Trial 2_ Churn Prediction Model 4 (XGBoost).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyNCcupx5HMAVz6JnX/z6a2+"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["# Setting up"],"metadata":{"id":"IElxH53uRgT8"}},{"cell_type":"code","execution_count":64,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nhq53RG6pVWU","executionInfo":{"status":"ok","timestamp":1678669017175,"user_tz":-660,"elapsed":3696,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"66711f57-7d4d-4bf8-e43d-28ed199a0926"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount(\"/content/drive\")"]},{"cell_type":"code","source":["%cd /content/drive/MyDrive/Projects/1. Churn Prediction/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u30eksl4pe_r","executionInfo":{"status":"ok","timestamp":1678669017175,"user_tz":-660,"elapsed":39,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"3cfe666f-091a-48cf-8c29-fe59ea461e59"},"execution_count":65,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Projects/1. Churn Prediction\n"]}]},{"cell_type":"markdown","source":["# Importing dataset"],"metadata":{"id":"aBN6z-oyRkbX"}},{"cell_type":"code","source":["import os\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import plotly.express as px\n","import plotly.graph_objects as go"],"metadata":{"id":"d_6OjWFnpe9a","executionInfo":{"status":"ok","timestamp":1678669017176,"user_tz":-660,"elapsed":36,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":66,"outputs":[]},{"cell_type":"code","source":["DATASET = \"Trial 2: output_cleaned_dataset.csv\""],"metadata":{"id":"xVkdbEwJpe60","executionInfo":{"status":"ok","timestamp":1678669017176,"user_tz":-660,"elapsed":36,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":67,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv(DATASET)"],"metadata":{"id":"AvQNUrvvpe4b","executionInfo":{"status":"ok","timestamp":1678669017177,"user_tz":-660,"elapsed":37,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":68,"outputs":[]},{"cell_type":"code","source":["df.shape"],"metadata":{"id":"G0Z-eEKnpe11","executionInfo":{"status":"ok","timestamp":1678669017177,"user_tz":-660,"elapsed":36,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"38285c66-0b67-4190-9949-0910103117ca"},"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(6589, 41)"]},"metadata":{},"execution_count":69}]},{"cell_type":"code","source":["df.head()"],"metadata":{"id":"3DFK5YNxpezW","executionInfo":{"status":"ok","timestamp":1678669017177,"user_tz":-660,"elapsed":34,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":235},"outputId":"d2e66a69-7f42-4226-949e-8dbe9fa72e67"},"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     0      1     2        3        4        5    6    7    8    9  ...   31  \\\n","0  0.0  42.39  16.0   593.30   974.81   4498.0  1.0  0.0  1.0  1.0  ...  0.0   \n","1  0.0  10.69  10.0   542.40   610.28  31297.0  0.0  0.0  1.0  0.0  ...  0.0   \n","2  0.0  33.65  30.0   280.85   415.45  62069.0  0.0  1.0  1.0  1.0  ...  0.0   \n","3  0.0  27.82   4.0  1237.85  1599.51  46677.0  1.0  0.0  1.0  1.0  ...  0.0   \n","4  0.0   7.38  11.0   267.40   289.54  42853.0  1.0  0.0  1.0  1.0  ...  0.0   \n","\n","    32   33   34   35   36   37   38   39   40  \n","0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  \n","1  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  \n","2  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  1.0  \n","3  0.0  0.0  0.0  1.0  0.0  0.0  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<th>36</th>\n","      <th>37</th>\n","      <th>38</th>\n","      <th>39</th>\n","      <th>40</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>42.39</td>\n","      <td>16.0</td>\n","      <td>593.30</td>\n","      <td>974.81</td>\n","      <td>4498.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0</td>\n","      <td>10.69</td>\n","      <td>10.0</td>\n","      <td>542.40</td>\n","      <td>610.28</td>\n","      <td>31297.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      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-1]"],"metadata":{"id":"TfH6cfjT_NAo","executionInfo":{"status":"ok","timestamp":1678669017178,"user_tz":-660,"elapsed":34,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":71,"outputs":[]},{"cell_type":"code","source":["X.shape"],"metadata":{"id":"6Y_9U0Xx_Sa1","executionInfo":{"status":"ok","timestamp":1678669017178,"user_tz":-660,"elapsed":34,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"f8993518-067c-4a88-a3ef-c8d62067a4e6"},"execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(6589, 40)"]},"metadata":{},"execution_count":72}]},{"cell_type":"code","source":["y.