e_commerce_dataset.ipynb

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   "source": [
    "<h2 style=\"text-align: center;\">FINAL PROJECT - TEAM TWO</h2>\n",
    "<h3 style=\"text-align: center;\">USD MADS - 500B</h3>\n",
    "<h3 style=\"text-align: center;\">Fall 2024, Professor Jules Malin</h3>\n",
    "<div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9c3a89-5cd9-46e3-80e6-69f7658c9134",
   "metadata": {},
   "source": [
    "\n",
    "<br>\n",
    "<h3 style=\"text-align: center;\">Team Two:</h3>\n",
    "<h2 style=\"text-align: center;\">Franky Hernandez</h2>\n",
    "<h2 style=\"text-align: center;\">Katie Kimberling</h2>\n",
    "<h2 style=\"text-align: center;\">Jason Avalos</h2>\n",
    "\n",
    "<div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e485681-ffdd-4de8-afa0-e7a2537044df",
   "metadata": {},
   "source": [
    "## <span style=\"color:black\"><div align=\"center\">INTRODUCTION AND EXPLANATION OF BUSINESS PROBLEM </div></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db4889e-38fa-472a-88c0-27f5c4e5445e",
   "metadata": {},
   "source": [
    "Team Two chose the <strong>e-commerce dataset from the UCI Machine Learning Repository</strong>. This dataset has 12,330 records with 17 different features (variables) describing the behavior of a potential customer on an e-commerce retail site (think Amazon or Zappos - a site that only offers products through internet shopping, not brick-and-mortar retail stores with physical locations). For the purposes of this project, a \"customer\" is defined as an individual who has successfully made a purchase on the website.\n",
    "\n",
    "<strong>Our overall goal is to make data-driven recommendations to our clients that increase customer engagement.</srong> \n",
    "\n",
    "Increasing customer engagement not only leads to higher sales and revenue, but it increases brand loyalty and word-of-mouth marketing by customers. Increasing customer engagement could include such things as: \n",
    "1. Determining ways to decrease bounce rates and exit rates (defined below),\n",
    "2. Increasing purchase rates by suggesting modifications to the website (what pages are experiencing the highest sale conversions and/or exit rates), and\n",
    "3. Identifying characteristcs of viewers who make purchases so that targeted advertising can be used for non-customers with the same characteristics (conversions).\n",
    "\n",
    "To make suggestions to increase cusomter engagement, we used the dataset to assess the following question:\n",
    "*  On which pages are viewers are spending the most time, especially prior to a purchase? More specifically, which types of pages (i.e. administrative, informational or product-related) have the highest duration of time spent on them (\"dwell time\") prior to a customer making a purchase?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5dce962-27be-400a-9577-be0d06978610",
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   "source": [
    "## <span style=\"color:black\"><div align=\"center\"><strong>PART ONE:</strong> Data Importing and Pre-processing </div></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fffd77-a035-4ebf-832f-ac6eff21e7f1",
   "metadata": {},
   "source": [
    "### Import dataset and describe characteristics such as dimensions, data types, file types, and import methods used"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a54ae89-2a0c-421d-9c3c-27f5450fe54c",
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   "source": [
    "The following packages were imported for the following reasons: \n",
    "- <strong>pandas</strong> in python for data anlysis\n",
    "- <strong>numpy</strong> to be used for calculations \n",
    "- <strong>seaborn</strong> -- to be used in conjunction with matplotlib for statistical data visualizations\n",
    "- <strong>matplotlib.pyplot</strong> for data visualizations and graphs\n",
    "- <strong>scipy</strong> in order to use the Winsorization function to adjust skewness and handle outliers\n",
    "- <strong>sklearn</strong> for supervised and unsupervised learning, regression and classification, splitting data into training and testing datasets, and calculating confusion matrices and accuracy scores of logistic regressions\n",
    "- <strong>statsmodels</strong> in order to calculate variance inflaction factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97fb258d-3639-44a1-b01f-206d9ce65e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats.mstats import winsorize\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import statsmodels.api as sm\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ae01dd3-80c5-4e33-8938-d860fe45100f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
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      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0               0                      0.0            0.0   \n",
       "1               0                      0.0            0.0   \n",
       "2               0                      0.0            0.0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0               1                      0.0   \n",
       "1                     0.0               2                     64.0   \n",
       "2                     0.0               1                      0.0   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0          0.2        0.2         0.0         0.0   Feb               1.0   \n",
       "1          0.0        0.1         0.0         0.0   Feb               2.0   \n",
       "2          0.2        0.2         0.0         0.0   Feb               4.0   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dataset\n",
    "e_df = pd.read_csv('online_shoppers_intention.csv')\n",
    "e_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db6d3807-5e60-4947-a7a1-3ab3599cfde3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Dimensions: (12330, 18)\n",
      "\n",
      "Data Types:\n",
      "Administrative               int64\n",
      "Administrative_Duration    float64\n",
      "Informational              float64\n",
      "Informational_Duration     float64\n",
      "ProductRelated               int64\n",
      "ProductRelated_Duration    float64\n",
      "BounceRates                float64\n",
      "ExitRates                  float64\n",
      "PageValues                 float64\n",
      "SpecialDay                 float64\n",
      "Month                       object\n",
      "OperatingSystems           float64\n",
      "Browser                      int64\n",
      "Region                       int64\n",
      "TrafficType                  int64\n",
      "VisitorType                 object\n",
      "Weekend                       bool\n",
      "Revenue                       bool\n",
      "dtype: object\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Administrative           12330 non-null  int64  \n",
      " 1   Administrative_Duration  12330 non-null  float64\n",
      " 2   Informational            12202 non-null  float64\n",
      " 3   Informational_Duration   12330 non-null  float64\n",
      " 4   ProductRelated           12330 non-null  int64  \n",
      " 5   ProductRelated_Duration  12330 non-null  float64\n",
      " 6   BounceRates              12330 non-null  float64\n",
      " 7   ExitRates                12330 non-null  float64\n",
      " 8   PageValues               12195 non-null  float64\n",
      " 9   SpecialDay               12330 non-null  float64\n",
      " 10  Month                    12330 non-null  object \n",
      " 11  OperatingSystems         12207 non-null  float64\n",
