ex3_balanceData.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOiU8pCrnGSWxRIgGbu4Vci",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/frankfaisca/machine-learning/blob/main/ex3_balanceData.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "eWBuOnhPHt0x"
      },
      "outputs": [],
      "source": [
        "# Load libs\n",
        "\n",
        "import numpy as np\n",
        "import pickle\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "from sklearn import utils\n",
        "\n",
        "from sklearn.utils import resample\n",
        "\n",
        "\n",
        "#Graphs\n",
        "import matplotlib.pyplot as plt #visualization\n",
        "from matplotlib import pyplot #visualization\n",
        "%matplotlib inline\n",
        "#static images of your plot embedded in the workbook\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "#Evaluation\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
        "\n",
        "import warnings\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load pickle file into train and test samples\n",
        "\n",
        "outfilename = \"/content/sample_data/A602.pickle\"\n",
        "file = open(outfilename , 'rb')\n",
        "(X_train , y_train , X_val , y_val , X_test , y_test) = pickle.load(file)\n",
        "print(f\"Shapes: \", X_train.shape , X_val.shape , X_test.shape)\n",
        "\n",
        "#transform array to dataframe\n",
        "\n",
        "X_train = pd.DataFrame(X_train)\n",
        "y_train = pd.DataFrame(y_train)\n",
        "X_val = pd.DataFrame(X_val)\n",
        "y_val = pd.DataFrame(y_val)\n",
        "X_test = pd.DataFrame(X_test)\n",
        "y_test = pd.DataFrame(y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aSphyWfGe7K",
        "outputId": "05ada5f7-67ae-44de-b7e9-68162c96eb37"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shapes:  (10178, 24) (2565, 24) (765, 24)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# transform y to 0 and 1\n",
        "\n",
        "y_train[0] = y_train.applymap(lambda x: 1 if x > 0 else x)\n",
        "y_val[0] = y_val.applymap(lambda x: 1 if x > 0 else x)\n",
        "y_test[0] = y_test.applymap(lambda x: 1 if x > 0 else x)\n",
        "\n"
      ],
      "metadata": {
        "id": "e7jv7dvrRf8t"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "data/"
      ],
      "metadata": {
        "id": "luZblsN8LypC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot a chart showing unbalance data\n",
        "\n",
        "class_division = [ y_train[y_train[0] == 1 ].shape[0], y_train[y_train[0] == 0 ].shape[0] ]\n",
        "\n",
        "my_labels = ['Minority Class', 'Majority Class']\n",
        "\n",
        "plt.pie(class_division, labels = my_labels)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "opfVTxfY-nYI",
        "outputId": "8f7e3fb9-0433-4361-d327-2129c4296640"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# list each item of class division variable without balance\n",
        "\n",
        "for item in class_division:\n",
        "  print(item)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvazLPEqENFK",
        "outputId": "75314594-af14-4aa0-f9e8-8f05499f9cfa"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "892\n",
            "9286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Oversampling\n",
        "\n",
        "ros = RandomOverSampler(random_state=0)\n",
        "ros.fit(X_train, y_train)\n",
        "X_Oversampled, y_Oversampled = ros.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "w3TRP288ah71"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Y after OverSampled\n",
        "\n",
