Trial 2_ Churn Prediction Model 3 (modifying network architecture).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyP2/0GHXuyg/msNtDhOpqVO"},"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":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nhq53RG6pVWU","executionInfo":{"status":"ok","timestamp":1678668396990,"user_tz":-660,"elapsed":2838,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"1bee9904-8971-44c4-e113-ebb7f4761514"},"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":1678668396990,"user_tz":-660,"elapsed":21,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"051eb811-dab0-40f4-9246-5eadbcd9ca15"},"execution_count":48,"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":1678668396991,"user_tz":-660,"elapsed":18,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":49,"outputs":[]},{"cell_type":"code","source":["DATASET = \"Trial 2: output_cleaned_dataset.csv\""],"metadata":{"id":"xVkdbEwJpe60","executionInfo":{"status":"ok","timestamp":1678668396991,"user_tz":-660,"elapsed":18,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":50,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv(DATASET)"],"metadata":{"id":"AvQNUrvvpe4b","executionInfo":{"status":"ok","timestamp":1678668396991,"user_tz":-660,"elapsed":18,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":51,"outputs":[]},{"cell_type":"code","source":["df.shape"],"metadata":{"id":"G0Z-eEKnpe11","executionInfo":{"status":"ok","timestamp":1678668396992,"user_tz":-660,"elapsed":19,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"d5e5d309-9f9b-4810-8f49-91cec1c71c86"},"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(6589, 41)"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["df.head()"],"metadata":{"id":"3DFK5YNxpezW","executionInfo":{"status":"ok","timestamp":1678668396992,"user_tz":-660,"elapsed":16,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":235},"outputId":"fd6bb977-38b8-4df9-d522-5b67cb6cc428"},"execution_count":53,"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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<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>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0</td>\n","      <td>7.38</td>\n","      <td>11.0</td>\n","      <td>267.40</td>\n","      <td>289.54</td>\n","      <td>42853.0</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>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 41 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" 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-1]"],"metadata":{"id":"TfH6cfjT_NAo","executionInfo":{"status":"ok","timestamp":1678668396992,"user_tz":-660,"elapsed":14,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":54,"outputs":[]},{"cell_type":"code","source":["X.shape"],"metadata":{"id":"6Y_9U0Xx_Sa1","executionInfo":{"status":"ok","timestamp":1678668396993,"user_tz":-660,"elapsed":15,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"520dd9b3-7092-4972-cbce-16f855ace2f1"},"execution_count":55,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(6589, 40)"]},"metadata":{},"execution_count":55}]},{"cell_type":"code","source":["y.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iXMkjrgDovbB","executionInfo":{"status":"ok","timestamp":1678668396993,"user_tz":-660,"elapsed":13,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"72652bf5-3e88-4b56-b1d3-6b8d95707928"},"execution_count":56,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(6589,)"]},"metadata":{},"execution_count":56}]},{"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":1678668396993,"user_tz":-660,"elapsed":10,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":57,"outputs":[]},{"cell_type":"code","source":["X_train.shape"],"metadata":{"id":"D23uHGKuA32X","executionInfo":{"status":"ok","timestamp":1678668397512,"user_tz":-660,"elapsed":529,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"347d5255-0c49-491d-8758-1624354cb700"},"execution_count":58,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4612, 