJPpb3dxbLT89bWlKD8+d/2e\nrfoGnY6dve08snsQi8nA1cUowWSmIbpCZ8IJzk4HmArdWpWSzhf50bnr9yypFIVyt2s4eftUwUoU\nPhNJ8M5923h41xCmuwyilRWFK3MhfnRuAo/dyuefPc4zh3c2xBt7ZcOz1guxXifS53WRyGnVK61I\nLRudi7Ek+wZ867Cq+tOw7ocOi4mnDo0ythDizfF5utw2/L0dleHD5VmVIg/s6KPP62QmnOBnV+cZ\n6nSv6lbIYjSwf9DHiK+N8YUwV5citDuseJY90Dea6VCck9cXgbLD38qMQFVVOXV9kYJUWtXkeLvF\nSCCeotd762ZoNJVjIhBlxNfGe7fvuKuAK6rKVDDG+EKYEZ+HX3/vA5sywOJuGPU6+rxOAvF0xea4\nWoY73UzOL9ZpZRqNRCpfxGoyrnmjU1VVQsks/t7NvfNcLQ0r5HAjOu/1uPjppSnOTi0x7HNzemKB\ndEHimcOjCIJAn9dFr8eJUacjlMywrcuzajG2m40cGukhnslxZT7M+GIEt82Cx27esJKja0tRzk2X\nG5wclnLp5Eo0PhGIEU5lOTzSjWUVb0aXxcTVxSiKUk7F5CWZqWCMfLHEO/dto9d7Z9HLSyWuL0WZ\nDSfoarPzy08cbugd+32DPs5OBWoW8l6vk2xRqvzONFqHcDLLSGfbmoOzaDqH02Kqq0fQetLQQr7C\nSnQ+sRTlxLV5MgWJiaUol+ZC7OnvBMqinyuWOHl9gXgmjyQrPLZ7aNU+H26bhWOj/SSyeWbDCSYC\nMaxGA167BavJsK5R+spmm9tm5ri/v9LBmcwWuDgbxOe2rdrrQa8TMRl1BBNpMgWJSCrHnoFO9g34\nbhuFq6pKOJXl+lKUSDrLgaFu3nNwR1PMRh3taeeFcxPLczyr//uYjQZcFiORdJaOOjk0ajQGiWye\nI9t613zcbDjBvqHmSKtAkwg5lIV6e7eXoc42Ls8F+eZLZ3jh7AR6nYh/eePt4V0DIMDJiQUEoZyu\neGTXIPcNra6sDsqGTK4BM/6edhZiKaaCcWRFwW0102Y3160OPZjIUCzJ9HmddDjLHYbHRvtuuQuY\nDMbQ60QODnWvev2yCoWizKnrixwb7eeJvSO3dS2USjLToTjToTgGvY6Hdw5yYLi7IfLfq6XDacNq\nNBDP5Gt2pxvy2AnE05qQtxD5YglZVulYY1emrCgsRJP84kN712ll9adphHwFvU5k32AXv/+Rx/jK\n917hB6evEc/kODjUg9Gg45Fdg7TZzLx0cYqSrPDixSmuLkZ58r5ta3LhM+h1DHa4GWh3EU3nmA7F\nGV+MYjbosJuMOK2mu+aZ70RJVjg/E2AyGMdqNNDrcVRe561ivX/Qx45u76rEVVHLefBwKovVbKDT\naeP4rsFbnpPJF5mLJAkm0iRzBUa72/k3D+9jm2/1qahGQhAE9g52MRdJ1CzkPoeZ2aUsuYKEpYku\nZhp3JpzKMNjhQlzje3smFGeos61p0irQhEK+gttm4YsfepSvfv917GYTpycXcVpNdHsc7On3MdDh\n5vzUEi6bmZcvTXNqcpHH9w6v+XUEQcDrsOJ1WCkt15UG4mmmwwkEQcBuMuKymrAY9XcVQ1VVCSYz\nnLq+WHHuu9mofuXYXFHi7FSA/YM+rCbDPUW8WJIJp3IkMgU8DjMPjPZiMhi4ML2EoijEMnnmo0lC\niQwlWWHPgI9ndvgZ8XmquhA1Grv7Ojg7VftGpU4U2NnXwUIsxbauxm8A0bg38Uye+3f0r/m4qVCc\nD96/ex1WtH409SfZbjHx+x95DFEQyEslzlxfYDoYR1ZUOl12jvkH0IkC7Q4r//TzMdw2Mw6zkXAq\ny94B35pn8Ol1Ip0uG50uG3vVThLZPMFEhqVYmpxUwmzQYzLosBj1lYaeFYFO5gq8emW2Yj+wvcvD\n7r6OW8Q/ky/yypUZCpJMtiDdUcRVyo5skVSukp7Z09+JXieQyhUIJTIsxFJ892eX6XTZ2T/oQx+c\n4Lt/923O6vV87nOfw63z88UvfpFSqYRer+crX/kKHR3ra3a1HvS3uyhKMulcoWqb0hX8Pe1cnAkx\n3KltejY7xZJMJi/Rs0bXwmg6h6qqlXRts9DUQg5UbpuuzIX45ktn+cRj9zHQ4ebSbJCfX5vH47Dg\nMBvxOqy8dHEKnSggKypvjs+xvdvL/kEffV7nmlMLgiDgtllw2yyM9rRTkEokcwWS2QLxTJ6rS1HC\nyWzZwbHLU+kkNRv0HBjqwue233LLF8/keW1sFkVVeXjXwG09RAqlEpFkjmgmh14Uyzlis5GiVOLS\nbBCjXkeH08pAhxtBEPjQA7s5vK2XWCzG//Xrf0ZbWxuSJPHNb36T9vZ2PvrRj+JwOPjMZz7DX/7l\nX/KFL3yhhr/E5qATRR4Y7ePaUrTiV18tTquZ7jYbwUS64W1LNe7OQjTFtm7Pmmd0Xl+KcNw/2HQX\n8qYX8hX8Pe0M+9r4q5+c5tBIDx97ZD+iAOMLYa4tRun1Oul2O1iMp5gNJ1CB6VCMq4sRRnxtvP/o\nTqC8y+20mNYs7CaDnlI6RzCRYT6aZC5SbpW3m410OK0UJJm9A52Y9HoyBYkr82FEQUCvE0nlCkwG\n4xh0Itu7PZULggJIUolMQSK3XB7XZrfQ3eZYFnEDFqMBh8WI3WREEATa7BZMBj0CQqUa5oUXXqBQ\nKPC3f/u3ZLNZ/uzP/owvfvGLAHz2s5/F6XQSj1fvnLjZHN3ex2tXZtk34ENX41zF3f0+fnp5Cp/b\n8TbzNo3moFwDnrmleXA1FKUSgUSGT23rWaeVrR8tI+QWk4HPP/sQPzxzjX/++RjjC2F+6bEDHBrp\n5eBwD8lsgblIgslgjHaHlaIs4+9pJ5LOMbEY5QdnruF1WHjl8gyG5RSKz23HsyycXoeVVK5AMJGh\npCjIskIiWyCYSPPeg+UGm7NTS1yZD6NbvprrBIF+rxOP3UqhJGMzGSlIJQqlEpKsUJDK/1qMetps\nFrwOC0VJJpktUJBkpJKMIAiYjDqMBj06QSBblLgyH+LCbABJVijJCpIsI8sKigofObabJw/swGEx\nEUyW552+/vrrtLe38zu/8zskk0l+8zd/E6vVyp/+6Z/y3HPP8Xu/93s8++yzm/nnqwmvw8qIr43p\ncLxmg6NejxOzXk8klaXd2TybXRo3iKRyuKxmvGvs5rw0F+LwSE9VvuWbTcsIOZRvs586NMr+oS7+\n+ienK0OaBUHAZTPjspnZM+AjX5SYjyaZCsYplmTShSKBRJrLc+X0hyQrxNJ5luJpZEVlm8/DUKeb\npViKi3Oht73uP755GZ0oVlz0VFXFqBcRBZGJQLQ8U3MVDgCxzB1a8NdgzjcbKt8JWE0GluLloczJ\nZJJcrpz7KxQK/PZv/zZ/8zd/w+XLl5mZmcFkMvHggw+u/kUakAf9A/zDmxdrFnJRFDi6o4+XL07i\ncVhpsjtsDcoeKQeHV1+yC+VxiouxFB9/ZP86rmz9aCkhX6HX4+R3P/RIJQf96pVpBASO7ujFoNNh\nNhrY1uVlW5cXVVXJFiTCyQwz4QTXlqLMR5OYDfrljbQSr1yZYSIQfdvriEI5pbISgTssJmxmI4qq\noigqJVmhpAAIKKqKqqjI9ZjHdjeW37sWo74yj9JqtWK1Wvna177GwsICTz31FF/+8pex2WwMDg7i\ndDZ+88+92NHtRQCiqSyeGsvG+rxOXDYzgXjqbb7vGo1NriiRK5YYuoND6J24NBsoD6NZw2jDRqIl\nhRyoNO6oqsobY7OML0b4hzcv8fDOAR7dPVT5sAuCgM1sxGY2MtjZxiO7h5aNdorEM3myBQl/TzuJ\nXIF0rkhekihIMsVSCVEQEEURQRBW9LMs2KqKqrJcwWLAajagKiovXSqPpzMbdDy6e5gRXxu5okSh\nJJdFv5IqUSgpMiVZpSQvf09RbnqOXHnezemVkqywvbsckVqMBpK5wrJrosj8/Dyf/vSnKZVKlEol\nTpw4AZTTLplMhieeeIIf//jHG/53qheiKPCQf5AzUwvcX6OQC4LA0e19/ODMVXwue9NtfG1l5iMp\ndvW131Laey+S2TyRdI6Hdw2t38LWmZYV8hUEQeDz7z/O5bkQL12c5PkzV3n+zFU+/MAenjyw/Y7H\n2M2mu86sVBS1ItqyqpanFgsCIgKCKKATYDGeJphIc3C4B0VVmY0k2Tfo4+i2XozrUMMtywrCsujo\ndSKyolIsyXzxi19kbGyMXC5HMpnEZDKxa9cuisUidrudEydOcPjw4bqvZ6M5sr2Xn1y8TjJXwFlj\nntPnttPldjAfTTb8BHWNMgWpRDSd4/F9I2s67sJMkMf3jDRVV/NbaUkhz+dy/MVX/4Tzp07gcLr4\n8P/xaY4/8W729HcSSWX56aWpStPHUjzFy5emGWx3MdjhpvMtZYF3QhTLog2w8udPZvNMBmNMhxJc\nmQ8xsRTFbTVz32A3oijwa++5f71+ZICKiEP5YmQ26sum+j4fn/rUp/jOd75DJBLhD//wD/nwhz8M\nwGuvvcbFixf5yle+sq5r2wisJgOP7xnh5PV5HvQP1Hy+o9t7+d6JK3S1OTDUWA2jsf5MhxLs7u9Y\nU39IOJklL0k86F9741Aj0ZJC/vx3v8PE2GX+6Gv/g+/93bf4q//6pxx77Al0Oh1eh5UPPnCja+vS\nbIiXL04iyWVfa5NBx0C7m1991xFcNjPhZJZMoYhBJ6ITRVRVpSjLSCWFVK7ATDjBUwd3YNDr+OHZ\nCX509hqCAD1tTj5ybA8P7RxAEMreJkVZpijJFEvlhp9soVj+tyjdSKnI8lvSKCuplhtfy8vPkWSF\nUkm5KfUi8+ieYZ4+7AfAqNORLUh4HfDcc8+xe/duvv3tb1dEHOAb3/gG3/3udzf2D7SOHPP389PL\nU0RS2ZpbrNvsFvw93vIs1CaxM92qZAsSiWye9xzcsepjZEXh1PUFPvjArjXXmzcaLSnkH/zYL/HB\nj/0SABarHavNhngHs6sn9o3w2J4hFmMppkNxZkIJ5iIJbMtX9RfOXeMnFybv+nqX54LoRJFsQcLn\nsqETRWRF4cWLk/z4/MSNzU/lhjCvFzcP1hAFbpma9J3vfIcPfehDla8DgQDZbJaBgdqj10bBqNfx\n5IHt/OTCdR7dvXZLhrdyZFsv//DGRcKpLO1N5L2x1ZgOxblvqBvzGlKWF2eCjPjammZ4xN1oSSFf\n4d8/9wFEUcd//NKX71qKpBPLU2L6vC6O77z1e4/uHmJXXydSSUZWFARBwKjXYdCXW/F7Pc6KZ4mi\nqpRK5Yi7WJLJrzTzLEfdeal0S6RdkuUbUfhNG5c3R9+SXK5ZL29+3niOrNzY5Ly5tPGWohhBuGXy\n0ZtvvsmXvvSlytcvvfQSx44dq+l33IgcGunhxQuTLMVSNXdoGvQ6HtszzA/OXMNlNWsplgYklSuQ\nK0rs7l+9xUQklWUpnuLzzx5vSsO4t9LSQv7lr/45/+tbf8lffPVP+KP/+j/R69f+4/Z4nKseXCAK\nAkaDfl02Mm9GUcrpnWKpnKrJSxKZ5TRNn+fGxpzAjYg8EAhgs9kwGm/kD8+fP8/jjz++rmvdDFb6\nCb73s8v43PaaP6hdbQ5GtRRLQ6KqKlPBOIdHelc9CEZWFE5OLPDBB3Y3ZfPP7WjJ8OL8qZ9z6o1X\n6ejq5sF3vIvg4gLh4NJmL6tuiKKA2aDHaTHR7rTS53Xh7+ng4HDPLd7LKje8aEKhEB7Prc0yoVAI\nr7fxJ4RXw57+TtqdVsYXwnU535FtvRSlEuHUGrqzNNadQDyNXieyo2f17+NWSqms0JJCfvHMSf76\nv/0XQkuLXDl/BpPZTJtnC0ZS6g0Xv7179/L1r3/9lm//+Z//Offdd99mrGzdEQSBX3hwL9cDMZK5\nQs3nW0mxTCzFKEhyHVaoUSsFqcRMOMFje4ZXPfAlmEgTiKf5wP27WiKlskJLCvmzH/04wztG+dLn\n/h0nXn2ZX/2tL2AyN2fHVi3IqlrpOt2KeB1W3ntwBycn5m/ZBK6WrjYH9w11cWkudMveg8bGo6oq\nVxej7B/sWnV1UiZf5OTEAh97dH/LpFRWaMkcuc3u4Le+9J83exmbTkEqYTOtzXO91XhgRz/nppcY\nXwjj763db/3AUBexdJbxxQj+nnbNIXGTCMTT6ESB/UNdq3q+JMu8NjbDew7sYHtX66UTWzIi1yhH\nLAVJxm7Z2kIuivVNsQiCwCO7h1GBuUii9gVqrJm1plRUVeXE1Xl293U2fePPndCEvEUplGRMBt2q\nd/JbmZUUy8+vzdclJWLQiTx53zaCiQyR1B0cKzXWBVVVGV+IrCmlcnE2iMVk4P3372ypvPjNaELe\nouQKUsvlAWvh2Gg//e0uTl1fqMv57GYT77pvGxNLUTJ5qS7n1Lg3U8E4NpNh1SmVmXCcUDLDJx69\nr6WDGk3IW5RcUcLdpJac64EgCHz0ob1IsszVOpUkdrkdHN85wMXZILmCJubrTSCeJpkr8M7921aV\nUlmIJrkyF+LTjx9q+aBGE/IWJVcsNa238nphMuj55DsOMhmMEYin63LO7d1eHhjt4/xMkHyxVJdz\narydFV+jJw/swGy8t0vhUizFuekAv/zE4S3hKa8JeYuSyObpcts3exkNh9dh5ROPHeDk9QXSddj8\nBNjZ28GRbb2cnwmQ08S87hSkEpfnwzy2Z/i2Q8nfylIsxZmpRT79+MEtY0GsCXmLks4XVm0tsNUY\n8Xl438EdvD4+iyTXp7lnz0BnWcynA2S1NEvdUBSVy3Nh9g/4GOxw3/P5C9EkZ6aW+PTjh9Y8JaiZ\n0YS8BVFVlUSmsCVuKavlgdF+9g928erlaeQ6uVHu7u/kwdF+LswESeWKdTnnVkZRVS7Phehy2zkw\n3H3P58+E4lyYCfAr7zy8pUQcWrQhaKuTyhVwWExNPfFkvREEgWeP7KRYknl1bIZOsT6dmqO97ej1\nIj+9NM1Qp5vOm7xvNFaPqqpcmQvjspl5dM/QXcsGVVXlwkyQcCrDr7776JYMYLSIvAWJpnP0bZHc\nYC2IosBHju2h3+vi4lIcWalPZD7i8/D04VHmo0kmg3HWe952q6GqKlfmw9jNRp7YO3LXCpWVjk1Z\nUfiNp45tSREHTchbkmg6x6Am5KtCFAV+8aG9dNjNvHplpm5plnanjQ/ev5uSLHN5LoisebOsinI6\nJYzNZCyXGd7F/z2dL/LihUlGOtv4zLuOtHyJ4d3QhLwFiaVzWy5HWAt6ncjjo10Md7bx08tTSKX6\nbIBaTQbed8hPp8vOmclFrTzxHiiKyqXZEE6riXft34b+LiIeiKd5+eIU79y3jQ8+sPuuz90KbO2f\nvgVJ54soikqvVrGyJkShnGbZ3d/Jixcn61aaqNeJPLxrkIPDPZybDrAYTWmplttQkEqcm16iw2nl\niX0jd4zEZVnhzOQi56aW+NTjB5dn4rZm2/1a0DY7W4z5SII9A50VH3KN1SOK5Q3QLredfz45zuGR\nHnx1qMUXBIE9A510t9l56dIU4VSWHT3tmA2t2zK+FpLZAlfmwxwY7mb/oO+OwhxOZjl1fZ7t3V4+\n8dgB7OatbQh3M5qQtxjBRIYHRlvT4W0jEASB+3f00+608a2XzjDU2cZoT32GkngcVt5/ZCcXZoKc\nmVqk3+ukq82xpa1wl+JpZsMJHt83zED77evEZVnh/EyAUDLDRx7cw+6+Ti0KfwtaaqWFKJZkkrkC\nIz7PvZ+scVdGfB5+/aljRFPZurkmAuh0IvcNd/HsET+JXIELW7QbVFVVJpaiBBMZnj2y844iHkpk\n+NfzEzitJj7/7HH29N85Yt/KbHREXr6XVLRRWetBMJZgZ1cbqlyiIG89caiVQuHWvLjdqONXnjjI\n90+O8cbYFPsHu+p2O++xmfjAoR2ML0a4OBfEaTHR53Fi0Ld+uqUglZgMxrCZjbx73xAmg+FtmpDM\n5hlfiCDJMh+638+OLi+CoL7tb7RVKBYrDWa3fYMI9RiBtVpOnjz5MPDTDXtBDQ0NjdbikcOHD7/y\n1gc3OiI/ATwCLAJaWK6hoaGxOnRAN2UNfRsbGpFraGhoaNQfbbNTQ0NDo8nRhFxDQ0OjydGEXEND\nQ6PJ0YRcQ0NDo8nRhFxDQ0OjydGEXENDQ6PJ0YRcQ0NDo8nRhFxDQ0OjydGEXENDQ6PJ0WxsNbYE\nfr9/CBgDXl9+yABMA/8BSACfBz4JZAAL8E/Al8fGxmS/3/8LwBeA4vJzPzE2Nhbb0B9AQ+MuaBG5\nxlYiNDY29o7l/44D88CXKIv5+4BHlx8/DtwH/IHf7/cAXwOeGhsbe5jyxeBzm7N8DY3bo0XkGluZ\nl4HPAs8B7xobG0sCjI2N5fx+/yeAAlACdqx8DwgCvZuxWA2NO6EJucaWxO/364APU3aTe2xsbOzK\nzd8fGxtL3/RlcvmYNuBXgI9v1Do1NFaDJuQaW4kOv9//4vL/i5S98b9KOf99V/x+fw/wL8AfjY2N\n3dZKVENjs9CEXGMrERobG3vHWx/0+/1Bv99/cGxs7PRNj7mAnrGxsct+v78LeAH4g7GxsX/cuOVq\naKwObbNTQwP+b+Bryxub+P1+C/B14BeWv/9t4AuaiGs0KtpgCY0twXL54StjY2N9d/j+p4DfBtKU\nA5y/Gxsb+6rf7z9KOQXzxk1PPz82Nvab67xkDY1Vowm5hoaGRpOjpVY0NDQ0mhxNyDU0NDSaHE3I\nNTQ0NJocTcg1NDQ0mhxNyDU0NDSaHE3INTQ0NJocTcg1NDQ0mpz/Hwkrn1hatR5lAAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=9, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97de4eef0>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QyNuAeSLyfPh+29HHhiZseR6ALewvhc89Hjt0r3eL42fAFBF5HnvQ/Mbw/qewY/GfFpGn\ngXOxx3UIn3OdiHwZuAI7hvtp7EZmW/r+8pbqSmwL7cXwva9jkC/VIOvGQK9bEb7uynA9/SV2NNnL\n4Xq/ALgi/N69D9t1Anaj+Q7w7/CnB7vH1Pv9r8d+324Jv78vYrslPhjmfDG2P/uZ8L1Px24gjxgo\n7z48iN0IvRL+f0evx2/F9sc/ES6fjwB/DPc07gQeE5F/Y5f1CUOM3Z/LgU+Hn/ki4BPGmFzvJ4W9\nD3til++L4bI4Dzu65/5B6s9AfotdtjcO8j3s7TLsQfZnsH30PxrgeAwAjs6nrtTARMQpfGlF5HuA\nb4z5UsRpVSUp4eQ8VV46TYBSAwj7Uh8LuxOy2NFLD0WcllL92upTdpWqcb/Hjj1+Bnsc43ds6hJS\nqupo94tSStWQYW+pd3R0JLBjnhdjh6cppZQanIcdMvvYvHnzBjzBaCDl6H7ZlXcPYVJKKVWavdiK\nEVvlKOqLAWbNmkU8Hh/sucPuqaeeYu7cUqd0GflxI4u95550dXcTf+SRysZlFC7rCONGGXu0feau\nri6ee+452HwK5SErR1HPAcTjcRKJAeedKZvRFjeS2EuXQleXLutREDfK2KPxM7OV3dY6pFEppWpI\nTQ1pDIKA1atXs3jxVu29bJGo4kYVu37vvVn5zjs4WxC3eMSV4zi4rrvxp6mpCdfVtoZSW6oqi/ra\ntWv5+te/zsqVK+nu7uaUU05hr736mpr83VauXMnChSXNRTWsVq9eHUncqGIv/PznWbx4Me0lxs3n\n8/i+T1tbG/F4nFgshu/7xGIxXNfF8zwcx8FxRswFeJSqSlVZ1G+55RamTp3K6aefzltvvcVnPvMZ\n7ryzz5lEVZXL5/N4nsf06dO1Fa5UBVRlUW9qasIYe22HVatW0dTUNMgrVKWN++UvSa9axbpTT33X\nY0EQEAQBLS0tNDY2ajFXqoKqsqh/+MMf5uabb+aAAw5g1apV/OQnPxn8RapigiCg9cYbae7p4cn/\n+i8cx8H3fbLZLMlkklgsRiqVorGxcfA3U0oNq6os6rfddhvjx4/n5z//Oc8++yxnnnkmN9/c+2py\naiBBEJDP5wE2HoQsHJQsPjjZ+/fez+l9X+F33/cJgoCddtoJ3/e1Ja5UlajKov7EE0+w5572WgCz\nZ89myZIl5HI5PK+vK0ONLoViXWgdx2IxYrEYDQ0NeJ6H7/sb/0+lUqRSKXzfH/4DkK5Lj+NEcoKZ\nUqp/VVnUJ0+ezD//+U8OOuggFi5cSCaTGfUFPZ/PE4vFGD9+PHV1dSSTyY2t466uLmbOnBlxhkqp\nalCVRf3II4/kzDPP5Oijj6anp4dzzz036pQqLggCPM8jmUySSCRoamqiqalJh/wppQZUlUU9k8lw\n6aWXRp1G2RRGh/Tu825oaCCRSGzsVmlubq7ePZRMhny15qbUKFaVRX2kKxTswgHFQh+37/sb58RJ\nJBLE43Hi8Tie5208EDliPP00/+7oYN7gz1RKVZAW9WFSaH03NTWRSqU2/j+iCrVSasTToj4EQRBs\nbG17nrexhe37PmvWrGHOnDmk0+mo06yMhx8m8+yzME/b6kpVEy3qJSi0whsaGpg5c2afY7KXLVs2\nego6wIIFTO3qgmOPjToTpVSRUV3U+zpgWRj/XegLj8ViJJNJmpubyWQyEWeslFIDGxVFvVC4C8ME\n4/E4vu/T2tpKLBbbOGvgiDxgqZRSRUZUUQ+CgFwux4YNG1i9ejVdXV3kcjny+fzGn0QisXEoYOGU\nds/zSKfTJJNJ4vG4Fm2lVM0aMUX97bffZu3atXR2dm7sInEcZ+MBy4KGhgamTZsWYaZKKRWdEVPU\nW1paaGlpGfR5y5Ytq0A2SilVnUZMUVdV5qabeOmZZ9g26jyUUpvRoq62zLx5rIs6B6XUu9RUUQ+C\ngJYXO+npfK3isVveiCZulLGrLm4+IMjlcRqTuFMacRtSFc9NqajVVFEHcLsDgq5c5ePmiCRuVLH9\no/dlQk+O4Lq/VjQugBf+jd1xWfBd++O5OAkPEj5uQ7LiOSlVLWquqKsK6enByeUIKhAqyAc4AThJ\nD6cuwQpiTNx7Co6rQ1OV6k2Luqo6QS6P47o4TUmcdAwnG4eWNG7Crq7dHQu1oCvVj6os6jfccAO3\n3377xttPPfUUf//73yPMSJVLEAQ4uQASHk59AqcugdOQtD++XvdUqaGqyqJ+xBFHcMQRRwDw6KOP\ncscdd0SckdpaQT6AfIDjAI4DMQ8nE8NtzeA0pXAyMT3TV6lhUJVFvdgVV1zBRRddFHUaahBBEEAu\nsIU55uJk4zjpOMQc8D2chA8pHyfl24KuBVypsqjqov6vf/2L9vZ2Wltbo05F9ZL/xHGsXrWKbHjb\nTcdxZ7XY/m9fJ0VTKipVXdRvvPFGDjvssKjTUH3If+J4Vi9ebIt6AO60RtwmHReuVNSq+kjUI488\nwk477RR1GqNSkA+gJ29/8uHARd+OBXfSMZyGJJ1ZF3d8Hd7O7bit2YHfUClVEVXbUn/rrbfIZDLE\n4/GoUxkdcnmcpI9Tn4RMzPZ91ydxYp4t5r2HEJ5wAk3LluHddls0+Sql+lS1RX3p0qU0NzdHnUZN\nC4IAJx+A5+Lt1G5HoZTaF37PPdR1dZU3QaXUkFVtUZ87dy5XXXVV1GnUhnxgx38nPfA98JyNp9c7\nDUn7mJ7Mo1RNqNqirkqUD3Dy2LlPYi7EPPAdHN8LC7eD05LGbdSDmEqNBlrUR5ggCE/iCbAt7eY0\nS+ribLPr5KhTU0pVAS3qVSrI5Tf2dztxD+riOKkYJH17Yk82YU/ycRzoWBh1ukqpKqFFvYoE+QAn\nl4e4h/+ecZCJ4yT86uzv3nln1r3zDomo81BKbUaLeoVsnLjKc2zru3ge8KRvhxFmYpBN2KGF1X5G\n5s0381JHB/OizkMptRkt6mWycQIrz8VpSOC2pnHGZnHiOu+JUqp8tKhvpUILPCDAbUlD2t98Aqts\nHMer6hN3t8w119DyyiswT9vqSlUTLepDlQ/sZdPivv0/E8MZk2HZ8yuYvH1b1NlVzrnn0t7VBeee\nG3UmSqkiWtRDG8+udB1wXfAcnLD/G8ext8MhhO7E+ne/gXapKKWqQM0VdScAcvl+HnRw6hJ2fLfv\nbjxoWTjD0mlO2bMrtUArpUaomirqjuOwdGaCiTv1cyKO59Zm/7ZSSoVqqqgDBL6DE6+5j6WUUiXR\nZqtSStWQmmrSBkFA59I3eOfVyl+wYcOS1yKJG1nsP9zMM88+y7avmsrGBTrfXkTn2pkkMn0csFZq\nlKupok4QsOrff+PVla9UPPTqRYt5ddVrFY8bZeyVixbzavfSob0oCP8JAjs5WfHvgR3vb5/jkGxo\npq59CvF0Fj+VIZauI5bKEss2EUtlhv3zKFULqrao33777Vx11VX4vs/nP/955s+fX9oLHQfX9cqa\nW59hXTeSuBWLHQTEsg24fgzPixF7axnr8gmaJs3G8X1cz8Nx7PBPx3Fx3HCyMccBx8Nxw/sdB8fz\ncDwf1/VxPRfHi+F4Lq7n47jhe7kesXRdnyOR3EQqsmWtVLWryqK+YsUKrrjiCm666SbWrVvHZZdd\nVnpRV1skCPLke3oI8nlcz8f1Y8TSGeKZBhJ1jTRMmkW2dcKmF0yZQltXF4nTvxVd0kqpd6nKov7Q\nQw+x++67k81myWaznHfeeVGnVDOCICDI58jnenAcFy8Wp2nqdqQaxpBoaCaWqSeWSOH6sahTVUpt\ngaos6m+88QYbNmzgpJNOYtWqVZx22mnsvvvuUac1YgVBnlxXF/Xtk6mfOINYpo54pp5YKoMXT2pX\nhlI1pCqLOsA777zD5ZdfzqJFizjmmGO499579UzPEgRBQJDrwY3FybZNIlHXSCLbQGpMO6n6FhxX\nR7EqVcuqsqi3tLSw00474fs+kyZNIpPJsHz5clpaWqJOraoEQZ5U41gSXQ4tM3fAi8XxYknimTqS\nDWOIZ3XIn1KjTVUW9T333JMzzjiDz372s6xcuZJ169bR1NQUdVqRy+d6iCXTxNL1xDNZmqZvT/24\nyazq6GD8jjoFrlKqSot6W1sbBx10EJ/85CcB+MY3voE7SrsNNh7Y7Omhrn0yM/Y/IuqUrB//mNee\nf56ZUeehlNpMVRZ1gAULFrBgwYKo04hUkM8zRnYiM2Y8ycYxxNN1Uae0ycEHs2rs2KizUEr1UrVF\nvVYF+Tz5fA9BLh+eoOPi+j6uH8eLxfCTafxkhlgqQ8uMHUg26HEEpVTptKgPsyAIyPd04XoxUi3j\nSDWOsWdh+jEcP46fTBFL2lPe/XgCNxbH9Ubgn2HPPZE1a+Af/4g6E6VUkRFYTapHPteDn0gRS2eJ\nreuhaep2xNJ1ZMaMJ93SNjKLdaneeINYV1fUWSileqnhqrP1CmO+8/kcnh8nXtdIPNNALJnCT6TJ\ntI6nfsI0AFZ3dDBRL8KslIrYqCzqQRAQBHnI5wnyeYIgjxdL2BZ3OmsLdyqDn8yQqG8iUddELJ3V\nMy+VUlWvJou6ndskR7JxDKmGFvxUxk5S5fng+ni+jxtP4scT+MkUXjxFPF2nZ1sqpUa8EVfU1yx5\ngw3vLKNr3Wq616+lZ8Nacp0byPV0ke/qJNk2lYm77U22bZvqGgKolFIVMOKKenbsRLJjJ/b7eEdH\nB81Tt6tgRqPUpz7F8jffpD3qPJRSmxlxRV1ViW9/m0UdHVrUlaoy2omslFI1pLZa6kFAdumj8NI7\nFQ+dXWoiiRtZ7O9dxZTly+HbX6tsXHp93nwPdG+AeBrqJsBY7XpTo1vNFfX6ZY9D4q2Kh65bvghe\nXVLxuJHFvuUOGnI5ePX+ysYF6t9+HVbXQ7YN0mOhfgKkmsDTqzUpVVtFXdW+fA9dyTbY/ijQIahK\nvYsWdVXd8jkIchDPQKIexu/KsrdcJmtBV6pPWtRV9QnykOu2XSrN02HWIeAnNj2+pCO63JSqclrU\nVfSCPOS7oWWWPdiZaoamqZDWaYeVGiot6mrLtNaT6+rashUoyEGmDbLjIFEHyUZonQOJ7HBnqdSo\no0VdbZkfHs/bixcxfrDn5bptF0osBX4K/CRkxsL4nSuRpVKjTlUW9UceeYQvfOELzJxpr4A5a9Ys\nzj777IizUiUJ8pDrtAW8bjyM30ULuFIV1G9RF5H9BnqhMebPw5/OJrvtths//OEPyxlCbY1Hniex\nfDm0j7cnAI2da1vgyQbIttuuFR2holTFDdRSH6hpHABlLeqqyl3ye3vy0X8cA1P3h6xehFqpatBv\nUTfG7Ft8W0QcY0xQ/pSsF154gZNOOomVK1dy6qmnsscee1QqtOotyNuDm7EsxMMfL0GePGz/qaiz\nU0oVGbRPXUTeA/wcyAKzReRs4C5jzCPlSmrKlCmceuqpHHzwwbz++uscc8wx3HXXXcTj8XKFVP0K\nYJv3w9R9Nx8rHvsWQaDXKFWq2pTS6Xk5cDywOLx9PfD9smUEtLW18aEPfQjHcZg0aRJjxozhrbcq\nP5/LqJTvgVwXeHGonwjbfQJmfnDzgq6UqlqljH7pNsb8S0QAMMY8JyI95Uzq9ttvZ+nSpZxwwgks\nXbqUt99+m7a2tnKGVEEABDBhN5h+gB2CqJQacUop6j0iMhV7cBQRORhwypnUfvvtx1e+8hXuuece\nuru7Offcc7XrZTjluyGWsSf9FH4SDdC2AyT1EoBKjWSlFPXTgdsAEZFVwMvAZ8qZVDab5corryxn\niNEpCGw3yg6fhpaZW/def/4zzz35JNsPT2ZKqWEyaFE3xjwJ7CAirUCnMWZV+dNSwyafA8eBTCtk\nxsG0D0C6aevfd9o0ulas2Pr3UUoNq1JGv2wHnAvMAQIReRI41xhjypybGqogbw9yOq7tXkmPsRNj\nTd13+C8gsWYN7rp1w/ueSqmtVkr3yy+BHwHfxPal7wn8Gti1jHmp3vK5cFRKzBbs+vF2fnEvwerO\nV2Ha9vb+VDPUjSv/aJW5c9muqwsWLSpvHKXUkJRS1NcYY64uuv2MiHy8XAmpUK7bzp3SMBHi9XYa\n2mw7pBrf1epes7oDJs+LKFGlVDUZaO6Xwhj2u0XkcOBuIA/sD/y1ArnVviCwI1GCHLgxiKUhXme7\nTcbMgnE76fwpSqkhGail3oMdxtjX8MUe4IKyZFRL8jnId4HjgevbE3r8BKTGQOMke9p9usX+JOrB\n9aLOWCk1wg0090u/TUQR2cqSQFsUAAAgAElEQVTxcKNE21yYdoBtgfsJOwpFKaXKqJTRLx5wEDAm\nvCsBnAVMKV9aI0xPpx15EkuHE15lYNzOME5HcSulKquUA6W/BpqA9wAPAO8DzilnUlUv32Nb3akW\naNgG2ndm8QtLGL/re6POrHLOPZfFr7yiW3alqkwpRX2iMWYvEbnPGHOEiEwGzgCuHuyFI1YQQCwJ\n6VbbD977J9sOrbM3H4XiLo8u3ygceyxvd3RoUVeqygzlcna+iCSNMa+KyJyyZVQp+Zwt3PF68OPg\nJWy/txuzl2KbvKe9io9SSo0gpRT1P4vI14BbgSdE5GVKm7K3uiUbYPcv6YiTLXX44Ux75x34s14A\nS6lqUsrcL+eIiGeMyYnIg0AbcFf5UxsGQd6eMu+nbKvcT9mDmekxMO49WtC3xhNPkO7Si2QoVW0G\nOvno+F63i28eSTX2qTsOXamxUD8BcGzf94yDwBtKL5NSSo1cA1W7vQZ4LKACRX3Dhg0ccsghnHzy\nyRx++OGDv8BxWDblE0yep6fMK6VGp4FOPjqukon05cc//jENDXqwUimlSlW1BzxffPFFXnjhBebP\nnx91KkopNWJUbWfzhRdeyNlnn82tt946hFcFNDc/DCwpV1r9amp6OZK4kcXeX+hcvZoEd1Q2LqNw\nWUcYN8rYWx+3E+gG0sBOwPjhSKvqlTJNwMHGmIp+c2+99VZ23HFHttlmmyG+MiCZfJMotlXJ5BIg\nmtE0kcT++eGsWbSYel6rbFxG4bKOMG6UsYcWN8AW8Cx2gF47dmaTJiBDmS+rXFVKqX5fFpE/GWN6\nyp5N6L777uP111/nvvvu48033yQejzNu3Dje//73VyoFpdSIsgswHWhkNBXwvpRS1N8B/i0iTwAb\nByYbY44pV1KXXHLJxt8vu+wyJkyYoAW92lxyD5mVq+Ccw6LORI1qOWAmtqgrKK2o/y78UWqTS+4h\nk8tpUVcRyIc/rcA0YOdo06kypZxR+gsRmQvMMMbcKiKNxph3KpAbAKeddlqlQimlql4A7Ig98JmM\nOJfqVMqB0i8Bn8LOo34rcLaIrDDGnF/u5JRSo1GAvbhaDnugMwXEsUW8HZgVXWojQCndL5/CzqF+\nT3j7q8CDgBZ1pdQWCrBdKAG2GyUT/qSwQxAbePPNNxg/Xo+lDVUpRX21MSZfmPsl/D1f3rSUUtUt\nCH9y4Y+DPZfRxQ5DHIcdXuhhy0zh/sJPDNv6rg+f20eEYGk5P0DNKqWovygi5wBNInI4djKvZ8qb\n1pZyeOed99DeLoM/dZitXPks48fPrnjcyGLHsuSDLqDyLalRt6wjjDtw7Bi2VZ0Kf2LhT9WeqD4q\nlFLUTwG+ACwEjsZe0u7ycia15RzWrZsKVP7aoOvWdUUSN7LYz7/Kkx0dzNNlXdNxo46thq6kom6M\nuQi4qHCHiHyL0X6dUqWUqkIDzae+L7AfcLSINBc9FAOOo0qLej4XkOvOjZq4kcV+ooPks4bcDjtW\nNi6jcFlHGDfK2EOJ67gOrqfdPjBwS/1Z7PghsEdCCrqBBWXLaCsE+YCF9y4lb/5V8dhvvhlN3Khi\n7/jljzE1l+Nxd25F48LoW9ZRxo0y9mBxg3xArieP53vMPUTItqQrmF31Gmg+9cXAtSLyoDHmFQAR\nSQBjjTGvlzOp9evXc8YZZ/D222/T2dnJySefzL777lvSax0XPL/yW2zHdSKJG3VsXda1HTfK2H3F\nDYKAXHeeZF2SCTu0kW3NkMjGtZVepKRx6iKyBrgK6ABWi8hdxpizy5XUvffey9y5c/nsZz/LwoUL\nOf7440su6kqp2hMEAY7rMnHHdurbsqQaklrI+1FKUT8U2AM4BvitMebrIlLWS8h/6EMf2vj74sWL\naWtrK2c4pVQVy3XlSDYk2e7gWcQSVXsJiKpRyhLqNsYEInIwcGl4X0UmV16wYAFvvvkmV155ZSXC\nKaWqSBAEeDGP6XtNoWmiXtayVCVNvSsivwcmGmMeEpFDsOf3lt11113HM888w1e/+lVuv/12HGd0\nz5Os1GiR686TbI4z99DZ2jofolKW1lHAAcDfwtudwGfKlhHw1FNP0dLSQnt7O9tuuy25XI7ly5fT\n0tJSzrBqCF467UKWLVtGKupEVE3J5wLSTUm2mTeB598wWtC3QClL7Mjw/0ML878A2wBXlyUj4PHH\nH2fhwoWcddZZLFu2jHXr1tHU1FSucGoLrJ25Aysyi7Woq2GT78nTPqeNiTu14zgOzkLdM98SpRT1\nvYp+jwPvxbbay1bUFyxYwFlnncVRRx3Fhg0b+OY3v4nr6pFupWpVPhfQPreNiTu2azfrVirlIhnH\nFd8WkTTwf8uWEZBMJrn44ovLGUJtpTlfPZxZPT0894Pbo05FjVBBEJDrCfA8h0m7jGfc7LFRp1QT\nhtxhZYxZJyIzypGMGjnczvX4uWhOW1cjWz4XkKyL0zy5kYbx9WRa0jrmfBiVcuWj+7ETJxdMAKI5\nX1kpNSIF+YAgD8n6BGNntdA2u1W7WcqklJb6N4p+D4BVwD/Lk45SqhbkevI4OMQyPqn6JNnWDO3b\njcWLVeQUl1FtoFka9+vnoWZgX6CsZ5UqpUaeIAgIcgHjt29j/Jw2LeIRGKilPtDcLgFa1JVSoVxP\nnpYpTaQbkzRMbCDTpINdozLQLI0bZ9ASkQZjzMrw93HGmDcrkZyqXksP+CSrVq2OOg0VkSAfkM/l\nCfIBfsKnob2e6XtMxnG1nzxqpRwoPRk4EPhYeNe1InKzMaZKL2mnKuHNQ49j0aLFjI86ETUsgiDY\nOByi8Hvh/3x3np7OHpJ1SRom1JHIJoglfRJ1ceLpOPF0TEevVJFSDpT+B5ufgHQg8Feq9DqlXsLF\nT1b+1GIvHk3cKGOPtrhRxi4pbgCu7xJL+HhxDz/h4XourueA49hWtOPgug44FP3v2v89d9Mc5p79\n33Fd8k93seO8OSSyCR2xMgKUsnZ6xpieotsBUJV/Wcd1aN9jDDvOm1Px2LmODZHEjSz2SScxYelS\nWm+6qbJxGYXLOsK4APG6GMm6ZCSx1dCVUtRvF5EHgfsBF9gfqPw3WVWXO++kvqsr6iyUUr0M2hFm\njDkf+BqwBFgMnGyM+Z9yJ6aUUmroSuocNMY8ADxQ5ly2WhAExB97nPXLV1Q8duLFFyKJG1XsxIYN\nON3drP/T3WWLEXR3QWcnOC5OMolTl8WtrwfdQ1CqX7U1WXEQEH/2WbrXrq146NiiRXSvXVfxuFHF\nTnR14eRydP9r+GaMCHJ5YnO3w81kcTJpnPp63OZm3EwGJ5HAKczU2dExbDGVqjW1VdRV1QtyOZxE\nAjedxslkIJ3BzaRwMhncpibi224bdYpKjWha1NUWyY0bR+eGzs3uC3I5O3QumcRNpXBSKUgmcOJx\n29JOJHDiCeK77Yrj6enjSpVD1Rb17373u3R0dNDT08PnPvc5DjzwwKhTUkXWn3Aii157jfbubpxM\nBq+hAW/WLBK77bqpm0QpVXFVWdQffvhhnn/+ea6//npWrFjBYYcdpkW9CgT5PORyeGNb8cZPYP2s\nWdR/8CCcWCzq1JRSoaos6rvuuis77LADAPX19axfv55cLoenu+wVFQQBdHfj1jcQm7MdblMj3owZ\neJkMXHstDS+/jHPoIVGnqZQqUpVF3fM80uk0ADfeeCN77723FvRhFgQB5PPQ00OQD3B8D1wXJ5XC\nzWZw0hm8trH4M2fitbe/u0vlzDOZ0NUFZ50VzQdQSvWpKot6wd13382NN97I1VeX7RrXo07Q3U1s\n5ky8yZPsiJO6Opx0BieVtAc0deOp1IhWtUX9/vvv58orr+Sqq66irq4u6nRGhCCftyNP6uttP3cs\nBjEfJxbD8WPg+/jTpxGbMiXqVJVSZVKVRX316tV897vf5ZprrqGxsTHqdKpW0NUFQUBAgFNXR2zK\nZBLz5+PG41GnppSKSFUW9T/84Q+sWLGCL37xixvvu/DCCxk/fnTP3h0EATgOXnMTTjZLfOed8caN\n44VnnqFul12iTk8pVQWqsqgfeeSRHHnkkVGnUXUcxyFzwvF4vfdedI5rpVSoKou6ClvlPT24zU24\nDY04dXV449vfXdCj8ve/88w//sGOUeehlNqMFvUqEnR3g+/jtbbiTRhPfPfd7ZjwatTURK6+Puos\nlFK9aFGPUBAEBJ2duJkMXksL/swZxHcdIfOivPEGsbfeijoLpVQvWtTLaOMJPrmc/XFde4JPOoU/\nZSreuDb86dNxm5tH3rUf99wT6eqCRYuizkQpVUSL+lYqnEofEEA8jptM2rHiY1txW1pw0mncTAay\nWdxwpkJ8f+QVcaXUiKBFfRCFA5ZOJo3X2ATxOCTi9oSeeBwnkyG2446s/fe/qddhhUqpiI26or6x\nZZ3L2flMXA98DydRmPM7iZNK2cunpZK4dXW4rWPxtpk48Ek92vJWSlWBUVXUg64u/BkzSOy6C059\nA04yvHDDSDgwqZRSJaiZoh4Egb0gcXc3+fXrbaGO+biZrG1tt4whtt1svEmTtD9bKVWzRnRRz739\nNl2PPUbujUUEPd2Qz7Nhjz2oO/iDuMkkFF+sWA2vSy/l9RdeYEbUeSilNjOii7rX0kLqgx/c7L6e\njg68pqaIMhpFPvpRVnZ0RJ2FUqoXbcYqpVQNGdEtdRWhffdl1urV8PjjUWeilCpSU0U9CAL+tfZf\nrH19dcVjv7z25UjiRhV7txeewenp4a+v31fRuDC0zxsA63vW47k+49JtTG+cQTqWLm+CSkWotoo6\nAUu7l5BYU/mLRCzrWUYsgrhRxd45nyMX5HljzcKKxoW+P28QBOSCHHnyxN0YmViWpmQTLclmJmQn\n0pho1FFPalSoqaKuakuhUAOkYyniboKY67PWW0t7ph3f9fFdn7gbJ+mnqI/X0ZhsIhPL4Dp6uEiN\nTlrUVVXpyfcwITuBxkQDST9NQ6Ke1vRYEl5i43M6VnQwb9K8CLNUqnpVbVF/7rnnOPnkkzn22GM5\n+uijo05HbaUgCMgHefLk7YliBDiOg4OL53o0J5tpSY5hcsNk2tJtUaer1IhVlUV93bp1nHfeeey+\n++5Rp6J6CcILXb96wC6sXbeGzlwnruPg4uJ7MeJePOwOSZKNZUj7GWJuDM/1SXhxEl6SuB8n4cbx\nwu4Tz/G0u0SpYVKVRT0ej/Ozn/2Mn/3sZ1GnMqrlgh7q4w20pceS9NN4rofveMTdOInL5vPWv5/n\n4zN3IRYWc8/ROXSUilpVFnXf9/H9qkytpuWCHPkgR8pP05xsZmrDNKY1TOt31Mhb/hLqE3pJO6Wq\niVbOUS4IAnqCHiCgPTOevSbsTSqWGvyF55xD++LF8NOflj1HpVTptKiPQvkgDw5Ma5hKfbyRMckx\nNCUbiXuJ0sdy/+IXtHR1lTdRpdSQaVEfBXJBD67jURerY0yqhbHpNiZmJ5IspUWulBpRqrKoP/XU\nU1x44YUsXLgQ3/f54x//yGWXXUZjY2PUqVW9XJAjCPKk/BTZeB3ZWJYJdROZUj9FR5goNQpUZVGf\nO3cuv/rVr6JOo+qlYinibpx13jomZMeT9NOMSbUwPjOhtH5xpVTNqcqirgaWC3IcOPkg2jL2JJ2O\nFR3M20bPsFRKaVEfcYIg4H3tu28s6JEZO5aetWtJDP5MpVQFaVEfAXJBDy2pMTQnmpDmbWlKVsGV\nnR59lGc7OtD9A6Wqixb1KpYLcjQmGtiuZS7TG6dHnY5SagTQol5levI9AGTjWWY1zmLOmLnVOQ/4\n3XdT99xzME/b6kpVEy3qZVSYmTBHDgeHmOOR8FMkvAQJP0HcjRPzYsScGDEvTtyLUR9/91SzVenE\nE5nc1QUnnxx1JkqpIlrUh1ku6GFsehxNiSaysQyZeJaGeD2pWJq4G6/OVrdSqmZoUd9ChTlTgiCP\n78bw8KiP1zO7ZTYzGmdGnZ5SapTSol6CQgGvi9UxNjOWtJ8hHUvTlGiiLl5Hwk/wj/X/YN507V9W\nSkWrpoq6g0NrrJUJ2fHD9p4xN05dvI5xmXbGpMZo94lSqqrVVlF3HHbIvEfPrlRKjVo1VdRVBd15\nJ88//TRzo85DKbUZLepqy8yeTefatVFnoZTqpaaKehAE/PXl9bwevF7x2K+8Gk3cqGI73V28+uqq\nqljWnT05mrMJdprcxJi6ZMXzUaqa1FhRh3c25Fm6urPisVdFFDeq2As+NZ9cLscNv7m/7LFyuTye\n5xJzHXzfpTsPPfmAmOcS9112ntrMpJZM2fNQaiSoqaKuaksulycZ85i/7Thmjtt0geuOjuXMmzcp\nwsyUql5a1FVVCIKA7lyAA6TiHg3pODPasmy/TaMOI1VqCKq2qF9wwQX885//xHEczjzzTHbYYYeo\nU1JbqSeXJ5cPcB0Hz3HwfYfmTJwJzWlScZ+WbJzmTIJk3Is6VaVGrKos6o8++iivvvoq119/PS++\n+CJnnnkm119/fdRpqS2Uywd4rsOu01qY2polFfdIxDw8V1vgSg23qizqDz30EB/4wAcAmD59OitX\nrmTNmjVks9mIM1OlyOUDCAIyyRjZhM+McVm2Hd+A7+mFr5Uqt6os6suWLWPOnDkbbzc3N7N06VIt\n6lUil8vzxFEnsXb1KprSMeIxj6Tvkoh5JHyXpmyCaa1ZYr4WcaUqrSqLem9BEESdwqgUBAFdPXlc\nxyEV98gkfOpSPu2NaeZ+70z++Y+/M2/e5KjTVEoVqcqiPnbsWJYtW7bx9pIlS2htbY0wo9pVGHXi\nOZBO+KTiHqm4/b8xHWOb5jSNmQRxbXUrNSJUZVHfY489uOyyy1iwYAFPP/00Y8eO1a6XYdDTkyfm\nu6STHpl4jLqkT0M6Rntjita65NC6SxYsYOry5XDXXeVLWCk1ZFVZ1HfeeWfmzJnDggULcByHc845\nJ+qURpRcznaZZJI+6YRPXdInm/AZ15hi8pjM8Iz7fvhhMl1dW/8+SqlhVZVFHeArX/lK1CmMOLl8\nQC4f8N7pLew8pRlXhwwqNepUbVFXA+vJ5QkCaM7GiTf5bD+1mWzCp7U+SVMmHnV6SqmIaFGvYkEQ\nbGx9e45DIuaRSXpkEzGmtmaZMa6OuO/S0fE2O09pjjpdpVQV0KJeRQojUbIJjymtWRrScZozcRrS\nMTIJX0/eUUoNSot6hHL5gHENSdJxj2TcJxP3aGtIMq4xVf0FfK+9WPP22ySizkMptRkt6hVW6FLJ\nJn0O3mE8Y+pH6EUdfvUrXunooCXqPJRSm9GivhXyYYHO5wO68tAdnn3peQ5xzyWV8MjEfTJJn4Rv\nL+iQ8D2ySZ9xDUmScV38SqnhpVVlCIIgIJcLaGtMMrY+SVM6Tl06Rjru8ezTq9h13nRinjs6hhJe\ncQWtr70G8+ZFnYlSqsioLuqFA5MQ4Lu2JV34KVwqLe67xD2PeMwh7nlMac3QnH13T3IqZie0GjW+\n9z3aurrgwgujzkQpVaSmi3o+H9Cdy+O5Dq7j4HsOMc/F9x3qEjEmj8kwpi5BcyZBIubqFXaUUiNe\nzRX1hOeQiXvUpWI0ZxPsOKmJmO8S8xwt2kqpmldTRd11HQ6alWbevKlRp6KUUpGo8sHQSimlhqIc\nLXUPoCvCGfw6OztHVdxIYre20tXdDbqsaz5ulLFH02cuqplbNeLCGe6rCnV0dOwJ3D+sb6qUUqPH\nXvPmzXtgS19cjpb6Y8BewGIgV4b3V0qpWuQB7dgausWGvaWulFIqOnqgVCmlaogWdaWUqiFa1JVS\nqoZoUVdKqRqiRV0ppWrIsAxpFJEYcA0wGTuM8ThjzEu9ntME/D9gjTHmE8MQ8wfA+4AA+IIx5rGi\nxz4AXBDm8gdjzHlbG6/EuEngJ8AcY8wuwxWzxNj7At/GfmYDnGiMyVcg7meBE8K4/wROMcYM25Cq\ngWIXPefbwO7GmPmViCsirwCvs2nI7qeNMQsrFHsb7PcoDjxhjDmp3HFFZALwv0VPnQacYYy5ttyx\nw8dOAY7GLu/HjTFfrFDcjwLfADqB64wxlw9X3PD95wK3AT/o/d5bU8OGq6V+FPCOMWZP4H+wxaW3\nK4EtHlBfTET2AWYaY3bHFpQf9nrKD4GPA3sAB4rIdhWK+z3gH8MRawti/xT4hDFmD6AO+GC544pI\nGlgA7BXGnQ3sPhxxB4td9JztgL2HK2apcYGDjTHzw5/hLOiDxb4YuNgYsxuQE5FJ5Y5rjFlY+KzA\nB4DXgNuHI+5gsUWkHvgqdh3bE9hORN5XgbgucDnwIez6daiITByOuOH7Z4DLgHv6ecoW17DhKur7\nA7eEv98dJtLbiQxTUQ/j3QpgjHkGaAr/+IjINGC5Meb1sKX6h/D5ZY0bOpNNy2G4DRZ7njHmjfD3\npTBsV5rrN64xZp0xZn9jTHdY4BuAN4cp7oCxi1wMnDWMMUuNWy4Drdsu9sS+28PHTzHGvFbuuL0c\nC9xkjFkzTHEHi90V/mRFxAfSwPIKxB2DbaguDevIPdgN2nDpxG4wFvV+YGtr2HAV9XHYQkKYRCAi\n8eInGGNWD1OszeKFlob39fXYEuxZWuWOO9yfcaixVwGISDtwIHZFKHvcMOYZwIvAb3p3u5Uztogc\nC/wFeGUYYw4aN3SliDwgIt8RkeGc03mg2K3AauAHYey+9ojLEbfYicDPhzHugLGNMRuAbwEvAa8C\njxhjnit33PD3OhGZGXYv7wu0DVNcjDE9xpj1JeY1pBo25KIuIieKyMPFP8ABvZ5W6YnLB4pXzlyi\nnKD9XbFFZCzwW+BkY8zblYprjPkOtp/1gyLS117asMcWkWbgOGxLvdx6f+ZvAl8G5gNzsbvJlYjt\nABOAS4F9gJ1E5MMViAuAiOwOPFtoQJRR8d+5HrsHPAuYCrxXRN5T7rjhcaHPAFdj975fJrrv+5Di\nDvlAqTHmKuCq4vtE5Brs1uWf4VbNMcaUc5rGRWzeihiPnWumr8cm0McuThniltuAscOV/w7gLGPM\nXZWIGxbWucaYvxpj1ovIHdiut7+VOzawH7blej+QAKaLyA+MMV8qc1yMMb8s/C4ifwC2B24chriD\nxV4GvGqMeTGMfQ8wB/h9meMWHILtXh1uA8XeFnjJGLMMQETuB+ZhD8qXMy7GmL9gu7sKB+NfGYaY\nW5LXkGrYcHW/3AUcEf5+KHDvML3vQPE+ASAiOwOLCl0fxphXgHoRmRL2wR0SPr+scStgsNgXY4+i\n31nBuDHgGhHJhrd3w468KXtsY8yNxpjtjDHvAw7DjgQZjoI+YFwRaRCRPxZ1L+4DPDVMcQeMbYzp\nAV4SkZnhc+cxfMu7lHV7V4anmA4l9ivAtiKSCm/vAjxfgbiIyB0iMjY8qHko5dmgvcvW1rBhmdBL\nRDxs630m9gDAscaY18O+1r8Aj2IPNDRitzpPA/9tjPnzVsT8DvaodB44BdgJWGmMuUVE9gYKV0S+\nyRhz0ZbGGWLcG4BtsK2nDuCnwzzsq8/YwB+BFcBDRU+/1hjz03LGDT/zseF9Pdgv/H8N85DGfmMX\nPWcKcM0wD2kc6DN/Abtrvh74O3BapT6ziMzADh92gSexy3u4hq4OuKxF5EngA8aYt4YjXqmxReRz\n2K62HuBBY8zXKhT3cGxXWwBcZIz53/7fachx52EbYlOAbmAh9gD4y1tbw3SWRqWUqiF6RqlSStUQ\nLepKKVVDtKgrpVQN0aKulFI1RIu6UkrVEC3qo5iIXCIi80Rkvog8EN53XzhDXDnjHl30+1HhnCYV\nJSIXichTIrKLiHxBRJ4TkUNE5DqxsxL297oBHx8k5lZ/VhE5VkR+vTXvoWrbsEy9q0amwhSmIjK/\nUjHDcxq+CRQK07eA32DHCVfSYcAhxphnRORC7LSrdwC/G+hFxpgFWxEzqs+qRhEt6qOAiIzHzoft\nACngJ8aYq0XkPuB87EkdxfYXkS9h59v4ljHm1yLShp3IKYs9Lf+74UkS5wK+MeYbYaxXsCeovCAi\nF2CnDUhhT0L7GnYujckichf2ZKkZwD0ichjwHuCcMM9u4LPGmJd7fZb3ApdgZ+5bDhwDrAvvm4c9\nUeTPxpizw+efBnwSu64/C5yMndVxAvZs2N+Hr/tOOMXFD7Gz8b0U/l6YF/9iY8wNJXy+fYAzgDew\nJ6F1Y6dB/nrxZzXGLA/z+x6wwhhzQXj7G9ipk78P/CrMuwG4tHh6gj6W9XzgfGPMnmKn4/0RdkbD\nLHCmMeZuETkS+AqwNlzG77rugRr5tPtldDgSOxHTfGzRSQ/yfMcY82HsWXxfD+/7b+Av4Xt8FPix\niNT19wYicgQwwRizTzj39wzs6c7nAEuNMQcaY84Jn74/sAE75/7hxph9sHNN93UW3a+xxX4fbCH9\nMLZoT8UW2L2x80/vIyK7YVvke4dzZr+DvXjIN7BTBH/aGPPf2DnwTzfGFM8R/mmgLZyG4IPAseFe\nxmCfD+yc8meGMXPAQcWftVDQQ/9LeKp66EhsMR8PXG6M2S983+/3t6z78GPsRmg/4CPAVeHp5mcC\np4Z/w69hN2yqxmhLfXS4Azg5nHjt99irMw3kvvD/N7BTOwC8F1ssMMYsEZE3ABngPfYFdg/3BsC2\nNqdiT23vy1zs9KI3iwiAh211byQiY4BGY8xTYR6XhPdfAtwdnqqfCyd92hXbaJkB3Bu+Zwbbci7F\newmXgzHmHezGg/B9Bvp8/wKeMcYsCe9/FWjuL4gx5h8ikhA7h3YS6DHGPBXuGX1NRL6G3TAMZX78\nfbHTxhY2JN3AWOz0AteIyE3AzcaYR4bwnmqE0KI+ChhjnhV75ZR9sBOvfZG+L2RSUNwdU5j2s/d8\nEk54X+/7CxNddWLnvtmstR3O09KXTuC1QeZvCeh777K/3DqB240xpw7wnkONVdDf55vPu7uzBps6\n9Vpsaz3DpmMN5wPPG2M+FU6Y1tfEccWfu/j6BZ3YPZ5lvZ7/AxG5Frvn8RMRucoYM9gGXo0w2v0y\nCojIUcCuxpi7sX3Kk+lOpoEAAAGySURBVMLd8aF4GDgofL/x2Fa1AVZhJzFDROZgW4Rgr3J1eCGO\niHwznF0wj53dsSAIbz8HjBF73UZEZG8R+c/iBMI54peJyK7hc04XkZPD3A4QESeMt09439+Ag8Oi\niIicLHZO8FI8SHhJQBGpF5FHZPMLv/T3+QZS+Ky9XYudBfDQ8HewF2R4Ovz9KCAvIoler9u47LFT\nERfn9skwrzHhKCcvnLxqpTHmF8C52GtzqhqjRX10+DfwfRH5C3Za5AvDaVyH4hxgz7C74WbgP429\npNkN2Is13I+9Kk6hEN2MLaoPishD2CL1EnZe6DdFpEPslKZ3Ao9j+5CPBn4e5nkets+8t/8ALg2f\nsze2ZXsD8AK2mD0A3GqM+Zsx5nHgCuC+cMjmfEqfOvY3wMsi8iDwJ+D7va4R0N/nG8idwOMiMr34\nzvBgcIA91lCYz/ty4L9F5E/YVvo9bCr4BRdjl9ed2IOfBZ8HDgv/Jn/AHjjOYedjf1DsPOxfpu9j\nFmqE01kalVKqhmhLXSmlaogWdaWUqiFa1JVSqoZoUVdKqRqiRV0ppWqIFnWllKohWtSVUqqG/H9N\nl6ylKt15jgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97de64748>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4FMPrtgNQ5lCniBsOx/Hll2kUD4oC5/pH2LGmFs81tJtNpjOMRhNsbalZROs0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\nuYaGhkaFowm5hoaGRoXz/wNhS3oqNfo4KQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=10, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97d9feeb8>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oFPK0zBAaJ21D49TZJNKZartlGMYQqXuBb29vZ9dddyUMQ2bMmEE2m+WVV16h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\nAaYCsbY/CoCo83oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97d9fe320>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RoLM62MwmXDYLp8dr85VvaQkwHJ1mZKoxrfKjgxO0+91L76izJuhCPg/PnR2nyW3HYlp5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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THNu6P_gE6bE",
        "colab_type": "text"
      },
      "source": [
        "***According to the plots above, 5 is the best clustering number***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFzF0WwcFHks",
        "colab_type": "text"
      },
      "source": [
        "#### K-Means"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SRtG368FM9U",
        "colab_type": "code",
        "outputId": "f1051f96-e048-4bdb-e6be-5a5802cace4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "k_means = KMeans(init=\"k-means++\", n_clusters=5, random_state=23)\n",
        "k_means.fit(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrydEqHqFOWc",
        "colab_type": "code",
        "outputId": "9470cf1b-2b3a-4afc-8223-0ba80e9b91c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "result = df.copy()\n",
        "result[\"group\"] = k_means.labels_\n",
        "result.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
              "      <th>group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>77769</td>\n",
              "      <td>0.791329</td>\n",
              "      <td>6559.829923</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>59</td>\n",
              "      <td>81799</td>\n",
              "      <td>0.791082</td>\n",
              "      <td>5417.661426</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>74751</td>\n",
              "      <td>0.702657</td>\n",
              "      <td>9258.992965</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>59</td>\n",
              "      <td>74373</td>\n",
              "      <td>0.765680</td>\n",
              "      <td>7346.334504</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>87</td>\n",
              "      <td>17760</td>\n",
              "      <td>0.348778</td>\n",
              "      <td>16869.507130</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Income  SpendingScore       Savings  group\n",
              "0   58   77769       0.791329   6559.829923      1\n",
              "1   59   81799       0.791082   5417.661426      1\n",
              "2   62   74751       0.702657   9258.992965      1\n",
              "3   59   74373       0.765680   7346.334504      1\n",
              "4   87   17760       0.348778  16869.507130      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrvzPtJqFPo0",
        "colab_type": "code",
        "outputId": "f51dca97-db1b-4523-d896-a425606abc83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for label in result.group.unique():\n",
        "    print(\"\\nCluster {}:\".format(label))\n",
        "    result[result.group == label].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 1:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
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              "      <td>5417.661426</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>74751</td>\n",
              "      <td>0.702657</td>\n",
              "      <td>9258.992965</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>59</td>\n",
              "      <td>74373</td>\n",
              "      <td>0.765680</td>\n",
              "      <td>7346.334504</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>54</td>\n",
              "      <td>76500</td>\n",
              "      <td>0.785198</td>\n",
              "      <td>6878.884249</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Income  SpendingScore      Savings  group\n",
              "0   58   77769       0.791329  6559.829923      1\n",
              "1   59   81799       0.791082  5417.661426      1\n",
              "2   62   74751       0.702657  9258.992965      1\n",