        "y_Oversampled.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2euKofojUMgg",
        "outputId": "14b166f7-24fd-4b74-e2cb-baa42ca5b612"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    9286\n",
              "1.0    9286\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Undersampling\n",
        "\n",
        "ros = RandomUnderSampler(random_state=0)\n",
        "ros.fit(X_train, y_train)\n",
        "X_Undersampled, y_Undersampled = ros.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "MMhfX07-k2Wn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dPNQ5J5tU3Tk"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Y after OverSampled\n",
        "\n",
        "y_Undersampled.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dc40fc1-70a2-45e0-9593-0e6aada89793",
        "id": "Zm2BkJmGU3nr"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    892\n",
              "1.0    892\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#GBR result normal\n",
        "# Using r2 score and meand square error metrics instead of classification report because it is a Regressor Problem\n",
        "\n",
        "gbr = GradientBoostingClassifier()\n",
        "gbr.fit(X_train,y_train)\n",
        "test = gbr.predict(X_test)\n",
        "\n",
        "#Confusion matrix using Normal Data\n",
        "\n",
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=test)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        "\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "X-o5uyxOpowB",
        "outputId": "5e53f5c6-cdf6-4a8f-efee-aa099723f29e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_original =  round(precision_score(y_test, test), 4)\n",
        "recall_original = round(recall_score(y_test, test), 4)\n",
        "F1_score_original =  round(f1_score(y_test, test), 4)\n",
        "metrics_original = [precision_original, recall_original, F1_score_original]\n",
        "\n",
        "original = pd.DataFrame(metrics_original)\n",
        "original.index = ['Precision', 'Recall', 'F1_score']\n",
        "original.columns = ['Original Dataset']\n",
        "original['Comments'] = ['Only 54% of the customers targetted are actually interested in renewing their insurance',\n",
        "                       'Only 41% of the potentially interested customers are being identified',\n",
        "                       '']\n",
        "original"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "GWSwzGFRXr8q",
        "outputId": "99db5c73-c4e5-406e-d8f1-dcb2bc431203"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Original Dataset                                           Comments\n",
              "Precision            0.4786  Only 54% of the customers targetted are actual...\n",
              "Recall               0.6087  Only 41% of the potentially interested custome...\n",
              "F1_score             0.5359                                                   "
            ],
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              "      <th>Precision</th>\n",
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              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-990db44b-0b24-4731-a358-60d171c0cb08 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Threshold\n",
        "\n",
        "cols = ['Metrics', 'Original']\n",
        "adj_threshold = pd.DataFrame()\n",
        "adj_threshold['Original'] = metrics_original\n",
        "thresholds = [ j/20 for j in range(1,16)]\n",
        "for i in thresholds:\n",
        "\n",
        "    y_pred_new_threshold = (gbr.predict_proba(X_test)[:,1]>=i).astype(int)\n",
