Module3_Lab2_FMML20210660.ipynb

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        "<a href=\"https://colab.research.google.com/github/SreenadhSingamaneni/IIITH-FMML20210660/blob/main/Module3_Lab2_FMML20210660.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "ihr08YTg2ifn"
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      "source": [
        "# **FOUNDATIONS OF MODERN MACHINE LEARNING, IIIT Hyderabad**\n",
        "### MODULE: CLASSIFICATION-1\n",
        "### LAB-2 : Implement KNN from scratch and visualize algo. performance\n",
        "#### Module Coordinator: Sahil Manoj Bhatt\n",
        "\n",
        "NOTE: YOU ONLY NEED TO MAKE CHANGES/WRITE CODE IN CELLS THAT SPECIFICALLY MENTION TASK-1, TASK-2, etc.\n",
        "\n",
        "WRITE ANY OBSERVATION(S), IF REQUIRED BY THE TASK, IN A SEPARATE CELL AT THE BOTTOM OF THE NOTEBOOK.  \n",
        "\n",
        "---\n",
        "\n"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "HIJdUKMs1QV0"
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      "source": [
        "## KNN\n",
        "\n",
        "In the last lab, you learnt distance metrics and their importance in classification algorithms. You also saw how the KNN algorithm works for two cases: i) a synthetic dataset and ii) a real-world Iris dataset.  \n",
        "\n",
        "However, the question still remains: *What is KNN?*  \n",
        "### What is KNN ?\n",
        "\n",
        "K-NN (K- Nearest Neighbours) is a classification technique where the output is a class membership.  \n",
        "An object is classified by a **plurality vote of its neighbors**, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.  \n",
        "\n",
        "### What is so unique about KNN ?  \n",
        "First off, KNN is a **supervised learning** algorithm, ie, the idea is to learn a function that can map an input to an output given some example pairs.  \n",
        "\n",
        "It is a **non-parametric** algorithm, since it doesn't assume anything about the form of the mapping function, which makes it very flexible to use. Certain algorithms are parametric (such as Naive Bayes), which are constrained as they require a specified form. KNN can, thus, be a good algorithm to try if the form is unknown.  \n",
        "\n",
        "Moreover, KNN is an **instance-based algorithm**, since it compares new problems/inputs with those which were seen during model training and that were stored in memory.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7877e44n6Kd"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import preprocessing\n",
        "from collections import Counter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHzdQE_8oPM1"
      },
      "source": [
        "def predict(X_train, y_train, X_test, k):\n",
        "    distances = []\n",
        "    targets = []\n",
        "\n",
        "    for i in range(len(X_train)):\n",
        "        # compute and store L2 distance\n",
        "        distances.append([np.sqrt(np.sum(np.square(X_test - X_train[i, :]))), i])\n",
        "\n",
        "    distances = sorted(distances)\n",
        "\n",
        "    for i in range(k):\n",
        "        index = distances[i][1]\n",
        "        targets.append(y_train[index])\n",
        "\n",
        "    # return most common target\n",
        "    return Counter(targets).most_common(1)[0][0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_kulEM0oWJ-"
      },
      "source": [
        "def k_nearest_neighbor(X_train, y_train, X_test, k):\n",
        "\n",
        "    assert k <= len(X_train), \"[!] K cannot be larger than number of samples.\"\n",
        "\n",
        "    # loop over all observations\n",
        "    predictions = []\n",
        "    for i in range(len(X_test)):\n",
        "        predictions.append(predict(X_train, y_train, X_test[i, :], k))\n",
        "\n",
        "    return np.asarray(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KlLazZsPbXml"
      },
      "source": [
        "# **Iris Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7wJgGQOxbDv"
      },
      "source": [
        "Download the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "9gPMkdpxwq3k",
        "outputId": "44e04511-7ca8-4459-b99d-45847ccfdefe"
      },
      "source": [
        "# Upload the Iris data CSV file that has been shared with you.\n",
        "# Run this cell, click on the 'Choose files' button and upload the file.\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a529c6a5-0921-4451-beaa-31b621d05b1f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a529c6a5-0921-4451-beaa-31b621d05b1f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving iris.csv to iris.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2KaMd3xxfVK"
      },
      "source": [
        "Explore the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MReMSexyoXj4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "33aa3b85-690d-4011-812f-ee3967eaea8b"
      },
      "source": [
        "df = pd.read_csv('iris.csv')\n",
        "df = df.drop(columns='Id')\n",
        "df"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
              "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
              "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
              "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
              "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
              "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
              "..             ...           ...            ...           ...             ...\n",
              "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
              "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