              "3   59   74373       0.765680  7346.334504      1\n",
              "6   54   76500       0.785198  6878.884249      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 0:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
              "      <th>group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>87</td>\n",
              "      <td>17760</td>\n",
              "      <td>0.348778</td>\n",
              "      <td>16869.507130</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>87</td>\n",
              "      <td>42592</td>\n",
              "      <td>0.355290</td>\n",
              "      <td>18086.287158</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>83</td>\n",
              "      <td>34384</td>\n",
              "      <td>0.324719</td>\n",
              "      <td>14783.379086</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>84</td>\n",
              "      <td>27693</td>\n",
              "      <td>0.367063</td>\n",
              "      <td>17879.558906</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>87</td>\n",
              "      <td>31481</td>\n",
              "      <td>0.317424</td>\n",
              "      <td>16180.688082</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  Income  SpendingScore       Savings  group\n",
              "4    87   17760       0.348778  16869.507130      0\n",
              "7    87   42592       0.355290  18086.287158      0\n",
              "8    83   34384       0.324719  14783.379086      0\n",
              "9    84   27693       0.367063  17879.558906      0\n",
              "17   87   31481       0.317424  16180.688082      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 3:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>SpendingScore</th>\n",
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              "      <th>group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>29</td>\n",
              "      <td>131578</td>\n",
              "      <td>0.847034</td>\n",
              "      <td>3535.514352</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>30</td>\n",
              "      <td>122788</td>\n",
              "      <td>0.872872</td>\n",
              "      <td>5706.149573</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>17</td>\n",
              "      <td>134966</td>\n",
              "      <td>0.907242</td>\n",
              "      <td>4128.044796</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>20</td>\n",
              "      <td>129142</td>\n",
              "      <td>0.887052</td>\n",
              "      <td>5603.121028</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>18</td>\n",
              "      <td>130813</td>\n",
              "      <td>0.890891</td>\n",
              "      <td>5256.434560</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  Income  SpendingScore      Savings  group\n",
              "5    29  131578       0.847034  3535.514352      3\n",
              "25   30  122788       0.872872  5706.149573      3\n",
              "26   17  134966       0.907242  4128.044796      3\n",
              "42   20  129142       0.887052  5603.121028      3\n",
              "45   18  130813       0.890891  5256.434560      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 4:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
              "      <th>group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>85</td>\n",
              "      <td>111389</td>\n",
              "      <td>0.036795</td>\n",
              "      <td>16009.237763</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>92</td>\n",
              "      <td>122879</td>\n",
              "      <td>0.060724</td>\n",
              "      <td>13709.670275</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>88</td>\n",
              "      <td>120678</td>\n",
              "      <td>0.063273</td>\n",
              "      <td>14264.473847</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>84</td>\n",
              "      <td>122696</td>\n",
              "      <td>0.082187</td>\n",
              "      <td>13809.734087</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>89</td>\n",
              "      <td>119697</td>\n",
              "      <td>0.091679</td>\n",
              "      <td>16215.399077</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  Income  SpendingScore       Savings  group\n",
              "10   85  111389       0.036795  16009.237763      4\n",
              "15   92  122879       0.060724  13709.670275      4\n",
              "19   88  120678       0.063273  14264.473847      4\n",
              "21   84  122696       0.082187  13809.734087      4\n",
              "24   89  119697       0.091679  16215.399077      4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 2:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>SpendingScore</th>\n",
              "      <th>Savings</th>\n",
              "      <th>group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>36</td>\n",
              "      <td>99780</td>\n",
              "      <td>0.265433</td>\n",
              "      <td>16398.401333</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>30</td>\n",
              "      <td>99949</td>\n",
              "      <td>0.344679</td>\n",
              "      <td>13621.639726</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>31</td>\n",
              "      <td>107963</td>\n",
              "      <td>0.290509</td>\n",
              "      <td>13407.081391</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>30</td>\n",
              "      <td>101073</td>\n",
              "      <td>0.314387</td>\n",
              "      <td>14324.555977</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>33</td>\n",
              "      <td>101058</td>\n",
              "      <td>0.315082</td>\n",
              "      <td>14911.868398</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  Income  SpendingScore       Savings  group\n",
              "11   36   99780       0.265433  16398.401333      2\n",
              "12   30   99949       0.344679  13621.639726      2\n",
              "13   31  107963       0.290509  13407.081391      2\n",
              "20   30  101073       0.314387  14324.555977      2\n",
              "34   33  101058       0.315082  14911.868398      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvS5a5EbFRFl",
        "colab_type": "text"
      },
      "source": [
        "#### Find Examplars\n",
        "find the observation that is closest to the cluster center"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMRaf3eKFTo7",
        "colab_type": "code",
        "outputId": "05f64e31-1355-4885-89c0-b475a6b9cbed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from scipy.spatial import distance\n",
        "\n",
        "for i, label in enumerate(set(k_means.labels_)):\n",
        "    X_tmp = df[k_means.labels_ == label].copy()\n",
        "\n",
        "    exemplar_idx = distance.cdist([k_means.cluster_centers_[i]], X_tmp).argmin()\n",
        "    exemplar = pd.DataFrame(X_tmp.iloc[exemplar_idx])\n",
        "\n",
        "    print(\"\\nCluster {}:\".format(label))\n",
        "    exemplar"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 0:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>320</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>87.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>12000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpendingScore</th>\n",
              "      <td>0.342991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings</th>\n",
              "      <td>16180.265252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        320\n",
              "Age               87.000000\n",
              "Income         12000.000000\n",
              "SpendingScore      0.342991\n",
              "Savings        16180.265252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 1:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>278</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>59.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>56321.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpendingScore</th>\n",
              "      <td>0.806566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings</th>\n",
              "      <td>6129.288986</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        278\n",
              "Age               59.000000\n",
              "Income         56321.000000\n",
              "SpendingScore      0.806566\n",
              "Savings         6129.288986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 2:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>339</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>34.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>89598.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpendingScore</th>\n",
              "      <td>0.340152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings</th>\n",
              "      <td>12832.768721</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        339\n",
              "Age               34.000000\n",
              "Income         89598.000000\n",
              "SpendingScore      0.340152\n",
              "Savings        12832.768721"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 3:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>360</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>117108.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpendingScore</th>\n",
              "      <td>0.857078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings</th>\n",
              "      <td>4749.879237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         360\n",
              "Age                28.000000\n",
              "Income         117108.000000\n",
              "SpendingScore       0.857078\n",
              "Savings          4749.879237"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 4:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>368</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>84.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>110582.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpendingScore</th>\n",
              "      <td>0.079049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings</th>\n",
              "      <td>14937.995170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         368\n",
              "Age                84.000000\n",
              "Income         110582.000000\n",
              "SpendingScore       0.079049\n",
              "Savings         14937.995170"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPo-xc01FVF8",
        "colab_type": "text"
      },
      "source": [
        "#### Snake Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_-mtFPOFYfz",
        "colab_type": "code",
        "outputId": "315faa06-a450-4e13-8f62-f4aa42d9b76f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "dat = X.copy()\n",
        "\n",
        "dat[\"Cluster\"] = k_means.labels_\n",
        "\n",
        "datamart_melt = pd.melt(\n",
        "    dat.reset_index(),\n",
        "    id_vars=[\"Cluster\"],\n",
        "    value_vars=[\"Age\", \"Income\", \"SpendingScore\", \"Savings\"],\n",
        "    var_name=\"Feature\",\n",
        "    value_name=\"Value\",\n",
        ")\n",
        "\n",
        "plt.title(\"Snake Plot, K-Means, K=5\")\n",
        "sns.lineplot(x=\"Feature\", y=\"Value\", hue=\"Cluster\", data=datamart_melt)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Snake Plot, K-Means, K=5')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa97dae5588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xsZlQMNwtFoIgMEkZzyRlPL/zhy9zaP9RineWcurYOdQzl3jrJ79hUeE8ih4r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\nEv4/b7Hl6btWAQcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBV5pMwzFab0",
        "colab_type": "text"
      },
      "source": [
        "#### Relative Importance Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0-4fnimFepF",
        "colab_type": "code",
        "outputId": "57fb8d3d-7701-44ba-9f41-0c24aa214c06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cluster_avg = dat.groupby([\"Cluster\"]).mean()\n",
        "population_avg = dat.drop([\"Cluster\"], axis=1).mean()\n",
        "\n",
        "relative_imp = cluster_avg - population_avg\n",
        "\n",
        "plt.figure(figsize=(8, 2))\n",
        "plt.title(\"Relative importance of features\")\n",
        "sns.heatmap(data=relative_imp, annot=True, fmt=\".2f\", cmap=\"RdYlGn\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 576x144 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Relative importance of features')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa97ddcf940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VuAmNDqHlaU1o1DsZgLy0fCISD6/bprbZui+PhMgQGsaHExRktSTnb8gos+7+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\nYRiGFyZJGoZhGIYX/w8t6o/lC/luCgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oemKnOctFf3M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILKGocQvFxac",
        "colab_type": "text"
      },
      "source": [
        "## 1.2 Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxtZ9JBoGCr8",
        "colab_type": "text"
      },
      "source": [
        "### 1.\tDownload the customer dataset: jewelry_customers.csv."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-ap0nLlGVSM",
        "colab_type": "text"
      },
      "source": [
        "I have downloaded the dataset and run the code. Please check the Jupyter notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4VtDCMPGeYt",
        "colab_type": "text"
      },
      "source": [
        "### 2.\tPerform a clustering analysis of the dataset.  a.\tTry different values of parameters (e.g., K for K-means), and describe what you think the best values are and why."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvIV8PttGmFk",
        "colab_type": "text"
      },
      "source": [
        "I used k-means to do clustering.\n",
        "\n",
        "I first use Elbow method to find the best k (the number of clusters). According to the plots below, 5 is the best cluster number in this case. Here is the reason:\n",
        "\n",
        "In Silhouette score elbow for Kmeans Clustering, when k is 5, we got the highest Silhouette score, which means that, at this point, the cluster is farthest away from the closest neighbouring cluster. So, 5 is the best k number.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0Ljfy_xHkRV",
        "colab_type": "code",
        "outputId": "ab127a03-798c-4ab7-9a9e-d29c214604b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "model = KMeans(init=\"k-means++\", random_state=23)\n",
        "KElbowVisualizer(model, k=(2, 11), metric=\"silhouette\", timings=False).fit(X).poof()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ARCQKp1fRJPfxm1R1ffHjlw3r9mmKIyrKx5NDnc9MD3y1yOM0xhTMl5NT9LR2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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcrv05TiHk3s",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "I use a for loop to try different k value (from 2 to 10). Based on InterclusterDistance plot, we can find that 5 is the best clustering number. There is no overlapping between each two circles. Besides the distance between each other looks good.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNZShMgKHvu9",
        "colab_type": "code",
        "outputId": "0f32cd52-8b63-4ce5-e666-2d454bbe6bc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "kmeans = KMeans(init=\"k-means++\", n_clusters=5, random_state=23)\n",
        "kmeans.fit(X)\n",
        "\n",
        "visualizer = InterclusterDistance(kmeans, random_state=40)\n",
        "visualizer.fit(X)  # Fit the training data to the visualizer\n",