        "\n",
        "     #calculating the threshold with respect to class 1\n",
        "\n",
        "    precision_adj_threshold =  round(precision_score(y_test, y_pred_new_threshold, average='binary'), 4)\n",
        "    recall_adj_threshold = round(recall_score(y_test, y_pred_new_threshold, average='binary'), 4)\n",
        "    F1_score_adj_threshold =  round((f1_score(y_test, y_pred_new_threshold, average='binary')), 4)\n",
        "    metrics_adj_threshold = [precision_adj_threshold, recall_adj_threshold, F1_score_adj_threshold]\n",
        "    name = 'Threshold: ' + str(i)\n",
        "    adj_threshold[name] = metrics_adj_threshold\n",
        "    cols.append(name)\n",
        "\n",
        "\n",
        "adj_threshold.index = ['Precision', 'Recall', 'F1_score']\n",
        "adj_threshold = adj_threshold.reset_index()\n",
        "adj_threshold.columns = cols\n",
        "adj_threshold.transpose()\n",
        "\n",
        "adj_threshold1 = adj_threshold.transpose().iloc[1:, :]\n",
        "adj_threshold1.columns = adj_threshold.iloc[:,0]\n",
        "adj_threshold1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "_m-iJUp0ZRCc",
        "outputId": "341743b7-404e-4fdb-e101-cbf402a1c68e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metrics         Precision  Recall F1_score\n",
              "Original           0.2539  0.8913   0.3952\n",
              "Threshold: 0.05    0.1272     1.0   0.2258\n",
              "Threshold: 0.1     0.1454  0.9891   0.2535\n",
              "Threshold: 0.15    0.1594  0.9565   0.2733\n",
              "Threshold: 0.2     0.1743  0.9565   0.2948\n",
              "Threshold: 0.25    0.1863  0.9457   0.3113\n",
              "Threshold: 0.3      0.195  0.9348   0.3227\n",
              "Threshold: 0.35    0.2044   0.913    0.334\n",
              "Threshold: 0.4     0.2187  0.8913   0.3512\n",
              "Threshold: 0.45     0.237  0.8913   0.3744\n",
              "Threshold: 0.5     0.2539  0.8913   0.3952\n",
              "Threshold: 0.55     0.264  0.8696   0.4051\n",
              "Threshold: 0.6     0.2841   0.837   0.4242\n",
              "Threshold: 0.65    0.3149  0.8043   0.4526\n",
              "Threshold: 0.7     0.3433    0.75    0.471\n",
              "Threshold: 0.75    0.3757  0.7065   0.4906"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4375dc3-32d8-4408-9660-b38c599b4690\" class=\"colab-df-container\">\n",
              "    <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>Metrics</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Original</th>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.8913</td>\n",
              "      <td>0.3952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.05</th>\n",
              "      <td>0.1272</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.1</th>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.9891</td>\n",
              "      <td>0.2535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.15</th>\n",
              "      <td>0.1594</td>\n",
              "      <td>0.9565</td>\n",
              "      <td>0.2733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.2</th>\n",
              "      <td>0.1743</td>\n",
              "      <td>0.9565</td>\n",
              "      <td>0.2948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.25</th>\n",
              "      <td>0.1863</td>\n",
              "      <td>0.9457</td>\n",
              "      <td>0.3113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.3</th>\n",
              "      <td>0.195</td>\n",
              "      <td>0.9348</td>\n",