40)"]},"metadata":{},"execution_count":58}]},{"cell_type":"code","source":["y_train.shape"],"metadata":{"id":"KY_ij4dQA3zF","executionInfo":{"status":"ok","timestamp":1678668397513,"user_tz":-660,"elapsed":18,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"3d661501-5e8c-4260-de88-4597b82f193f"},"execution_count":59,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4612,)"]},"metadata":{},"execution_count":59}]},{"cell_type":"code","source":["X_test.shape"],"metadata":{"id":"-_ijNLlVBAKp","executionInfo":{"status":"ok","timestamp":1678668397513,"user_tz":-660,"elapsed":16,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"41119fd7-4888-48b1-91bd-1ac7d4fe49d7"},"execution_count":60,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988, 40)"]},"metadata":{},"execution_count":60}]},{"cell_type":"code","source":["y_test.shape"],"metadata":{"id":"n9lIPZ-vBCBH","executionInfo":{"status":"ok","timestamp":1678668397513,"user_tz":-660,"elapsed":14,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"bac4cf71-62f2-425e-982c-a13dc275c55c"},"execution_count":61,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988,)"]},"metadata":{},"execution_count":61}]},{"cell_type":"code","source":["X_val.shape"],"metadata":{"id":"BLvAvLHGBB2S","executionInfo":{"status":"ok","timestamp":1678668397513,"user_tz":-660,"elapsed":11,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"a6a67b3d-e3fe-42e0-8d55-b080b0821951"},"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(989, 40)"]},"metadata":{},"execution_count":62}]},{"cell_type":"code","source":["y_val.shape"],"metadata":{"id":"tvvoPmvcBBvF","executionInfo":{"status":"ok","timestamp":1678668397514,"user_tz":-660,"elapsed":10,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"cb1fbaa4-a0e5-42c5-b037-de73ce35ac1e"},"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(989,)"]},"metadata":{},"execution_count":63}]},{"cell_type":"markdown","source":["# Testing out scalers"],"metadata":{"id":"GDOMtaNoogTv"}},{"cell_type":"code","source":["from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score\n","\n","standard_scaler_pipeline = Pipeline([\n","    (\"scaler\", StandardScaler()),\n","    (\"classifier\", RandomForestClassifier())\n","])\n","\n","minmax_scaler_pipeline = Pipeline([\n","    (\"scaler\", MinMaxScaler()),\n","    (\"classifier\", RandomForestClassifier())\n","])\n","\n","normalizer_pipeline = Pipeline([\n","    (\"scaler\", Normalizer()),\n","    (\"classifier\", RandomForestClassifier())\n","])"],"metadata":{"id":"4slOx8-vojJv","executionInfo":{"status":"ok","timestamp":1678668397514,"user_tz":-660,"elapsed":7,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":64,"outputs":[]},{"cell_type":"code","source":["n = 1"],"metadata":{"id":"K2fOYkv3swyH","executionInfo":{"status":"ok","timestamp":1678668397514,"user_tz":-660,"elapsed":7,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":65,"outputs":[]},{"cell_type":"code","source":["# standard scaler baseline model\n","accs = []\n","for i in range(n):\n","  standard_scaler_model = standard_scaler_pipeline.fit(X_train, y_train)\n","  y_pred = standard_scaler_model.predict(X_test)\n","  accs.append(accuracy_score(y_test, y_pred))\n","sum(accs) / len(accs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RN_7-xzsojGU","executionInfo":{"status":"ok","timestamp":1678668397514,"user_tz":-660,"elapsed":7,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"0762c252-d397-49df-9dce-5830af0ebb7a"},"execution_count":66,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8491902834008097"]},"metadata":{},"execution_count":66}]},{"cell_type":"code","source":["# minmax scaler baseline model\n","accs = []\n","for i in range(n):\n","  minmax_scaler_model = minmax_scaler_pipeline.fit(X_train, y_train)\n","  y_pred = minmax_scaler_model.predict(X_test)\n","  accs.append(accuracy_score(y_test, y_pred))\n","sum(accs) / len(accs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b_eTHGY8oi7d","executionInfo":{"status":"ok","timestamp":1678668398173,"user_tz":-660,"elapsed":369,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"0461ca49-6bb6-4933-8ae0-89bdfe965a3a"},"execution_count":67,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8421052631578947"]},"metadata":{},"execution_count":67}]},{"cell_type":"code","source":["# normalizer baseline model\n","accs = []\n","for i in range(n):\n","  normalizer_model = normalizer_pipeline.fit(X_train, y_train)\n","  y_pred = normalizer_model.predict(X_test)\n","  