        "visualizer.poof()  # Draw/show/poof the data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=23, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fa97b9ddf60>,\n",
              "                     embedding='mds', legend=True, legend_loc='lower left',\n",
              "                     legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
              "                     random_state=40, scoring='membership')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RVVU5XtdGUXoyV5cXRdqceSfeFMd7rt2IXidxtLaVQDAYaZMWLULII0huSgJG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\n6NGj2O12wsLCKC8vJzGx+Z522aV6ovRasexQaLc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yTEu4ToNHho93H+Wjvx3y9qZdV/am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\nJ4JcEAQhxP0fB81qT+U3cOkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7sLdl6TXH-i1",
        "colab_type": "text"
      },
      "source": [
        "### 3.\tDocument your findings. Describe and interpret the clusters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fNwG6DGIzVv",
        "colab_type": "text"
      },
      "source": [
        "I tried three ways to interpret the clusters, which are snake plot, relative importance measures and exemplars. The three methods show the same conclusion.\n",
        "\n",
        "Cluster 0: The customers’ age is highest. The income is the lowest. The savings is the highest and the spending score is relative low. These customers are the senior people whose income is low and savings are high.\n",
        "\n",
        "Cluster 1: The customers’ age is around 59. The income is in the middle. The savings are relative low and the spending score is relative high. These customers are the middle-aged people whose income is about average and love jewelry.\n",
        "\n",
        "Cluster 2: The customers’ age is relative low, which is around 34. The income is in relative high. The savings are relative high and the spending score is relative low. These customers are the people around 34, whose has good income and savings but do not spend too much on jewelry. \n",
        "\n",
        "Cluster 3: The customers’ age is lowest. The income is the highest. The savings is the lowest and the spending score is the highest. These customers are the young people whose income is very high willing to spend on jewelry. \n",
        "\n",
        "Cluster 4: The customers’ age, income and saving is relative high. The spending score is the lowest. These customers are the old people whose income is high but spend little on jewelry. \n",
        "\n",
        "Obviously, the best customer is cluster 3. These young people has the highest income and spend a lot on jewelry. The store can be more focus on these customers.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8EQRt3TJKUK",
        "colab_type": "text"
      },
      "source": [
        "**SnakePlot**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cN0xMnulI3L9",
        "colab_type": "code",
        "outputId": "b8f4584e-1574-4a65-abfa-560a7e1b955d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.lineplot(x=\"Feature\", y=\"Value\", hue=\"Cluster\", data=datamart_melt)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa97b984b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qL99h+oJCChdMI+0xDBKLR2KUf+9Dwp1BZqwrIpYm4587l1gwQqQrSKQ7RKQ7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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewYzE726JWPE",
        "colab_type": "text"
      },
      "source": [
        "**Heatmap**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZ-yp5cQI8e8",
        "colab_type": "code",
        "outputId": "32bd0315-2b09-437b-d17b-dee4489d33cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.heatmap(data=relative_imp, annot=True, fmt=\".2f\", cmap=\"RdYlGn\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa97b8dca90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FtdjGnx9uJTu1AEuRjR43NvXKaJN7239Y835Ly2zXc07gxT7vJ3UpeSul/qGc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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uc8ZsAn6JgpM",
        "colab_type": "text"
      },
      "source": [
        "**Examplar**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "806nUbgGJCHW",
        "colab_type": "code",
        "outputId": "85680688-3124-41f7-db71-e6cfdfdef25a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        }
      },
      "source": [
        "for i, label in enumerate(set(k_means.labels_)):\n",
        "    X_tmp = df[k_means.labels_ == label].copy()\n",
        "\n",
        "    exemplar_idx = distance.cdist([k_means.cluster_centers_[i]], X_tmp).argmin()\n",
        "    exemplar = pd.DataFrame(X_tmp.iloc[exemplar_idx])\n",
        "\n",
        "    print(\"\\nCluster {}:\".format(label))\n",
        "    exemplar"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 0:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>320</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>87.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>12000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpendingScore</th>\n",
              "      <td>0.342991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings</th>\n",
              "      <td>16180.265252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        320\n",
              "Age               87.000000\n",
              "Income         12000.000000\n",
              "SpendingScore      0.342991\n",
              "Savings        16180.265252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 1:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>278</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>59.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>56321.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpendingScore</th>\n",
              "      <td>0.806566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings</th>\n",
              "      <td>6129.288986</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        278\n",
              "Age               59.000000\n",
              "Income         56321.000000\n",
              "SpendingScore      0.806566\n",
              "Savings         6129.288986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cluster 2:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "      <td>89598.000000</td>\n",
              "    </tr>\n",
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              "      <td>0.340152</td>\n",
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              "                        339\n",
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            "\n",
            "Cluster 3:\n"
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              "      <td>117108.000000</td>\n",
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              "      <th>SpendingScore</th>\n",
              "      <td>0.857078</td>\n",
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              "      <td>4749.879237</td>\n",
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              "                         360\n",
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            "\n",
            "Cluster 4:\n"
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              "                         368\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeBdOASkJGzl",
        "colab_type": "text"
      },
      "source": [
        "### 4.\tHow good are the results?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvU3MEvsJovN",
        "colab_type": "text"
      },
      "source": [
        "I think the result is very good. First of all, the Silhouette score is over 0.8, which is very high. It means the clusters has good distance between each other. The snake plot shows that lines are not overlapping and have different combination of low, medium and high value on the features. The InterclusterDistance plot clearly shows 5 area without overlapping. The exemplars give us a sample of each cluster, which is easy to understand. Overall, the result is satisfied."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1eou23rJq20",
        "colab_type": "text"
      },
      "source": [
        "# 2.\tCATCHING RECIDIVISTS BEFORE THEY STRIKE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHVTMlwbrHEQ",
        "colab_type": "text"
      },
      "source": [
        "A recidivist is a criminal that was released from prison, but commits another crime. You are a warden at a maximum-security prison in Kingston, and you want to determine which prisoners will likely become recidivists. Luckily, you have a Queen’s degree, so you are going to take a data-driven approach. You have collected some historical training data that include some basic metadata, and whether the prisoner ended up becoming a recidivist or not.\n",
        "Given the training data below, use the ID3 algorithm and entropy-based information gain to construct a decision tree by hand to predict which prisoners will become recidivists. Show all the steps. Use the resulting decision tree to predict the class of the following prisoner: Good Behavior = false, Age < 30 = false, Drug dependent = true.\n",
        "\n",
        "| id | Good Behavior | Age < 30 | Drug Dependent | Recidivist |\n",
        "|----|---------------|----------|----------------|------------|\n",
        "| 1  | False         | True     | False          | True       |\n",
        "| 2  | False         | False    | False          | False      |\n",
        "| 3  | False         | True     | False          | True       |\n",
        "| 4  | True          | False    | False          | False      |\n",
        "| 5  | True          | False    | True           | True       |\n",
        "| 6  | True          | False    | False          | false      |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZX33p3trSUY",
        "colab_type": "text"
      },
      "source": [
        "## 2.1 Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PE_fVoLSJvUt",
        "colab_type": "text"
      },
      "source": [
        "* Calculate information gain\n",
        "*\tE(Recidivist)=E(3, 3)=E(0.5, 0.5)= -0.5 log2(⁡0.5)-0.5 log2(⁡0.5)=0.97\n",
        "* E(Recidivist, GoodBehavior)=P(False)E(2,1)+P(True)E(1,2)=0.5*0.92+0.5*0.92=0.92\n",
        "* Gain(Recidivist, GoodBehavior)= E(Recidivist)- E(Recidivist, GoodBehavior)=0.97-0.92=0.05\n",
        "* Gain(Recidivist, age)= 0.97-2/6*0-4/6*0.81=0.43\n",
        "* Gain(Recidivist, DrugDependent)=0.97-1/6*0-5/6*0.97=0.16\n",
        "* Because the Gain(Recidivist, age) is the highest, we first split data from this one.\n",
        "* Since the prisoner we want to predict is older than 30, so we filter the data and get the following:\n",
        "\n",
        "  \n",
        "| id | Good Behavior | Age < 30 | Drug Dependent | Recidivist |\n",
        "|----|---------------|----------|----------------|------------|\n",
        "| 2  | False         | False    | False          | False      |\n",
        "| 4  | True          | False    | False          | False      |\n",
        "| 5  | True          | False    | True           | True       |\n",
        "| 6  | True          | False    | False          | false      |\n",
        "\n",
        "* Gain’(Recidivist, GoodBehavior)=0.81-1/4*0-3/4*0.92=0.12\n",
        "* Gain’(Recidivist, DrugDependent)=0.81-1/4*0-3/4*0=0.81\n",
        "* Because the Gain(Recidivist, DrugDependent) is the highest in this level, we split data from this one.\n",
        "* Since the prisoner we want to predict is has drug dependent, so we filter the data and get the following:\n",
        "\n",
        "| id | Good Behavior | Age < 30 | Drug Dependent | Recidivist |\n",
        "|----|---------------|----------|----------------|------------|\n",
        "| 5  | True          | False    | True           | True       |\n",
        "\n",
        "* So we can stop here. I will predict Recidivist as True."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWymD5u7J0rd",
        "colab_type": "text"
      },
      "source": [
        "# 3.\tMORE CLASSIFICATION MEASURES\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wspla4-xrdr_",
        "colab_type": "text"
      },
      "source": [
        "In class, we talked about several classification measures, such as accuracy, precision, recall, AUC, etc. There are many other measures out there, each with its own pros and cons. \n",
        "Do a bit of research of your own to find at least two classification measures that we did not discuss in class. For each measure, describe what it is, how it is calculated, how it is different from accuracy, and in which scenarios it is best used. Show some example datasets/confusion matrices to illustrate your points."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MoMR89irY-H",
        "colab_type": "text"
      },
      "source": [
        "## 3.1 Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ed8kYOezLcWt",
        "colab_type": "text"
      },
      "source": [
        "1.\t\n",
        "\n",
        "* classification measure name: Diagnostic odds ratio (DOR)\n",
        "\n",
        "* The Diagnostic odds ratio =TP x TN/(FP x FN)\n",
        "DOR is utilized for estimating the discriminative ability of the test and also for comparing between two diagnostic tests. The cons is that if there is a zero in FP or FN. Then DOR cannot be applied in this case.\n",
        "* Here is an example:\n",
        "\n",
        "| Confusion matrix 1 |       |\n",
        "|--------------------|-------|\n",
        "| TP 60              | FN 20 |\n",
        "| FP 10              | TN 10 |\n",
        "\n",
        "\n",
        "| Confusion matrix 2  || \n",
        "|-----------|-----------|\n",
        "| TP        65\t|FN   15 |\n",
        "| FP        15\t|TN   5  |\n",
        "\n",
        "* Accuracy_1=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
        "* DOR_1= TP x TN/(FP x FN)=60x10/(10*20)=3\n",
        "\n",
        "* Accuracy_2=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
        "* DOR_2= TP x TN/(FP x FN)=65x5/(15*15)=1.44\n",
        "\n",
        "  \n",
        "\n",
        "* In this example, the accuracy is the same for both confusion matrices. But the DOR_1 is larger than DOR_2. Because the formula of DOR use TP x TN comparing to Accuracy using TN + TP. As we all know, the product of two real number is maximum when the numbers are equal give sum is constant, So, if two models have the same accuracy, the model with higher DOR should be a more balanced model to give the TP and TN. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvPVe7tRLhL8",
        "colab_type": "text"
      },
      "source": [
        "2.\t\n",
        "* classification measure name: Youden’s index (YI)\n",
        "* Youden’s index=TPR+TNR-1=TP/(TP+FN)+TN/(TN+FP)-1\n",
        "YI evaluates the discriminative power of the test. The YI metric is ranged from zero when the test is poor to one which represents a perfect diagnostic test. It is also suitable with imbalanced data. One of the major disadvantages of this test is that it does not change concerning the differences between the sensitivity and specificity of the test.\n",
        "* Here is an example:\n",
        "\n",
        "| Confusion matrix 1 |       |\n",
        "|--------------------|-------|\n",
        "| TP 60              | FN 20 |\n",
        "| FP 10              | TN 10 |\n",
        "\n",
        "\n",
        "| Confusion matrix 2  || \n",
        "|-----------|-----------|\n",
        "| TP        65\t|FN   15 |\n",
        "| FP        15\t|TN   5  |\n",
        "\n",
        "\n",
        "* Accuracy_1=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
        "* YI_1= TP/(TP+FN)+TN/(TN+FP)-1=60/80+10/20-1=0.75+0.5-1=0.25\n",
        "\n",
        "* Accuracy_2=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
