Upload P2 - Secom Notebook - Mercury.ipynb

P2 - Secom Notebook - Mercury.ipynb

@@ -26,7 +26,7 @@
   },
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-   "execution_count": 97,
+   "execution_count": 117,
    "metadata": {
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@@ -53,7 +53,7 @@
   },
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+   "execution_count": 118,
    "metadata": {
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@@ -64,7 +64,7 @@
      "data": {
       "application/mercury+json": {
        "allow_download": true,
-       "code_uid": "App.0.40.24.1-rand0254d4eb",
+       "code_uid": "App.0.40.24.1-rand92992328",
        "continuous_update": false,
        "description": "Recumpute everything dynamically",
        "full_screen": true,
@@ -96,7 +96,7 @@
   },
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+   "execution_count": 119,
    "metadata": {
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@@ -138,7 +138,7 @@
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+   "execution_count": 120,
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@@ -195,7 +195,7 @@
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+   "execution_count": 121,
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@@ -290,7 +290,7 @@
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+   "execution_count": 122,
    "metadata": {
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@@ -341,7 +341,7 @@
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+   "execution_count": 123,
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@@ -419,7 +419,7 @@
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+   "execution_count": 124,
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@@ -499,7 +499,7 @@
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+   "execution_count": 125,
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@@ -585,7 +585,7 @@
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+   "execution_count": 126,
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@@ -648,7 +648,7 @@
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+   "execution_count": 127,
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@@ -737,7 +737,7 @@
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@@ -825,7 +825,7 @@
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+   "execution_count": 129,
    "metadata": {
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@@ -839,17 +839,17 @@
         "yes",
         "no"
        ],
-       "code_uid": "Select.0.40.16.25-rand2694baac",
+       "code_uid": "Select.0.40.16.25-rand7e848899",
        "disabled": false,
        "hidden": false,
        "label": "Drop Duplicates",
-       "model_id": "d2be0f72c8ad4556970977c13a04a1c8",
+       "model_id": "78db72d25e074b869614de47137d0448",
        "url_key": "",
        "value": "yes",
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-       "model_id": "d2be0f72c8ad4556970977c13a04a1c8",
+       "model_id": "78db72d25e074b869614de47137d0448",
        "version_major": 2,
        "version_minor": 0
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@@ -863,18 +863,18 @@
     {
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+       "code_uid": "Text.0.40.15.28-rand8e5732e8",
        "disabled": false,
        "hidden": false,
        "label": "Missing Value Threeshold",
-       "model_id": "80b2d08ffcd84257b8bc791ed6e20d5a",
+       "model_id": "f78ef6cc053648c19f15aa01597b534a",
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        "value": "80",
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@@ -888,18 +888,18 @@
     {
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+       "code_uid": "Text.0.40.15.31-rand6f7ca014",
        "disabled": false,
        "hidden": false,
        "label": "Variance Threshold",
-       "model_id": "be3aff94bc9946d283b7b34af9b61b1d",
+       "model_id": "5261497c6c9d48ff98150666a710b79f",
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@@ -913,18 +913,18 @@
     {
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+       "code_uid": "Text.0.40.15.34-rand08bf9f01",
        "disabled": false,
        "hidden": false,
        "label": "Correlation Threshold",
-       "model_id": "20a2b896579147a4a8cf1f8593ca263c",
+       "model_id": "4368fac8a54944ec8869b93c28f79673",
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-       "model_id": "20a2b896579147a4a8cf1f8593ca263c",
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@@ -944,17 +944,17 @@
         4,
         5
        ],
-       "code_uid": "Select.0.40.16.38-randf6cb87b9",
+       "code_uid": "Select.0.40.16.38-rand8c9dc1e9",
        "disabled": false,
        "hidden": false,
        "label": "Outlier Removal Threshold",
-       "model_id": "013f6eeb57534a49abd399f13c4814aa",
+       "model_id": "7a670fc3850143b39f8d41bb867b09c2",
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        "value": "none",
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "013f6eeb57534a49abd399f13c4814aa",
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@@ -975,17 +975,17 @@
         "minmax",
         "robust"
        ],
-       "code_uid": "Select.0.40.16.46-rand035a3a64",
+       "code_uid": "Select.0.40.16.46-rand3225540c",
        "disabled": false,
        "hidden": false,
        "label": "Scaling Variables",