accs.append(accuracy_score(y_test, y_pred))\n","sum(accs) / len(accs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2Q1k1HaCpq2W","executionInfo":{"status":"ok","timestamp":1678668399666,"user_tz":-660,"elapsed":1496,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"5a2e26e3-6154-4ab3-e85a-f6ff433ab623"},"execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8248987854251012"]},"metadata":{},"execution_count":68}]},{"cell_type":"markdown","source":["All 3 scalers seem to be producing more or less the same results."],"metadata":{"id":"hhI0D4R3sCBs"}},{"cell_type":"code","source":["scaler = StandardScaler()\n","X_train = scaler.fit_transform(X_train)\n","X_test = scaler.fit_transform(X_test)"],"metadata":{"id":"62kX6sw9tqFx","executionInfo":{"status":"ok","timestamp":1678668399666,"user_tz":-660,"elapsed":17,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":69,"outputs":[]},{"cell_type":"code","source":["X_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Dqw0ZCYPtzkb","executionInfo":{"status":"ok","timestamp":1678668399666,"user_tz":-660,"elapsed":16,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"42a22889-4406-4919-fec8-c7ca07be574c"},"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4612, 40)"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["y_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VhfYKSMdt2J6","executionInfo":{"status":"ok","timestamp":1678668399667,"user_tz":-660,"elapsed":14,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"7b4893ef-a22a-4c29-bb1e-1150f45fe974"},"execution_count":71,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4612,)"]},"metadata":{},"execution_count":71}]},{"cell_type":"code","source":["X_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c49pRXXht2Cv","executionInfo":{"status":"ok","timestamp":1678668399667,"user_tz":-660,"elapsed":11,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"38a137ad-067e-4763-a539-97ce9501a879"},"execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988, 40)"]},"metadata":{},"execution_count":72}]},{"cell_type":"code","source":["y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NGOG_ApGt1tp","executionInfo":{"status":"ok","timestamp":1678668399667,"user_tz":-660,"elapsed":8,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"26b3f2d5-7e41-4b7c-80b0-157b3773125b"},"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(988,)"]},"metadata":{},"execution_count":73}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"gAu4mHXKSRt1"}},{"cell_type":"markdown","source":["## Building model"],"metadata":{"id":"An-aaRetSZ5N"}},{"cell_type":"code","source":["X_train[0].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S2DbEThSuIZ4","executionInfo":{"status":"ok","timestamp":1678668399667,"user_tz":-660,"elapsed":6,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"outputId":"9e13db96-ed98-483d-b116-f3d9a075aa17"},"execution_count":74,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(40,)"]},"metadata":{},"execution_count":74}]},{"cell_type":"code","source":["# building the model\n","\n","import tensorflow as tf\n","from tensorflow.keras.layers import Dense, BatchNormalization, Dropout\n","from tensorflow.keras.models import Sequential\n","\n","model = Sequential(layers=[\n","    Dense(1024, activation=\"relu\", input_shape=(40,)),\n","    BatchNormalization(),\n","    Dense(2048, activation=\"relu\"),\n","    BatchNormalization(),\n","    Dropout(0.5),\n","    Dense(2048, activation=\"relu\"),\n","    BatchNormalization(),\n","    Dropout(0.5),\n","    Dense(1, activation=\"sigmoid\")\n","])"],"metadata":{"id":"dl6XCD5CBthB","executionInfo":{"status":"ok","timestamp":1678668400185,"user_tz":-660,"elapsed":521,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":75,"outputs":[]},{"cell_type":"markdown","source":["## Compiling and saving"],"metadata":{"id":"bOIOEP7dSbxj"}},{"cell_type":"code","source":["optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5)\n","model.compile(optimizer=optimizer, loss=\"binary_crossentropy\", metrics=[\"accuracy\"])"],"metadata":{"id":"ZQbX5LV5Ccdo","executionInfo":{"status":"ok","timestamp":1678668400185,"user_tz":-660,"elapsed":7,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":76,"outputs":[]},{"cell_type":"code","source":["checkpointer = tf.keras.callbacks.ModelCheckpoint(\n","    \"Trial 2: model3_weights.hdf5\", \n","    monitor=\"val_loss\", \n","    