              "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
              "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
              "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lI5ikdtwXICl"
      },
      "source": [
        "As you can see above, the last column consists of strings as classes. These need to be converted into appropriate numerical classes. For this, you can use the Label encoder module provided by scikit-learn.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3k-c7xhfpErX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "48e6f625-d0a0-4c85-dfa2-1261904eba2f"
      },
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "orig_species = df['Species']\n",
        "le.fit(df['Species'])\n",
        "df['Species']=le.transform(df['Species'])\n",
        "df"
      ],
      "execution_count": null,
      "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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0              5.1           3.5            1.4           0.2        0\n",
              "1              4.9           3.0            1.4           0.2        0\n",
              "2              4.7           3.2            1.3           0.2        0\n",
              "3              4.6           3.1            1.5           0.2        0\n",
              "4              5.0           3.6            1.4           0.2        0\n",
              "..             ...           ...            ...           ...      ...\n",
              "145            6.7           3.0            5.2           2.3        2\n",
              "146            6.3           2.5            5.0           1.9        2\n",
              "147            6.5           3.0            5.2           2.0        2\n",
              "148            6.2           3.4            5.4           2.3        2\n",
              "149            5.9           3.0            5.1           1.8        2\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWnZ-aQxUHmO"
      },
      "source": [
        "X = np.array(df[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']])\n",
        "y = np.array(df['Species'])\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=17)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HczF_VrRpLGH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ef59bd3-8ee1-40fa-c29c-80698c945bfb"
      },
      "source": [
        "# Making our predictions\n",
        "predictions = k_nearest_neighbor(X_train, y_train, X_test, 7)\n",
        "\n",
        "# evaluating accuracy\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "print(\"The accuracy of our classifier is {} %\".format(100*accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of our classifier is 94.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "weliPFpEXmEE",
        "outputId": "7e2aa7f3-1056-4d98-f712-1ee6b517fb2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 0,\n",
              "       2, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 2, 1, 0, 1, 1, 0, 1,\n",
              "       2, 1, 1, 2, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwQ3qP-UXY5x"
      },
      "source": [
        "Voila! You have implemented your own version of the K-Nearest Neighbours algorithm, which works very well on the Iris Dataset. Congratulations!  \n",
        "\n",
        "Now try out the sklearn implementation and compare your results.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0jJj5_7dg-o",
        "outputId": "d87375af-dcc2-4e5b-a016-decc5b2226e4"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "sklearn_knn = KNeighborsClassifier(n_neighbors=7)\n",
        "sklearn_knn.fit(X_train,y_train)\n",
        "sklearn_predictions = sklearn_knn.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, sklearn_predictions)\n",
        "print(\"The accuracy of Sklearn classifier is {} %\".format(100*accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of Sklearn classifier is 94.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hXZP4wXqtmK"
      },
      "source": [
        "## TASK-1\n",
        "## Modify the KNN function you wrote to return all the K-nearest neighbours along with their distances,\n",
        "## instead of just the output that was most common. You don't need to find out accuracy, just modify the function\n",
        "## and return the k-nearest neighbours and distances."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(X_train, y_train, X_test, k):\n",
        "    distances = []\n",
        "    targets = []\n",
        "    distt=[]\n",
        "\n",
        "    for i in range(len(X_train)):\n",
        "        # compute and store L2 distance\n",
        "        distances.append([np.sqrt(np.sum(np.square(X_test - X_train[i, :]))), i])\n",
        "\n",
        "    distances = sorted(distances)\n",
        "    #print(distances)\n",
        "\n",
        "    for i in range(k):\n",
        "        index = distances[i][1]\n",
        "        dist=distances[i][0]\n",
        "        distt.append(dist)\n",
        "        targets.append(y_train[index])\n",
        "    #print(targets)\n",
        "    #print(distt)\n",
        "\n",
        "    # return most common target\n",
        "    return targets,distt\n",
        "def k_nearest_neighbor(X_train, y_train, X_test, k):\n",
        "\n",
        "    assert k <= len(X_train), \"[!] K cannot be larger than number of samples.\"\n",
        "\n",
        "    # loop over all observations\n",
        "    predictions = []\n",
        "    disttt=[]\n",
        "    for i in range(len(X_test)):\n",
        "        disttt.append(predict(X_train, y_train, X_test[i, :], k))\n",
        "\n",