              "      <td>0.3227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.35</th>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.913</td>\n",
              "      <td>0.334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.4</th>\n",
              "      <td>0.2187</td>\n",
              "      <td>0.8913</td>\n",
              "      <td>0.3512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.45</th>\n",
              "      <td>0.237</td>\n",
              "      <td>0.8913</td>\n",
              "      <td>0.3744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.5</th>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.8913</td>\n",
              "      <td>0.3952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.55</th>\n",
              "      <td>0.264</td>\n",
              "      <td>0.8696</td>\n",
              "      <td>0.4051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.6</th>\n",
              "      <td>0.2841</td>\n",
              "      <td>0.837</td>\n",
              "      <td>0.4242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.65</th>\n",
              "      <td>0.3149</td>\n",
              "      <td>0.8043</td>\n",
              "      <td>0.4526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.7</th>\n",
              "      <td>0.3433</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Threshold: 0.75</th>\n",
              "      <td>0.3757</td>\n",
              "      <td>0.7065</td>\n",
              "      <td>0.4906</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4375dc3-32d8-4408-9660-b38c599b4690')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a4375dc3-32d8-4408-9660-b38c599b4690 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a4375dc3-32d8-4408-9660-b38c599b4690');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-95bae1c0-6131-4550-8e0c-a84399c33eb8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95bae1c0-6131-4550-8e0c-a84399c33eb8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-95bae1c0-6131-4550-8e0c-a84399c33eb8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adj_threshold.plot(x='Metrics',\n",
        "        kind='bar',\n",
        "        stacked=False,\n",
        "        title='Metrics across Varying Thresholds',\n",
        "        figsize=(15, 5),\n",
        "        cmap = 'twilight').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "BLY0dxKacD11",
        "outputId": "d6590ef2-7a04-42b6-fea2-4d922f40ace8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7abfa4b5b880>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_new_threshold = (gbr.predict_proba(X_test)[:,1]>=0.3).astype(int)\n",
        "\n",
        "\n",
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred_new_threshold)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(7, 7))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Accent, alpha=0.4)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='x-large')\n",
        "\n",
        "plt.xlabel('Predictions', fontsize=10)\n",
        "plt.ylabel('Actuals', fontsize=10)\n",
        "plt.title('Confusion Matrix', fontsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "7c_mH5Wpb2VV",