verbose=1, \n","    save_best_only=True, \n","    save_weights_only=True\n",")\n","\n","earlystopping = tf.keras.callbacks.EarlyStopping(\n","    monitor=\"val_loss\",\n","    patience=50,\n","    verbose=1\n",")"],"metadata":{"id":"zGUniSo7EdHY","executionInfo":{"status":"ok","timestamp":1678668400186,"user_tz":-660,"elapsed":7,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":77,"outputs":[]},{"cell_type":"code","source":["with open(\"Trial 2: model3.json\", \"w\") as json_file:\n","  json_file.write(model.to_json())"],"metadata":{"id":"CTYOksLUWiMN","executionInfo":{"status":"ok","timestamp":1678668400186,"user_tz":-660,"elapsed":7,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":78,"outputs":[]},{"cell_type":"markdown","source":["## Fitting"],"metadata":{"id":"CUmMspIdSeU-"}},{"cell_type":"code","source":["history = model.fit(\n","    X_train, \n","    y_train, \n","    batch_size=16, \n","    validation_data=(X_val, y_val), \n","    verbose=1,\n","    callbacks=[checkpointer, earlystopping],\n","    shuffle=True,\n","    epochs=400\n",")"],"metadata":{"id":"L04teeBvER6O","executionInfo":{"status":"ok","timestamp":1678668522913,"user_tz":-660,"elapsed":122734,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"f1496ac2-d724-4dc0-b5cf-b3939d73731e"},"execution_count":79,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/400\n","280/289 [============================>.] - ETA: 0s - loss: 0.8403 - accuracy: 0.6440\n","Epoch 1: val_loss improved from inf to 384.92499, saving model to Trial 2: model3_weights.hdf5\n","289/289 [==============================] - 6s 10ms/step - loss: 0.8362 - accuracy: 0.6455 - val_loss: 384.9250 - val_accuracy: 0.6896\n","Epoch 2/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.7040 - accuracy: 0.6977\n","Epoch 2: val_loss did not improve from 384.92499\n","289/289 [==============================] - 2s 6ms/step - loss: 0.7050 - accuracy: 0.6973 - val_loss: 726.2357 - val_accuracy: 0.6906\n","Epoch 3/400\n","289/289 [==============================] - ETA: 0s - loss: 0.6504 - accuracy: 0.7244\n","Epoch 3: val_loss improved from 384.92499 to 185.59924, saving model to Trial 2: model3_weights.hdf5\n","289/289 [==============================] - 2s 6ms/step - loss: 0.6504 - accuracy: 0.7244 - val_loss: 185.5992 - val_accuracy: 0.6764\n","Epoch 4/400\n","281/289 [============================>.] - ETA: 0s - loss: 0.6572 - accuracy: 0.7233\n","Epoch 4: val_loss did not improve from 185.59924\n","289/289 [==============================] - 2s 6ms/step - loss: 0.6550 - accuracy: 0.7240 - val_loss: 570.7385 - val_accuracy: 0.6785\n","Epoch 5/400\n","284/289 [============================>.] - ETA: 0s - loss: 0.5912 - accuracy: 0.7504\n","Epoch 5: val_loss did not improve from 185.59924\n","289/289 [==============================] - 2s 7ms/step - loss: 0.5916 - accuracy: 0.7502 - val_loss: 334.7108 - val_accuracy: 0.6512\n","Epoch 6/400\n","289/289 [==============================] - ETA: 0s - loss: 0.5971 - accuracy: 0.7550\n","Epoch 6: val_loss did not improve from 185.59924\n","289/289 [==============================] - 2s 8ms/step - loss: 0.5971 - accuracy: 0.7550 - val_loss: 797.5593 - val_accuracy: 0.6845\n","Epoch 7/400\n","280/289 [============================>.] - ETA: 0s - loss: 0.5693 - accuracy: 0.7536\n","Epoch 7: val_loss did not improve from 185.59924\n","289/289 [==============================] - 2s 6ms/step - loss: 0.5657 - accuracy: 0.7554 - val_loss: 1000.3679 - val_accuracy: 0.6906\n","Epoch 8/400\n","281/289 [============================>.] - ETA: 0s - loss: 0.5396 - accuracy: 0.7762\n","Epoch 8: val_loss did not improve from 185.59924\n","289/289 [==============================] - 2s 6ms/step - loss: 0.5406 - accuracy: 0.7758 - val_loss: 589.7881 - val_accuracy: 0.6279\n","Epoch 9/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.5360 - accuracy: 0.7729\n","Epoch 9: val_loss did not improve from 185.59924\n","289/289 [==============================] - 2s 6ms/step - loss: 0.5357 - accuracy: 0.7728 - val_loss: 692.5145 - val_accuracy: 0.6906\n","Epoch 10/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.5223 - accuracy: 0.7726\n","Epoch 10: val_loss improved from 185.59924 to 72.85638, saving model to Trial 2: model3_weights.hdf5\n","289/289 [==============================] - 