      " 12  Browser                  12330 non-null  int64  \n",
      " 13  Region                   12330 non-null  int64  \n",
      " 14  TrafficType              12330 non-null  int64  \n",
      " 15  VisitorType              12330 non-null  object \n",
      " 16  Weekend                  12330 non-null  bool   \n",
      " 17  Revenue                  12330 non-null  bool   \n",
      "dtypes: bool(2), float64(9), int64(5), object(2)\n",
      "memory usage: 1.5+ MB\n",
      "\n",
      "Dataset Information:\n",
      "None\n",
      "\n",
      "Dataset Summary:\n",
      "       Administrative  Administrative_Duration  Informational  \\\n",
      "count    12330.000000             12330.000000   12202.000000   \n",
      "mean         2.315166                80.818611       0.503770   \n",
      "std          3.321784               176.779107       1.270882   \n",
      "min          0.000000                 0.000000       0.000000   \n",
      "25%          0.000000                 0.000000       0.000000   \n",
      "50%          1.000000                 7.500000       0.000000   \n",
      "75%          4.000000                93.256250       0.000000   \n",
      "max         27.000000              3398.750000      24.000000   \n",
      "\n",
      "       Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
      "count            12330.000000    12330.000000             12330.000000   \n",
      "mean                34.472398       31.731468              1194.746220   \n",
      "std                140.749294       44.475503              1913.669288   \n",
      "min                  0.000000        0.000000                 0.000000   \n",
      "25%                  0.000000        7.000000               184.137500   \n",
      "50%                  0.000000       18.000000               598.936905   \n",
      "75%                  0.000000       38.000000              1464.157214   \n",
      "max               2549.375000      705.000000             63973.522230   \n",
      "\n",
      "        BounceRates     ExitRates    PageValues    SpecialDay  \\\n",
      "count  12330.000000  12330.000000  12195.000000  12330.000000   \n",
      "mean       0.022191      0.043073      5.911196      0.061427   \n",
      "std        0.048488      0.048597     18.632116      0.198917   \n",
      "min        0.000000      0.000000      0.000000      0.000000   \n",
      "25%        0.000000      0.014286      0.000000      0.000000   \n",
      "50%        0.003112      0.025156      0.000000      0.000000   \n",
      "75%        0.016813      0.050000      0.000000      0.000000   \n",
      "max        0.200000      0.200000    361.763742      1.000000   \n",
      "\n",
      "       OperatingSystems       Browser        Region   TrafficType  \n",
      "count      12207.000000  12330.000000  12330.000000  12330.000000  \n",
      "mean           2.123618      2.357097      3.147364      4.069586  \n",
      "std            0.911829      1.717277      2.401591      4.025169  \n",
      "min            1.000000      1.000000      1.000000      1.000000  \n",
      "25%            2.000000      2.000000      1.000000      2.000000  \n",
      "50%            2.000000      2.000000      3.000000      2.000000  \n",
      "75%            3.000000      2.000000      4.000000      4.000000  \n",
      "max            8.000000     13.000000      9.000000     20.000000  \n"
     ]
    }
   ],
   "source": [
    "# Describe the dataset\n",
    "print(f\"Dataset Dimensions: {e_df.shape}\")\n",
    "print(f\"\\nData Types:\\n{e_df.dtypes}\")\n",
    "print(f\"\\nDataset Information:\\n{e_df.info()}\")\n",
    "print(f\"\\nDataset Summary:\\n{e_df.describe()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e66455c-6c08-4929-826c-de2610a3a52f",
   "metadata": {},
   "source": [
    "### Clean, wrangle, and handle missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04d3a7ee-774a-4fd3-b939-07dcfb050f7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values:\n",
      "Administrative               0\n",
      "Administrative_Duration      0\n",
      "Informational              128\n",
      "Informational_Duration       0\n",
      "ProductRelated               0\n",
      "ProductRelated_Duration      0\n",
      "BounceRates                  0\n",
      "ExitRates                    0\n",
      "PageValues                 135\n",
      "SpecialDay                   0\n",
      "Month                        0\n",
      "OperatingSystems           123\n",
      "Browser                      0\n",
      "Region                       0\n",
      "TrafficType                  0\n",
      "VisitorType                  0\n",
      "Weekend                      0\n",
      "Revenue                      0\n",
      "dtype: int64\n",
      "Number of rows after dropping missing values: 11948\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(f\"Missing Values:\\n{e_df.isnull().sum()}\")\n",
    "\n",
    "# --------Handle Missing Values--------\n",
    "\n",
    "# Fill numeric columns with the median\n",
    "e_df['BounceRates'].fillna(e_df['BounceRates'].median())\n",
    "e_df['ExitRates'].fillna(e_df['ExitRates'].median())\n",
    "\n",
    "# Replace page duration columns with 0\n",
    "e_df['Administrative_Duration'].fillna(0)\n",
    "e_df['Informational_Duration'].fillna(0)\n",
    "e_df['ProductRelated_Duration'].fillna(0)\n",
    "\n",
    "# Fill categorical values with mode \n",
    "e_df['Month'].fillna(e_df['Month'].mode()[0])\n",
    "e_df['VisitorType'].fillna(e_df['VisitorType'].mode()[0])\n",
    "\n",
    "# Drop any remaining rows that have missing values \n",
    "e_df.dropna(inplace=True)\n",
    "\n",
    "print(\"Number of rows after dropping missing values:\", len(e_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3abda17-6314-41d8-9cc4-5f56c66740d3",
   "metadata": {},
   "source": [
    "### Transform data appropriately using techniques such as aggregation, normalization, and feature construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1ab883f-d19d-4e0c-9c93-c0a064bdd2b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize numerical features to ensure consistent scales\n",
    "scaler = MinMaxScaler()\n",
    "e_df[['BounceRates', 'ExitRates', 'ProductRelated_Duration']] = scaler.fit_transform(\n",
    "    e_df[['BounceRates', 'ExitRates', 'ProductRelated_Duration']]\n",
    ")\n",
    "\n",
    "# Aggregate the average product-related duration by Region and reset the index\n",
    "region_product_duration = e_df.groupby('Region')['ProductRelated_Duration'].mean().reset_index()\n",
    "region_product_duration.rename(columns={'ProductRelated_Duration': 'Avg_Product_Duration'}, inplace=True)\n",
    "\n",
    "# Drop any duplicate 'Avg_Product_Duration' columns\n",
    "if 'Avg_Product_Duration' in e_df.columns:\n",
    "    e_df = e_df.drop(columns=['Avg_Product_Duration'])\n",
    "\n",
    "# Now merge with the main dataframe\n",
    "e_df = e_df.merge(region_product_duration, on='Region', how='left')\n",
    "\n",
    "# Feature Construction: Create a \"Total Duration\" feature for overall session length\n",
    "e_df['TotalDuration'] = e_df['Administrative_Duration'] + e_df['Informational_Duration'] + e_df['ProductRelated_Duration']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf53d17-c578-450d-ab03-aaa52bccac51",
   "metadata": {},
   "source": [
    "### Reduce redundant data and perform need based discretization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f6956fe-f4ff-4bb1-8fc8-88e26e66ba9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11948 entries, 0 to 11947\n",
      "Data columns (total 21 columns):\n",