        "outputId": "8c1247a1-e973-46a4-9f15-438a2ee5286b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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BAAAYQFQBAAAYQFQBAAAYQFQBAAAYQFQBAAAYQFQh35k8ebLKlCkjDw8P1a9fX1u2bHH0SADysLVr16pTp04KCQmRzWbTkiVLHD0SnBRRhXxl0aJFGjhwoEaMGKFff/1V1atX18MPP6wzZ844ejQAeVRiYqKqV6+uyZMnO3oUODl+TQ3ylfr166tu3br69NNPJUlpaWkKDQ1V//79NWzYMAdPByCvs9ls+uabb9SlSxdHjwInxJUq5BvXrl1TVFSUWrdubV/m4uKi1q1ba9OmTQ6cDABwLyCqkG+cO3dOqampCg4OzrA8ODhYMTExDpoKAHCvIKoAAAAMIKqQbxQpUkSurq46ffp0huWnT59WsWLFHDQVAOBeQVQh33Bzc1Pt2rUVGRlpX5aWlqbIyEg1bNjQgZMBAO4FBRw9AGDSwIEDFR4erjp16qhevXoaP368EhMT1bdvX0ePBiCPunTpkg4dOmT/Pjo6Wjt27FBgYKBKlSrlwMngbPhIBeQ7n376qT7++GPFxMSoRo0amjhxourXr+/osQDkUWvWrFHLli0zLQ8PD9fs2bPv/kBwWkQVAACAAdxTBQAAYABRBQAAYABRBQAAYABRBQAAYABRBQAAYABRBQAAYABRBQAAYABRhXwpKSlJI0eOVFJSkqNHAeAEeM6ACXz4J/Kl+Ph4+fn5KS4uTr6+vo4eB0Aex3MGTOBKFQAAgAFEFQAAgAEFHD3AnUhLS9PJkyfl4+Mjm83m6HGQh8THx2f4XwC4EZ4zkB3LspSQkKCQkBC5uNz4WpRT31N14sQJhYaGOnoMAACQzx0/flwlS5a84TZOfaXKx8dHkrR48WJ5eXk5eBoAAJDfXL58WY899pi9OW7EqaMq/SU/Ly8veXt7O3gaAACQX93KbUbcqA4AAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGAAUQUAAGBAAUcPAGRl2rRp2r9/v44fP664uDi5u7srODhYTZs21aOPPio/Pz/7tikpKVqyZIkOHjyoQ4cO6ciRI0pJSdGQIUPUsWPHbI8RGxurhQsX6pdfflFMTIwKFiyoYsWKqVWrVurcubO8vLzuxqkCMCQnzxunTp1Sjx49st1Xq1atNGLEiAzLvv/+e40aNSrbxwwcOFCdO3e+8xOB0yKqkCd9/fXXqlChgurUqaOAgABduXJFe/bs0axZs7R06VJNnTpVQUFBkqQrV65o0qRJkqTAwEAFBgbqzJkzN9z/qVOn9MILLyg2NlY1atRQ/fr1lZSUpG3btmnatGlavXq1pk6dKnd391w/VwBm5OR5I11YWJiaNGmSaV/33Xdftsdp0qSJwsLCMi2vWLHinZ8EnBpRhTxp+fLlWQbNjBkzNG/ePM2bN08DBw6UJHl4eGj06NEqX768ChcurFmzZmn27Nk33P/ChQsVGxurvn37qk+fPvblqampGjx4sH799Vf99NNPatu2rcnTApCLcvK8kS4sLEx9+/bN0XGaNGmidu3a3dGsyJ+4pwp5UnZXiFq2bClJOnHihH1ZwYIF1aBBAxUuXPiW93/y5ElJUqNGjTIsd3V1VYMGDSRJcXFxOZoZgGPl5HkDyA15IqomT56sMmXKyMPDQ/Xr19eWLVscPRLyqI0bN0qSypUrd0f7Sb+0v3nz5gzL09LS9Msvv8jFxUU1a9a8o2MAyBtu9Lxx7tw5RUREaO7cuYqIiNDhw4dvur9Dhw7p66+/1vz587Vy5cqb3m6Ae4fDX/5btGiRBg4cqGnTpql+/foaP368Hn74Ye3fvz/Ta9+49yxcuFBXrlxRYmKi9u3bp927d6tcuXLq2bPnHe33iSee0KZNmzRz5kxt375dFSpUUHJysrZu3aoLFy5oyJAhqlChgqGzAHA35eR5Y9u2bdq2bVuGZTVq1NDrr7+u4ODgLPe/ePHiDN+7urqqQ4cOevnll7kP8x7n8KgaO3asnnnmGftr2tOmTdOyZcv0n//8R8OGDXPwdHC0RYsW6cKFC/bv69Wrp+HDh8vf3/+O9hsQEKApU6Zo9OjRWrdunX799VdJks1mU8eOHVWnTp072j8Ax7mV5w0PDw/17t1bTZs2VfHixSVJf/zxh2bNmqXt27fr1Vdf1cyZM+Xp6Wl/TPHixfXKK6+obt26Klq0qBITE7Vr1y7NmDFDERERSkxM1FtvvXXXzhN5j82yLMtRB7927Zq8vLy0ePFidenSxb48PDxcFy9e1Lfffpth+6SkJCUlJdm/j4+PV2hoqJYvXy5vb++7NTYc4MKFC/rtt980ffp0Xb58WaNGjcr2SlL6jeo3+kiFU6dO6fXXX1dSUpIGDBigatWq6erVq9qwYYOmTJkiT09PTZkyxf5kC8D55OR5I11KSor69++vPXv2qH///nrsscduepwzZ86oX79+SkhI0MyZM7N8ZyCcV2Jiotq3b6+4uDj5+vrecFuH3lN17tw5paamZrrEGhwcrJiYmEzbf/jhh/Lz87N/hYaG3q1R4WCBgYFq1qyZxowZo/j4eH3wwQd3tL9Ro0bpjz/+0LvvvqsGDRrI29tbhQsX1iOPPKJ//vOfunDhwk3fQQggb7ud540CBQqoQ4cOkqSdO3fe0nGCgoLsb3C51ccgf8oTN6rfquHDhysuLs7+dfz4cUePhLusWLFiKl26tKKjo3Xx4sXb2sfly5e1Y8cO+fr6ZnnjavoN6vv377+TUQHkETl93kh/mfDq1au3fIzbeQzyH4feU1WkSBG5urrq9OnTGZafPn1axYoVy7S9u7s7NwFC58+fl3T95tDbkZycLOn6Jd3k5GQVLFgww/r0J92/LwfgvHLyvLFnzx5JytHL/7fzGOQ/Dr1S5ebmptq1aysyMtK+LC0tTZGRkWrYsKEDJ4MjHT9+XJcuXcq0PC0tTTNmzFBsbKyqVq0qHx+f29q/n5+fSpcurdTUVM2ZMyfDuqSkJPuyWrVq3db+Adx9OX3eOHDggNLS0jJtHxUVpa+//lqS1KZNmwzr9u3bl+X+582bp99//11+fn6qX7++idOBk3L4u/8GDhyo8PBw1alTR/Xq1dP48eOVmJiY40+4Rf6xefNmTZ8+XdWqVVPx4sXl5+enCxcuaOfOnTp58qQCAwM1ZMiQDI+ZP3++jh07Jun6Z8hI139P1+7duyVJ1apVy3DT+oABAzRs2DDNmTNH27ZtU5UqVZSUlKQtW7YoJiZGJUqU0JNPPnmXzhjAncrp88bkyZN14sQJValSxf7xPYcPH7a/E/jpp59W1apVMxzjueee03333aewsDAVKVJEiYmJ2r17t6Kjo+Xh4aE333yTN03d4xz67r90n376qT7++GPFxMSoRo0amjhx4i3Vfnx8vPz8/Hj3Xz7zxx9/KCIiQrt379bZs2d16dIleXh4qGTJkmrYsKG6deuW6R0Yr7zyinbs2JHtPtu2bavhw4dnWHb48GEtWLBAO3fu1IULF+Ti4qKQkBA1btxYTzzxxG1fCQNw9+X0eWPZsmVat26doqOjFRcXp5SUFAUEBKhKlSp69NFHVb169UzHmDp1qvbu3asTJ04oISFBNptNwcHBql27trp3766QkJC7ecq4S3Ly7r88EVW3i6gCAAC5yWk+UgEAACC/IKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMMBJVFy9eNLEbAAAAp5XjqBo9erQWLVpk/7579+4qXLiwSpQooZ07dxodDgAAwFnkOKqmTZum0NBQSdLq1au1evVqff/992rXrp2GDBlifEAAAABnUCCnD4iJibFH1Xfffafu3burTZs2KlOmjOrXr298QAAAAGeQ4ytVAQEBOn78uCRpxYoVat26tSTJsiylpqaanQ4AAMBJ5PhKVdeuXfXkk0+qfPnyOn/+vNq1aydJ2r59u8LCwowPCAAA4AxyHFXjxo1TmTJldPz4cX300UcqVKiQJOnUqVN68cUXjQ8IAADgDGyWZVmOHuJ2xcfHy8/PT8uXL5e3t7ejxwEAAPlMYmKi2rdvr7i4OPn6+t5w21u6UhUREXHLB3/kkUdueVsAAID84paiqkuXLre0M5vNxs3qAADgnnRLUZWWlpbbcwAAADg1fvcfAACAATl+9590/aatn3/+WceOHdO1a9cyrBswYICRwQAAAJxJjqNq+/btat++vS5fvqzExEQFBgbq3Llz8vLyUlBQEFEFAADuSTl++e/VV19Vp06dFBsbK09PT23evFlHjx5V7dq1NWbMmNyYEQAAIM/LcVTt2LFDgwYNkouLi1xdXZWUlKTQ0FB99NFHev3113NjRgAAgDwvx1FVsGBBubhcf1hQUJCOHTsmSfLz87P/TkAAAIB7TY7vqapZs6a2bt2q8uXLq3nz5nrrrbd07tw5zZ07V1WrVs2NGQEAAPK8HF+p+uCDD1S8eHFJ0vvvv6+AgAC98MILOnv2rKZPn258QAAAAGeQ4ytVderUsf//oKAgrVixwuhAAAAAzogP/wQAADAgx1eq7rvvPtlstmzX//HHH3c0EAAAgDPKcVT961//yvB9cnKytm/frhUrVmjIkCGm5gIAAHAqOY6qV155JcvlkydP1rZt2+54oNvhWcFTXj5eDjk2AOcSkljW0SMAcCIJCfG3vK2xe6ratWun//73v6Z2BwAA4FSMRdXixYsVGBhoancAAABO5bY+/POvN6pblqWYmBidPXtWU6ZMMTocAACAs8hxVHXu3DlDVLm4uKho0aJq0aKFKlWqZHQ4AAAAZ5HjqBo5cmQujAEAAODccnxPlaurq86cOZNp+fnz5+Xq6mpkKAAAAGeT46iyLCvL5UlJSXJzc7vjgQAAAJzRLb/8N3HiREmSzWbT559/rkKFCtnXpaamau3atdxTBQAA7lm3HFXjxo2TdP1K1bRp0zK81Ofm5qYyZcpo2rRp5icEAABwArccVdHR0ZKkli1b6n//+58CAgJybSgAAABnk+N3//3000+5MQcAAIBTy/GN6t26ddPo0aMzLf/oo4/0+OOPGxkKAADA2eQ4qtauXav27dtnWt6uXTutXbvWyFAAAADOJsdRdenSpSw/OqFgwYKKj7/13+QMAACQn+Q4qqpVq6ZFixZlWr5w4ULdf//9RoYCAABwNjm+Uf3NN99U165ddfjwYbVq1UqSFBkZqS+//FKLFy82PiAAAIAzyHFUderUSUuWLNEHH3ygxYsXy9PTU9WrV9ePP/6owMDA3JgRAAAgz8txVElShw4d1KFDB0lSfHy8FixYoMGDBysqKkqpqalGBwQAAHAGOb6nKt3atWsVHh6ukJAQffLJJ2rVqpU2b95scjYAAACnkaMrVTExMZo9e7Zmzpyp+Ph4de/eXUlJSVqyZAk3qQMAgHvaLV+p6tSpkypWrKhdu3Zp/PjxOnnypCZNmpSbswEAADiNW75S9f3332vAgAF64YUXVL58+dycCQAAwOnc8pWq9evXKyEhQbVr11b9+vX16aef6ty5c7k5GwAAgNO45ahq0KCBZsyYoVOnTum5557TwoULFRISorS0NK1evVoJCQm5OScAAECeluN3/3l7e6tfv35av369du/erUGDBmnUqFEKCgrSI488khszAgAA5Hm3/ZEKklSxYkV99NFHOnHihBYsWGBqJgAAAKdzR1GVztXVVV26dFFERISJ3QEAADgdI1EFAABwryOqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCjg6AEA07as3aKv//O1dm3bpYS4BPkF+Cmscph6PNNDjVs3dvR4ABzghx9Xaebsz3Tg0H5djI1VUFCwqlWtrmefflF1atXNtH1qaqoWLf5Si/+3SPv271FSUpKCgoJV/YGaGvLqcJUrG+aAs0BeR1QhX5n4zkTNnTxXQSFBavZwM/kH+iv2fKz27dqnqI1RRBVwD3p/1EhNmT5JAQGBavtQewUGBCr6aLRW/fC9lq9YqgmfTFG3Lt3t2ycmXlLfZ3tpw6Z1qnJ/NT3WtYc83N116vQpbdm6WX9EHyaqkCWiCvnGN3O/0dzJc9XhHx307zH/VkG3ghnWpySnOGgyAI5y5uxpTft8sooWCdIPy9eqSJGi9nUbNq1T955dNGbcqAxRNfTfg7Rh0zqNeu8TPfVkn0z7TE5OvhujwwlxTxXyhWtJ1zT1w6kqVrJYlkElSQUK8m8I4F5z4s/jSktLU80atTIElSQ1bthUhQoV0vkL5+zLdv+2U99ELNYjHR/NMqgkqWDBzM8vgOTgqFq7dq06deqkkJAQ2Ww2LVmyxJHjwIn98vMvij0fq5btW8rmYtP61ev1xaQvtGD6Au3ausvR4wFwkPvKlJObm5t27NyuCxfOZ1i3ectGXbp0SU0bN7cv+ybiv5KkLp26Kj4+Xv9d8pUmTRmneQu+UPSRP+7q7HA+Dv2ne2JioqpXr65+/fqpa9eujhwFTm7Pjj2SJDd3N/V6sJcO7zucYX3NhjU1+vPRCigS4IjxADhIgH+AXn9thN5+/w21eLiR2j7UXgH+ATpy7IhW/7BCzZq00Oj3xtq337lru6TrV7gatayt2NgL9nU2m029e/bVuyNGydXV9a6fC/I+h0ZVu3bt1K5dO0eOgHwi9lysJGnelHm6r8J9mhExQxWqVtDJoyc14e0J2rxms4Y9M0yfffOZgycFcLc90+95hZYM1aChAzR/4Rz78jKly6p7tycyvCx47vxZSdLb77+phx9qr6GDXlfxYiH6dUeUhr0xWF/M+48KBxbRoH8NvevngbzPqe6pSkpKUnx8fIYvQJLS0tIkSa4FXPXJnE9Uo34NeXl7Kez+MH0862MFhQTp142/8lIgcA+a8tlEPftSXz3e7QltXBOlQ78f14qIH1W6VGm9/Opzem/USPu26c8lYeXKa9qkmQorV0He3oXUtHFzTZ8ySy4uLpr+nym6du2ag84GeZlTRdWHH34oPz8/+1doaKijR0Ie4ePnI0mqWLWiQkqFZFjn4eWhhi0aSpJ+3/77XZ8NgONs3Lxe749+W20ebKuRb7yn0qXKyNPTS9WqVtfMaXNUrFhxffb5ZB09dkSS5OfrJ0l6qNXDmV7iq1K5qkqFltalS5d08PCBu30qcAJOFVXDhw9XXFyc/ev48eOOHgl5ROlypSVJhfwKZbnex/96dCVdTbprMwFwvB9+XCVJatSwSaZ1np5eqvlALaWlpem3369fxS77/z9/yvf/x9XfpUfX1atXcmNcODmniip3d3f5+vpm+AIkqW7TurLZbIo+EG2/fP9X6Teu//0qFoD87dq16/+QOv+3d/6lS19e0M1NkuzvBNx3YG+mbZOSkuzvAAwtWcr4rHB+ThVVQHaKhxZX0zZNFXMiRgunL8ywbvOazdr802b5+PmoUatGDpoQgCPUq3v9pf/5C+boVMzJDOt+XPODtkb9Ig93D9WpVU+S1KFtJxULLqaly5Zo+86oDNuP/3SM4hPi1ahhEwUVDb47JwCn4tB3/126dEmHDh2yfx8dHa0dO3YoMDBQpUrxrwDkzGujXtP+3/Zr3IhxWv/DelWsVlEnj53Uz9//LBdXF70x9g0V8s365UEA+VPHdo/oy8bNtW7Dz2rRpqHatumgoCJBOnj4oH74caUsy9Lw195SYECgJMnLy1vjPvpU4c88qa7/6Kh2bTqqWLHi2r4jSlu2bVaRwkUzfAQD8Fc2y7IsRx18zZo1atmyZabl4eHhmj179k0fHx8fLz8/P/106CcV8uEvS1z/aIXPP/lca1et1bnT5+Tt462a9Wuqz4A+qlKriqPHQx4QkljW0SPgLktOTtbsuTP17Xf/08FD+3XlyhX5+weoxgO19HSfZ9W8aea/h37f+5vGTxqjzVs2KiEhXkWLBOnBlg/pX/0Hq1hwcQecBRwlISFelarfp7i4uJveduTQqLpTRBWAnCKqAORETqKKe6oAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMIKoAAAAMKODoAe6EZVmSpMSERAdPAsBZJCTGO3oEAE7k0qUESf/XHDfi1FGVkHD9RDvW7OjgSQAAQH6WkJAgPz+/G25js24lvfKotLQ0nTx5Uj4+PrLZbI4eB3lIfHy8QkNDdfz4cfn6+jp6HAB5HM8ZyI5lWUpISFBISIhcXG5815RTX6lycXFRyZIlHT0G8jBfX1+eIAHcMp4zkJWbXaFKx43qAAAABhBVAAAABhBVyJfc3d01YsQIubu7O3oUAE6A5wyY4NQ3qgMAAOQVXKkCAAAwgKgCAAAwgKgCAAAwgKgCkC/06dNHXbp0sX/fokUL/etf/7qjfZrYB4B7B1EFIFf16dNHNptNNptNbm5uCgsL0zvvvKOUlJRcPe7//vc/vfvuu7e07Zo1a2Sz2XTx4sXb3gcAOPUnqgNwDm3bttWsWbOUlJSk5cuX66WXXlLBggU1fPjwDNtdu3ZNbm5uRo4ZGBiYJ/YB4N7BlSoAuc7d3V3FihVT6dKl9cILL6h169aKiIiwv2T3/vvvKyQkRBUrVpQkHT9+XN27d5e/v78CAwPVuXNnHTlyxL6/1NRUDRw4UP7+/ipcuLBee+21TL9B/u8v3SUlJWno0KEKDQ2Vu7u7wsLCNHPmTB05ckQtW7aUJAUEBMhms6lPnz5Z7iM2Nla9e/dWQECAvLy81K5dOx08eNC+fvbs2fL399fKlStVuXJlFSpUSG3bttWpU6fs26xZs0b16tWTt7e3/P391bhxYx09etTQTxqAIxFVAO46T09PXbt2TZIUGRmp/fv3a/Xq1fruu++UnJyshx9+WD4+Plq3bp02bNhgj5P0x3zyySeaPXu2/vOf/2j9+vW6cOGCvvnmmxses3fv3lqwYIEmTpyovXv36rPPPlOhQoUUGhqq//73v5Kk/fv369SpU5owYUKW++jTp4+2bdumiIgIbdq0SZZlqX379kpOTrZvc/nyZY0ZM0Zz587V2rVrdezYMQ0ePFiSlJKSoi5duqh58+batWuXNm3apGeffZZfCA/kE7z8B+CusSxLkZGRWrlypfr376+zZ8/K29tbn3/+uf1lv3nz5iktLU2ff/65PTZmzZolf39/rVmzRm3atNH48eM1fPhwde3aVZI0bdo0rVy5MtvjHjhwQF999ZVWr16t1q1bS5LKli1rX5/+Ml9QUJD8/f2z3MfBgwcVERGhDRs2qFGjRpKk+fPnKzQ0VEuWLNHjjz8uSUpOTta0adNUrlw5SdLLL7+sd955R5IUHx+vuLg4dezY0b6+cuXKOf9BAsiTuFIFINd99913KlSokDw8PNSuXTv94x//0MiRIyVJ1apVy3Af1c6dO3Xo0CH5+PioUKFCKlSokAIDA3X16lUdPnxYcXFxOnXqlOrXr29/TIECBVSnTp1sj79jxw65urqqefPmt30Oe/fuVYECBTIct3DhwqpYsaL27t1rX+bl5WUPJkkqXry4zpw5I+l6vPXp00cPP/ywOnXqpAkTJmR4aRCAc+NKFYBc17JlS02dOlVubm4KCQlRgQL/99Tj7e2dYdtLly6pdu3amj9/fqb9FC1a9LaO7+npeVuPux0FCxbM8L3NZstwv9esWbM0YMAArVixQosWLdIbb7yh1atXq0GDBndtRgC5gytVAHKdt7e3wsLCVKpUqQxBlZVatWrp4MGDCgoKUlhYWIYvPz8/+fn5qXjx4vrll1/sj0lJSVFUVFS2+6xWrZrS0tL0888/Z7k+/UpZampqtvuoXLmyUlJSMhz3/Pnz2r9/v+6///4bntPf1axZU8OHD9fGjRtVtWpVffnllzl6PIC8iagCkKf07NlTRYoUUefOnbVu3TpFR0drzZo1GjBggE6cOCFJeuWVVzRq1CgtWbJE+/bt04svvpjpM6b+qkyZMgoPD1e/fv20ZMkS+z6/+uorSVLp0qVls9n03Xff6ezZs7p06VKmfZQvX16dO3fWM888o/Xr12vnzp3q1auXSpQooc6dO9/SuUVHR2v48OHatGmTjh49qlWrVungwYPcVwXkE0QVgDzFy8tLa9euValSpdS1a1dVrlxZTz/9tK5evSpfX19J0qBBg/TUU08pPDxcDRs2lI+Pjx599NEb7nfq1Kl67LHH9OKLL6pSpUp65plnlJiYKEkqUaKE3n77bQ0bNkzBwcF6+eWXs9zHrFmzVLt2bXXs2FENGzaUZVlavnx5ppf8bnRu+/btU7du3VShQgU9++yzeumll/Tcc8/l4CcEIK+yWX//cBcAAADkGFeqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADCCqAAAADPh/ucgHMTbPUa0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GBR result with Oversampling\n",
        "\n",
        "gbr = GradientBoostingClassifier()\n",
        "gbr.fit(X_Oversampled,y_Oversampled)\n",
        "test = gbr.predict(X_test)\n",
        "\n",
        "# Confusion matrix using OverSampling\n",
        "\n",
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=test)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        "\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "3RuBNl11NvlD",
        "outputId": "b866f8d9-c1bf-4137-f258-14da6b389ec9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_original =  round(precision_score(y_test, test), 4)\n",
        "recall_original = round(recall_score(y_test, test), 4)\n",
        "F1_score_original =  round(f1_score(y_test, test), 4)\n",
