ml_a.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"ml_a.ipynb","version":"0.3.2","provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"metadata":{"id":"9l7HD94rODqX","colab_type":"code","colab":{}},"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","from google.colab import drive"],"execution_count":0,"outputs":[]},{"metadata":{"id":"30qfWl1uOGVv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"d7a4288d-514e-4025-e52a-fd5809eba6bf","executionInfo":{"status":"ok","timestamp":1546097185883,"user_tz":-330,"elapsed":685,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}}},"cell_type":"code","source":["drive.mount('/content/drive')"],"execution_count":99,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"metadata":{"id":"uXcnDs55OhzG","colab_type":"code","colab":{}},"cell_type":"code","source":["file_path = '/content/drive/My Drive/YouTube/Presentations/Accuracy_Python/data.csv'"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Ps5StOSZOzc3","colab_type":"code","outputId":"2515eb83-0771-47fe-9241-93dfd612af44","executionInfo":{"status":"ok","timestamp":1546097189104,"user_tz":-330,"elapsed":797,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":202}},"cell_type":"code","source":["df = pd.read_csv(file_path)\n","df.head()\n"],"execution_count":101,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>y_act</th>\n","      <th>y_pred_random_forest</th>\n","      <th>y_pred_logistic</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.639816</td>\n","      <td>0.531904</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>0.490993</td>\n","      <td>0.414496</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>0.623815</td>\n","      <td>0.569883</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>0.506616</td>\n","      <td>0.443674</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>0.418302</td>\n","      <td>0.369532</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   y_act  y_pred_random_forest  y_pred_logistic\n","0      1              0.639816         0.531904\n","1      0              0.490993         0.414496\n","2      1              0.623815         0.569883\n","3      1              0.506616         0.443674\n","4      0              0.418302         0.369532"]},"metadata":{"tags":[]},"execution_count":101}]},{"metadata":{"id":"djBcS1sPO2hP","colab_type":"code","outputId":"8807e51b-eefd-456d-9e24-5c87c6603716","executionInfo":{"status":"ok","timestamp":1546097229854,"user_tz":-330,"elapsed":1144,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":202}},"cell_type":"code","source":["thresh = 0.5\n","df['y_pred_rf'] = (df.y_pred_random_forest >= 0.5).astype('int')\n","df['y_pred_lr'] = (df.y_pred_logistic >= 0.5).astype('int')\n","df.head()"],"execution_count":102,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>y_act</th>\n","      <th>y_pred_random_forest</th>\n","      <th>y_pred_logistic</th>\n","      <th>y_pred_rf</th>\n","      <th>y_pred_lr</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.639816</td>\n","      <td>0.531904</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>0.490993</td>\n","      <td>0.414496</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>0.623815</td>\n","      <td>0.569883</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>0.506616</td>\n","      <td>0.443674</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>0.418302</td>\n","      <td>0.369532</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   y_act  y_pred_random_forest  y_pred_logistic  y_pred_rf  y_pred_lr\n","0      1              0.639816         0.531904          1          1\n","1      0              0.490993         0.414496          0          0\n","2      1              0.623815         0.569883          1          1\n","3      1              0.506616         0.443674          1          0\n","4      0              0.418302         0.369532          0          0"]},"metadata":{"tags":[]},"execution_count":102}]},{"metadata":{"id":"MyCUFrItPlPO","colab_type":"code","outputId":"af2a1839-e29b-4522-815f-00caea24855e","executionInfo":{"status":"ok","timestamp":1546097251348,"user_tz":-330,"elapsed":4338,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"cell_type":"code","source":["df.shape"],"execution_count":103,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(15758, 5)"]},"metadata":{"tags":[]},"execution_count":103}]},{"metadata":{"id":"odU7dUxRPJ0l","colab_type":"code","colab":{}},"cell_type":"code","source":["def compute_tp_tn_fn_fp(y_act, y_pred):\n","\t'''\n","\tTrue positive - actual = 1, predicted = 1\n","\tFalse positive - actual = 1, predicted = 0\n","\tFalse negative - actual = 0, predicted = 1\n","\tTrue negative - actual = 0, predicted = 0\n","\t'''\n","\ttp = sum((y_act == 1) & (y_pred == 1))\n","\ttn = sum((y_act == 0) & (y_pred == 0))\n","\tfn = sum((y_act == 1) & (y_pred == 0))\n","\tfp = sum((y_act == 0) & (y_pred == 1))\n","\treturn tp, tn, fp, fn"],"execution_count":0,"outputs":[]},{"metadata":{"id":"NUr_BhRdPScf","colab_type":"code","outputId":"5786a607-cdad-46c1-9d32-83763207535e","executionInfo":{"status":"ok","timestamp":1546097336659,"user_tz":-330,"elapsed":1306,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":87}},"cell_type":"code","source":["tp_lr, tn_lr, fp_lr, fn_lr = compute_tp_tn_fn_fp(df.y_act, df.y_pred_lr)\n","print('TP for Logistic Reg :', tp_lr)\n","print('TN for Logistic Reg :', tn_lr)\n","print('FP for Logistic Reg :', fp_lr)\n","print('FN for Logistic Reg :', fn_lr)"],"execution_count":105,"outputs":[{"output_type":"stream","text":["TP for Logistic Reg : 4279\n","TN for Logistic Reg : 5425\n","FP for Logistic Reg : 2454\n","FN for Logistic Reg : 3600\n"],"name":"stdout"}]},{"metadata":{"id":"I_g7lSw0QXSW","colab_type":"code","outputId":"84247f47-1030-4d33-e6ca-2659ef24ae29","executionInfo":{"status":"ok","timestamp":1546097353474,"user_tz":-330,"elapsed":1046,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":87}},"cell_type":"code","source":["tp_rf, tn_rf, fp_rf, fn_rf = compute_tp_tn_fn_fp(df.y_act, df.y_pred_rf)\n","print('TP for Random Forest :', tp_rf)\n","print('TN for Random Forest :', tn_rf)\n","print('FP for Random Forest :', fp_rf)\n","print('FN for Random Forest :', fn_rf)"],"execution_count":106,"outputs":[{"output_type":"stream","text":["TP for Random Forest : 5047\n","TN for Random Forest : 5519\n","FP for Random Forest : 2360\n","FN for Random Forest : 2832\n"],"name":"stdout"}]},{"metadata":{"id":"bgZP9T4CZ1Li","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","tn_rf1, fp_rf1, fn_rf1, tp_rf1 = confusion_matrix(df.y_act, df.y_pred_rf).ravel()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_rtIci_rZ9J7","colab_type":"code","outputId":"da53b54c-4deb-4ea6-cfb4-2c29322b66d0","executionInfo":{"status":"ok","timestamp":1546097398708,"user_tz":-330,"elapsed":1256,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":87}},"cell_type":"code","source":["print('TP for Random Forest :', tp_rf1)\n","print('TN for Random Forest :', tn_rf1)\n","print('FP for Random Forest :', fp_rf1)\n","print('FN for Random Forest :', fn_rf1)"],"execution_count":108,"outputs":[{"output_type":"stream","text":["TP for Random Forest : 5047\n","TN for Random Forest : 5519\n","FP for Random Forest : 2360\n","FN for Random Forest : 2832\n"],"name":"stdout"}]},{"metadata":{"id":"G8RHrb5kQ_I_","colab_type":"code","colab":{}},"cell_type":"code","source":["def compute_accuracy(tp, tn, fn, fp):\n","\t'''\n","\tAccuracy = TP + TN / FP + FN + TP + TN\n","\n","\t'''\n","\treturn ((tp + tn) * 100)/ float( tp + tn + fn + fp)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"NR5DT6tHRvc5","colab_type":"code","outputId":"c0855715-33c0-43e6-c1ba-d009cba45489","executionInfo":{"status":"ok","timestamp":1546097455545,"user_tz":-330,"elapsed":1145,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"cell_type":"code","source":["print('Accuracy for Logistic Regression :', compute_accuracy(tp_lr, tn_lr, fn_lr, fp_lr))\n","print('Accuracy for Random Forest :', compute_accuracy(tp_rf, tn_rf, fn_rf, fp_rf))"],"execution_count":110,"outputs":[{"output_type":"stream","text":["Accuracy for Logistic Regression : 61.58141896179718\n","Accuracy for Random Forest : 67.05165630156111\n"],"name":"stdout"}]},{"metadata":{"id":"Oa_Yf9boSN3M","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.metrics import accuracy_score"],"execution_count":0,"outputs":[]},{"metadata":{"id":"SUiyyqq-SWer","colab_type":"code","outputId":"805d8380-281f-40fa-9f8a-58e4eb0903df","executionInfo":{"status":"ok","timestamp":1546097475232,"user_tz":-330,"elapsed":961,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"cell_type":"code","source":["print('Accuracy for Logistic Regression :', 100* accuracy_score(df.y_act, \n","                                                           df.y_pred_lr))\n","print('Accuracy for Logistic Regression :', 100*accuracy_score(df.y_act, \n","                                                           df.y_pred_rf))"],"execution_count":112,"outputs":[{"output_type":"stream","text":["Accuracy for Logistic Regression : 61.58141896179718\n","Accuracy for Logistic Regression : 67.05165630156111\n"],"name":"stdout"}]},{"metadata":{"id":"Lirv9_L0SZ85","colab_type":"code","colab":{}},"cell_type":"code","source":["def compute_precision(tp, fp):\n","\t'''\n","\tPrecision = TP  / FP + TP \n","\n","\t'''\n","\treturn (tp  * 100)/ float( tp + fp)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"fXBCPFF9XVqR","colab_type":"code","outputId":"aa0e468f-11d1-4288-bd3d-4b0a45a79b3c","executionInfo":{"status":"ok","timestamp":1546097528255,"user_tz":-330,"elapsed":1166,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"cell_type":"code","source":["print('Precision for Logistic