      " #   Column                   Non-Null Count  Dtype   \n",
      "---  ------                   --------------  -----   \n",
      " 0   Administrative           11948 non-null  int64   \n",
      " 1   Administrative_Duration  11948 non-null  float64 \n",
      " 2   Informational            11948 non-null  float64 \n",
      " 3   Informational_Duration   11948 non-null  float64 \n",
      " 4   ProductRelated           11948 non-null  int64   \n",
      " 5   ProductRelated_Duration  11948 non-null  float64 \n",
      " 6   BounceRates              11948 non-null  float64 \n",
      " 7   ExitRates                11948 non-null  float64 \n",
      " 8   PageValues               11948 non-null  float64 \n",
      " 9   SpecialDay               11948 non-null  float64 \n",
      " 10  Month                    11948 non-null  object  \n",
      " 11  OperatingSystems         11948 non-null  float64 \n",
      " 12  Browser                  11948 non-null  int64   \n",
      " 13  Region                   11948 non-null  int64   \n",
      " 14  TrafficType              11948 non-null  int64   \n",
      " 15  VisitorType              11948 non-null  object  \n",
      " 16  Weekend                  11948 non-null  bool    \n",
      " 17  Revenue                  11948 non-null  bool    \n",
      " 18  Avg_Product_Duration     11948 non-null  float64 \n",
      " 19  TotalDuration            11948 non-null  float64 \n",
      " 20  DurationCategory         11948 non-null  category\n",
      "dtypes: bool(2), category(1), float64(11), int64(5), object(2)\n",
      "memory usage: 1.7+ MB\n",
      "\n",
      "Final DataFrame Summary:\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Drop redundant columns\n",
    "# Discretize 'TotalDuration' into categories\n",
    "e_df['DurationCategory'] = pd.cut(e_df['TotalDuration'], bins=3, labels=[\"Low\", \"Medium\", \"High\"])\n",
    "\n",
    "# Final review of the cleaned and preprocessed data\n",
    "print(f\"\\nFinal DataFrame Summary:\\n{e_df.info()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29ea3098-3b86-4d86-bbea-ab01ffa0a033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sample Data:\n",
      "   Administrative  Administrative_Duration  Informational  \\\n",
      "0               0                      0.0            0.0   \n",
      "1               0                      0.0            0.0   \n",
      "2               0                      0.0            0.0   \n",
      "\n",
      "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
      "0                     0.0               1                    0.000   \n",
      "1                     0.0               2                    0.001   \n",
      "2                     0.0               1                    0.000   \n",
      "\n",
      "   BounceRates  ExitRates  PageValues  SpecialDay  ... OperatingSystems  \\\n",
      "0          1.0        1.0         0.0         0.0  ...              1.0   \n",
      "1          0.0        0.5         0.0         0.0  ...              2.0   \n",
      "2          1.0        1.0         0.0         0.0  ...              4.0   \n",
      "\n",
      "   Browser  Region  TrafficType        VisitorType Weekend  Revenue  \\\n",
      "0        1       1            1  Returning_Visitor   False    False   \n",
      "1        2       1            2  Returning_Visitor   False    False   \n",
      "2        1       9            3  Returning_Visitor   False    False   \n",
      "\n",
      "   Avg_Product_Duration  TotalDuration  DurationCategory  \n",
      "0              0.019887          0.000               Low  \n",
      "1              0.019887          0.001               Low  \n",
      "2              0.017696          0.000               Low  \n",
      "\n",
      "[3 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "# Sample final data\n",
    "print(f\"\\nSample Data:\\n{e_df.head(3)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1096efaf-d0e5-46ea-8430-2f23fa7a322b",
   "metadata": {},
   "source": [
    "## <span style=\"color:black\"><div align=\"center\"><strong>PART TWO:</strong> Data Analysis and Visualization </div></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb4e8b8-9ac2-4f52-924b-4e7b1ba9afd6",
   "metadata": {},
   "source": [
    "### Identify categorical, ordinal, and numerical variables within the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5760acc-0e6d-4150-a205-1920f6728057",
   "metadata": {},
   "source": [
    "<p><strong>CATEGORICAL VARIABLES:</strong>\n",
    "Administrative, Informational, ProductRelated, Month, Weekend, Revenue,\n",
    "DurationCategory, Operating Systems, Browser,Region, TrafficType, VisitorType, Weekendd, Revenue, and DurationCategory\n",
    "\n",
    "<strong>NUMERICAL VARIABLES:</strong>\n",
    "ProductRelated_Duration, BounceRates, ExitRates, PageValues, \n",
    "SpecialDay, Avg_Product_Duration, and TotalDuration</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd3b035-b67e-4f23-a90d-bc25f3148e44",
   "metadata": {},
   "source": [
    "### Provide measures of centrality and distribution with visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d0146b0-e944-434c-bba4-0dc290c336cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overview of the Data with Histograms\n",
    "\n",
    "#Selecting the numerical variables based on datatype\n",
    "numeric_features = e_df.select_dtypes(include=['int64', 'float64']).columns\n",
    "\n",
    "# Adjust the figure size to accommodate all plots\n",
    "plt.figure(figsize=(20, 20))  # Increased height to fit more rows\n",
    "\n",
    "# Calculate the number of rows needed for the subplots\n",
    "num_rows = (len(numeric_features) + 2) // 3  # Adding 2 to ensure enough space\n",
    "\n",
    "for i, col in enumerate(numeric_features, 1):\n",
    "    plt.subplot(num_rows, 3, i)  # Dynamically calculate number of rows\n",
    "    plt.hist(e_df[col], bins=30, color='skyblue')\n",
    "    plt.title(col)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5133ce88-e058-413b-a9f4-44a072ecb3ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List all numerical variables for analysis\n",
    "numerical_variables = ['ProductRelated_Duration', 'BounceRates', 'ExitRates', 'PageValues', 'Avg_Product_Duration', 'TotalDuration', 'SpecialDay']\n",
    "\n",
    "# Filter numerical \n",
    "numerical_df = e_df[ numerical_variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17f107d1-2918-4f55-b77e-ee36f9c76592",
   "metadata": {},
   "outputs": [
    {
     "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>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>Avg_Product_Duration</th>\n",
       "      <th>TotalDuration</th>\n",
       "      <th>SpecialDay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11948.000000</td>\n",
       "      <td>11948.000000</td>\n",
       "      <td>11948.000000</td>\n",
       "      <td>11948.000000</td>\n",
       "      <td>11948.000000</td>\n",
       "      <td>11948.000000</td>\n",
       "      <td>11948.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.018638</td>\n",
       "      <td>0.110817</td>\n",
       "      <td>0.215265</td>\n",
       "      <td>5.938418</td>\n",
       "      <td>0.018638</td>\n",
       "      <td>115.565329</td>\n",
       "      <td>0.061383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.029818</td>\n",
       "      <td>0.242254</td>\n",
       "      <td>0.242886</td>\n",
       "      <td>18.719907</td>\n",
       "      <td>0.001150</td>\n",
       "      <td>251.068151</td>\n",
