Created using Colaboratory

Author: sandipanpaul21

08_Logistic_Regression.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "name": "08 Logistic Regression.ipynb",
       "provenance": [],
-      "authorship_tag": "ABX9TyOzdICGm/7utO3jg6uo4fdn",
+      "authorship_tag": "ABX9TyOu3EEwuHsR/3JTtbVCA2NW",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -661,13 +661,14 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 323
+          "height": 341
         },
-        "outputId": "0ec5d4c8-56ff-4b9c-997b-1c61cff2886e"
+        "outputId": "df986f17-81c9-4bf4-e8b6-1e10fc337083"
       },
       "source": [
         "# Model Prediction\n",
         "\n",
+        "print(\"Sample Prediction\")\n",
         "pred = result.predict(X_test)\n",
         "model_prediction = pd.DataFrame(pred.round(2),columns = ['Prediction'])\n",
         "model_prediction['temp'] = 'temp'\n",
@@ -686,15 +687,16 @@
         "print(\"Model 1, F1 Score :\",model_1_fscore)\n",
         "model_1_roc = roc_auc_score(y_test,y_pred)\n",
         "print(\"Model 1, AUC :\",model_1_roc)\n",
-        "print(\"\\nConfusion Matrix Model 1\")\n",
+        "print(\"\\nConfusion Matrix, Model 1\")\n",
         "model_1_cm = confusion_matrix(y_test,y_pred)\n",
         "print(model_1_cm)"
       ],
-      "execution_count": 10,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
+            "Sample Prediction\n",
             "    Prediction  temp  Final_Class\n",
             "23        0.61  temp            1\n",
             "81        0.62  temp            1\n",
@@ -709,7 +711,7 @@
             "Model 1, F1 Score : 0.46\n",
             "Model 1, AUC : 0.5\n",
             "\n",
-            "Confusion Matrix Model 1\n",
+            "Confusion Matrix, Model 1\n",
             "[[ 0 21]\n",
             " [ 0  9]]\n"
           ],