        "    return np.asarray(disttt)\n"
      ],
      "metadata": {
        "id": "tOdax9EVYgu_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dist=k_nearest_neighbor(X_train, y_train, X_test, 9)\n",
        "print(dist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nz6fL61AhKNa",
        "outputId": "71a8255f-b187-451b-bee6-3a6b8753d66e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.34641016 0.38729833 0.41231056 0.45825757 0.45825757 0.46904158\n",
            "   0.47958315 0.51961524 0.51961524]]\n",
            "\n",
            " [[1.         1.         1.         2.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.2        0.24494897 0.33166248 0.43588989 0.48989795 0.5\n",
            "   0.51961524 0.54772256 0.57445626]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.24494897 0.36055513 0.36055513 0.37416574 0.42426407 0.57445626\n",
            "   0.57445626 0.59160798 0.6164414 ]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.17320508 0.2236068  0.26457513 0.36055513 0.42426407 0.51961524\n",
            "   0.55677644 0.678233   0.7       ]]\n",
            "\n",
            " [[2.         2.         2.         1.         1.         1.\n",
            "   1.         1.         2.        ]\n",
            "  [0.17320508 0.28284271 0.38729833 0.42426407 0.43588989 0.4472136\n",
            "   0.46904158 0.5        0.51961524]]\n",
            "\n",
            " [[2.         2.         1.         1.         1.         2.\n",
            "   1.         2.         2.        ]\n",
            "  [0.14142136 0.36055513 0.42426407 0.45825757 0.45825757 0.47958315\n",
            "   0.5        0.53851648 0.55677644]]\n",
            "\n",
            " [[1.         1.         1.         1.         2.         1.\n",
            "   1.         2.         2.        ]\n",
            "  [0.48989795 0.50990195 0.51961524 0.58309519 0.7        0.70710678\n",
            "   0.71414284 0.73484692 0.73484692]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.41231056 0.81853528 0.86023253 1.00498756 1.0198039  1.13578167\n",
            "   1.22065556 1.24096736 1.28452326]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.42426407 0.4472136  0.52915026 0.54772256 0.58309519 0.58309519\n",
            "   0.64031242 0.70710678 0.71414284]]\n",
            "\n",
            " [[2.         2.         2.         1.         2.         2.\n",
            "   2.         2.         1.        ]\n",
            "  [0.26457513 0.33166248 0.51961524 0.54772256 0.60827625 0.64807407\n",
            "   0.65574385 0.67082039 0.78740079]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.24494897 0.3        0.31622777 0.4        0.43588989 0.47958315\n",
            "   0.54772256 0.57445626 0.6244998 ]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.17320508 0.17320508 0.17320508 0.2        0.26457513 0.26457513\n",
            "   0.31622777 0.31622777 0.33166248]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         2.         1.        ]\n",
            "  [0.31622777 0.33166248 0.45825757 0.45825757 0.46904158 0.46904158\n",
            "   0.6244998  0.68556546 0.73484692]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.2236068  0.28284271 0.3        0.3        0.3        0.3\n",
            "   0.31622777 0.34641016 0.34641016]]\n",
            "\n",
            " [[2.         2.         1.         2.         2.         2.\n",
            "   2.         2.         1.        ]\n",
            "  [0.2236068  0.37416574 0.42426407 0.46904158 0.50990195 0.51961524\n",
            "   0.55677644 0.55677644 0.60827625]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.14142136 0.26457513 0.31622777 0.33166248 0.33166248\n",
            "   0.37416574 0.37416574 0.41231056]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.2        0.24494897 0.3        0.3        0.36055513\n",
            "   0.42426407 0.42426407 0.46904158]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         1.        ]\n",
            "  [0.14142136 0.24494897 0.36055513 0.38729833 0.38729833 0.5\n",
            "   0.50990195 0.51961524 0.54772256]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.42426407 0.50990195 0.55677644 0.60827625 0.64031242 0.64807407\n",
            "   0.70710678 0.74161985 0.76157731]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.1        0.3        0.42426407 0.43588989 0.46904158 0.5\n",
            "   0.50990195 0.50990195 0.6       ]]\n",
            "\n",
            " [[1.         1.         2.         1.         2.         1.\n",
            "   2.         1.         2.        ]\n",
            "  [0.33166248 0.36055513 0.37416574 0.46904158 0.46904158 0.5\n",
            "   0.50990195 0.53851648 0.55677644]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.14142136 0.17320508 0.2236068  0.2236068  0.26457513\n",
            "   0.28284271 0.3        0.33166248]]\n",
            "\n",
            " [[2.         2.         1.         2.         2.         1.\n",
            "   2.         2.         2.        ]\n",
            "  [0.31622777 0.33166248 0.37416574 0.45825757 0.53851648 0.60827625\n",
            "   0.6244998  0.64031242 0.64031242]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.3        0.41231056 0.43588989 0.51961524 0.52915026 0.53851648\n",
            "   0.53851648 0.57445626 0.76811457]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   2.         2.         1.        ]\n",
            "  [0.42426407 0.4472136  0.46904158 0.51961524 0.51961524 0.55677644\n",
            "   0.58309519 0.67082039 0.76157731]]\n",
            "\n",
            " [[1.         1.         1.         1.         2.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.37416574 0.45825757 0.46904158 0.51961524 0.53851648 0.58309519\n",