        "metrics_original = [precision_original, recall_original, F1_score_original]\n",
        "\n",
        "original = pd.DataFrame(metrics_original)\n",
        "original.index = ['Precision', 'Recall', 'F1_score']\n",
        "original.columns = ['Original Dataset']\n",
        "original['Comments'] = ['Only 54% of the customers targetted are actually interested in renewing their insurance',\n",
        "                       'Only 41% of the potentially interested customers are being identified',\n",
        "                       '']\n",
        "original"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "McGqw4IqX2t3",
        "outputId": "84612a7a-f5b0-4176-f011-b6c39fd50f1a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Original Dataset                                           Comments\n",
              "Precision            0.3490  Only 54% of the customers targetted are actual...\n",
              "Recall               0.7283  Only 41% of the potentially interested custome...\n",
              "F1_score             0.4718                                                   "
            ],
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              "      <th>Precision</th>\n",
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              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed24451f-089d-4745-aed0-593b41f992d7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GBR result with Undersampling\n",
        "# Using r2 score and meand square error metrics instead of classification report because it is a Regressor Problem\n",
        "\n",
        "gbr = GradientBoostingClassifier()\n",
        "gbr.fit(X_Undersampled,y_Undersampled)\n",
        "test = gbr.predict(X_test)\n",
        "\n",
        "#Confusion matrix using UnderSampling\n",
        "\n",
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=test)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        "\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "_23aVzzflSpw",
        "outputId": "39da25b2-9eb0-4a1d-fe04-6856a013c107"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_original =  round(precision_score(y_test, test), 4)\n",
        "recall_original = round(recall_score(y_test, test), 4)\n",
        "F1_score_original =  round(f1_score(y_test, test), 4)\n",
        "metrics_original = [precision_original, recall_original, F1_score_original]\n",
        "\n",
        "original = pd.DataFrame(metrics_original)\n",
        "original.index = ['Precision', 'Recall', 'F1_score']\n",
        "original.columns = ['Original Dataset']\n",
        "original['Comments'] = ['Only 54% of the customers targetted are actually interested in renewing their insurance',\n",
        "                       'Only 41% of the potentially interested customers are being identified',\n",
        "                       '']\n",
        "original"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "sJnjZ7VBX7z9",
        "outputId": "44fa483a-88db-4f00-b065-329a20201abe"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Original Dataset                                           Comments\n",
              "Precision            0.2539  Only 54% of the customers targetted are actual...\n",
              "Recall               0.8913  Only 41% of the potentially interested custome...\n",
              "F1_score             0.3952                                                   "
            ],
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              "      <th>Precision</th>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-657732c0-b12f-448e-9233-117380f7e7fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}