       "      <td>0.198728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.002877</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017748</td>\n",
       "      <td>0.005443</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.009367</td>\n",
       "      <td>0.015535</td>\n",
       "      <td>0.125543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018512</td>\n",
       "      <td>19.004501</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.022849</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019887</td>\n",
       "      <td>121.276726</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>361.763742</td>\n",
       "      <td>0.019887</td>\n",
       "      <td>5949.125000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProductRelated_Duration   BounceRates     ExitRates    PageValues  \\\n",
       "count             11948.000000  11948.000000  11948.000000  11948.000000   \n",
       "mean                  0.018638      0.110817      0.215265      5.938418   \n",
       "std                   0.029818      0.242254      0.242886     18.719907   \n",
       "min                   0.000000      0.000000      0.000000      0.000000   \n",
       "25%                   0.002877      0.000000      0.071429      0.000000   \n",
       "50%                   0.009367      0.015535      0.125543      0.000000   \n",
       "75%                   0.022849      0.083333      0.250000      0.000000   \n",
       "max                   1.000000      1.000000      1.000000    361.763742   \n",
       "\n",
       "       Avg_Product_Duration  TotalDuration    SpecialDay  \n",
       "count          11948.000000   11948.000000  11948.000000  \n",
       "mean               0.018638     115.565329      0.061383  \n",
       "std                0.001150     251.068151      0.198728  \n",
       "min                0.016197       0.000000      0.000000  \n",
       "25%                0.017748       0.005443      0.000000  \n",
       "50%                0.018512      19.004501      0.000000  \n",
       "75%                0.019887     121.276726      0.000000  \n",
       "max                0.019887    5949.125000      1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive Statistics for Numerical Variables\n",
    "numerical_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "342a93e5-ddf1-4b46-aea9-f4bf82034d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots\n",
    "# Axes 2*3 matrix axes\n",
    "# Winsorize 10% from both the lower and upper tails\n",
    "# numerical_df = winsorize(numerical_df, limits=[0.1, 0.1])\n",
    "# Winsorize 10% from both the lower and upper tails\n",
    "def winsorize_dataframe(df, limits=(0.2, 0.2)):\n",
    "    \"\"\"Winsorizes all numeric columns in the dataframe.\"\"\"\n",
    "    df_winsorized = df.copy()\n",
    "    for col in df_winsorized.select_dtypes(include='number'):\n",
    "        df_winsorized[col] = winsorize(df_winsorized[col], limits=limits)\n",
    "    return df_winsorized\n",
    "\n",
    "numerical_df = winsorize_dataframe(numerical_df)\n",
    "\n",
    "fig, axes = plt.subplots(3, 3, figsize=(10, 5))\n",
    "\n",
    "# Boxplot for ProductRelated_Duration\n",
    "sns.boxplot(y=numerical_df['ProductRelated_Duration'], ax=axes[0,0], color='skyblue')\n",
    "axes[0,0].set_title('ProductRelated_Duration')\n",
    "\n",
    "# Boxplot for BounceRates\n",
    "sns.boxplot(y=numerical_df['BounceRates'],  ax=axes[0,1], color='skyblue')\n",
    "axes[0,1].set_title('BounceRates')\n",
    "\n",
    "# Boxplot for ExitRates\n",
    "sns.boxplot(y=numerical_df['ExitRates'], ax=axes[0,2], color='skyblue')\n",
    "axes[0,2].set_title('Exit Rates')\n",
    "\n",
    "# Boxplot for PageValues\n",
    "sns.boxplot(y=numerical_df['PageValues'], ax=axes[1,0], color='skyblue')\n",
    "axes[1,0].set_title('PageValues')\n",
    "\n",
    "# Boxplot for SpecialDay\n",
    "sns.boxplot(y=numerical_df['SpecialDay'],  ax=axes[1,1], color='skyblue')\n",
    "axes[1,1].set_title('SpecialDay')\n",
    "\n",
    "# Boxplot for Avg_Product_Duration\n",
    "sns.boxplot(y=numerical_df['Avg_Product_Duration'], ax=axes[1,2], color='skyblue' )\n",
    "axes[1,2].set_title('Avg_Product_Duration')\n",
    "\n",
    "# Boxplot for Total_Duration\n",
    "sns.boxplot(y=numerical_df['TotalDuration'], ax=axes[2,0], color='skyblue')\n",
    "axes[2,0].set_title('TotalDuration')\n",
    "\n",
    "axes[2,1].set_visible(False)\n",
    "axes[2,2].set_visible(False)\n",
    "\n",
    "# Removes y-labels from all plots\n",
    "for row in axes:\n",
    "    for element in row:\n",
    "        element.set_ylabel(\"\")\n",
    "        \n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# SPECIAL DAY STILL CONTAINS NAN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6eb5a5f-095b-4083-bad4-27a43c5710a9",
   "metadata": {},
   "source": [
    "### Diagnose for correlations between variables and determine independent and dependent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d61395-9171-4728-99e9-8c21bdb863d9",
   "metadata": {},
   "source": [
    "<p>Based on the correlation matrix show above. The only two variables that are strongly correlated are: BounceRates and ExitRates, with a positive correlation coeeficient of .91. The remaining numerical variables have weak correlation ranging from .01 to .4.</p> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "915ba8ce-e5f8-4ba5-ae5c-1e81a4bec0bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = numerical_df.iloc[:, :-1].corr()\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61089314-fde2-48b0-93f7-8372e7a9002d",
   "metadata": {},
   "source": [
    "### Variance Inflation Factor (VIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "15d57f39-f0cd-4051-8573-67944cd4a0e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   feature        VIF\n",
      "0  ProductRelated_Duration   3.666940\n",
      "1              BounceRates   3.138316\n",
      "2                ExitRates   9.387867\n",
      "3               PageValues   1.546095\n",
      "4     Avg_Product_Duration  10.898621\n",
      "5            TotalDuration   2.300693\n",
      "6               SpecialDay        NaN\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Create dummy df to store VIF values\n",
    "vif_df = pd.DataFrame()\n",
    "vif_df[\"feature\"] = numerical_df.columns\n",
    "\n",
    "# calculating VIF for each feature\n",
    "vif_df[\"VIF\"] = [variance_inflation_factor(numerical_df.values, i) for i in range(len(numerical_df.columns))]\n",
    "\n",
    "print(vif_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98dd92e-9cdf-45e5-8aa3-92d1bc7c00f5",
   "metadata": {},
   "source": [
    "This implies that ExitRates and Avg_Product_Duration are likely to be correlated, representing multicollinearity concerns. However, our final model does not utilize those variables and multicolinearity does not affect the result."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27892294-2427-47de-b0d8-599bf3c297d4",
   "metadata": {},
   "source": [
    "### Perform exploratory analysis in combination with visualization techniques to discover patterns and features of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a315f0-959d-4eb2-a1cb-582e0f90d8f2",
   "metadata": {},
   "source": [
    "#### Visitor Type Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e48a716-d83f-4507-a375-507a2159834e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_df['VisitorType'].value_counts().plot(kind='bar', color='skyblue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c5ad49-8614-4615-b9bd-bcb87007bca2",
   "metadata": {},
   "source": [