        "* YI_2= TP/(TP+FN)+TN/(TN+FP)-1=65/80+5/20-1=0.8125+0.25-1=0.0625\n",
        "\n",
        "* In this example, two models have the same accuracy. But if the accuracy is the same, the Yi score is quite different, the one with higher YI score has overall better balance of specificity and sensitivity.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZKKfwZONXNq",
        "colab_type": "text"
      },
      "source": [
        "# 4.\tTHE INTERN\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFehBkPCrjTe",
        "colab_type": "text"
      },
      "source": [
        "You are an in-demand, world-traveling, work-all-night consultant who specializes in designing supervised machine learning solutions for clients in a wide-range of industries. You have seen it all and you know what to do. To help you get more done in less time, you have hired an intern from Ivey, who, unfortunately, needs some handholding. Your intern does not understand when to use which classification measure. Your intern keeps getting it wrong. To help your intern learn from your experience, you have decided to look at some previous projects and describe which measure you used, and more importantly, why. \n",
        "For each project below, describe which measure(s) are best, and why. Also, give an example of a measure which would be horrible to use, and why. List any assumptions you are making, about the dataset, problem, or business priorities that were involved in the project.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xncl15Rrrl64",
        "colab_type": "text"
      },
      "source": [
        "## 4.1 Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuQOC_N4N7Zh",
        "colab_type": "text"
      },
      "source": [
        "#### a)\tThe fraud department at a bank wanted to predict which transactions were fraudulent. The training dataset had 100K credit card transactions, of which 97K are legit and 3K are fraud."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YuJp58WhN_BD",
        "colab_type": "text"
      },
      "source": [
        "1.\tI will use recall to measure in this case. \n",
        "2.\tAssumption: A fraud will cause a huge loss and the cost of following up potentially fraudulent.\n",
        "3.\tBecause the huge loss and not much on following up the case, so we do not want to miss the true positive, which means when the Fraud is actually true our model should predict it as true. Also this may cause more false positive. Since in my assumption that the cost of following up potentially fraudulent is not high, Recall is a good measurement to use.\n",
        "4.\tIt is horrible to use accuracy to measure in this case. Because the data is imbalance, the model will predict all the result as legit and get 97% accuracy. So, it is not wise to use accuracy in the case.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxURfhJjlzuY",
        "colab_type": "text"
      },
      "source": [
        "#### b)\tA hospital wanted to predict whether a MRI scan contained cancer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_o3EQXYl5DH",
        "colab_type": "text"
      },
      "source": [
        "1.\tI will use F1 score to measure in this case.\n",
        "2.\tAssumption: Most of people who have MRI scan do not have cancer. So, the data may not imbalance.\n",
        "3.\tIn this case, because we need to deal with the imbalance data, we want to balance both recall and precision. Since F1 score equals 2 * precision * recall / (precision + recall), F1 score is a good way to measure the model.\n",
        "4.\tIt is horrible to use accuracy to measure in this case. Because the data is imbalance, the model will predict all the result as no cancer and get high accuracy. So, it is not wise to use accuracy in the case.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzkzRZ27l7Q6",
        "colab_type": "text"
      },
      "source": [
        "#### c)\tAn IT team wanted to filter spam from email inboxes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fxWSn2q8l_M4",
        "colab_type": "text"
      },
      "source": [
        "1.\tI will use precision to measure in this case.\n",
        "2.\tAssumption: We need to avoid predicting an important email as spam (Avoid FP)\n",
        "3.\tIn this case, if the model predict the spam as true, we want to have high confidence to make sure that it is actual spam. Precision equals to TP/ (TP + FP), which is what we want in this case.\n",
        "4.\tIt is horrible to use specificity to measure in this case. The model will only predict a mail as non-spam when it is very sure, which means it will cause more normal mail be treated as spam.  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxQ6kYzgmBdQ",
        "colab_type": "text"
      },
      "source": [
        "#### d)\tA sports analytics department wants to predict which team will win the match."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lv-6uQfVFzsO",
        "colab_type": "text"
      },
      "source": [
        "1. I will use ROC/AUC to measure in this case.\n",
        "2. Assumption: The department wants to know some of which matches they have high probability to win and some of which mataches they will probably lose.\n",
        "3. In this case, we will choose the model with highest AUC score. Then, we can check the prediction result to tell which part of prediction we have high confidence to believe the result.\n",
        "4. It is horrible to use specificity to measure in this case. The model will only predict a game as lose when it is very sure, which means that the accuracy to for predicting winning will be sacrifised."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7gbrMxNmEZg",
        "colab_type": "text"
      },
      "source": [
        "#### e)\tA city government wanted to build a system to monitor Twitter to see if any local residents were tweeting about emergencies that needed quick response from the police department. They don’t trust Twitter that much; they only want to send police in true emergencies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mS9IMiOwhQ63",
        "colab_type": "text"
      },
      "source": [
        "1.\tI will use precision to measure in this case.\n",
        "2.\tAssumption: We need to avoid wrong emergency alert (Avoid FP).\n",
        "3.\tIn this case, if the model predict the emergency as true, we want to have high confidence to make sure that it is actual emergency. Precision equals to TP/ (TP + FP), which is what we want in this case.\n",
        "4.\tIt is horrible to use specificity TN/(TN+FP) to measure in this case. The model will only predict non-emergency when it is very sure, which means it will cause more emergency alerts.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnNg_wghmHki",
        "colab_type": "text"
      },
      "source": [
        "#### f)\t [Describe one more project, whereby the best measure is one that you have not yet listed in parts a-e above.]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTPQrIDdx53q",
        "colab_type": "text"
      },
      "source": [
        "1. Project: A company is preparing an event, it has invited a lot of guests. Since they have the history data of previous events, they want to have a model to predict who will come on event day.\n",
        "2. I will use log loss to measure in this case.\n",
        "2. Assumption: They only care about how many people in total will come.\n",
        "3. In this case, since I conly care about the overal situation instead of individual, I want to use log loss to penalize models being very wrong.\n",
        "4. It is horriable to use precision to measure in this case. The model will make sure the precise of prediacting people who will come. But it will sacrifice the overall accuracy. So, it is better to use log loss in this case."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qJuBhuCmLWw",
        "colab_type": "text"
      },
      "source": [
        "# 5.Uncle STEVE’S GROCERY STORE\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VW3Tngchr2TA",
        "colab_type": "text"
      },
      "source": [
        "Uncle Steve runs a small, local grocery store. Looking for some customer insights, he has hired you to do some data science. He has given you a few years’ worth of customer transactions, i.e., sets of items that customers have purchased. You have applied an association rules learning algorithm to the data, and the algorithm has generated a large set of association rules.  \n",
        "For each of the following scenarios, provide an example of one of the discovered association rules that satisfies the following conditions. (Just make up the rule, using your human experience and intuition!) Also, describe whether and why each rule would be considered subjectively interesting or uninteresting for Uncle Steve."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koWJMRdKr39_",
        "colab_type": "text"
      },
      "source": [
        "## 5.1 Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHzMFoFemPA5",
        "colab_type": "text"
      },
      "source": [
        "#### a)\tA rule that has high support and high confidence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhgpyPi2mbJx",
        "colab_type": "text"
      },
      "source": [
        "{egg} → {milk} \n",
        "\n",
        "* These two items are both high frequency purchasing items. It is uninteresting for Uncle Steve because it is a common sense that people will often buy egg and milk together (High support). Besides, Uncle Steve will not be surprised that people will buy milk when they buy eggs (High confidence).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBaR0lVsmdHQ",
        "colab_type": "text"
      },
      "source": [
        "#### b)\tA rule that has reasonably high support but low confidence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvHYHGGvmhWZ",
        "colab_type": "text"
      },
      "source": [
        "{egg} → {bacon} \n",
        "\n",
        "* Most people will buy eggs when they shop at a grocery store. But, bacon is not as popular as eggs. \n",
        "\n",
        "* This rule has reasonably high support since they are both daily food which has a relative high purchasing frequency. However, the confidence is low because when people buy eggs, they do not always buy bacon.\n",
        "\n",
        "* I think this is interesting to Uncle Steve because he will realize that giving a discount to eggs will not influence the sales of bacon much.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpM5b3iZmkDg",
        "colab_type": "text"
      },
      "source": [
        "#### c)\tA rule that has low support and low confidence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRj7-X70moli",
        "colab_type": "text"
      },
      "source": [
        "{Christmas tree} → {Halloween pumpkin} \n",
        "\n",
        "* These two items are both low frequency purchasing items. People probably buy once a year. Furthermore, since Halloween is in October, there is very low possibility that people will buy Halloween pumpkin when people buy Christmas tree. So, this rule has low support and low confidence.\n",
        "\n",
        "* This rule is not interesting to Uncle Steve because it is a common sense and he must know this result.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YX7T04bYmryA",
        "colab_type": "text"
      },
      "source": [
        "#### d)\tA rule that has low support and high confidence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UPOW6XS9mw9p",
        "colab_type": "text"
      },
      "source": [
        "{outdoor grills} → {barbecue charcoal}\n",
        "\n",
        "* Since outdoor grills are very low frequency purchasing item, the support should be very low. But, when people by outdoor grills will always buy barbecue charcoal when they buy outdoor grill, which will give this rule a high confidence.\n",
        "\n",
        "* I think this is interesting to Uncle Steve. He will realize that giving discount to barbecue charcoal will not increase the sales of outdoor grills. Instead, he can give a bundle promotion of these two items since the confidence is high. This may help to increase the sale of outdoor grills."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fT1WYN0mzdg",
        "colab_type": "text"
      },
      "source": [
        "# 6.\tASSOCIATION RULES, TWO WAYS\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WWS1laushxX",
        "colab_type": "text"
      },
      "source": [
        "Consider the following table of customer transactions.\n",
        "\n",
        "| Customer ID | Transaction ID | Items Bought |\n",
        "|-------------|----------------|--------------|\n",
        "| 1           | 0001           | {a, d, e}    |\n",
        "| 1           | 0024           | {a, b, c, e} |\n",
        "| 2           | 0012           | {a, b, d, e} |\n",
        "| 2           | 0031           | {a, c, d, e} |\n",
        "| 3           | 0015           | {b, c, e}    |\n",
        "| 3           | 0022           | {b, d, e}    |\n",
        "| 4           | 0029           | {c, d}       |\n",
        "| 4           | 0040           | {a, b, c}    |\n",
        "| 5           | 0033           | {a, d, e}    |\n",
        "| 5           | 0038           | {a, b, e}    |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RoKXEsQUsjrH",
        "colab_type": "text"
      },
      "source": [
        "## 6.1 Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfZK3HmlnRcW",
        "colab_type": "text"
      },
      "source": [
        "#### a)\tCompute the support for itemsets {e}, {b,d}, and {b,d,e} by treating each transaction ID as a market basket (i.e., the normal way)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jO88RCg_nc0B",
        "colab_type": "text"
      },
      "source": [
        "| Customer ID | Transaction ID | Items Bought | a | b | c | d | e |\n",
        "|-------------|----------------|--------------|---|---|---|---|---|\n",
        "| 1           | 1              | {a, d, e}    | 1 | 0 | 0 | 1 | 1 |\n",
        "| 1           | 24             | {a, b, c, e} | 1 | 1 | 1 | 0 | 1 |\n",
        "| 2           | 12             | {a, b, d, e} | 1 | 1 | 0 | 1 | 1 |\n",
        "| 2           | 31             | {a, c, d, e} | 1 | 0 | 1 | 1 | 1 |\n",
        "| 3           | 15             | {b, c, e}    | 0 | 1 | 1 | 0 | 1 |\n",
        "| 3           | 22             | {b, d, e}    | 0 | 1 | 0 | 1 | 1 |\n",
        "| 4           | 29             | {c, d}       | 0 | 0 | 1 | 1 | 0 |\n",
        "| 4           | 40             | {a, b, c}    | 1 | 1 | 1 | 0 | 0 |\n",
        "| 5           | 33             | {a, d, e}    | 1 | 0 | 0 | 1 | 1 |\n",
        "| 5           | 38             | {a, b, e}    | 1 | 1 | 0 | 0 | 1 |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6euLmZBniwP",
        "colab_type": "text"
      },
      "source": [
        "* S{e}=8/10=0.8\n",
        "* S{b,d}=2/10=0.2\n",
        "* S{b,d,e}=2/10=0.2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRbWM2g6nlUX",
        "colab_type": "text"
      },
      "source": [
        "#### b)\tUse the results in part (a) to compute the confidence for the association rules {b,d} → {e} and {e} → {b,d}. Is confidence a symmetric measure?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjY21BbEnrCb",
        "colab_type": "text"
      },
      "source": [
        "* C({b,d} → {e})=0.2/0.2=1\n",
        "* C({e} → {b,d})=0.2/0.8=0.25\n",
        "* Confidence is not a symmetric measure.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOXblC_nnwUx",
        "colab_type": "text"
      },
      "source": [
        "#### c)\tRepeat part (a) by treating each customer ID as a market basket. Each item should be treated as a binary feature (i.e., 1 if an item appears in at least one transaction bought by the customer, and 0 otherwise). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4Rj5ZXPnzEg",