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iXMkjrgDovbB","executionInfo":{"status":"ok","timestamp":1678669017178,"user_tz":-660,"elapsed":32,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"1faf968e-1b8e-43f0-b629-159a1876fe3d"},"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(6589,)"]},"metadata":{},"execution_count":73}]},{"cell_type":"markdown","source":["## Splitting dataset into train, test and val"],"metadata":{"id":"e77OLbs1SE5Q"}},{"cell_type":"code","source":["# splitting into train (60%), test (20%) and val (20%)\n","\n","from sklearn.model_selection import train_test_split\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True)\n","X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, shuffle=True)"],"metadata":{"id":"Y7JJNyy9_4vo","executionInfo":{"status":"ok","timestamp":1678669017178,"user_tz":-660,"elapsed":29,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":74,"outputs":[]},{"cell_type":"code","source":["X_train.shape"],"metadata":{"id":"D23uHGKuA32X","executionInfo":{"status":"ok","timestamp":1678669017179,"user_tz":-660,"elapsed":30,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"a9871e3b-64e6-40f4-ae02-d8f64c0e9edf"},"execution_count":75,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4612, 40)"]},"metadata":{},"execution_count":75}]},{"cell_type":"code","source":["y_train.shape"],"metadata":{"id":"KY_ij4dQA3zF","executionInfo":{"status":"ok","timestamp":1678669017179,"user_tz":-660,"elapsed":28,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"2a04112c-b1fe-4449-d20c-ad851efdbd3a"},"execution_count":76,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4612,)"]},"metadata":{},"execution_count":76}]},{"cell_type":"code","source":["X_test.shape"],"metadata":{"id":"-_ijNLlVBAKp","executionInfo":{"status":"ok","timestamp":1678669017179,"user_tz":-660,"elapsed":26,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"4853537f-4c52-4623-d747-97a3127f08f3"},"execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988, 40)"]},"metadata":{},"execution_count":77}]},{"cell_type":"code","source":["y_test.shape"],"metadata":{"id":"n9lIPZ-vBCBH","executionInfo":{"status":"ok","timestamp":1678669017180,"user_tz":-660,"elapsed":25,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"56f189d9-bb41-4806-ac7e-0e4f39e97795"},"execution_count":78,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988,)"]},"metadata":{},"execution_count":78}]},{"cell_type":"code","source":["X_val.shape"],"metadata":{"id":"BLvAvLHGBB2S","executionInfo":{"status":"ok","timestamp":1678669017180,"user_tz":-660,"elapsed":23,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"bce1188a-0738-4535-e895-a4b31f176cc2"},"execution_count":79,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(989, 40)"]},"metadata":{},"execution_count":79}]},{"cell_type":"code","source":["y_val.shape"],"metadata":{"id":"tvvoPmvcBBvF","executionInfo":{"status":"ok","timestamp":1678669017180,"user_tz":-660,"elapsed":22,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"5fdc3bcc-8d5e-4760-9d97-77fafc8d5046"},"execution_count":80,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(989,)"]},"metadata":{},"execution_count":80}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"gAu4mHXKSRt1"}},{"cell_type":"markdown","source":["## Building model"],"metadata":{"id":"An-aaRetSZ5N"}},{"cell_type":"code","source":["from xgboost.callback import EarlyStopping, TrainingCheckPoint\n","\n","checkpointer = TrainingCheckPoint(\"/\", \"model_4\")"],"metadata":{"id":"WHtbPBY2dO-q","executionInfo":{"status":"ok","timestamp":1678669017180,"user_tz":-660,"elapsed":20,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":81,"outputs":[]},{"cell_type":"code","source":["from xgboost import XGBClassifier\n","\n","bst = XGBClassifier(n_estimators=1000, learning_rate=0.001, callbacks=[checkpointer])\n","bst.fit(X_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":251},"id":"WderblBhdO6z","executionInfo":{"status":"ok","timestamp":1678669035310,"user_tz":-660,"elapsed":18149,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"0f430f35-9da0-45f5-f4c8-2c6d840aa8fb"},"execution_count":82,"outputs":[{"output_type":"execute_result","data":{"text/plain":["XGBClassifier(base_score=None, booster=None,\n","              callbacks=[<xgboost.callback.TrainingCheckPoint object at 0x7f50009e86d0>],\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=0.001, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n","              predictor=None, random_state=None, ...)"],"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None,\n","              callbacks=[&lt;xgboost.callback.TrainingCheckPoint object at 0x7f50009e86d0&gt;],\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=0.001, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n","              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None,\n","              callbacks=[&lt;xgboost.callback.TrainingCheckPoint object at 0x7f50009e86d0&gt;],\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=0.001, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n","              