            "   0.73484692 0.75498344 0.79372539]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.45825757 0.50990195 0.51961524 0.51961524 0.59160798 0.68556546\n",
            "   0.71414284 0.72801099 0.82462113]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.2236068  0.3        0.50990195 0.50990195 0.51961524 0.52915026\n",
            "   0.58309519 0.6164414  0.6244998 ]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.1        0.14142136 0.14142136 0.14142136 0.17320508 0.17320508\n",
            "   0.2236068  0.3        0.31622777]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.14142136 0.31622777 0.46904158 0.48989795 0.5        0.54772256\n",
            "   0.54772256 0.54772256 0.64807407]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.17320508 0.2236068  0.3        0.31622777 0.31622777\n",
            "   0.31622777 0.34641016 0.36055513]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.38729833 0.38729833 0.72111026 0.79372539 0.9        0.97467943\n",
            "   1.161895   1.32287566 1.33041347]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.17320508 0.17320508 0.17320508 0.2        0.26457513 0.26457513\n",
            "   0.31622777 0.31622777 0.33166248]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.33166248 0.34641016 0.37416574 0.38729833 0.41231056 0.45825757\n",
            "   0.47958315 0.52915026 0.53851648]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.34641016 0.50990195 0.6        0.6164414  0.6164414  0.64807407\n",
            "   0.66332496 0.67082039 0.72111026]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.2        0.34641016 0.38729833 0.42426407 0.45825757 0.47958315\n",
            "   0.5        0.55677644 0.60827625]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.17320508 0.2236068  0.3        0.38729833 0.42426407 0.58309519\n",
            "   0.60827625 0.6244998  0.64807407]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.34641016 0.38729833 0.43588989 0.46904158 0.64807407 0.65574385\n",
            "   0.66332496 0.74161985 0.74161985]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.34641016 0.4        0.43588989 0.54772256 0.54772256 0.64031242\n",
            "   0.64807407 0.67082039 0.678233  ]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.17320508 0.17320508 0.17320508 0.2        0.26457513 0.26457513\n",
            "   0.31622777 0.31622777 0.33166248]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.17320508 0.33166248 0.36055513 0.43588989 0.48989795 0.6\n",
            "   0.6244998  0.678233   0.68556546]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.14142136 0.24494897 0.26457513 0.31622777 0.31622777 0.48989795\n",
            "   0.53851648 0.53851648 0.55677644]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.1        0.2236068  0.24494897 0.28284271 0.31622777 0.33166248\n",
            "   0.34641016 0.36055513 0.37416574]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.28284271 0.37416574 0.46904158 0.47958315 0.54772256 0.6\n",
            "   0.6164414  0.64807407 0.72801099]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.17320508 0.34641016 0.36055513 0.37416574 0.43588989 0.43588989\n",
            "   0.45825757 0.46904158 0.48989795]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.3        0.38729833 0.38729833 0.42426407 0.51961524 0.57445626\n",
            "   0.6244998  0.65574385 0.7       ]]\n",
            "\n",
            " [[2.         1.         1.         1.         2.         2.\n",
            "   1.         2.         1.        ]\n",
            "  [0.2236068  0.46904158 0.52915026 0.54772256 0.54772256 0.6\n",
            "   0.6244998  0.64807407 0.65574385]]\n",
            "\n",
            " [[2.         2.         1.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.         0.31622777 0.36055513 0.47958315 0.50990195 0.54772256\n",
            "   0.54772256 0.63245553 0.678233  ]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.28284271 0.31622777 0.31622777 0.33166248 0.34641016 0.37416574\n",
            "   0.37416574 0.37416574 0.45825757]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.2        0.26457513 0.37416574 0.38729833 0.38729833 0.41231056\n",
            "   0.42426407 0.43588989 0.43588989]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SxcBnLl8GHWz"
      },
      "source": [
        "## Visualizing Data  \n",
        "\n",
        "We will look into something called **Voronoi** diagrams.  \n",
        "\n",
        "**Note**: Ideally, we should perform data visualization to see what the data looks like before we apply any Machine Learning algorithm.  Only for the purpose of this lab session, we're explaining it after you've applied KNN."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aDsDfmXG79k"
      },
      "source": [
        "### Voronoi Diagrams  \n",
        "\n",
        "In simple terms, Voronoi diagrams help you to visualize the dataset by partioning the plane into regions that are close to a given set of points. These regions are also called Voronoi cells.  \n",
        "\n",
        "Note that the cells/regions depend on the Distance metric being used. One way of interpreting this is by understanding that the distance metric decides the degree to which a 'point' or 'seed' in the Voronoi diagram has influence.  For each seed there is a corresponding region, called a Voronoi cell, consisting of all points of the plane closer to that seed than to any other. \n",
        "\n",