2s 7ms/step - loss: 0.5224 - accuracy: 0.7723 - val_loss: 72.8564 - val_accuracy: 0.6279\n","Epoch 11/400\n","286/289 [============================>.] - ETA: 0s - loss: 0.5078 - accuracy: 0.7880\n","Epoch 11: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.5105 - accuracy: 0.7864 - val_loss: 315.5846 - val_accuracy: 0.6795\n","Epoch 12/400\n","285/289 [============================>.] - ETA: 0s - loss: 0.4979 - accuracy: 0.7904\n","Epoch 12: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.4976 - accuracy: 0.7908 - val_loss: 160.6903 - val_accuracy: 0.6754\n","Epoch 13/400\n","288/289 [============================>.] - ETA: 0s - loss: 0.4810 - accuracy: 0.7941\n","Epoch 13: val_loss did not improve from 72.85638\n","289/289 [==============================] - 3s 9ms/step - loss: 0.4810 - accuracy: 0.7940 - val_loss: 284.7992 - val_accuracy: 0.6876\n","Epoch 14/400\n","288/289 [============================>.] - ETA: 0s - loss: 0.4722 - accuracy: 0.7975\n","Epoch 14: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 9ms/step - loss: 0.4726 - accuracy: 0.7975 - val_loss: 1075.2112 - val_accuracy: 0.6906\n","Epoch 15/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.4467 - accuracy: 0.8105\n","Epoch 15: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 7ms/step - loss: 0.4470 - accuracy: 0.8107 - val_loss: 878.1001 - val_accuracy: 0.6906\n","Epoch 16/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.4604 - accuracy: 0.8016\n","Epoch 16: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4617 - accuracy: 0.8010 - val_loss: 1055.5642 - val_accuracy: 0.6876\n","Epoch 17/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.4475 - accuracy: 0.8101\n","Epoch 17: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4463 - accuracy: 0.8107 - val_loss: 1255.7695 - val_accuracy: 0.6906\n","Epoch 18/400\n","284/289 [============================>.] - ETA: 0s - loss: 0.4350 - accuracy: 0.8160\n","Epoch 18: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4358 - accuracy: 0.8159 - val_loss: 1013.4978 - val_accuracy: 0.6906\n","Epoch 19/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.4256 - accuracy: 0.8190\n","Epoch 19: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 7ms/step - loss: 0.4262 - accuracy: 0.8183 - val_loss: 264.7006 - val_accuracy: 0.6987\n","Epoch 20/400\n","283/289 [============================>.] - ETA: 0s - loss: 0.4239 - accuracy: 0.8174\n","Epoch 20: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.4233 - accuracy: 0.8185 - val_loss: 955.7911 - val_accuracy: 0.6906\n","Epoch 21/400\n","289/289 [==============================] - ETA: 0s - loss: 0.4212 - accuracy: 0.8179\n","Epoch 21: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4212 - accuracy: 0.8179 - val_loss: 954.2372 - val_accuracy: 0.6906\n","Epoch 22/400\n","289/289 [==============================] - ETA: 0s - loss: 0.4275 - accuracy: 0.8179\n","Epoch 22: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4275 - accuracy: 0.8179 - val_loss: 1067.0852 - val_accuracy: 0.6906\n","Epoch 23/400\n","280/289 [============================>.] - ETA: 0s - loss: 0.4175 - accuracy: 0.8292\n","Epoch 23: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4212 - accuracy: 0.8265 - val_loss: 1519.5449 - val_accuracy: 0.6906\n","Epoch 24/400\n","285/289 [============================>.] - ETA: 0s - loss: 0.4055 - accuracy: 0.8331\n","Epoch 24: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4058 - accuracy: 0.8326 - val_loss: 1572.0234 - val_accuracy: 0.6906\n","Epoch 25/400\n","289/289 [==============================] - ETA: 0s - loss: 0.4002 - accuracy: 0.8341\n","Epoch 25: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.4002 - accuracy: 0.8341 - val_loss: 1231.2666 - val_accuracy: 0.6906\n","Epoch 26/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.3993 - accuracy: 0.8328\n","Epoch 26: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 7ms/step - loss: 0.3990 - accuracy: 0.8326 - val_loss: 879.8669 - val_accuracy: 0.6906\n","Epoch 27/400\n","284/289 [============================>.] - ETA: 0s - loss: 0.3815 - accuracy: 0.8380\n","Epoch 27: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.3830 - accuracy: 0.8380 - val_loss: 795.5658 - val_accuracy: 0.6906\n","Epoch 28/400\n","285/289 [============================>.] - ETA: 