    "#### Clustered Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd5325dd-16cc-459d-af8d-206eff16ece5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary to map month acronyms to their numerical values\n",
    "# To sort by month\n",
    "month_order = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'June': 6, \n",
    "               'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}\n",
    "\n",
    "# Group by the 'Month' and Sub-group by Revenue\n",
    "grouped = e_df.groupby(['Month'])['Revenue'].value_counts().unstack()\n",
    "grouped_df = grouped.reset_index() # Converts result to DF\n",
    "\n",
    "# Add a new column with the numerical month values\n",
    "grouped_df['month_num'] = grouped_df['Month'].map(month_order)\n",
    "\n",
    "# Sort the DataFrame by the numerical month column\n",
    "grouped_df.sort_values('month_num', inplace=True)\n",
    "\n",
    "# Drop the temporary numerical month column\n",
    "grouped_df.drop('month_num', axis=1, inplace=True)\n",
    "\n",
    "grouped_df.plot(x='Month', kind='bar', cmap='tab20c')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Clustered Bar Plot -- Relationship between Month and Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21051b93-fe38-4512-bf54-df4cf6633c4e",
   "metadata": {},
   "source": [
    "#### Scatterplot between ExitRates and BounceRates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aaabbac3-9609-4166-a2fd-da010a067c16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot between ExitRates and Bounces\n",
    "plt.scatter(e_df['ExitRates'], e_df['BounceRates'], color='skyblue')\n",
    "plt.xlabel('ExitRates')\n",
    "plt.ylabel('BounceRates')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2df30f68-c745-40ae-aea4-0e80990e002d",
   "metadata": {},
   "source": [
    "\n",
    "## <span style=\"color:black\"><div align=\"center\"><strong>PART THREE:</strong> Data Analytics </div></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbb213b-be51-4b2e-8f00-6d682a31c77e",
   "metadata": {},
   "source": [
    "#### As a reminder, we want to assess the following question:\n",
    "*  Which types of pages (i.e. administrative, informational or product-related) have the highest duration of time spent on them (\"dwell time\") prior to a customer making a purchase?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54954bb2-7658-4929-a2ea-ee3523e6d599",
   "metadata": {},
   "source": [
    "### Determine the need for a supervised or unsupervised learning method and identify dependent and independent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a1ffdb-e0dc-4a69-9745-d482a9aff0f9",
   "metadata": {},
   "source": [
    "For research question #1: Supervised learning as the outcome/target is known. The dependent variable is \"revenue,\" where making a purchase is defined as a binary revenue outcome of 1 - indicating a sale. The independent variables are continuous, ratio-level numerical (quantitative) variables measuring the amount of time spent on various pages - an Administrative page, an Informational page, or a Product-related page. Due to the fact that the predictor variables are continuous and quantitative, and the output variable is qualitative (categorical), a logistic regression is appropriate for this analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "49d9f641-e3e9-426c-8621-9ec94e05b424",
   "metadata": {},
   "source": [
    "### Train, test, and provide accuracy and evaluation metrics for model results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2565aabf-caa4-42c6-841f-196f450078df",
   "metadata": {},
   "source": [
    "### Research Question: Logistic regression using duration on various pages as predictor variables for the response variable of revenue (binary)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f93b6da-b4db-4806-af5d-e1378fa04a62",
   "metadata": {},
   "source": [
    "*   independent (predictor) variables are continuous, ratio-level, numerical variables measuring \"dwell time\" - the amount of time a site viewer spent on 3 different types of web pages: Administrative, Information, and Product Related.\n",
    "\n",
    "*  The dependent (response) variable is a categorial, binary variable of revenue where a 0 means no purchase/sale was made, and a 1 indicates a the customer made a purchase.\n",
    "\n",
    "*  The data was split into training and testing datasets, where the training data represented 20% (or 2,390 records) of the total dataset, with randomly selected records.\n",
    "\n",
    "*  A confusion matrix run on the training data indicated a model accuracy of 84.52%, meaning the model is correclty predicting the known revenue outcomes 84.52 percent of the time.\n",
    "\n",
    "*  The original log model gave the following equation: Revenue(predicted true) = -1.91552694 + 8.76515079e-04Administrative_Duration + 5.42868244e-04Informational_Duration + 5.64771996*ProductRelated_Duration\n",
    "\n",
    "*  We converted the coefficients of the logistic model into odds, for better ease of understanding. This rendered the equation: Revenue(predicted true) = 0.1472642127 + 1.00078567Administrative_Duration + 1.0004435Informational_Duration + 394.29338707*ProductRelated_Duration\n",
    "\n",
    "*  The independent variables of Administrative Duration and ProductRelated Duration are both significately associated with Revenue (p<0.05), but Informational Duration is not a significant contributor to Revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "881e9e02-25b3-41a7-99b1-570901d2cc7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8451882845188284\n",
      "Confusion:\n",
      " [[2018    6]\n",
      " [ 364    2]]\n"
     ]
    }
   ],
   "source": [
    "# Defining features and target variable\n",
    "\n",
    "# The independent (predictor) variables are the amount of time spent on three different kinds of pages - \n",
    "# Administrative, Informational or Product Related\n",
    "X = e_df[['Administrative_Duration', 'Informational_Duration', 'ProductRelated_Duration']]\n",
    "\n",
    "# The response (dependent) variable is the binary categorical variable of Revenue, where a value of \n",
    "# 0 means no sale was made, and 1 means a purchase was made by the customer on the site\n",
    "y = e_df['Revenue']\n",
    "\n",
    "# Split data into training and testing sets, using a training size ratio of 20%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a logistic regression model\n",
    "model = LogisticRegression()\n",
    "\n",
    "# Fit the model on the training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Get coefficients and intercept for the logistic model\n",
    "coefficients = model.coef_\n",
    "intercept = model.intercept_\n",
    "print(\"Intercept:\", intercept)\n",
    "print(\"Coefficients:\", coefficients)\n",
    "\n",
    "\n",
    "# Evaluate the model and calculate the accuracy of the model and the confusion matrix\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "confusion = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print('Accuracy:', accuracy)\n",
    "print('Confusion:\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ce0f5f0-6803-4a4c-872c-f8c44e42197f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sm' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Obtaining the values of the coefficients for the logistic model.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43msm\u001b[49m\u001b[38;5;241m.\u001b[39madd_constant(X)\n\u001b[1;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m sm\u001b[38;5;241m.\u001b[39mLogit(y, X)\n\u001b[1;32m      4\u001b[0m result \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnewton\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sm' is not defined"