        "This [link](https://en.wikipedia.org/wiki/Voronoi_diagram#Illustration) provides a wonderful illustration of Voronoi plots for 20 points in two cases: (1) Using Euclidean distance, and (2) Using Manhattan distance.  \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdcdjthOKwra"
      },
      "source": [
        "Since our Iris data is 4-dimensional (as it has 4 attributes), we need to convert into a form that can be represented in 2-D.   \n",
        "\n",
        "While there are methods to visualize data higher than 2-dimensions, that is beyond scope for now.  \n",
        "\n",
        "For simplicity, we just take the first two columns of the iris dataset attributes and observe the Voronoi diagram generated for that.  \n",
        "Alternatively, one can also perform PCA (Principal Component Analysis), to reduce the 4D data to just two dimensions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNOPcS3f7sZg"
      },
      "source": [
        "#@title Plotting Voronoi regions\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
        "\n",
        "def voronoi_finite_polygons_2d(vor, radius=None):\n",
        "    \"\"\"\n",
        "    Reconstruct infinite voronoi regions in a 2D diagram to finite\n",
        "    regions.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    vor : Voronoi\n",
        "        Input diagram\n",
        "    radius : float, optional\n",
        "        Distance to 'points at infinity'.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    regions : list of tuples\n",
        "        Indices of vertices in each revised Voronoi regions.\n",
        "    vertices : list of tuples\n",
        "        Coordinates for revised Voronoi vertices. Same as coordinates\n",
        "        of input vertices, with 'points at infinity' appended to the\n",
        "        end.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    if vor.points.shape[1] != 2:\n",
        "        raise ValueError(\"Requires 2D input\")\n",
        "\n",
        "    new_regions = []\n",
        "    new_vertices = vor.vertices.tolist()\n",
        "\n",
        "    center = vor.points.mean(axis=0)\n",
        "    if radius is None:\n",
        "        radius = vor.points.ptp().max()\n",
        "\n",
        "    # Construct a map containing all ridges for a given point\n",
        "    all_ridges = {}\n",
        "    for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):\n",
        "        all_ridges.setdefault(p1, []).append((p2, v1, v2))\n",
        "        all_ridges.setdefault(p2, []).append((p1, v1, v2))\n",
        "\n",
        "    # Reconstruct infinite regions\n",
        "    for p1, region in enumerate(vor.point_region):\n",
        "        vertices = vor.regions[region]\n",
        "\n",
        "        if all(v >= 0 for v in vertices):\n",
        "            # finite region\n",
        "            new_regions.append(vertices)\n",
        "            continue\n",
        "\n",
        "        # reconstruct a non-finite region\n",
        "        ridges = all_ridges[p1]\n",
        "        new_region = [v for v in vertices if v >= 0]\n",
        "\n",
        "        for p2, v1, v2 in ridges:\n",
        "            if v2 < 0:\n",
        "                v1, v2 = v2, v1\n",
        "            if v1 >= 0:\n",
        "                # finite ridge: already in the region\n",
        "                continue\n",
        "\n",
        "            # Compute the missing endpoint of an infinite ridge\n",
        "\n",
        "            t = vor.points[p2] - vor.points[p1] # tangent\n",
        "            t /= np.linalg.norm(t)\n",
        "            n = np.array([-t[1], t[0]])  # normal\n",
        "\n",
        "            midpoint = vor.points[[p1, p2]].mean(axis=0)\n",
        "            direction = np.sign(np.dot(midpoint - center, n)) * n\n",
        "            far_point = vor.vertices[v2] + direction * radius\n",
        "\n",
        "            new_region.append(len(new_vertices))\n",
        "            new_vertices.append(far_point.tolist())\n",
        "\n",
        "        # sort region counterclockwise\n",
        "        vs = np.asarray([new_vertices[v] for v in new_region])\n",
        "        c = vs.mean(axis=0)\n",
        "        angles = np.arctan2(vs[:,1] - c[1], vs[:,0] - c[0])\n",
        "        new_region = np.array(new_region)[np.argsort(angles)]\n",
        "\n",
        "        # finish\n",
        "        new_regions.append(new_region.tolist())\n",
        "\n",
        "    return new_regions, np.asarray(new_vertices)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "9NIVhxz8KvG3",
        "outputId": "08bd5939-2b10-49b4-edd4-0fd796d54340"
      },
      "source": [
        "## Plotting Voronoi regions for the Iris dataset\n",
        "\n",
        "points = []\n",
        "xpts = np.array(df['SepalLengthCm'])\n",
        "ypts = np.array(df['SepalWidthCm'])\n",
        "for i in range(len(xpts)):\n",
        "  points.append([xpts[i],ypts[i]])\n",
        "# print(points)\n",
        "points = np.array(points)\n",
        "# compute Voronoi tesselation\n",
        "vor = Voronoi(points)\n",
        "\n",
        "regions, vertices = voronoi_finite_polygons_2d(vor)\n",
        "\n",
        "for region in regions:\n",
        "    polygon = vertices[region]\n",
        "    plt.fill(*zip(*polygon), alpha=0.4)\n",
        "\n",
        "plt.plot(points[:,0], points[:,1], 'ko')\n",
        "plt.xlim(vor.min_bound[0] - 0.1, vor.max_bound[0] + 0.1)\n",
        "plt.ylim(vor.min_bound[1] - 0.1, vor.max_bound[1] + 0.1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSK-GI0Knurk"
      },
      "source": [
        "## Understanding Decision Boundaries  \n",