0s - loss: 0.3788 - accuracy: 0.8406\n","Epoch 28: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3805 - accuracy: 0.8395 - val_loss: 1175.3848 - val_accuracy: 0.6906\n","Epoch 29/400\n","288/289 [============================>.] - ETA: 0s - loss: 0.3944 - accuracy: 0.8314\n","Epoch 29: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3941 - accuracy: 0.8315 - val_loss: 866.8322 - val_accuracy: 0.6906\n","Epoch 30/400\n","280/289 [============================>.] - ETA: 0s - loss: 0.3835 - accuracy: 0.8384\n","Epoch 30: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3842 - accuracy: 0.8385 - val_loss: 1402.8370 - val_accuracy: 0.6906\n","Epoch 31/400\n","288/289 [============================>.] - ETA: 0s - loss: 0.3717 - accuracy: 0.8429\n","Epoch 31: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3715 - accuracy: 0.8430 - val_loss: 820.8361 - val_accuracy: 0.6906\n","Epoch 32/400\n","285/289 [============================>.] - ETA: 0s - loss: 0.3580 - accuracy: 0.8452\n","Epoch 32: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.3577 - accuracy: 0.8450 - val_loss: 1319.5978 - val_accuracy: 0.6906\n","Epoch 33/400\n","286/289 [============================>.] - ETA: 0s - loss: 0.3751 - accuracy: 0.8376\n","Epoch 33: val_loss did not improve from 72.85638\n","289/289 [==============================] - 3s 9ms/step - loss: 0.3774 - accuracy: 0.8372 - val_loss: 1952.7117 - val_accuracy: 0.6906\n","Epoch 34/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.3558 - accuracy: 0.8484\n","Epoch 34: val_loss did not improve from 72.85638\n","289/289 [==============================] - 3s 9ms/step - loss: 0.3560 - accuracy: 0.8484 - val_loss: 1795.9301 - val_accuracy: 0.6906\n","Epoch 35/400\n","282/289 [============================>.] - ETA: 0s - loss: 0.3662 - accuracy: 0.8424\n","Epoch 35: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3667 - accuracy: 0.8422 - val_loss: 1874.7821 - val_accuracy: 0.6906\n","Epoch 36/400\n","283/289 [============================>.] - ETA: 0s - loss: 0.3553 - accuracy: 0.8474\n","Epoch 36: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3538 - accuracy: 0.8476 - val_loss: 1317.7722 - val_accuracy: 0.6906\n","Epoch 37/400\n","286/289 [============================>.] - ETA: 0s - loss: 0.3550 - accuracy: 0.8468\n","Epoch 37: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3549 - accuracy: 0.8469 - val_loss: 1495.4994 - val_accuracy: 0.6906\n","Epoch 38/400\n","284/289 [============================>.] - ETA: 0s - loss: 0.3566 - accuracy: 0.8451\n","Epoch 38: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3562 - accuracy: 0.8454 - val_loss: 2098.6582 - val_accuracy: 0.6906\n","Epoch 39/400\n","288/289 [============================>.] - ETA: 0s - loss: 0.3360 - accuracy: 0.8596\n","Epoch 39: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3357 - accuracy: 0.8597 - val_loss: 1372.0444 - val_accuracy: 0.6906\n","Epoch 40/400\n","286/289 [============================>.] - ETA: 0s - loss: 0.3446 - accuracy: 0.8560\n","Epoch 40: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 7ms/step - loss: 0.3450 - accuracy: 0.8556 - val_loss: 1853.3318 - val_accuracy: 0.6906\n","Epoch 41/400\n","287/289 [============================>.] - ETA: 0s - loss: 0.3445 - accuracy: 0.8506\n","Epoch 41: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.3447 - accuracy: 0.8502 - val_loss: 1801.4949 - val_accuracy: 0.6906\n","Epoch 42/400\n","283/289 [============================>.] - ETA: 0s - loss: 0.3431 - accuracy: 0.8564\n","Epoch 42: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3423 - accuracy: 0.8569 - val_loss: 2245.1848 - val_accuracy: 0.6906\n","Epoch 43/400\n","284/289 [============================>.] - ETA: 0s - loss: 0.3311 - accuracy: 0.8596\n","Epoch 43: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3317 - accuracy: 0.8584 - val_loss: 1923.4713 - val_accuracy: 0.6906\n","Epoch 44/400\n","283/289 [============================>.] - ETA: 0s - loss: 0.3189 - accuracy: 0.8584\n","Epoch 44: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3185 - accuracy: 0.8584 - val_loss: 1824.0198 - val_accuracy: 0.6906\n","Epoch 45/400\n","283/289 [============================>.] - ETA: 0s - loss: 0.3388 - accuracy: 0.8598\n","Epoch 45: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3383 - accuracy: 0.8593 - val_loss: 1715.2723 - val_accuracy: 0.6906\n","Epoch 46/400\n","280/289 [============================>.] - ETA: 0s - loss: 0.3153 - accuracy: 0.8647\n","Epoch 46: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3132 - accuracy: 0.8658 - val_loss: 830.3947 - val_accuracy: 0.6906\n","Epoch 47/400\n","289/289 [==============================] - ETA: 0s - loss: 0.3265 - accuracy: 0.8539\n","Epoch 47: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 7ms/step - loss: 0.3265 - accuracy: 0.8539 - val_loss: 729.2501 - val_accuracy: 0.6906\n","Epoch 48/400\n","282/289 [============================>.] - ETA: 0s - loss: 0.3328 - accuracy: 0.8575\n","Epoch 48: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.3314 - accuracy: 0.8584 - val_loss: 1461.4534 - val_accuracy: 0.6906\n","Epoch 49/400\n","288/289 [============================>.] - ETA: 0s - loss: 0.3088 - accuracy: 0.8672\n","Epoch 49: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 7ms/step - loss: 0.3088 - accuracy: 0.8673 - val_loss: 1036.1553 - val_accuracy: 0.6906\n","Epoch 50/400\n","285/289 [============================>.] - ETA: 0s - loss: 0.3156 - accuracy: 0.8625\n","Epoch 50: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3158 - accuracy: 0.8623 - val_loss: 619.8160 - val_accuracy: 0.6906\n","Epoch 51/400\n","284/289 [============================>.] - ETA: 0s - loss: 0.3162 - accuracy: 0.8649\n","Epoch 51: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.3143 - accuracy: 0.8662 - val_loss: 1068.9266 - val_accuracy: 0.6906\n","Epoch 52/400\n","280/289 [============================>.] - ETA: 0s - loss: 0.3141 - accuracy: 0.8612\n","Epoch 52: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 7ms/step - loss: 0.3155 - accuracy: 0.8610 - val_loss: 1472.3698 - val_accuracy: 0.6906\n","Epoch 53/400\n","281/289 [============================>.] - ETA: 0s - loss: 0.3073 - accuracy: 0.8683\n","Epoch 53: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3100 - accuracy: 0.8675 - val_loss: 2079.3003 - val_accuracy: 0.6906\n","Epoch 54/400\n","285/289 [============================>.] - ETA: 0s - loss: 0.3078 - accuracy: 0.8724\n","Epoch 54: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.3067 - accuracy: 0.8725 - val_loss: 1982.9159 - val_accuracy: 0.6906\n","Epoch 55/400\n","285/289 [============================>.] - ETA: 0s - loss: 0.2881 - accuracy: 0.8763\n","Epoch 55: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 8ms/step - loss: 0.2891 - accuracy: 0.8760 - val_loss: 1504.8142 - val_accuracy: 0.6906\n","Epoch 56/400\n","281/289 [============================>.] - ETA: 0s - loss: 0.2976 - accuracy: 0.8781\n","Epoch 56: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3010 - accuracy: 0.8771 - val_loss: 1699.8533 - val_accuracy: 0.6906\n","Epoch 57/400\n","282/289 [============================>.] - ETA: 0s - loss: 0.2825 - accuracy: 0.8805\n","Epoch 57: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.2857 - accuracy: 0.8794 - val_loss: 2165.4121 - val_accuracy: 0.6906\n","Epoch 58/400\n","288/289 [============================>.] - ETA: 0s - loss: 0.2981 - accuracy: 0.8761\n","Epoch 58: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.2985 - accuracy: 0.8760 - val_loss: 1642.7073 - val_accuracy: 0.6906\n","Epoch 59/400\n","286/289 [============================>.] - ETA: 0s - loss: 0.3022 - accuracy: 0.8739\n","Epoch 59: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3015 - accuracy: 0.8738 - val_loss: 2240.8315 - val_accuracy: 0.6906\n","Epoch 60/400\n","289/289 [==============================] - ETA: 0s - loss: 0.3014 - accuracy: 0.8727\n","Epoch 60: val_loss did not improve from 72.85638\n","289/289 [==============================] - 2s 6ms/step - loss: 0.3014 - accuracy: 0.8727 - val_loss: 1835.7130 - val_accuracy: 0.6906\n","Epoch 60: early stopping\n"]}]},{"cell_type":"markdown","source":["# Analysing"],"metadata":{"id":"92kqgVABSWbv"}},{"cell_type":"code","source":["training_acc = history.history[\"accuracy\"]\n","training_loss = history.history[\"loss\"]\n","val_acc = history.history[\"val_accuracy\"]\n","val_loss = history.history[\"val_loss\"]"],"metadata":{"id":"7mOdK2hWHIOS","executionInfo":{"status":"ok","timestamp":1678668522913,"user_tz":-660,"elapsed":7,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":80,"outputs":[]},{"cell_type":"code","source":["plt.figure(figsize=(7, 