Regression :', compute_precision(tp_lr, fp_lr))\n","print('Precision for Random Forest :', compute_precision(tp_rf, fp_rf))"],"execution_count":114,"outputs":[{"output_type":"stream","text":["Precision for Logistic Regression : 63.55265112134264\n","Precision for Random Forest : 68.1382476036182\n"],"name":"stdout"}]},{"metadata":{"id":"bKvrMNKSYpWW","colab_type":"code","outputId":"dfaffbec-eacb-4ea7-d56d-0290e086b16b","executionInfo":{"status":"ok","timestamp":1546097560013,"user_tz":-330,"elapsed":1117,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"cell_type":"code","source":["from sklearn.metrics import precision_score\n","print('Precision for Logistic Regression :', 100* precision_score(df.y_act, \n","                                                           df.y_pred_lr))\n","print('Precision for Random Forest :', 100* precision_score(df.y_act, \n","                                                           df.y_pred_rf))"],"execution_count":115,"outputs":[{"output_type":"stream","text":["Precision for Logistic Regression : 63.55265112134264\n","Precision for Random Forest : 68.1382476036182\n"],"name":"stdout"}]},{"metadata":{"id":"ZsrGqhaMY2j9","colab_type":"code","colab":{}},"cell_type":"code","source":["def compute_recall(tp, fn):\n","\t'''\n","\tRecall = TP /FN + TP \n","\n","\t'''\n","\treturn (tp  * 100)/ float( tp + fn)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"t9nsMZwBbFqF","colab_type":"code","outputId":"f154eb19-edb0-4df5-b58f-92dfaf831367","executionInfo":{"status":"ok","timestamp":1546097592003,"user_tz":-330,"elapsed":694,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"cell_type":"code","source":["print('Recall for Logistic Regression :', compute_recall(tp_lr, fn_lr))\n","print('Recall for Random Forest :', compute_recall(tp_rf, fn_rf))"],"execution_count":117,"outputs":[{"output_type":"stream","text":["Recall for Logistic Regression : 54.30892245208783\n","Recall for Random Forest : 64.05635232897576\n"],"name":"stdout"}]},{"metadata":{"id":"hctMfFOCa-OX","colab_type":"code","outputId":"956ef858-a360-45c4-9b8c-efa68f0d8b42","executionInfo":{"status":"ok","timestamp":1546097610581,"user_tz":-330,"elapsed":978,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"cell_type":"code","source":["from sklearn.metrics import recall_score\n","\n","print('Recall for Logistic Regression :', 100* recall_score(df.y_act, \n","                                                           df.y_pred_lr))\n","print('Recall for Random Forest :', 100* recall_score(df.y_act, \n","                                                           df.y_pred_rf))"],"execution_count":118,"outputs":[{"output_type":"stream","text":["Recall for Logistic Regression : 54.30892245208783\n","Recall for Random Forest : 64.05635232897576\n"],"name":"stdout"}]},{"metadata":{"id":"oYF1hlSGbiN4","colab_type":"code","colab":{}},"cell_type":"code","source":["def compute_f1_score(y_true, y_pred):\n","    # calculates the F1 score\n","    tp, tn, fp, fn = compute_tp_tn_fn_fp(y_true, y_pred)\n","    precision = compute_precision(tp, fp)/100\n","    recall = compute_recall(tp, fn)/100\n","    f1_score = (2*precision*recall)/ (precision + recall)\n","    return f1_score"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6BQl8guud5c8","colab_type":"code","outputId":"faef0a47-551b-4654-e0ed-6117090999e0","executionInfo":{"status":"ok","timestamp":1546097672645,"user_tz":-330,"elapsed":1107,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"cell_type":"code","source":["print('F1 score for Logistic Regression :', compute_f1_score(df.y_act, \n","                                                             df.y_pred_lr))\n","print('F1 score for Random Forest :', compute_f1_score(df.y_act, \n","                                                             df.y_pred_rf))"],"execution_count":120,"outputs":[{"output_type":"stream","text":["F1 score for Logistic Regression : 0.5856830002737475\n","F1 score for Random Forest : 0.660342797330891\n"],"name":"stdout"}]},{"metadata":{"id":"qBrixsxLeFHq","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"526bd5de-9a67-43eb-e431-bfea7a79ef1f","executionInfo":{"status":"ok","timestamp":1546097707873,"user_tz":-330,"elapsed":1247,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg","userId":"01561702845917398436"}}},"cell_type":"code","source":["from sklearn.metrics import f1_score\n","print('F1 score for Logistic Regression :', f1_score(df.y_act, df.y_pred_lr))\n","print('F1 score for Random Forest :', f1_score(df.y_act, df.y_pred_rf))"],"execution_count":121,"outputs":[{"output_type":"stream","text":["F1 score for Logistic Regression : 0.5856830002737475\n","F1 score for Random Forest : 0.660342797330891\n"],"name":"stdout"}]},{"metadata":{"id":"_ehAwBc4ecS_","colab_type":"code","colab":{}},"cell_type":"code","source":[""],"execution_count":0,"outputs":[]}]}