        "colab_type": "text"
      },
      "source": [
        "| Customer ID | a | b | c | d | e |\n",
        "|-------------|---|---|---|---|---|\n",
        "| 1           | 1 | 1 | 1 | 1 | 1 |\n",
        "| 2           | 1 | 1 | 1 | 1 | 1 |\n",
        "| 3           | 0 | 1 | 1 | 1 | 1 |\n",
        "| 4           | 1 | 1 | 1 | 1 | 0 |\n",
        "| 5           | 1 | 1 | 0 | 1 | 1 |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QDsEQq8Vn5KZ",
        "colab_type": "text"
      },
      "source": [
        "* S{e}=4/5=0.8\n",
        "* S{b,d}=5/5=1\n",
        "* S{b,d,e}=4/5=0.8\n",
        "\n",
        "* C({b,d} → {e})=0.8/1=0.8\n",
        "* C({e} → {b,d})=0.8/0.8=1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGpOpHg8n9nA",
        "colab_type": "text"
      },
      "source": [
        "# 7.\tVIVA LA VINO\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCjzX2SOsrFm",
        "colab_type": "text"
      },
      "source": [
        "Some Smith faculty have started a wine club. At each meeting, members of the club perform blind taste tests of different wine varietals. Members indicate how much they enjoy each varietal, using an integer scale of 1 (worst) to 7 (best). After the most recent meeting, here are the ratings.\n",
        "\n",
        "|  |Zin    | Pinot Noir | Chard | Merlot | Cab | Pinot Gris |  \n",
        "|--------|------------|-------|--------|-----|------------|---|\n",
        "| Yuri   | 7          | 6     | 7      | 4   | 5          | 4 |\n",
        "| Steve  |            | 7     | 6      | 4   | 3          | 4 |\n",
        "| Gary   | 3          | 3     | 3      | 1   | 1          | 5 |\n",
        "| Qurat  | 2          | 2     | 1      | 3   | 7          | 4 |\n",
        "| Brigid | 5          | 6     | 7      | 2   | 3          | 3 |\n",
        "\n",
        "Unfortunately, the club ran out of Zin before Steve had a chance to try it. Luckily, the club has you, a data-driven, clever, and charming Queen’s student. Use your skills to predict what Steve would rate Zin. Use user-based collaborative filtering with cosine distance. To predict the rating, find the two nearest neighbors and take the weighted average of their scores of the item in question.\n",
        "Hints:\n",
        "\tRecall that the cosine distance is calculated as  〖dist〗_cos (A,B)=1-(∑_(i=0)^n▒〖A_i B_i 〗)/(√(∑_(i=0)^n▒A_i^2 ) √(∑_(i=0)^n▒B_i^2 ))\n",
        "\tWhen comparing two users, only include items that both users have rated.  For example, to compare Yuri and Steve, the calculation would ignore Zin (since Steve hasn’t rated it) and would be 1-(7*6+6*7+4*4+5*3+4*4)/(√(7^2+6^2+4^2+5^2+4^2 ) √(6^2+7^2+4^2+3^2+4^2 ))=0.021.\n",
        "\tTo find the weighted average of two ratings R1 and R2, which have a distances D1 and D2 respectively, use the formula: Weighted_Average = ((1-D1)*R1 + (1-D2)*R2) / (2-D1 - D2).\n",
        "\tRound the predicted rating to the nearest integer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2N1rEz73K2O",
        "colab_type": "text"
      },
      "source": [
        "## 7.1 Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ipm7B_D3TWR",
        "colab_type": "text"
      },
      "source": [
        "Here is how I caculate this manually."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVHiNvAhoRRc",
        "colab_type": "code",
        "outputId": "1f5c7e51-350c-4b8f-ee3d-4c38036adb00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# import a package to find the smallest number\n",
        "from heapq import nsmallest\n",
        "\n",
        "# create the table\n",
        "dict_df={\"Zin\":[7,np.nan,3,2,5],\n",
        "        \"Pinot\":[6,7,3,2,6],\n",
        "        \"Chard\":[7,6,3,1,7],\n",
        "        \"Merlot\":[4,4,1,3,2],\n",
        "        \"Cab\":[5,3,1,7,3],\n",
        "        \"Pinot Gris\":[4,4,5,4,3]}\n",
        "df_club=pd.DataFrame(dict_df)\n",
        "df_club.index=['Yuri',\"Steve\",\"Gary\",\"Qurat\",\"Brigid\"]\n",
        "\n",
        "# slice the table to remove the column Zin\n",
        "df_without_zin=df_club.iloc[:,1:]\n",
        "\n",
        "# define a function to caculate the distance\n",
        "def dist_cos(A,B):\n",
        "  '''Caculate the cosine distance'''\n",
        "  result=1-sum(A*B)/((sum(A**2))**0.5*(sum(B**2))**0.5)\n",
        "  return result\n",
        "\n",
        "# create a dictionary to save the distance value\n",
        "dict_dist={}\n",
        "for i in ['Yuri',\"Gary\",\"Qurat\",\"Brigid\"]:\n",
        "  dict_dist[i]=dist_cos(df_without_zin.loc[i],df_without_zin.loc['Steve'])\n",
        "\n",
        "# find the dictionary key of the 2 smallest value\n",
        "two_smallest_distance=nsmallest(2,dict_dist,key=dict_dist.get)\n",
        "\n",
        "# caculate the rating\n",
        "rating=int(round(((1-dict_dist[two_smallest_distance[0]])*df_club.loc[two_smallest_distance[0],'Zin']+(1-dict_dist[two_smallest_distance[1]])*df_club.loc[two_smallest_distance[1],'Zin'])/(2-dict_dist[two_smallest_distance[0]]-dict_dist[two_smallest_distance[1]])))\n",
        "rating"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nybiwo7e498b",
        "colab_type": "text"
      },
      "source": [
        "So, my prediction is 6."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUIjHIxToIbY",
        "colab_type": "text"
      },
      "source": [
        "# 8.\tYUM, ORANGE JUICE!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pdmb0hqsxZB",
        "colab_type": "text"
      },
      "source": [
        "[For this question, please answer the parts below in English, and also turn in your code. Your code should be submitted as a notebook – either a Python Jupyter Notebook (.ipynb) or an R Markdown file (.Rmd), along with a PDF rendering of the notebook.]\n",
        "One cup of fresh orange juice has 124 mg of vitamin C, which is 200% of the recommended daily intake of vitamin C for an adult. With this as (completely unrelated) motivation, build a model to predict whether a grocery store customer will Purchase Citrus Hill (CH) or Minute Maid (MM) orange juice.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3mMl1upoqyu",
        "colab_type": "text"
      },
      "source": [
        "## 8.1 Coding part"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40M-E1EyvRnN",
        "colab_type": "text"
      },
      "source": [
        "### Load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4IFPH1DovGH",
        "colab_type": "code",
        "outputId": "a088c61f-d799-4233-e243-f6da1e9cdc99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df8 = pd.read_csv(\"/content/drive/My Drive/MMA/MMA869/OJ.CSV\")\n",
        "df8.head()\n",
        "df8.info()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Purchase</th>\n",
              "      <th>WeekofPurchase</th>\n",
              "      <th>StoreID</th>\n",
              "      <th>PriceCH</th>\n",
              "      <th>PriceMM</th>\n",
              "      <th>DiscCH</th>\n",
              "      <th>DiscMM</th>\n",
              "      <th>SpecialCH</th>\n",
              "      <th>SpecialMM</th>\n",
              "      <th>LoyalCH</th>\n",
              "      <th>SalePriceMM</th>\n",
              "      <th>SalePriceCH</th>\n",
              "      <th>PriceDiff</th>\n",
              "      <th>Store7</th>\n",
              "      <th>PctDiscMM</th>\n",
              "      <th>PctDiscCH</th>\n",
              "      <th>ListPriceDiff</th>\n",
              "      <th>STORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CH</td>\n",
              "      <td>237</td>\n",
              "      <td>1</td>\n",
              "      <td>1.75</td>\n",
              "      <td>1.99</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.99</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.24</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>CH</td>\n",
              "      <td>239</td>\n",
              "      <td>1</td>\n",
              "      <td>1.75</td>\n",
              "      <td>1.99</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.69</td>\n",
              "      <td>1.75</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>No</td>\n",
              "      <td>0.150754</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>CH</td>\n",
              "      <td>245</td>\n",
              "      <td>1</td>\n",
              "      <td>1.86</td>\n",
              "      <td>2.09</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.680000</td>\n",
              "      <td>2.09</td>\n",
              "      <td>1.69</td>\n",
              "      <td>0.40</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.091398</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>MM</td>\n",
              "      <td>227</td>\n",
              "      <td>1</td>\n",
              "      <td>1.69</td>\n",
              "      <td>1.69</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>1.69</td>\n",
              "      <td>1.69</td>\n",
              "      <td>0.00</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>CH</td>\n",
              "      <td>228</td>\n",
              "      <td>7</td>\n",
              "      <td>1.69</td>\n",
              "      <td>1.69</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.956535</td>\n",
              "      <td>1.69</td>\n",
              "      <td>1.69</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0 Purchase  WeekofPurchase  StoreID  PriceCH  PriceMM  DiscCH  \\\n",
              "0           1       CH             237        1     1.75     1.99    0.00   \n",
              "1           2       CH             239        1     1.75     1.99    0.00   \n",
              "2           3       CH             245        1     1.86     2.09    0.17   \n",
              "3           4       MM             227        1     1.69     1.69    0.00   \n",
              "4           5       CH             228        7     1.69     1.69    0.00   \n",
              "\n",
              "   DiscMM  SpecialCH  SpecialMM   LoyalCH  SalePriceMM  SalePriceCH  \\\n",
              "0     0.0          0          0  0.500000         1.99         1.75   \n",
              "1     0.3          0          1  0.600000         1.69         1.75   \n",
              "2     0.0          0          0  0.680000         2.09         1.69   \n",
              "3     0.0          0          0  0.400000         1.69         1.69   \n",
              "4     0.0          0          0  0.956535         1.69         1.69   \n",
              "\n",
              "   PriceDiff Store7  PctDiscMM  PctDiscCH  ListPriceDiff  STORE  \n",
              "0       0.24     No   0.000000   0.000000           0.24      1  \n",
              "1      -0.06     No   0.150754   0.000000           0.24      1  \n",
              "2       0.40     No   0.000000   0.091398           0.23      1  \n",
              "3       0.00     No   0.000000   0.000000           0.00      1  \n",
              "4       0.00    Yes   0.000000   0.000000           0.00      0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1070 entries, 0 to 1069\n",
            "Data columns (total 19 columns):\n",
            "Unnamed: 0        1070 non-null int64\n",
            "Purchase          1070 non-null object\n",
            "WeekofPurchase    1070 non-null int64\n",
            "StoreID           1070 non-null int64\n",
            "PriceCH           1070 non-null float64\n",
            "PriceMM           1070 non-null float64\n",
            "DiscCH            1070 non-null float64\n",
            "DiscMM            1070 non-null float64\n",
            "SpecialCH         1070 non-null int64\n",
            "SpecialMM         1070 non-null int64\n",
            "LoyalCH           1070 non-null float64\n",
            "SalePriceMM       1070 non-null float64\n",
            "SalePriceCH       1070 non-null float64\n",
            "PriceDiff         1070 non-null float64\n",
            "Store7            1070 non-null object\n",
            "PctDiscMM         1070 non-null float64\n",
            "PctDiscCH         1070 non-null float64\n",
            "ListPriceDiff     1070 non-null float64\n",
            "STORE             1070 non-null int64\n",
            "dtypes: float64(11), int64(6), object(2)\n",
            "memory usage: 158.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTCkEB8SVVlW",
        "colab_type": "text"
      },
      "source": [
        "### preprocess the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQDXC4YqIuf-",
        "colab_type": "code",
        "outputId": "57978c8b-55c0-4adc-c45a-60600a46f4de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "df8.WeekofPurchase=df8.WeekofPurchase.apply(str)\n",
        "df8.StoreID=df8.StoreID.apply(str)\n",
        "df8.STORE=df8.STORE.apply(str)\n",
        "df8['Store7_dummy']=np.where(df8.Store7=='Yes',1,0)\n",
        "df8.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1070 entries, 0 to 1069\n",
            "Data columns (total 20 columns):\n",
            "Unnamed: 0        1070 non-null int64\n",
            "Purchase          1070 non-null object\n",
            "WeekofPurchase    1070 non-null object\n",
            "StoreID           1070 non-null object\n",
            "PriceCH           1070 non-null float64\n",
            "PriceMM           1070 non-null float64\n",
            "DiscCH            1070 non-null float64\n",
            "DiscMM            1070 non-null float64\n",
            "SpecialCH         1070 non-null int64\n",
            "SpecialMM         1070 non-null int64\n",
            "LoyalCH           1070 non-null float64\n",
            "SalePriceMM       1070 non-null float64\n",
            "SalePriceCH       1070 non-null float64\n",
            "PriceDiff         1070 non-null float64\n",
            "Store7            1070 non-null object\n",
            "PctDiscMM         1070 non-null float64\n",
            "PctDiscCH         1070 non-null float64\n",
            "ListPriceDiff     1070 non-null float64\n",
            "STORE             1070 non-null object\n",
            "Store7_dummy      1070 non-null int64\n",
            "dtypes: float64(11), int64(4), object(5)\n",
            "memory usage: 167.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEvs0vmmUUZu",
        "colab_type": "code",
        "outputId": "32e3b78c-88d2-4ad0-f369-7b53e1918d27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        }
      },
      "source": [
        "corr=df8.corr()\n",
        "corr_triu = corr.where(~np.tril(np.ones(corr.shape)).astype(np.bool))\n",
        "corr_triu = corr_triu.stack()\n",
        "corr_triu.name = 'Pearson Correlation Coefficient'\n",
        "corr_triu.index.names = ['Col1', 'Col2']\n",
        "corr_triu[(corr_triu > 0.5) | (corr_triu < -0.5) ].to_frame()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Pearson Correlation Coefficient</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">PriceCH</th>\n",
              "      <th>PriceMM</th>\n",
              "      <td>0.616402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SalePriceCH</th>\n",
              "      <td>0.586716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">PriceMM</th>\n",
              "      <th>SalePriceMM</th>\n",
              "      <td>0.532859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ListPriceDiff</th>\n",
              "      <td>0.665187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">DiscCH</th>\n",
              "      <th>SpecialCH</th>\n",
              "      <td>0.534775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SalePriceCH</th>\n",
              "      <td>-0.711274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PctDiscCH</th>\n",
              "      <td>0.999022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">DiscMM</th>\n",