7))\n","plt.plot(history.epoch, training_acc, label=\"Training Accuracy\")\n","plt.plot(history.epoch, val_acc, label=\"Validation Accuracy\")\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Accuracy\")\n","plt.legend()\n","plt.show()"],"metadata":{"id":"HLHpIe-BHUYI","executionInfo":{"status":"ok","timestamp":1678668523592,"user_tz":-660,"elapsed":685,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":442},"outputId":"9c50a9ae-8eb2-4546-89d5-0c4fa9277aad"},"execution_count":81,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 504x504 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.figure(figsize=(7, 7))\n","plt.plot(history.epoch, training_loss, label=\"Training Loss\")\n","plt.plot(history.epoch, val_loss, label=\"Validation Loss\")\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Loss\")\n","plt.legend()\n","plt.show()"],"metadata":{"id":"wVazbkhFHepg","executionInfo":{"status":"ok","timestamp":1678668523592,"user_tz":-660,"elapsed":15,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":442},"outputId":"d8322189-e282-466a-bb7d-f14e35460cd4"},"execution_count":82,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 504x504 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["y_pred = np.round(model.predict(X_test))"],"metadata":{"id":"i95AQ-0MIN3f","executionInfo":{"status":"ok","timestamp":1678668523592,"user_tz":-660,"elapsed":13,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"7995045a-a051-41f6-ca0e-b22044ee71d7"},"execution_count":83,"outputs":[{"output_type":"stream","name":"stdout","text":["31/31 [==============================] - 0s 3ms/step\n"]}]},{"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":1678668523593,"user_tz":-660,"elapsed":11,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":265},"outputId":"33165601-44ae-4f41-eb91-0388114e4c54"},"execution_count":84,"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":1678668523593,"user_tz":-660,"elapsed":10,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"2affbe81-bf9e-4b0f-8c84-ae74630323af"},"execution_count":85,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8279352226720648"]},"metadata":{},"execution_count":85}]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred))"],"metadata":{"id":"O7wxUCLwKukU","executionInfo":{"status":"ok","timestamp":1678668523979,"user_tz":-660,"elapsed":393,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b051d1b2-2c36-4a46-e79b-dbdcca689674"},"execution_count":86,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","         0.0       0.88      0.88      0.88       712\n","         1.0       0.69      0.69      0.69       276\n","\n","    accuracy                           0.83       988\n","   macro avg       0.79      0.79      0.79       988\n","weighted avg       0.83      0.83      0.83       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":1678668523980,"user_tz":-660,"elapsed":12,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":87,"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":1678668523980,"user_tz":-660,"elapsed":12,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":88,"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":1678668523980,"user_tz":-660,"elapsed":11,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":89,"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":1678668523980,"user_tz":-660,"elapsed":11,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":90,"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":1678668523981,"user_tz":-660,"elapsed":12,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"c6786207-50c2-4925-9319-c08e1ca83b69"},"execution_count":91,"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":1678668523981,"user_tz":-660,"elapsed":11,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}},"colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"a7bc387c-0369-4574-c87f-80cd9f905655"},"execution_count":92,"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":[],"metadata":{"id":"t4aiJw1q6KcU","executionInfo":{"status":"ok","timestamp":1678668523983,"user_tz":-660,"elapsed":12,"user":{"displayName":"Raj Pulapakura","userId":"15990840560051697646"}}},"execution_count":92,"outputs":[]}]}