        "So you have seen the Voronoi diagram of the dataset, implemented KNN, and also seen your algorithm's performance in terms of accuracy? Impressive!  \n",
        "Wouldn't it also be great to know how exactly these 'votes' or neighbours are decided through some kind of visualization?  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytP_AImALiZX"
      },
      "source": [
        "### Decision Boundary\n",
        "\n",
        "While the Voronoi diagram gave us a good idea of the points present in our dataset, to understand how KNN performed on our dataset we can plot decision boundaries. Decision boundaries, as the name suggests, divide the plane into different regions of classification.  \n",
        "\n",
        "Note that here again, for simplicity, we have only considered first two attributes of the DataFrame (ie, Sepal Length and Sepal Width).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8P8Pqav4DI4N"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "def decision_boundary_plot(x_dec,y_dec,k):\n",
        "  h = .02  # step size in the mesh\n",
        "\n",
        "  # Create color maps\n",
        "  n = len(set(y_dec))\n",
        "  cmap_light = ListedColormap(['pink', 'green', 'cyan','yellow'][:n])\n",
        "  cmap_bold = ['pink', 'darkgreen', 'blue','yellow'][:n]\n",
        "\n",
        "  for weights in ['uniform', 'distance']:\n",
        "      # we create an instance of Neighbours Classifier and fit the data.\n",
        "      clf = KNeighborsClassifier(n_neighbors=k, weights=weights)\n",
        "      clf.fit(x_dec, y_dec)\n",
        "\n",
        "      # Plot the decision boundary. For that, we will assign a color to each\n",
        "      # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
        "      x_min, x_max = x_dec[:, 0].min() - 1, x_dec[:, 0].max() + 1\n",
        "      y_min, y_max = x_dec[:, 1].min() - 1, x_dec[:, 1].max() + 1\n",
        "      xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                          np.arange(y_min, y_max, h))\n",
        "      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "\n",
        "      # Put the result into a color plot\n",
        "      Z = Z.reshape(xx.shape)\n",
        "      plt.figure(figsize=(8, 6))\n",
        "      plt.contourf(xx, yy, Z, cmap=cmap_light)\n",
        "\n",
        "      # Plot also the training points\n",
        "      sns.scatterplot(x=x_dec[:, 0], y=x_dec[:, 1], hue=y_dec,\n",
        "                      palette=cmap_bold, alpha=1.0, edgecolor=\"black\")\n",
        "      plt.xlim(xx.min(), xx.max())\n",
        "      plt.ylim(yy.min(), yy.max())\n",
        "      plt.title(\"Multi-Classification (k = %i, weights = '%s')\"% (k, weights))\n",
        "\n",
        "  plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "bgAO62vWKPHt",
        "outputId": "74e518db-1688-4698-9535-afbe42536863"
      },
      "source": [
        "x_pts = X[:,:2]\n",
        "y_pts = y\n",
        "decision_boundary_plot(x_pts,y_pts,7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFRJidIgr6gt"
      },
      "source": [
        "## TASK-2\n",
        "## In the above cells, we saw the Voronoi diagram of the data and plotted the KNN decision boundaries\n",
        "## by only considering two attributes of the dataset. You must be already familiar with PCA.\n",
        "## Apply PCA on the dataset above to reduce it to two dimensions. \n",
        "## Plot the Voronoi diagram and Decision boundaries after that."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gti-Jgu_VBDl"
      },
      "source": [
        "## Confusion Matrix  \n",
        "In classification problems, a confusion matrix, also known as an error matrix, is a table that allows visualization of the performance of an algorithm, typically a supervised learning one. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPfc8YFBA8Oh"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "XMXIM9apA99s",
        "outputId": "3ef0c662-70ea-4eb6-ed12-f7a0619257e4"
      },
      "source": [
        "print(confusion_matrix(y_test,predictions))\n",
        "pd.crosstab(y_test, predictions, rownames=['True'], colnames=['Predicted'], margins=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[13  0  0]\n",
            " [ 0 22  0]\n",
            " [ 0  3 12]]\n"
          ]
        },
        {
          "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>Predicted</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Predicted   0   1   2  All\n",
              "True                      \n",
              "0          13   0   0   13\n",
              "1           0  22   0   22\n",
              "2           0   3  12   15\n",
              "All        13  25  12   50"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "5cgYG0E5UHdy",
        "outputId": "447b7705-089b-4109-ac35-e61aeee6b556"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, predictions)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"viridis\" ,fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTPQOPStVtmI"
      },
      "source": [
        "## Classification Report\n",
        "\n",
        "Precision, Recall, and F1-Score are other metrics besides accuracy that one might look for in an algorithm.  Depending on the use-case, one might consider one metric more important than the other.  \n",
        "\n",
        "Note: *T-> True, F->False, P->Positive, N->Negative*\n",
        "    \n",
        "Mathematically, Accuracy is :  \n",
        "\n",
        "$Accuracy = \\frac{TP+TN}{TP+TN+FP+FN}$  \n",
        "\n",
        "**Precision**: The accuracy of positive predictions\n",
        "\n",
        "\n",
        "$Precision = \\frac{TP}{TP+FP}$ \n",
        "\n",