     ]
    }
   ],
   "source": [
    "# Obtaining the values of the coefficients for the logistic model.\n",
    "X = sm.add_constant(X)\n",
    "model = sm.Logit(y, X)\n",
    "result = model.fit(method='newton')\n",
    "result.params\n",
    "\n",
    "# Get p-values for the coefficients to determine variable significance using statsmodels\n",
    "p_values = result.pvalues\n",
    "print(\"P-values:\", p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc1f23c0-0d1a-440d-8f1a-737edca69609",
   "metadata": {},
   "outputs": [],
   "source": [
    "result.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f5d680e-36c5-4955-b4df-b6c4b91e6151",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting coefficients of the logistic regression model into odds for better understanding\n",
    "log_odds = logr.coef_\n",
    "odds = np.exp(log_odds)\n",
    "\n",
    "print(odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf688261-be36-436c-ae34-e700bcf34fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 427.9555555555555, 'Predicted label')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aNm2a3nvvPU2dOtWhz4EDB/Txxx/rq6++0qpVq/TTTz/phRdesF9ftGiRRo4cqbFjx2rfvn0aN26cXn/9dS1YsCDf8QBAgbEBcCuRkZG29u3b22w2my03N9cWFxdns1qttqFDh9qvBwYG2jIzM+33LFy40BYaGmrLzc21t2VmZtp8fHxs3377rc1ms9mCg4NtEydOtF/Pzs623X777fZn2Ww2W+PGjW0vvviizWaz2ZKSkmySbHFxcVeMc/369TZJttOnT9vbLly4YCtWrJht06ZNDn179uxpe+qpp2w2m80WFRVlq1mzpsP1ESNG5Bnr7yTZli1bdtXrkyZNsoWFhdnPR40aZfP09LQdOXLE3vbNN9/YPDw8bMeOHbPZbDZb1apVbYsXL3YY54033rCFh4fbbDab7eDBgzZJtp9++umqzwWAgsaaOcANrVixQiVKlFB2drZyc3P19NNPKzo62n69du3aDuvkfv75Zx04cEAlS5Z0GOfChQtKTk7W2bNndezYMTVs2NB+rUiRImrQoEGeqdZLEhMT5enpqcaNGxuO+8CBAzp//rweeeQRh/asrCzVr19fkrRv3z6HOCQpPDzc8DMuWbp0qaZPn67k5GSdO3dOOTk58vX1dehToUIF3XbbbQ7Pyc3NVVJSkkqWLKnk5GT17NlTvXv3tvfJycmRn59fvuMBgIJCMge4oaZNm2rWrFny8vJSSEiIihRx/Ee5ePHiDufnzp1TWFiYFi1alGescuXKXVcMPj4++b7n3LlzkqSVK1c6JFHSX+sAnSUhIUFdunTR6NGjFRERIT8/Py1ZskSTJ0/Od6zvvfdenuTS09PTabECwI0imQPcUPHixVWtWjXD/e+++24tXbpUAQEBeapTlwQHB2vLli1q1KiRpL8qUNu3b9fdd999xf61a9dWbm6uNm7cqGbNmuW5fqkyePHiRXtbzZo1ZbValZKSctWKXo0aNewvc1yyefPma3/Iy2zatEkVK1bUq6++am87fPhwnn4pKSk6evSoQkJC7M/x8PBQaGioAgMDFRISol9//VVdunTJ1/MB4GbiBQigEOjSpYvKli2r9u3b67vvvtPBgwe1YcMGDRw4UEeOHJEkvfjiixo/fryWL1+uX375RS+88MI/7hFXqVIlRUZGqkePHlq+fLl9zI8//liSVLFiRVksFq1YsUInTpzQuXPnVLJkSQ0dOlSDBw/WggULlJycrB07dujtt9+2v1Tw/PPPa//+/Ro2bJiSkpK0ePFixcbG5uvz3nHHHUpJSdGSJUuUnJys6dOnX/FlDm9vb0VGRurnn3/Wd999p4EDB6pTp04KCgqSJI0ePVoxMTGaPn26/vOf/2jXrl2aP3++pkyZkq94AKAgkcwBhUCxYsUUHx+vChUqqEOHDqpRo4Z69uypCxcu2Ct1L730kp555hlFRkYqPDxcJUuW1GOPPfaP486aNUtPPPGEXnjhBd15553q3bu3MjIyJEm33XabRo8erZdfflmBgYHq37+/JOmNN97Q66+/rpiYGNWoUUMtWrTQypUrVblyZUl/rWP77LPPtHz5ctWtW1ezZ8/WuHHj8vV527Vrp8GDB6t///6qV6+eNm3apNdffz1Pv2rVqqlDhw5q1aqVmjdvrjp16jhsPdKrVy+9//77mj9/vmrXrq3GjRsrNjbWHisAmIHFdrXVzQAAADA9KnMAAABujGQOAADAjZHMAQAAuDGSOQAAADd2S+4z51O/v6tDAGDA6W3vuDoEANfg7aJMoSD+Xf7nT8b/NycmJkaff/65fvnlF/n4+Ohf//qXJkyYoNDQUHufCxcu6KWXXtKSJUuUmZmpiIgIzZw5U4GBgfY+KSkp6tu3r9avX68SJUooMjJSMTExDpu9b9iwQUOGDNGePXtUvnx5vfbaa+revbvhWKnMAQAA/M3GjRvVr18/bd68WXFxccrOzlbz5s3t2y9J0uDBg/XVV1/pk08+0caNG3X06FF16NDBfv3ixYtq3bq1srKytGnTJi1YsECxsbEaOXKkvc/BgwfVunVrNW3aVImJiRo0aJB69eqlb7/91nCst+TWJFTmAPdAZQ4wP5dV5u4e6PQx/9wx/brvPXHihAICArRx40Y1atRIZ8+eVbly5bR48WI98cQTkqRffvlFNWrUUEJCgu677z598803atOmjY4ePWqv1s2ePVsjRozQiRMn5OXlpREjRmjlypXavXu3/VmdO3fWmTNntGrVKkOxUZkDAACFQmZmptLT0x2OzMxMQ/eePXtWklS6dGlJ0vbt25Wdne3wc4Z33nmnKlSooISEBEl//U507dq1HaZdIyIilJ6erj179tj7/P0nESMiIuxjGEEyBwAAzMdicfoRExMjPz8/hyMmJuaaoeTm5mrQoEG6//77VatWLUlSamqqvLy85O/v79A3MDBQqamp9j6XJ3KXrl+69k990tPT9eeffxr6qm7JFyAAAICbszi/3hQVFaUhQ4Y4tFmt1mve169fP+3evVvff/+902NyBpI5AABQKFitVkPJ2+X69++vFStWKD4+Xrfffru9PSgoSFlZWTpz5oxDdS4tLU1BQUH2Plu3bnUYLy0tzX7t0n9faru8j6+vr3x8fAzFyDQrAAAwnwKYZs0Pm82m/v37a9myZVq3bp0qV67scD0sLExFixbV2rVr7W1JSUlKSUlReHi4JCk8PFy7du3S8ePH7X3i4uLk6+urmjVr2vtcPsalPpfGMILKHAAAMJ8CmGbNj379+mnx4sX64osvVLJkSfsaNz8/P/n4+MjPz089e/bUkCFDVLp0afn6+mrAgAEKDw/XfffdJ0lq3ry5atasqWeeeUYTJ05UamqqXnvtNfXr189eIXz++ef1zjvvaPjw4erRo4fWrVunjz/+WCtXrjQcK5U5AACAv5k1a5bOnj2rJk2aKDg42H4sXbrU3mfq1Klq06aNHn/8cTVq1EhBQUH6/PPP7dc9PT21YsUKeXp6Kjw8XF27dlW3bt00ZswYe5/KlStr5cqViouLU926dTV58mS9//77ioiIMBwr+8wBcBn2mQPMz2X7zDUc5vQx/9wyyeljmgGVOQAAADfGmjkAAGA+Ll4z505I5gAAgPnk8+3Twoy0FwAAwI1RmQMAAObDNKthfFMAAABujMocAAAwH9bMGUYyBwAAzIdpVsP4pgAAANwYlTkAAGA+TLMaRmUOAADAjVGZAwAA5sOaOcNI5gAAgPmQzBnGNwUAAODGqMwBAADz8eAFCKOozAEAALgxKnMAAMB8WDNnGMkcAAAwH/aZM4y0FwAAwI1RmQMAAObDNKthfFMAAABujMocAAAwH9bMGUYyBwAAzIdpVsP4pgAAANwYlTkAAGA+TLMaRmUOAADAjVGZAwAA5sOaOcNI5gAAgPkwzWoYaS8AAIAbozIHAADMh2lWw/imAAAA3BiVOQAAYD6smTOMZA4AAJgP06yG8U0BAAC4MSpzAADAfKjMGcY3BQAA4MaozAEAAPPhBQjDSOYAAID5MM1qGN8UAACAG6MyBwAAzIdpVsOozAEAALgxKnMAAMB8WDNnGN8UAAAwH4vF+Uc+xMfHq23btgoJCZHFYtHy5cv/Fp7lisekSZPsfSpVqpTn+vjx4x3G2blzpx588EF5e3urfPnymjhxYr6/KpI5AACAv8nIyFDdunU1Y8aMK14/duyYwzFv3jxZLBY9/vjjDv3GjBnj0G/AgAH2a+np6WrevLkqVqyo7du3a9KkSYqOjtacOXPyFSvTrAAAwHQsLn4BomXLlmrZsuVVrwcFBTmcf/HFF2ratKmqVKni0F6yZMk8fS9ZtGiRsrKyNG/ePHl5eemuu+5SYmKipkyZoj59+hiOlcocAAAoFDIzM5Wenu5wZGZm3vC4aWlpWrlypXr27Jnn2vjx41WmTBnVr19fkyZNUk5Ojv1aQkKCGjVqJC8vL3tbRESEkpKSdPr0acPPJ5kDAACmc7U1aTdyxMTEyM/Pz+GIiYm54VgXLFigkiVLqkOHDg7tAwcO1JIlS7R+/Xo999xzGjdunIYPH26/npqaqsDAQId7Lp2npqYafj7TrAAAwHwKYJY1KipKQ4YMcWizWq03PO68efPUpUsXeXt7O7Rf/qw6derIy8tLzz33nGJiYpzy3EtI5gAAQKFgtVqdmkRJ0nfffaekpCQtXbr0mn0bNmyonJwcHTp0SKGhoQoKClJaWppDn0vnV1tndyVMswIAANMpiGnWgjB37lyFhYWpbt261+ybmJgoDw8PBQQESJLCw8MVHx+v7Oxse5+4uDiFhoaqVKlShmMgmQMAAKbj6mTu3LlzSkxMVGJioiTp4MGDSkxMVEpKir1Penq6PvnkE/Xq1SvP/QkJCXrrrbf0888/69dff9WiRYs0ePBgde3a1Z6oPf300/Ly8lLPnj21Z88eLV26VNOmTcszFXwtTLMCAAD8zY8//qimTZvazy8lWJGRkYqNjZUkLVmyRDabTU899VSe+61Wq5YsWaLo6GhlZmaqcuXKGjx4sEOi5ufnp9WrV6tfv34KCwtT2bJlNXLkyHxtSyJJFpvNZruOz2hqPvX7uzoEAAac3vaOq0MAcA3eLir7+Hb+wOljpi/p5vQxzYBpVgAAADfGNCsAADAdV/8ChDshmQMAAOZDLmcY06wAAABujMocAAAwHaZZjaMyBwAA4MaozAEAANOhMmccyRwAADAdkjnjmGYFAABwY1TmAACA6VCZM47KHAAAgBujMgcAAMyHwpxhJHMAAMB0mGY1jmlWAAAAN0ZlDgAAmA6VOeOozAEAALgxKnMAAMB0qMwZRzIHAADMh1zOMKZZAQAA3BiVOQAAYDpMsxpHZQ4AAMCNUZkDAACmQ2XOOJI5AABgOiRzxjHNCgAA4MaozAEAANOhMmcclTkAAAA3RmUOAACYD4U5w0jmAACA6TDNahzTrAAAAG6MyhwAADAdKnPGUZkDAABwY1TmAACA6VCZM45kDgAAmA+5nGFMswIAALgxKnMAAMB0mGY1jsocAACAG6MyBwAATIfKnHEkc7gphvZorkcfqqvqlQL1Z2a2tvz8q16d9oX2Hz5u72P1KqLxQzqoY0SYrF5FtCZhn14ct1THT/1h7zN5+BO6r24V3VUtWL8cTNN9ncfneVaz8Bp6/flWqlE1WBeysvXDjmSNmPy5Uo6duimfFSiM0tLS9NaUSfrhu+904cKfKl+hosb8e5zuqlXb1aHBTZHMGcc0K26KB++uptlL49W425tq0/cdFSniqRWz+quYt5e9z8Shj6t1o1rqMnyumvd6S8Hl/LRkcq88Y33wxWZ9unrHFZ9TMaSMPpnaRxu2/UcNO49XuxdmqIx/cS2Z3LvAPhtQ2KWfPavuXZ9SkSJFNWP2e/r8y5V6adgI+fr6uTo0oFCgMoebon3/mQ7nfUZ9qN/WjVf9muX1w45k+ZbwVvdHw9X9lVht3PYfe5+fl72ue2tX0tZdhyRJL038VJJUtlQr1brjtjzPubtmeXl6eCh6xgrZbDZJ0lsfrNUnU/uoSBEP5eTkFuCnBAqneXPfU2BQkN4YG2Nvu/328i6MCLcCKnPGuTSZO3nypObNm6eEhASlpqZKkoKCgvSvf/1L3bt3V7ly5VwZHgqQbwlvSdLps+clSfVrVJBX0SJatznJ3uc/h9KUcuyUGtapbE/mrmXH3t+Ua8tVt/b3aeGXm1WimFVPt75X67YkkcgBBWTj+nX61/0PaOjggfrxx20KCAjUk52f1uMdO7k6NKBQcNk067Zt21S9enVNnz5dfn5+atSokRo1aiQ/Pz9Nnz5dd955p3788cdrjpOZman09HSHw5Z78SZ8Alwvi8WiSUOf0KafkrU3+ZgkKaiMrzKzsnX23J8OfY//nq7AMr6Gxz589He1eWGGRvdvq7Nb3lLad2/qtkB/dR0+z6mfAcD/HDnymz5e+pEqVKykWXPmqtOTT2lCzL/15fJlrg4N7sxSAEc+xMfHq23btgoJCZHFYtHy5csdrnfv3l0Wi8XhaNGihUOfU6dOqUuXLvL19ZW/v7969uypc+fOOfTZuXOnHnzwQXl7e6t8+fKaOHFi/gKVCytzAwYMUMeOHTV79uw8pVSbzabnn39eAwYMUEJCwj+OExMTo9GjRzu0eQbeo6LB9zo9ZjjHW1GddFe1YD387FSnjx1YpqRmvv60Fn21RR+v2q4Sxa0a2beNFr/ZU62ff8fpzwMg5ebadFetWho4aIgkqUaNmjpwYL8++XiJ2j36mIujg7ty9TRrRkaG6tatqx49eqhDhw5X7NOiRQvNnz/ffm61Wh2ud+nSRceOHVNcXJyys7P17LPPqk+fPlq8eLEkKT09Xc2bN1ezZs00e/Zs7dq1Sz169JC/v7/69OljOFaXJXM///yzYmNjr/iXZbFYNHjwYNWvX/+a40RFRWnIkCEObQEPjnBanHCuqSM6qtWDtdSs51v67/Ez9vbU39Nl9SoqvxI+DtW5gDK+Svs93fD4zz3ZSOnn/tSr076wt/V4dYEOfPtvh7V3AJynXLlyqlK1qkNblSpVtCbuWxdFBNy4li1bqmXLlv/Yx2q1Kigo6IrX9u3bp1WrVmnbtm1q0KCBJOntt99Wq1at9OabbyokJESLFi1SVlaW5s2bJy8vL911111KTEzUlClT8pXMuWyaNSgoSFu3br3q9a1btyowMPCa41itVvn6+jocFg9PZ4YKJ5k6oqPaPVRXLZ6brsNHf3e49tO+FGVl56hpw1B72x0VA1QhuLS27Dxo+BnFvL2Um2tzaLuY+9daOQ8PFtMCBaFe/bt16KDjP6eHDx1SSEjel5QAo/4+hemM40pLszIzM687xg0bNiggIEChoaHq27evfv/9f/9uS0hIkL+/vz2Rk6RmzZrJw8NDW7Zssfdp1KiRvLz+t7NDRESEkpKSdPr0acNxuKwyN3ToUPXp00fbt2/Xww8/bE/c0tLStHbtWr333nt68803XRUenOytqE56smUDdRw8R+cyLiiwTElJ0tlzF3QhM1vp5y4odnmCJrzUQafOZuiPjAuaMqKjNv/8q0M1rUr5sirhY1VgWV/5WIuqTvW//mWx79dUZedc1Dff7dGALk0V1aeFPl61XSWLWTW6fzsdPvq7En854oqPDtzyunaLVGTXp/T+nNlqHtFSu3ft1KeffqyR0WNcHRrg4EpLs0aNGqXo6Oh8j9WiRQt16NBBlStXVnJysl555RW1bNlSCQkJ8vT0VGpqqgICAhzuKVKkiEqXLm1/6TM1NVWVK1d26HMpH0pNTVWpUqUMxeKyZK5fv34qW7aspk6dqpkzZ+rixb9eWvD09FRYWJhiY2PVqRNvQt0qnuvUSJIU9/4gh/beIxfqw6/++n8ow9/8TLm5Nn30Zq+/Ng3etE8vxix16D9rZBc1anCH/XzL0ihJUmirkUo5dkobt/1H3V9ZoMGRzTQk8hGdv5ClLTsPql2/mbqQmV2AnxAovGrVrqMp097R9Lem6N1ZM3Tb7bdr+IhX1LpNO1eHBjdWEEvmrrQ06+/r3Izq3Lmz/c+1a9dWnTp1VLVqVW3YsEEPP/zwDcWZXy7dmuTJJ5/Uk08+qezsbJ08eVKSVLZsWRUtWtSVYaEA+NTvf80+mVk5Gjz+Yw0e//FV+0T0nnbNcT75drs++XZ7vuIDcGMaN2mqxk2aujoM3EIK4gUIq9V63cnbtVSpUkVly5bVgQMH9PDDDysoKEjHjx936JOTk6NTp07Z19kFBQUpLS3Noc+l86utxbsSU/wCRNGiRRUcHKzg4GASOQAA4HaOHDmi33//XcHBwZKk8PBwnTlzRtu3/6+4sG7dOuXm5qphw4b2PvHx8crO/t/MUVxcnEJDQw1PsUomSeYAAAAuZ7E4/8iPc+fOKTExUYmJiZKkgwcPKjExUSkpKTp37pyGDRumzZs369ChQ1q7dq3at2+vatWqKSIiQpJUo0YNtWjRQr1799bWrVv1ww8/qH///urcubNCQkIkSU8//bS8vLzUs2dP7dmzR0uXLtW0adPyTAVfC8kcAADA3/z444+qX7++fZu0IUOGqH79+ho5cqQ8PT21c+dOtWvXTtWrV1fPnj0VFham7777zmEad9GiRbrzzjv18MMPq1WrVnrggQc0Z84c+3U/Pz+tXr1aBw8eVFhYmF566SWNHDkyX9uSSJLFdukHLG8hRtZnAXC909vYyBkwO28Xra4PHeH8fQqTJkQ4fUwzcOkLEAAAAFfi4h+AcCtMswIAALgxKnMAAMB0+NUe40jmAACA6TDNahzTrAAAAG6MyhwAADCdgvgFiFsVlTkAAAA3RmUOAACYDoU540jmAACA6TDNahzTrAAAAG6MyhwAADAdKnPGUZkDAABwY1TmAACA6VCYM45kDgAAmA7TrMYxzQoAAODGqMwBAADToTBnHJU5AAAAN0ZlDgAAmA5r5owjmQMAAKZDLmcc06wAAABujMocAAAwHaZZjaMyBwAA4MaozAEAANOhMGccyRwAADAdplmNY5oVAADAjVGZAwAApkNhzjgqcwAAAG6MyhwAADAd1swZRzIHAABMh1zOOKZZAQAA3BiVOQAAYDpMsxpHZQ4AAMCNUZkDAACmQ2HOOJI5AABgOkyzGsc0KwAAgBujMgcAAEyHypxxVOYAAADcGJU5AABgOhTmjCOZAwAApsM0q3FMswIAALgxKnMAAMB0KMwZR2UOAADAjZHMAQAA07FYLE4/8iM+Pl5t27ZVSEiILBaLli9fbr+WnZ2tESNGqHbt2ipevLhCQkLUrVs3HT161GGMSpUq5Ylh/PjxDn127typBx98UN7e3ipfvrwmTpyY7++KZA4AAJiOxeL8Iz8yMjJUt25dzZgxI8+18+fPa8eOHXr99de1Y8cOff7550pKSlK7du3y9B0zZoyOHTtmPwYMGGC/lp6erubNm6tixYravn27Jk2apOjoaM2ZMydfsbJmDgAA4G9atmypli1bXvGan5+f4uLiHNreeecd3XvvvUpJSVGFChXs7SVLllRQUNAVx1m0aJGysrI0b948eXl56a677lJiYqKmTJmiPn36GI6VyhwAADAdD4vF6UdmZqbS09MdjszMTKfEe/bsWVksFvn7+zu0jx8/XmXKlFH9+vU1adIk5eTk2K8lJCSoUaNG8vLysrdFREQoKSlJp0+fNvxskjkAAFAoxMTEyM/Pz+GIiYm54XEvXLigESNG6KmnnpKvr6+9feDAgVqyZInWr1+v5557TuPGjdPw4cPt11NTUxUYGOgw1qXz1NRUw89nmhUAAJhOQWxNEhUVpSFDhji0Wa3WGxozOztbnTp1ks1m06xZsxyuXf6sOnXqyMvLS88995xiYmJu+LmXI5kDAACmUxC/AGG1Wp2aRF1K5A4fPqx169Y5VOWupGHDhsrJydGhQ4cUGhqqoKAgpaWlOfS5dH61dXZXwjQrAABAPl1K5Pbv3681a9aoTJky17wnMTFRHh4eCggIkCSFh4crPj5e2dnZ9j5xcXEKDQ1VqVKlDMdCZQ4AAJiOh4t/AeLcuXM6cOCA/fzgwYNKTExU6dKlFRwcrCeeeEI7duzQihUrdPHiRfsat9KlS8vLy0sJCQnasmWLmjZtqpIlSyohIUGDBw9W165d7Yna008/rdGjR6tnz54aMWKEdu/erWnTpmnq1Kn5ipVkDgAA4G9+/PFHNW3a1H5+af1bZGSkoqOj9eWXX0qS6tWr53Df+vXr1aRJE1mtVi1ZskTR0dHKzMxU5cqVNXjwYId1dH5+flq9erX69eunsLAwlS1bViNHjszXtiSSZLHZbLbr/Jym5VO/v6tDAGDA6W3vuDoEANfg7aKyT6vZW50+5tfP3+v0Mc2AyhwAADCdgnib9VbFCxAAAABujMocAAAwHYsozRlFMgcAAEzH1W+zuhOmWQEAANyYocrc9OnTDQ84cODA6w4GAABAKphfgLhVGUrmjG5eZ7FYSOYAAABuIkPJ3MGDBws6DgAAADsKc8Zd95q5rKwsJSUlKScnx5nxAAAAyMNicfpxq8p3Mnf+/Hn17NlTxYoV01133aWUlBRJ0oABAzR+/HinBwgAAICry3cyFxUVpZ9//lkbNmyQt7e3vb1Zs2ZaunSpU4MDAACFk8Xi/ONWle995pYvX66lS5fqvvvuc3jT5K677lJycrJTgwMAAMA/y3cyd+LECQUEBORpz8jI4DViAADgFOQUxuV7mrVBgwZauXKl/fzSl/3+++8rPDzceZEBAIBCi2lW4/JdmRs3bpxatmypvXv3KicnR9OmTdPevXu1adMmbdy4sSBiBAAAwFXkuzL3wAMPKDExUTk5Oapdu7ZWr16tgIAAJSQkKCwsrCBiBAAAhQxbkxiX78qcJFWtWlXvvfees2MBAABAPl1XMnfx4kUtW7ZM+/btkyTVrFlT7du3V5Ei1zUcAACAg1u3juZ8+c6+9uzZo3bt2ik1NVWhoaGSpAkTJqhcuXL66quvVKtWLacHCQAAChfeZjUu32vmevXqpbvuuktHjhzRjh07tGPHDv3222+qU6eO+vTpUxAxAgAA4CryXZlLTEzUjz/+qFKlStnbSpUqpbFjx+qee+5xanAAAKBw8qAwZ1i+K3PVq1dXWlpanvbjx4+rWrVqTgkKAAAAxhiqzKWnp9v/HBMTo4EDByo6Olr33XefJGnz5s0aM2aMJkyYUDBRAgCAQoU1c8YZSub8/f0dvlSbzaZOnTrZ22w2mySpbdu2unjxYgGECQAAChNyOeMMJXPr168v6DgAAABwHQwlc40bNy7oOAAAAOyYZjXuunf5PX/+vFJSUpSVleXQXqdOnRsOCgAAAMbkO5k7ceKEnn32WX3zzTdXvM6aOQAAcKPYmsS4fG9NMmjQIJ05c0ZbtmyRj4+PVq1apQULFuiOO+7Ql19+WRAxAgCAQsZisTj9uFXluzK3bt06ffHFF2rQoIE8PDxUsWJFPfLII/L19VVMTIxat25dEHECAADgCvJdmcvIyFBAQICkv3754cSJE5Kk2rVra8eOHc6NDgAAFEqWAjhuVflO5kJDQ5WUlCRJqlu3rt59913997//1ezZsxUcHOz0AAEAAHB1+Z5mffHFF3Xs2DFJ0qhRo9SiRQstWrRIXl5eio2NdXZ8AACgEPK4hde4OVu+k7muXbva/xwWFqbDhw/rl19+UYUKFVS2bFmnBgcAAAoncjnjrnufuUuKFSumu+++2xmxAAAAIJ8MJXNDhgwxPOCUKVOuOxgAAACJX4DID0PJ3E8//WRoML54AACAm8tQMrd+/fqCjgMAAMCO+pBxN7xmDgAAwNl4m9W4fO8zBwAAAPOgMgcAAEyHwpxxVOYAAAD+Jj4+Xm3btlVISIgsFouWL1/ucN1ms2nkyJEKDg6Wj4+PmjVrpv379zv0OXXqlLp06SJfX1/5+/urZ8+eOnfunEOfnTt36sEHH5S3t7fKly+viRMn5jtWkjkAAGA6FovF6Ud+ZGRkqG7dupoxY8YVr0+cOFHTp0/X7NmztWXLFhUvXlwRERG6cOGCvU+XLl20Z88excXFacWKFYqPj1efPn3s19PT09W8eXNVrFhR27dv16RJkxQdHa05c+bk77uy2Wy2a3X68ssvDQ/Yrl27fAVQEPb8N8PVIQAwoGpgcVeHAOAavF20IGvAsn1OH/Ptx2pc130Wi0XLli3To48+KumvqlxISIheeuklDR06VJJ09uxZBQYGKjY2Vp07d9a+fftUs2ZNbdu2TQ0aNJAkrVq1Sq1atdKRI0cUEhKiWbNm6dVXX1Vqaqq8vLwkSS+//LKWL1+uX375xXB8hv6KLgVv5MNevHjR8MMBAABulszMTGVmZjq0Wa1WWa3WfI1z8OBBpaamqlmzZvY2Pz8/NWzYUAkJCercubMSEhLk7+9vT+QkqVmzZvLw8NCWLVv02GOPKSEhQY0aNbIncpIUERGhCRMm6PTp0ypVqpSheAxNs+bm5ho6SOQAAIAzFMQ0a0xMjPz8/ByOmJiYfMeWmpoqSQoMDHRoDwwMtF9LTU1VQECAw/UiRYqodOnSDn2uNMblzzCCt1kBAEChEBUVlecnSvNblTOj60rmMjIytHHjRqWkpCgrK8vh2sCBA50SGAAAKLw8CmBrkuuZUr2SoKAgSVJaWpqCg4Pt7WlpaapXr569z/Hjxx3uy8nJ0alTp+z3BwUFKS0tzaHPpfNLfYzIdzL3008/qVWrVjp//rwyMjJUunRpnTx5UsWKFVNAQADJHAAAuGEFkcw5S+XKlRUUFKS1a9fak7f09HRt2bJFffv2lSSFh4frzJkz2r59u8LCwiRJ69atU25urho2bGjv8+qrryo7O1tFixaVJMXFxSk0NNTwejnpOrYmGTx4sNq2bavTp0/Lx8dHmzdv1uHDhxUWFqY333wzv8MBAACYzrlz55SYmKjExERJf730kJiYqJSUFFksFg0aNEj//ve/9eWXX2rXrl3q1q2bQkJC7C+N1qhRQy1atFDv3r21detW/fDDD+rfv786d+6skJAQSdLTTz8tLy8v9ezZU3v27NHSpUs1bdq0PFPB15LvylxiYqLeffddeXh4yNPTU5mZmapSpYomTpyoyMhIdejQIb9DAgAAOMjvvnDO9uOPP6pp06b280sJVmRkpGJjYzV8+HBlZGSoT58+OnPmjB544AGtWrVK3t7e9nsWLVqk/v376+GHH5aHh4cef/xxTZ8+3X7dz89Pq1evVr9+/RQWFqayZctq5MiRDnvRGWFon7nLlStXTps2bdIdd9yh6tWr6+2331ZERIR++eUXhYWFKSPD9Xu8sc8c4B7YZw4wP1ftM/fSV0lOH3Ny21Cnj2kG+f4rql+/vrZt26Y77rhDjRs31siRI3Xy5EktXLhQtWrVKogYAQBAIWPmNXNmk+81c+PGjbO/uTF27FiVKlVKffv21YkTJ/L98xMAAABXYrE4/7hV5bsyd/lOxgEBAVq1apVTAwIAAIBxbBoMAABMx+NWLqU5Wb6TucqVK//jGya//vrrDQUEAACQ73VghVi+k7lBgwY5nGdnZ+unn37SqlWrNGzYMGfFBQAAAAPyncy9+OKLV2yfMWOGfvzxxxsOCAAAgFlW45xWxWzZsqU+++wzZw0HAAAAA5z2AsSnn36q0qVLO2s4AABQiPEChHHXtWnw5S9A2Gw2paam6sSJE5o5c6ZTgwMAAIUTuZxx+U7m2rdv75DMeXh4qFy5cmrSpInuvPNOpwYHAACAf5bvZC46OroAwgAAAPgffs7LuHy/AOHp6anjx4/naf/999/l6enplKAAAABgTL4rczab7YrtmZmZ8vLyuuGAAAAAeAHCOMPJ3PTp0yVJFotF77//vkqUKGG/dvHiRcXHx7NmDgAAOAW5nHGGk7mpU6dK+qsyN3v2bIcpVS8vL1WqVEmzZ892foQAAAC4KsPJ3MGDByVJTZs21eeff65SpUoVWFAAAKBw4wUI4/K9Zm79+vUFEQcAAACuQ77fZn388cc1YcKEPO0TJ05Ux44dnRIUAAAo3CwF8J9bVb6Tufj4eLVq1SpPe8uWLRUfH++UoAAAQOHmYXH+cavKdzJ37ty5K25BUrRoUaWnpzslKAAAABiT72Sudu3aWrp0aZ72JUuWqGbNmk4JCgAAFG5U5ozL9wsQr7/+ujp06KDk5GQ99NBDkqS1a9fqo48+0ieffOL0AAEAAHB1+U7m2rZtq+XLl2vcuHH69NNP5ePjozp16mjNmjVq3LhxQcQIAAAKGQu7BhuW72ROklq3bq3WrVvnad+9e7dq1ap1w0EBAIDC7VaeFnW2fK+Z+7s//vhDc+bM0b333qu6des6IyYAAAAYdN3JXHx8vLp166bg4GC9+eabeuihh7R582ZnxgYAAAopi8X5x60qX9Osqampio2N1dy5c5Wenq5OnTopMzNTy5cv501WAAAAFzBcmWvbtq1CQ0O1c+dOvfXWWzp69KjefvvtgowNAAAUUh4Wi9OPW5Xhytw333yjgQMHqm/fvrrjjjsKMiYAAFDI8QKEcYYrc99//73++OMPhYWFqWHDhnrnnXd08uTJgowNAAAA12A4mbvvvvv03nvv6dixY3ruuee0ZMkShYSEKDc3V3Fxcfrjjz8KMk4AAFCI8AKEcfl+m7V