              "      <th>SalePriceMM</th>\n",
              "      <td>-0.846868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PriceDiff</th>\n",
              "      <td>-0.823908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PctDiscMM</th>\n",
              "      <td>0.998793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">SpecialCH</th>\n",
              "      <th>SalePriceCH</th>\n",
              "      <td>-0.508586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PctDiscCH</th>\n",
              "      <td>0.549015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">SalePriceMM</th>\n",
              "      <th>PriceDiff</th>\n",
              "      <td>0.852798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PctDiscMM</th>\n",
              "      <td>-0.856749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SalePriceCH</th>\n",
              "      <th>PctDiscCH</th>\n",
              "      <td>-0.722776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PriceDiff</th>\n",
              "      <th>PctDiscMM</th>\n",
              "      <td>-0.828097</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Pearson Correlation Coefficient\n",
              "Col1        Col2                                          \n",
              "PriceCH     PriceMM                               0.616402\n",
              "            SalePriceCH                           0.586716\n",
              "PriceMM     SalePriceMM                           0.532859\n",
              "            ListPriceDiff                         0.665187\n",
              "DiscCH      SpecialCH                             0.534775\n",
              "            SalePriceCH                          -0.711274\n",
              "            PctDiscCH                             0.999022\n",
              "DiscMM      SalePriceMM                          -0.846868\n",
              "            PriceDiff                            -0.823908\n",
              "            PctDiscMM                             0.998793\n",
              "SpecialCH   SalePriceCH                          -0.508586\n",
              "            PctDiscCH                             0.549015\n",
              "SalePriceMM PriceDiff                             0.852798\n",
              "            PctDiscMM                            -0.856749\n",
              "SalePriceCH PctDiscCH                            -0.722776\n",
              "PriceDiff   PctDiscMM                            -0.828097"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffZZaQOvWESB",
        "colab_type": "text"
      },
      "source": [
        "Because the correlation between DiscCH and PctDiscCH and the correlation between DiscMM and PctDiscMM are extremely high (almost 1), so I deceide to drop PctDiscMM and PctDiscCH. Also, I will drop PriceDiff, SalePriceCH and SalePriceMM, since the correlation coefficient is high."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPWI6oNhWpfi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=['WeekofPurchase','StoreID','PriceCH','PriceMM','DiscCH','DiscMM','SpecialCH','SpecialMM','LoyalCH', 'Store7_dummy','ListPriceDiff','STORE']\n",
        "X=df8[features]\n",
        "df8['Purchase_dummy']=np.where(df8['Purchase']=='CH',1,0)\n",
        "y=df8['Purchase_dummy']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3AEAEm0pBU2",
        "colab_type": "code",
        "outputId": "fb2444f7-08b3-426a-fd62-918b1bf0c2e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "corr=X.corr()\n",
        "corr_triu = corr.where(~np.tril(np.ones(corr.shape)).astype(np.bool))\n",
        "corr_triu = corr_triu.stack()\n",
        "corr_triu.name = 'Pearson Correlation Coefficient'\n",
        "corr_triu.index.names = ['Col1', 'Col2']\n",
        "corr_triu[(corr_triu > 0.5) | (corr_triu < -0.5) ].to_frame()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Pearson Correlation Coefficient</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PriceCH</th>\n",
              "      <th>PriceMM</th>\n",
              "      <td>0.616402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PriceMM</th>\n",
              "      <th>ListPriceDiff</th>\n",
              "      <td>0.665187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiscCH</th>\n",
              "      <th>SpecialCH</th>\n",
              "      <td>0.534775</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       Pearson Correlation Coefficient\n",
              "Col1    Col2                                          \n",
              "PriceCH PriceMM                               0.616402\n",
              "PriceMM ListPriceDiff                         0.665187\n",
              "DiscCH  SpecialCH                             0.534775"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzQlHpjSUXy7",
        "colab_type": "code",
        "outputId": "36923e3e-8a3d-4437-9534-c792911494b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "features_to_be_scaled = ['PriceCH', 'PriceMM', 'DiscCH', 'DiscMM','LoyalCH', 'ListPriceDiff']\n",
        "# Separating out the features\n",
        "\n",
        "X[features_to_be_scaled] = StandardScaler().fit_transform(X[features_to_be_scaled])\n",
        "X.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  self.obj[item] = s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>WeekofPurchase</th>\n",
              "      <th>StoreID</th>\n",
              "      <th>PriceCH</th>\n",
              "      <th>PriceMM</th>\n",
              "      <th>DiscCH</th>\n",
              "      <th>DiscMM</th>\n",
              "      <th>SpecialCH</th>\n",
              "      <th>SpecialMM</th>\n",
              "      <th>LoyalCH</th>\n",
              "      <th>Store7_dummy</th>\n",
              "      <th>ListPriceDiff</th>\n",
              "      <th>STORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>237</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.152062</td>\n",
              "      <td>-0.710313</td>\n",
              "      <td>-0.441664</td>\n",
              "      <td>-0.577188</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.213788</td>\n",
              "      <td>0</td>\n",
              "      <td>0.204766</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>239</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.152062</td>\n",
              "      <td>-0.710313</td>\n",
              "      <td>-0.441664</td>\n",
              "      <td>0.826428</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.111205</td>\n",
              "      <td>0</td>\n",
              "      <td>0.204766</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>245</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.072806</td>\n",
              "      <td>0.034162</td>\n",
              "      <td>1.006140</td>\n",
              "      <td>-0.577188</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.371200</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111730</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>227</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.740747</td>\n",
              "      <td>-2.943741</td>\n",
              "      <td>-0.441664</td>\n",
              "      <td>-0.577188</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.538781</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.028100</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>228</td>\n",
              "      <td>7</td>\n",
              "      <td>-1.740747</td>\n",
              "      <td>-2.943741</td>\n",
              "      <td>-0.441664</td>\n",
              "      <td>-0.577188</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.269920</td>\n",
              "      <td>1</td>\n",
              "      <td>-2.028100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  WeekofPurchase StoreID   PriceCH   PriceMM    DiscCH    DiscMM  SpecialCH  \\\n",
              "0            237       1 -1.152062 -0.710313 -0.441664 -0.577188          0   \n",
              "1            239       1 -1.152062 -0.710313 -0.441664  0.826428          0   \n",
              "2            245       1 -0.072806  0.034162  1.006140 -0.577188          0   \n",
              "3            227       1 -1.740747 -2.943741 -0.441664 -0.577188          0   \n",
              "4            228       7 -1.740747 -2.943741 -0.441664 -0.577188          0   \n",
              "\n",
              "   SpecialMM   LoyalCH  Store7_dummy  ListPriceDiff STORE  \n",
              "0          0 -0.213788             0       0.204766     1  \n",
              "1          1  0.111205             0       0.204766     1  \n",
              "2          0  0.371200             0       0.111730     1  \n",
              "3          0 -0.538781             0      -2.028100     1  \n",
              "4          0  1.269920             1      -2.028100     0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNsxVJP50kSJ",
        "colab_type": "text"
      },
      "source": [
        "### Split data into test and train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMXXBdTWYUt2",
        "colab_type": "code",
        "outputId": "0f0512cc-7f93-48f6-911e-5ea0880a2797",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "sum(y)/len(y) # check the ratio of True in target"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6102803738317757"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sr3zS8Km0qjh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1120)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umOTMtAECu3G",
        "colab_type": "text"
      },
      "source": [
        "### Define a function to measure the score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TWnUYTtC39i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score, cohen_kappa_score, f1_score, log_loss,roc_auc_score,precision_score,recall_score\n",
        "def score_list(predicion,y_test=y_test):\n",
        "  '''this function will print Accurarcy, Kappa, F1 score, Log Loss and AUC'''\n",
        "  print('\\n ****************************')\n",
        "  print(\"Accuracy = {:.2f}\".format(accuracy_score(y_test, predicion)))\n",
        "  print(\"F1 Score = {:.2f}\".format(f1_score(y_test, predicion)))\n",
        "  print(\"AUC = {:.2f}\".format(roc_auc_score(y_test, predicion)))\n",
        "  print(\"Precision = {:.2f}\".format(precision_score(y_test, predicion)))\n",
        "  print(\"Recall = {:.2f}\".format(recall_score(y_test, predicion)))\n",
        "  print('**************************** \\n')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Opf-10cg1sgZ",
        "colab_type": "text"
      },
      "source": [
        "### Five models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CjIhL0o1yXP",
        "colab_type": "text"
      },
      "source": [
        "#### Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJqm5KOq3eWn",
        "colab_type": "code",
        "outputId": "e8515916-f605-4273-c1a4-04e6efbc6602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# build model using grid search with 5Fold Cross validation to train and test\n",
        "treeclf = DecisionTreeClassifier(splitter='best', presort=True, class_weight=None, random_state=42)\n",
        "parameters = {'criterion':('gini', 'entropy'), 'max_depth':[2, 4, 6, 8, 10], 'min_samples_split':[2, 10, 50], 'min_samples_leaf':[1, 5, 10],\n",
        "             'max_features':[None, 'auto'], 'max_leaf_nodes':[None, 5, 10, 50], 'min_impurity_decrease':[0, 0.1, 0.2]}\n",
        "cv_clf = GridSearchCV(treeclf, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
        "cv_clf.fit(X_train, y_train)\n",
        "\n",
        "# predict on test data\n",
        "y_pred_dt_cv = cv_clf.predict(X_test)\n",
        "\n",
        "# print the score with the function defined myself\n",
        "score_list(y_pred_dt_cv)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=DecisionTreeClassifier(class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features=None,\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              presort=True, random_state=42,\n",
              "                                              splitter='best'),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid={'criterion': ('gini', 'entropy'),\n",
              "                         'max_depth': [2, 4, 6, 8, 10],\n",
              "                         'max_features': [None, 'auto'],\n",
              "                         'max_leaf_nodes': [None, 5, 10, 50],\n",
              "                         'min_impurity_decrease': [0, 0.1, 0.2],\n",
              "                         'min_samples_leaf': [1, 5, 10],\n",
              "                         'min_samples_split': [2, 10, 50]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
              "             scoring='roc_auc', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " ****************************\n",
            "Accuracy = 0.75\n",
            "F1 Score = 0.78\n",
            "AUC = 0.75\n",
            "Precision = 0.81\n",
            "Recall = 0.75\n",
            "**************************** \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s61gsWx25g8Y",
        "colab_type": "text"
      },
      "source": [
        "#### Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-Pv0oeL5mS3",
        "colab_type": "code",
        "outputId": "ff847af9-6763-4598-9b90-ba34b923ce50",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "# build model using grid search with 5Fold Cross validation to train and test\n",
        "gnb = GaussianNB()\n",
        "parameters = {\"var_smoothing\":[1e-7,1e-8,1e-9,1e-10,1e-11]}\n",
        "cv_gnb = GridSearchCV(gnb, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
        "cv_gnb.fit(X_train, y_train)\n",
        "\n",
        "# predict on test data\n",
        "y_pred_gnb_cv = cv_gnb.predict(X_test)\n",
        "\n",
        "# print the score with the function defined myself\n",
        "score_list(y_pred_gnb_cv)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=GaussianNB(priors=None, var_smoothing=1e-09), iid='warn',\n",
              "             n_jobs=None,\n",
              "             param_grid={'var_smoothing': [1e-07, 1e-08, 1e-09, 1e-10, 1e-11]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
              "             scoring='roc_auc', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " ****************************\n",
            "Accuracy = 0.75\n",
            "F1 Score = 0.78\n",
            "AUC = 0.75\n",
            "Precision = 0.81\n",
            "Recall = 0.76\n",
            "**************************** \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYPN1day5vAQ",
        "colab_type": "text"
      },
      "source": [
        "#### KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCg4Wa5T6bzv",
        "colab_type": "code",
        "outputId": "d6a8389d-e366-4c3c-b1d7-167dde2570ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "\n",
        "# build model using grid search with 5Fold Cross validation to train and test\n",
        "knn_clf = KNeighborsClassifier()\n",
        "parameters = {\"n_neighbors\":range(2,11)}\n",
        "cv_knn_clf = GridSearchCV(knn_clf, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
        "cv_knn_clf.fit(X_train, y_train)\n",
        "\n",
        "# predict on test data\n",
        "y_pred_knn_clf_cv = cv_knn_clf.predict(X_test)\n",
        "\n",
        "# print the score with the function defined myself\n",
        "score_list(y_pred_knn_clf_cv)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
              "                                            metric='minkowski',\n",
              "                                            metric_params=None, n_jobs=None,\n",
              "                                            n_neighbors=5, p=2,\n",