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":82}]},{"cell_type":"code","source":["y_pred = bst.predict(X_test)"],"metadata":{"id":"3G2C28PSdO4R","executionInfo":{"status":"ok","timestamp":1678669035310,"user_tz":-660,"elapsed":20,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":83,"outputs":[]},{"cell_type":"code","source":["y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3Fg7ZtEqdO2C","executionInfo":{"status":"ok","timestamp":1678669035311,"user_tz":-660,"elapsed":20,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"9e2a62b0-e1a1-40a5-bfb8-936a38db580d"},"execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988,)"]},"metadata":{},"execution_count":84}]},{"cell_type":"code","source":["y_pred.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MD3fYlx-dOzl","executionInfo":{"status":"ok","timestamp":1678669035311,"user_tz":-660,"elapsed":18,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"d3197694-cb5e-407c-dfdd-b98e411ce8f1"},"execution_count":85,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988,)"]},"metadata":{},"execution_count":85}]},{"cell_type":"markdown","source":["# Analysing"],"metadata":{"id":"92kqgVABSWbv"}},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, precision_score, recall_score\n","\n","cm = confusion_matrix(y_test, y_pred)\n","sns.heatmap(cm, annot=True, cbar=False)\n","plt.show()"],"metadata":{"id":"i-LYsLFVJfux","executionInfo":{"status":"ok","timestamp":1678669035312,"user_tz":-660,"elapsed":17,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":265},"outputId":"018faa38-99e4-4441-f377-b06b392415d9"},"execution_count":86,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["accuracy_score(y_test, y_pred)"],"metadata":{"id":"mqSTeAQxJxsj","executionInfo":{"status":"ok","timestamp":1678669035312,"user_tz":-660,"elapsed":16,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"f796fb5c-c407-4298-f74c-2678fe9fc767"},"execution_count":87,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.861336032388664"]},"metadata":{},"execution_count":87}]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred))"],"metadata":{"id":"O7wxUCLwKukU","executionInfo":{"status":"ok","timestamp":1678669035312,"user_tz":-660,"elapsed":14,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"abd2fc0b-7d83-4a44-ed40-1faf99ff14b6"},"execution_count":88,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","         0.0       0.87      0.95      0.91       702\n","         1.0       0.83      0.65      0.73       286\n","\n","    accuracy                           0.86       988\n","   macro avg       0.85      0.80      0.82       988\n","weighted avg       0.86      0.86      0.86       988\n","\n"]}]},{"cell_type":"code","source":["stayed_precision = precision_score(y_test, y_pred, labels=[0, 1], pos_label=0)"],"metadata":{"id":"akTRZNJaMvs7","executionInfo":{"status":"ok","timestamp":1678669035313,"user_tz":-660,"elapsed":12,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":89,"outputs":[]},{"cell_type":"code","source":["churned_precision = precision_score(y_test, y_pred, labels=[0, 1], pos_label=1)"],"metadata":{"id":"U0GLq7X5Kxx8","executionInfo":{"status":"ok","timestamp":1678669035313,"user_tz":-660,"elapsed":12,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":90,"outputs":[]},{"cell_type":"code","source":["stayed_recall = recall_score(y_test, y_pred, labels=[0, 1], pos_label=0)"],"metadata":{"id":"sp05mez4M5lk","executionInfo":{"status":"ok","timestamp":1678669035314,"user_tz":-660,"elapsed":13,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":91,"outputs":[]},{"cell_type":"code","source":["churned_recall = recall_score(y_test, y_pred, labels=[0, 1], pos_label=1)"],"metadata":{"id":"6AmF6bOgM1T8","executionInfo":{"status":"ok","timestamp":1678669035314,"user_tz":-660,"elapsed":13,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":92,"outputs":[]},{"cell_type":"code","source":["plt.bar([\"0\", \"1\"], [stayed_precision, churned_precision])\n","plt.xlabel(\"Class\")\n","plt.ylabel(\"Precision\")\n","plt.title(\"Precision (0 = Stayed, 1 = Churned)\")\n","plt.show()"],"metadata":{"id":"GWTgSDA4NMha","executionInfo":{"status":"ok","timestamp":1678669035315,"user_tz":-660,"elapsed":13,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"e18c30ef-7e72-4c27-e3e9-e4b8abbef581"},"execution_count":93,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.bar([\"0\", \"1\"], [stayed_recall, churned_recall])\n","plt.xlabel(\"Class\")\n","plt.ylabel(\"Recall\")\n","plt.title(\"Recall (0 = Stayed, 1 = Churned)\")\n","plt.show()"],"metadata":{"id":"wJ7FddSwNMeJ","executionInfo":{"status":"ok","timestamp":1678669036006,"user_tz":-660,"elapsed":704,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"70636607-39d4-4b22-9545-cd01ca3bbec6"},"execution_count":94,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}