48eLq0aOHvv/+e+3atUsvvfSSxo8fr4CAALVr164gYgQAAMBV3NA+c6GhoZo4caKOHDmijz76yFkxAQCAQs5DFqcft6rr+gWIv/P09NSjjz6qRx991BnDAQCAQu5WnhZ1thv+BQgAAAC4jlMqcwAAAM7E1iTGUZkDAABwY1TmAACA6dzKv9jgbCRzAADAdMjljGOaFQAAwI1RmQMAAKbDNKtxVOYAAAD+plKlSrJYLHmOfv36SZKaNGmS59rzzz/vMEZKSopat26tYsWKKSAgQMOGDVNOTo7TY6UyBwAATMfVhblt27bp4sWL9vPdu3frkUceUceOHe1tvXv31pgxY+znxYoVs//54sWLat26tYKCgrRp0yYdO3ZM3bp1U9GiRTVu3DinxkoyBwAATMfVU4flypVzOB8/fryqVq2qxo0b29uKFSumoKCgK96/evVq7d27V2vWrFFgYKDq1aunN954QyNGjFB0dLS8vLycFqurvysAAICbIjMzU+np6Q5HZmbmNe/LysrShx9+qB49eshyWclw0aJFKlu2rGrVqqWoqCidP3/efi0hIUG1a9dWYGCgvS0iIkLp6enas2ePUz8XyRwAADCdK61Xu9EjJiZGfn5+DkdMTMw1Y1m+fLnOnDmj7t2729uefvppffjhh1q/fr2ioqK0cOFCde3a1X49NTXVIZGTZD9PTU11zpf0/5hmBQAAhUJUVJSGDBni0Ga1Wq9539y5c9WyZUuFhITY2/r06WP/c+3atRUcHKyHH35YycnJqlq1qvOCNoBkDgAAmE5BvP9gtVoNJW+XO3z4sNasWaPPP//8H/s1bNhQknTgwAFVrVpVQUFB2rp1q0OftLQ0SbrqOrvrxTQrAAAwHQ+LxenH9Zg/f74CAgLUunXrf+yXmJgoSQoODpYkhYeHa9euXTp+/Li9T1xcnHx9fVWzZs3riuVqqMwBAABcQW5urubPn6/IyEgVKfK/lCk5OVmLFy9Wq1atVKZMGe3cuVODBw9Wo0aNVKdOHUlS8+bNVbNmTT3zzDOaOHGiUlNT9dprr6lfv375rg5eC8kcAAAwHTP8/sOaNWuUkpKiHj16OLR7eXlpzZo1euutt5SRkaHy5cvr8ccf12uvvWbv4+npqRUrVqhv374KDw9X8eLFFRkZ6bAvnbNYbDabzemjutie/2a4OgQABlQNLO7qEABcg7eLyj6Lth9x+phdwm53+phmQGUOAACYjqt/AcKdkMwBAADTsZDNGcbbrAAAAG6MyhwAADAdqk3G8V0BAAC4MSpzAADAdFgzZxzJHAAAMB1SOeOYZgUAAHBjVOYAAIDpMM1qHMkcAAAwHaYOjeO7AgAAcGNU5gAAgOkwzWoclTkAAAA3RmUOAACYDnU540jmAACA6TDLahzTrAAAAG6MyhwAADAdDyZaDaMyBwAA4MaozAEAANNhzZxxJHMAAMB0LEyzGsY0KwAAgBujMgcAAEyHaVbjqMwBAAC4MSpzAADAdNiaxDiSOQAAYDpMsxrHNCsAAIAbozIHAABMh8qccVTmAAAA3BiVOQAAYDpsGmwcyRwAADAdD3I5w5hmBQAAcGNU5gAAgOkwzWoclTkAAAA3RmUOAACYDluTGEcyBwAATIdpVuOYZgUAAHBjVOYAAIDpsDWJcVTmAAAA3BiVOQAAYDqsmTOOZA4useqLT/TtV5/oeOoxSVL5SlXU6Zk+urvh/fY+SXt+1qK5M7T/l93y8PBU5arV9frEGbJavR3Gys7K0oh+3XQo+T+aPOcjVa4WelM/C1DYzX3vXa2NW62DB3+V1dtb9erV16AhQ1WpchVXhwY3xtusxpHMwSXKlAtQ114DFXx7Bclm0/rVX2n864P15rsfqULlqkra87PeeHmAOjz1rHoNGCFPT08d+vU/8rDkXRnwwZxpKl2mnA4l/8cFnwTAj9u26smnuuiu2rV1Meei3p42Rc/37qnPv1ypYsWKuTo84JZHMgeXuOdfjR3Ou/Tsr2+//FT/2bdLFSpX1byZk9Xqsc7q8PSz9j63VaiUZ5wdW35Q4o8JGh79pnZs/aGgwwZwBbPmzHU4HzN2vJo+GK59e/corME9LooK7o7CnHG8AAGXu3jxor5f960uXPhToTXr6MzpU9q/b7f8/Esrqn93Pft4M702qJf27frJ4b4zp37XzMlv6MWof8vq7X2V0QHcbOf++EOS5Ovn5+JIgOsXHR0ti8XicNx555326xcuXFC/fv1UpkwZlShRQo8//rjS0tIcxkhJSVHr1q1VrFgxBQQEaNiwYcrJyXF6rKZO5n777Tf16NHjH/tkZmYqPT3d4cjKzLxJEeJGHP51v55udb+ejLhPs6eO1YjRk1W+UhWlHTsiSVr6wbtq1voxvT7+HVW5406NGvq8jh5JkSTZbDa9PXGUIto+oWqhNV35MQBcJjc3VxMnjFO9+nfrjjuquzocuDEPi8XpR37dddddOnbsmP34/vvv7dcGDx6sr776Sp988ok2btyoo0ePqkOHDvbrFy9eVOvWrZWVlaVNmzZpwYIFio2N1ciRI53y/VzO1MncqVOntGDBgn/sExMTIz8/P4fjvXfevEkR4kaElK+kye99pAkzF6hFu456e8JI/XboV9lybZKk5m066OGW7VXljjvVo99Q3Va+otZ984Uk6etlS/Tn+fMO07AAXG/cv0cref9+TXxzqqtDgZuzFMCRX0WKFFFQUJD9KFu2rCTp7Nmzmjt3rqZMmaKHHnpIYWFhmj9/vjZt2qTNmzdLklavXq29e/fqww8/VL169dSyZUu98cYbmjFjhrKysq7zW7lKnE4dLZ++/PLLf7z+66+/XnOMqKgoDRkyxKEt+aTzS5hwvqJFiyr4tgqSpKrVa+pA0h6t+HyxOjz1V4JWvqLjm3C3VaisE8dTJUm7ftqm/+zdqScj7nPoM+z5rmrUrKUGvjzmJnwCAJcb9+8xit+4QfMWfKjAoCBXhwPkkZmZqcy/zd5ZrVZZrdYr9t+/f79CQkLk7e2t8PBwxcTEqEKFCtq+fbuys7PVrFkze98777xTFSpUUEJCgu677z4lJCSodu3aCgwMtPeJiIhQ3759tWfPHtWvX99pn8ulydyjjz4qi8Uim8121T6Wa5RFr/SX4PVHhlPiw82Vm5urnOxsBQSFqHSZcvrvb4cdrh87kqL69/5LktSz/zA91eMF+7XTJ09ozIh+emnkeN1Ro9ZNjRso7Gw2m2LGvqF1a+M0N3ahbr+9vKtDwq2gAN6AiImJ0ejRox3aRo0apejo6Dx9GzZsqNjYWIWGhurYsWMaPXq0HnzwQe3evVupqany8vKSv7+/wz2BgYFKTf2r6JCamuqQyF26fumaM7k0mQsODtbMmTPVvn37K15PTExUWFjYTY4KN8OH772t+vf+S+UCg/Xn+Qx9t3aV9vy8Xa9PmCGLxaL2T3bT0gXvqlLV6qpcrbrWf7tC/005pGGjJkqSygUGO4zn4/PX9gdBIberbLnAPM8DUHDGvTFa33y9Qm+9PVPFixXXyRMnJEklSpaUNy8nwUSuNJt3tapcy5Yt7X+uU6eOGjZsqIoVK+rjjz+Wj49PgcaZXy5N5sLCwrR9+/arJnPXqtrBfZ09c0rTx4/U6VMnVax4CVWqcodenzBD9Rr8NW3a9okuys7K0vyZk3Xuj7OqVKW6Rk2aqaDb+H/8gNl8vPQjSVLP7s84tI/5d4zaP9bhSrcA11QQvwDxT1Oq1+Lv76/q1avrwIEDeuSRR5SVlaUzZ844VOfS0tIU9P9LDIKCgrR161aHMS697Rrk5GUILk3mhg0bpoyMq0+JVqtWTevXr7+JEeFm6Tds1DX7dHj6WcMvOAQEhejzdTtuNCwA1+HnPUmuDgG3ILP9AsS5c+eUnJysZ555RmFhYSpatKjWrl2rxx9/XJKUlJSklJQUhYeHS5LCw8M1duxYHT9+XAEBAZKkuLg4+fr6qmZN5+7CYLHdgqWvPf9lzRzgDqoGFnd1CACuwdtFZZ+tv551+pj3VjG+9+HQoUPVtm1bVaxYUUePHtWoUaOUmJiovXv3qly5curbt6++/vprxcbGytfXVwMGDJAkbdq0SdJfW5PUq1dPISEhmjhxolJTU/XMM8+oV69eGjdunFM/F78AAQAATMfVhbkjR47oqaee0u+//65y5crpgQce0ObNm1WuXDlJ0tSpU+Xh4aHHH39cmZmZioiI0MyZM+33e3p6asWKFerbt6/Cw8NVvHhxRUZGaswY5++2QGUOgMtQmQPMz1WVuW0FUJm7Jx+VOXdCZQ4AAJiPq0tzboRkDgAAmE5BvM16qzL1z3kBAADgn1GZAwAApmO2rUnMjMocAACAG6MyBwAATIfCnHEkcwAAwHzI5gxjmhUAAMCNUZkDAACmw9YkxpHMAQAA0+FtVuOYZgUAAHBjVOYAAIDpUJgzjsocAACAG6MyBwAAzIfSnGEkcwAAwHR4m9U4plkBAADcGJU5AABgOmxNYhyVOQAAADdGZQ4AAJgOhTnjSOYAAID5kM0ZxjQrAACAG6MyBwAATIetSYyjMgcAAODGqMwBAADTYWsS40jmAACA6ZDLGcc0KwAAgBujMgcAAMyH0pxhVOYAAADcGJU5AABgOmxNYhzJHAAAMB3eZjWOaVYAAAA3RmUOAACYDoU546jMAQAAuDEqcwAAwHwozRlGMgcAAEyHt1mNY5oVAADAjVGZAwAApsPWJMZRmQMAAHBjVOYAAIDpUJgzjmQOAACYD9mcYUyzAgAAuDGSOQAAYDqWAvhPfsTExOiee+5RyZIlFRAQoEcffVRJSUkOfZo0aSKLxeJwPP/88w59UlJS1Lp1axUrVkwBAQEaNmyYcnJybvj7uRzTrAAAAH+zceNG9evXT/fcc49ycnL0yiuvqHnz5tq7d6+KFy9u79e7d2+NGTPGfl6sWDH7ny9evKjWrVsrKChImzZt0rFjx9StWzcVLVpU48aNc1qsFpvNZnPaaCax578Zrg4BgAFVA4tfuxMAl/J2Udnn4MkLTh+zclnv6773xIkTCggI0MaNG9WoUSNJf1Xm6tWrp7feeuuK93zzzTdq06aNjh49qsDAQEnS7NmzNWLECJ04cUJeXl7XHc/lmGYFAACmYymAIzMzU+np6Q5HZmamoXjOnj0rSSpdurRD+6JFi1S2bFnVqlVLUVFROn/+vP1aQkKCateubU/kJCkiIkLp6enas2dP/r6Qf0AyBwAACoWYmBj5+fk5HDExMde8Lzc3V4MGDdL999+vWrVq2duffvppffjhh1q/fr2ioqK0cOFCde3a1X49NTXVIZGTZD9PTU110qdizRwAADCjAtiaJCoqSkOGDHFos1qt17yvX79+2r17t77//nuH9j59+tj/XLt2bQUHB+vhhx9WcnKyqlat6pygDaAyBwAACgWr1SpfX1+H41rJXP/+/bVixQqtX79et99++z/2bdiwoSTpwIEDkqSgoCClpaU59Ll0HhQUdL0fIw+SOQAAYDqu3prEZrOpf//+WrZsmdatW6fKlStf857ExERJUnBwsCQpPDxcu3bt0vHjx+194uLi5Ovrq5o1a+Yrnn/CNCsAADAdi4t/AaJfv35avHixvvjiC5UsWdK+xs3Pz08+Pj5KTk7W4sWL1apVK5UpU0Y7d+7U4MGD1ahRI9WpU0eS1Lx5c9WsWVPPPPOMJk6cqNTUVL322mvq16+foeldo9iaBIDLsDUJYH6u2pok5ZSxt0zzo0Jp4wmU5SrZ5Pz589W9e3f99ttv6tq1q3bv3q2MjAyVL19ejz32mF577TX5+vra+x8+fFh9+/bVhg0bVLx4cUVGRmr8+PEqUsR5XyzJHACXIZkDzM9VydxvBZDMlc9HMudOWDMHAADgxlgzBwAATMfVa+bcCckcAAAwIbI5o5hmBQAAcGNU5gAAgOkwzWoclTkAAAA3RmUOAACYDoU540jmAACA6TDNahzTrAAAAG6MyhwAADAdCxOthpHMAQAA8yGXM4xpVgAAADdGZQ4AAJgOhTnjqMwBAAC4MSpzAADAdNiaxDiSOQAAYDq8zWoc06wAAABujMocAAAwHwpzhlGZAwAAcGNU5gAAgOlQmDOOZA4AAJgOb7MaxzQrAACAG6MyBwAATIetSYyjMgcAAODGqMwBAADTYc2ccVTmAAAA3BjJHAAAgBtjmhUAAJgO06zGUZkDAABwY1TmAACA6bA1iXEkcwAAwHSYZjWOaVYAAAA3RmUOAACYDoU546jMAQAAuDEqcwAAwHwozRlGMgcAAEyHt1mNY5oVAADAjVGZAwAApsPWJMZRmQMAAHBjVOYAAIDpUJgzjmQOAACYD9mcYUyzAgAAuDEqcwAAwHTYmsQ4KnMAAABujMocAAAwHbYmMc5is9lsrg4CuJbMzEzFxMQoKipKVqvV1eEAuAL+OQVcg2QObiE9PV1+fn46e/asfH19XR0OgCvgn1PANVgzBwAA4MZI5gAAANwYyRwAAIAbI5mDW7BarRo1ahSLqgET459TwDV4AQIAAMCNUZkDAABwYyRzAAAAboxkDgAAwI2RzAEAALgxkjkAAAA3RjIH05sxY4YqVaokb29vNWzYUFu3bnV1SAAuEx8fr7Zt2yokJEQWi0XLly93dUhAoUIyB1NbunSphgwZolGjRmnHjh2qW7euIiIidPz4cVeHBuD/ZWRkqG7dupoxY4arQwEKJfaZg6k1bNhQ99xzj9555x1JUm5ursqXL68BAwbo5ZdfdnF0AP7OYrFo2bJlevTRR10dClBoUJmDaWVlZWn79u1q1qyZvc3Dw0PNmjVTQkKCCyMDAMA8SOZgWidPntTFixcVGBjo0B4YGKjU1FQXRQUAgLmQzAEAALgxkjmYVtmyZeXp6am0tDSH9rS0NAUFBbkoKgAAzIVkDqbl5eWlsLAwrV271t6Wm5urtWvXKjw83IWRAQBgHkVcHQDwT4YMGaLIyEg1aNBA9957r9566y1lZGTo2WefdXVoAP7fuXPndODAAfv5wYMHlZiYqNKlS6tChQoujAwoHNiaBKb3zjvvaNKkSUpNTVW9evU0ffp0NWzY0NVhAfh/GzZsUNOmTfO0R0ZGKjY29uYHBBQyJHMAAABujDVzAAAAboxkDgAAwI2RzAEAALgxkjkAAAA3RjIHAADgxkjmAAAA3BjJHAAAgBsjmQMAAHBjJHMAAABujGQOAADAjZHMAQAAuLH/AxKQV/Qwc4OkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the confusion matrix using a heatmap\n",
    "class_names=[0,1] # name  of classes \n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(confusion), annot=True, cmap=\"Blues\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5df0b5a-1021-4aa0-b07a-6deec3dde76e",
   "metadata": {},
   "source": [
    "![references.png](references.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}