              "                                            weights='uniform'),\n",
              "             iid='warn', n_jobs=None, param_grid={'n_neighbors': range(2, 11)},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
              "             scoring='roc_auc', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " ****************************\n",
            "Accuracy = 0.76\n",
            "F1 Score = 0.80\n",
            "AUC = 0.74\n",
            "Precision = 0.77\n",
            "Recall = 0.84\n",
            "**************************** \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yvm8tdZm6fZH",
        "colab_type": "text"
      },
      "source": [
        "#### NN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mz3T550W6zJP",
        "colab_type": "code",
        "outputId": "82f81033-6176-4da1-e236-9ac2df129558",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "\n",
        "# build model using grid search with 5Fold Cross validation to train and test\n",
        "nn_clf = MLPClassifier(solver='lbfgs',random_state=1, verbose=False)\n",
        "parameters = {'activation':['logistic','tanh','relu'],'alpha':[0.001,0.003,0.01,0.03,0.1,0.3,1,3],'hidden_layer_sizes':range(2,11)}\n",
        "cv_nn_clf = GridSearchCV(nn_clf, parameters, scoring='roc_auc', cv=5, return_train_score=False)\n",
        "cv_nn_clf.fit(X_train, y_train)\n",
        "\n",
        "# predict on test data\n",
        "y_pred_nn_clf_cv = cv_nn_clf.predict(X_test)\n",
        "\n",
        "# print the score with the function defined myself\n",
        "score_list(y_pred_nn_clf_cv)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=MLPClassifier(activation='relu', alpha=0.0001,\n",
              "                                     batch_size='auto', beta_1=0.9,\n",
              "                                     beta_2=0.999, early_stopping=False,\n",
              "                                     epsilon=1e-08, hidden_layer_sizes=(100,),\n",
              "                                     learning_rate='constant',\n",
              "                                     learning_rate_init=0.001, max_iter=200,\n",
              "                                     momentum=0.9, n_iter_no_change=10,\n",
              "                                     nesterovs_momentum=True, power_t=0.5,\n",
              "                                     random_state=1, shuffle=True,\n",
              "                                     solver='lbfgs', tol=0.0001,\n",
              "                                     validation_fraction=0.1, verbose=False,\n",
              "                                     warm_start=False),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid={'activation': ['logistic', 'tanh', 'relu'],\n",
              "                         'alpha': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3],\n",
              "                         'hidden_layer_sizes': range(2, 11)},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='roc_auc', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " ****************************\n",
            "Accuracy = 0.80\n",
            "F1 Score = 0.83\n",
            "AUC = 0.79\n",
            "Precision = 0.83\n",
            "Recall = 0.83\n",
            "**************************** \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ie0Vry88TuO",
        "colab_type": "text"
      },
      "source": [
        "#### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrGf9TqL662d",
        "colab_type": "code",
        "outputId": "c4f75013-3779-4c3d-8146-2ed2bec88ed3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "# build model using grid search with 5Fold Cross validation to train and test\n",
        "rf_clf = RandomForestClassifier(random_state=1120)\n",
        "parameters = {\"n_estimators\":[50,100,200,300],'max_depth':[2,5,8,11]}\n",
        "cv_rf_clf = GridSearchCV(rf_clf, parameters, scoring='roc_auc', cv=5, return_train_score=False)\n",
        "cv_rf_clf.fit(X_train, y_train)\n",
        "\n",
        "# predict on test data\n",
        "y_pred_rf_clf_cv = cv_rf_clf.predict(X_test)\n",
        "\n",
        "# print the score with the function defined myself\n",
        "score_list(y_pred_rf_clf_cv)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators='warn', n_jobs=None,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=1120, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid={'max_depth': [2, 5, 8, 11],\n",
              "                         'n_estimators': [50, 100, 200, 300]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='roc_auc', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " ****************************\n",
            "Accuracy = 0.80\n",
            "F1 Score = 0.83\n",
            "AUC = 0.80\n",
            "Precision = 0.84\n",
            "Recall = 0.82\n",
            "**************************** \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sh_ra80fo6Op",
        "colab_type": "text"
      },
      "source": [
        "## 8.2 Answer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ww-6PLIp4c5",
        "colab_type": "text"
      },
      "source": [
        "#### 1.\tDownload the file OJ.csv. The target feature is Purchase. The rest of the features are self-explanatory, hopefully."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pu6iRid9qLsX",
        "colab_type": "text"
      },
      "source": [
        "I have downloaded the data and loaded in the coding part."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0ETWIRGqQUX",
        "colab_type": "text"
      },
      "source": [
        "#### 2.\tPreprocess the data however you see fit. Describe what you did and why."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQoIcVnwVNL2",
        "colab_type": "text"
      },
      "source": [
        "* I set WeekofPurchase, StoreID and STORE to object in pandas dataframe.\n",
        "* I made a correlation table to check the correlation between each two features. Then I list the correlation coefficients which are larger than 0.5\n",
        "* Because the correlation between DiscCH and PctDiscCH and the correlation between DiscMM and PctDiscMM are extremely high (almost 1), so I deceide to drop PctDiscMM and PctDiscCH. \n",
        "* Also, I drop PriceDiff since the correlation coefficient is high.\n",
        "* I set the target to 1 and 0.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lv3Lg5PTqT0H",
        "colab_type": "text"
      },
      "source": [
        "#### 3.\tSplit the data into training and testing sets. Describe what you did and why."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGW5baE9YE_9",
        "colab_type": "text"
      },
      "source": [
        "* I check the ratio of true and false in target. Since around 60% is true and 40% is false, I consider this is a balance data.\n",
        "* I just randomly split the data into train and test. The train data is 80% and the test data is 20%\n",
        "* I will use 5-foldcross validation to tune the hyper parameter with train data and test the model with test data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzyvATC1qVlH",
        "colab_type": "text"
      },
      "source": [
        "#### 4.\tChoose an appropriate metric to analyze a model’s performance. Justify."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvpmqK-4Zsv4",
        "colab_type": "text"
      },
      "source": [
        "* I use AUC score to measure the performance\n",
        "* The target is not 60% true and 40% false, I do not want to use accuracy as the measure.\n",
        "* I do not care too much about either specificity or sensitivity.\n",
        "* I may want to know top 10 percent of situations that customer will buy CH.\n",
        "* So, AUC is a good choice in this case."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RyqyV4NlqaC4",
        "colab_type": "text"
      },
      "source": [
        "#### 5.\tBuild five different models, using five different classifier algorithms. (Any five will do.) a.\tTune each model. What were the best parameter values for each model?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsZY6DZChvoo",
        "colab_type": "text"
      },
      "source": [
        "* I have build 5 different models to test, which are Decvision Trees, Naive Bayes, K-nearest neighbors, neural network and random forest.\n",
        "* For each model, I use grid search with 5-foldcross validation to tune the hyper parameter.\n",
        "* Here are the best parameter values for each model, please check the output below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJIDyDwBf3R6",
        "colab_type": "code",
        "outputId": "7fafee78-ecce-4fe6-897a-a92290ead5c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        }
      },
      "source": [
        "print('\\n **** Decision Trees ****')\n",
        "cv_clf.best_estimator_\n",
        "print('\\n')\n",
        "\n",
        "print('\\n **** Naive Bayes ****')\n",
        "cv_gnb.best_estimator_\n",
        "print('\\n')\n",
        "\n",
        "print('\\n **** k-nearest neighbors ****')\n",
        "cv_knn_clf.best_estimator_\n",
        "print('\\n')\n",
        "\n",
        "print('\\n **** neural network ****')\n",
        "cv_nn_clf.best_estimator_\n",
        "print('\\n')\n",
        "\n",
        "print('\\n **** random forest ****')\n",
        "cv_rf_clf.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " **** Decision Trees ****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
              "                       max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0, min_impurity_split=None,\n",
              "                       min_samples_leaf=10, min_samples_split=50,\n",
              "                       min_weight_fraction_leaf=0.0, presort=True,\n",
              "                       random_state=42, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            " **** Naive Bayes ****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            " **** k-nearest neighbors ****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            " **** neural network ****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(activation='tanh', alpha=3, batch_size='auto', beta_1=0.9,\n",
              "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
              "              hidden_layer_sizes=7, learning_rate='constant',\n",
              "              learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
              "              n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
              "              random_state=1, shuffle=True, solver='lbfgs', tol=0.0001,\n",
              "              validation_fraction=0.1, verbose=False, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            " **** random forest ****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "                       max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=1120,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogulDkNjqdLQ",
        "colab_type": "text"
      },
      "source": [
        "#### 6.\tDescribe and compare the performance of each fine-tuned model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G7W9s6R306ON",
        "colab_type": "text"
      },
      "source": [
        "* Random forest gets the highest score on  AUC, accuracy ad f1 score. \n",
        "* Here are the scores, please check the table below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9QSTrzIqldf",
        "colab_type": "code",
        "outputId": "4d222f81-d16b-45bc-f494-3053477cc9a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dict_score={}\n",
        "for i in [accuracy_score, f1_score,roc_auc_score,precision_score,recall_score]:\n",
        "  dict_score[i.__name__]=[]\n",
        "  for k in [y_pred_dt_cv,y_pred_gnb_cv,y_pred_knn_clf_cv,y_pred_nn_clf_cv,y_pred_rf_clf_cv]:\n",
        "    dict_score[i.__name__].append(round(i(y_test,k),3))\n",
        "\n",
        "df8_6=pd.DataFrame(dict_score,index=['Decision Trees','Naive Bayes','k-nearest neighbors','neural network','random forest'])\n",
        "df8_6.sort_values('roc_auc_score',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy_score</th>\n",
              "      <th>f1_score</th>\n",
              "      <th>roc_auc_score</th>\n",
              "      <th>precision_score</th>\n",
              "      <th>recall_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>random forest</th>\n",
              "      <td>0.804</td>\n",
              "      <td>0.831</td>\n",
              "      <td>0.801</td>\n",
              "      <td>0.844</td>\n",
              "      <td>0.817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neural network</th>\n",
              "      <td>0.799</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.793</td>\n",
              "      <td>0.832</td>\n",
              "      <td>0.825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Naive Bayes</th>\n",
              "      <td>0.752</td>\n",
              "      <td>0.784</td>\n",
              "      <td>0.750</td>\n",
              "      <td>0.807</td>\n",
              "      <td>0.762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Trees</th>\n",
              "      <td>0.748</td>\n",
              "      <td>0.779</td>\n",
              "      <td>0.746</td>\n",
              "      <td>0.805</td>\n",
              "      <td>0.754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>k-nearest neighbors</th>\n",
              "      <td>0.757</td>\n",
              "      <td>0.803</td>\n",
              "      <td>0.739</td>\n",
              "      <td>0.768</td>\n",
              "      <td>0.841</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     accuracy_score  f1_score  roc_auc_score  precision_score  \\\n",
              "random forest                 0.804     0.831          0.801            0.844   \n",
              "neural network                0.799     0.829          0.793            0.832   \n",
              "Naive Bayes                   0.752     0.784          0.750            0.807   \n",
              "Decision Trees                0.748     0.779          0.746            0.805   \n",
              "k-nearest neighbors           0.757     0.803          0.739            0.768   \n",
              "\n",
              "                     recall_score  \n",
              "random forest               0.817  \n",
              "neural network              0.825  \n",
              "Naive Bayes                 0.762  \n",
              "Decision Trees              0.754  \n",
              "k-nearest neighbors         0.841  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-7G71bIqhF_",
        "colab_type": "text"
      },
      "source": [
        "#### 7.\tSelect the best model. Justify."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWtQ4cdU7yec",
        "colab_type": "text"
      },
      "source": [
        "I select random forest as the best model in this case. Because it has the highest AUC, F1 score and accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCHSJrEfqjWg",
        "colab_type": "text"
      },
      "source": [
        "#### 8.\tIs this model good enough to deploy today? Justify."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJy0WhcG35TU",
        "colab_type": "text"
      },
      "source": [
        "* Since the AUC is over 0.8, I think it is good enough to deploy today.\n",
        "* Besides, I think the model will definitely help the business. At least they can have the support to predict the demond of the CH and MM orange juice. \n",
        "* If the merchant can collect data in more dimensions such as weather, temperature, holiday, and season, I think we can make a better model for the business."
      ]
    }
  ]
}