        "**Recall**:Fraction of positives that were correctly identified\n",
        "\n",
        "\n",
        "$Recall = \\frac{TP}{TP+FN}$\n",
        "\n",
        "\n",
        "**F1-score**: Harmonic mean of precision and recall  \n",
        "\n",
        "\n",
        "$F1 = \\frac{2*Precision*Recall}{Precision+Recall} = \\frac{2*TP}{2*TP+FP+FN}$  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VH3KEfEYW190",
        "outputId": "ee74398a-a4b6-4b1f-ed4d-6486662a6287"
      },
      "source": [
        "#import classification_report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        13\n",
            "           1       0.88      1.00      0.94        22\n",
            "           2       1.00      0.80      0.89        15\n",
            "\n",
            "    accuracy                           0.94        50\n",
            "   macro avg       0.96      0.93      0.94        50\n",
            "weighted avg       0.95      0.94      0.94        50\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtYNFoJh0LU-"
      },
      "source": [
        "# **Car Evaluation Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsBukCMi4UjJ",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "8efe25dc-17d7-42a1-f806-2fcc46cd7b70"
      },
      "source": [
        "# Upload the Car evaluation data CSV file that has been shared with you.\n",
        "# Run this cell, click on the 'Choose files' button and upload the file.\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e89db293-ce80-4b65-ac75-236c2cdd2800\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e89db293-ce80-4b65-ac75-236c2cdd2800\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving car_evaluation.csv to car_evaluation.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0T5yvzVH4jrQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "6d090afd-c77d-4d60-9132-7cd384f8b3a7"
      },
      "source": [
        "car_df = pd.read_csv('car_evaluation.csv')\n",
        "car_df"
      ],
      "execution_count": null,
      "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>buying price</th>\n",
              "      <th>maintenance cost</th>\n",
              "      <th>num_doors</th>\n",
              "      <th>num_persons</th>\n",
              "      <th>lug_boot</th>\n",
              "      <th>safety</th>\n",
              "      <th>decision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>small</td>\n",
              "      <td>med</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>small</td>\n",
              "      <td>high</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>med</td>\n",
              "      <td>low</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>med</td>\n",
              "      <td>med</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>med</td>\n",
              "      <td>high</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1722</th>\n",
              "      <td>low</td>\n",
              "      <td>low</td>\n",
              "      <td>5more</td>\n",
              "      <td>more</td>\n",
              "      <td>med</td>\n",
              "      <td>med</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1723</th>\n",
              "      <td>low</td>\n",
              "      <td>low</td>\n",
              "      <td>5more</td>\n",
              "      <td>more</td>\n",
              "      <td>med</td>\n",
              "      <td>high</td>\n",
              "      <td>vgood</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1724</th>\n",
              "      <td>low</td>\n",
              "      <td>low</td>\n",
              "      <td>5more</td>\n",
              "      <td>more</td>\n",
              "      <td>big</td>\n",
              "      <td>low</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>low</td>\n",
              "      <td>low</td>\n",
              "      <td>5more</td>\n",
              "      <td>more</td>\n",
              "      <td>big</td>\n",
              "      <td>med</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>low</td>\n",
              "      <td>low</td>\n",
              "      <td>5more</td>\n",
              "      <td>more</td>\n",
              "      <td>big</td>\n",
              "      <td>high</td>\n",
              "      <td>vgood</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1727 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     buying price maintenance cost num_doors  ... lug_boot safety decision\n",
              "0           vhigh            vhigh         2  ...    small    med    unacc\n",
              "1           vhigh            vhigh         2  ...    small   high    unacc\n",
              "2           vhigh            vhigh         2  ...      med    low    unacc\n",
              "3           vhigh            vhigh         2  ...      med    med    unacc\n",
              "4           vhigh            vhigh         2  ...      med   high    unacc\n",
              "...           ...              ...       ...  ...      ...    ...      ...\n",
              "1722          low              low     5more  ...      med    med     good\n",
              "1723          low              low     5more  ...      med   high    vgood\n",
              "1724          low              low     5more  ...      big    low    unacc\n",
              "1725          low              low     5more  ...      big    med     good\n",
              "1726          low              low     5more  ...      big   high    vgood\n",
              "\n",
              "[1727 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwjBankjB9Os",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b454836c-e0cd-40cf-96f9-2e2bc4abb96d"
      },
      "source": [
        "for x in car_df.columns:\n",
        "  # print(x)\n",
        "  le = preprocessing.LabelEncoder()\n",
        "  le.fit(car_df[x])\n",
        "  car_df[x]=le.transform(car_df[x])\n",
        "\n",
        "car_df"
      ],
      "execution_count": null,
      "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>buying price</th>\n",
              "      <th>maintenance cost</th>\n",
              "      <th>num_doors</th>\n",
              "      <th>num_persons</th>\n",
              "      <th>lug_boot</th>\n",
              "      <th>safety</th>\n",
              "      <th>decision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1722</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1723</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1724</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1727 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      buying price  maintenance cost  num_doors  ...  lug_boot  safety  decision\n",
              "0                3                 3          0  ...         2       2         2\n",
              "1                3                 3          0  ...         2       0         2\n",
              "2                3                 3          0  ...         1       1         2\n",
              "3                3                 3          0  ...         1       2         2\n",
              "4                3                 3          0  ...         1       0         2\n",
              "...            ...               ...        ...  ...       ...     ...       ...\n",
              "1722             1                 1          3  ...         1       2         1\n",
              "1723             1                 1          3  ...         1       0         3\n",
              "1724             1                 1          3  ...         0       1         2\n",
              "1725             1                 1          3  ...         0       2         1\n",
              "1726             1                 1          3  ...         0       0         3\n",
              "\n",
              "[1727 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5Klx4yMCgKa"
      },
      "source": [
        "dataset = car_df.values\n",
        "X = dataset[:,0:6]\n",
        "y = np.array(dataset[:,6])\n",
        "X_train, X_test, y_train, Y_test = train_test_split(X, y, test_size=0.2, random_state=19)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HL5ufCHDANh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "445e6283-d6a3-4c70-f37c-d2af833e2c91"
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "predictions1 = knn.predict(X_test)\n",
        "score = accuracy_score(Y_test, predictions1)\n",
        "print(\"The accuracy of the classifier on Car evaluation dataset is {:.2f} %\".format(100*score))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the classifier on Car evaluation dataset is 89.88 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYCimUC_A56C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "758ac00d-dcf5-4dc2-935e-0e2fa493225a"
      },
      "source": [
        "## TASK-3\n",
        "## Plot a Confusion Matrix for the results of the Car evaluation dataset\n",
        "confusion_matrix(Y_test,predictions1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 56,   2,  14,   0],\n",
              "       [  9,   6,   0,   0],\n",
              "       [  3,   0, 239,   0],\n",
              "       [  5,   0,   2,  10]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8TRknp0XTqJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dd5d947-064c-4dc8-8397-1a56b55349e2"
      },
      "source": [
        "## TASK-4\n",
        "## Print a Classification Report for the results of the Car evaluation dataset\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y_test,predictions1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.78      0.77        72\n",
            "           1       0.75      0.40      0.52        15\n",
            "           2       0.94      0.99      0.96       242\n",
            "           3       1.00      0.59      0.74        17\n",
            "\n",
            "    accuracy                           0.90       346\n",
            "   macro avg       0.86      0.69      0.75       346\n",
            "weighted avg       0.90      0.90      0.89       346\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xr9dI1Kqsprz"
      },
      "source": [
        "### Questions to Think About and Answer\n",
        "1. In the section on Decision boundaries, you must have seen that we ran the KNN algorithm twice: first with the _weights_ set to 'uniform' and then set to 'distance'. Find out the difference between these two.  \n",
        "2. What do you think could be the drawbacks of using KNN ?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. uniform - All points in each neighborhood are weighted equally.\n",
        "\n",
        " 'distance' - weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.\n",
        "2. As training data increases the speed at which calculations are made rapidly decrease.\n",
        " If K- value is choosen incorrectly then it will be under or overfitted to the data.When majority of the data the model is being trained on represents 1 label then that label will have a high likelihood of being predicted"
      ],
      "metadata": {
        "id": "yBKSGanEoiq0"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRlrn4EctZrC"
      },
      "source": [
        "### Useful Resources for further reading\n",
        "1. Interactive KNN visualization, with class boundaries: http://vision.stanford.edu/teaching/cs231n-demos/knn/  \n"
      ]
    }
  ]
}