-       "model_id": "4c799a2109f0475dad281094174aff03",
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        "version_minor": 0
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@@ -1005,17 +1005,17 @@
         "knn",
         "most_frequent"
        ],
-       "code_uid": "Select.0.40.16.50-rand1c821039",
+       "code_uid": "Select.0.40.16.50-rand6b935ac8",
        "disabled": false,
        "hidden": false,
        "label": "Imputation Methods",
-       "model_id": "7c5bf031dc55488688a5878edb7cc55f",
+       "model_id": "343d094ce57041bea6fc249e1e6b3fc0",
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        "value": "mean",
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@@ -1036,17 +1036,17 @@
         "pca",
         "boruta"
        ],
-       "code_uid": "Select.0.40.16.55-randf440b52a",
+       "code_uid": "Select.0.40.16.55-rand0bacb10c",
        "disabled": false,
        "hidden": false,
        "label": "Feature Selection",
-       "model_id": "f2b440a394e6473dbcf3ab959393d76f",
+       "model_id": "6cb844c4413442c7af4907d9f0af5a79",
        "url_key": "",
-       "value": "lasso",
+       "value": "none",
        "widget": "Select"
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2b440a394e6473dbcf3ab959393d76f",
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@@ -1066,17 +1066,17 @@
         "undersampling",
         "rose"
        ],
-       "code_uid": "Select.0.40.16.59-randd37e3f6b",
+       "code_uid": "Select.0.40.16.59-randb88939bd",
        "disabled": false,
        "hidden": false,
        "label": "Imbalance Treatment",
-       "model_id": "59021cfbc5d8465ba0dae8da4581ba65",
+       "model_id": "23f135fd27ca4174b4f80b53f9e2878b",
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        "value": "none",
        "widget": "Select"
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "59021cfbc5d8465ba0dae8da4581ba65",
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@@ -1099,17 +1099,17 @@
         "decision_tree",
         "xgboost"
        ],
-       "code_uid": "Select.0.40.16.64-rand7458a327",
+       "code_uid": "Select.0.40.16.64-rand2cb8e572",
        "disabled": false,
        "hidden": false,
        "label": "Model Selection",
-       "model_id": "2e45c8c32a4e44a6b612ad6943d9890e",
+       "model_id": "ac627c0a6ae64f34a97ce1b2f803d50a",
        "url_key": "",
        "value": "random_forest",
        "widget": "Select"
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e45c8c32a4e44a6b612ad6943d9890e",
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@@ -1217,7 +1217,7 @@
   },
   {
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-   "execution_count": 116,
+   "execution_count": 130,
    "metadata": {
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@@ -1243,8 +1243,8 @@
       "Number of missing values after imputation:  0\n",
       "Number of missing values before imputation:  6954\n",
       "Number of missing values after imputation:  0\n",
-      "Selected method is:  lasso\n",
-      "Shape of the training set after feature selection with LassoCV:  (1175, 6)\n"
+      "Selected method is:  none\n",
+      "Shape of the training set after no feature selection:  (1175, 445)\n"
      ]
     }
    ],
@@ -1298,7 +1298,7 @@
   },
   {
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-   "execution_count": null,
+   "execution_count": 131,
    "metadata": {
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@@ -1336,7 +1336,7 @@
   },
   {
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-   "execution_count": null,
+   "execution_count": 132,
    "metadata": {
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@@ -1351,20 +1351,121 @@
       "Missing values threshold is: 80  - Variance threshold is:, 0.0  - Correlation threshold is: 1.0\n",
       "Outlier removal threshold is: none\n",
       "Scaling method is: none\n",
-      "Imputation method is: mean\n"
+      "Imputation method is: mean\n",
+      "Feature selection method is: none\n",
+      "Imbalance treatment method is: none\n"
      ]
     },
     {
-     "ename": "NameError",
-     "evalue": "name 'feature_selection_var' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_34908\\804542050.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mevaluation_score_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluation_counts_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mevaluate_models\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_model\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# check if the model has already been evaluated and if not, append the results to the dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mevaluation_score_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mevaluation_score_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluation_score_df\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_34908\\293505729.py\u001b[0m in \u001b[0;36mevaluate_models\u001b[1;34m(model)\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Scaling method is:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_model_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Imputation method is:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimputation_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Feature selection method is:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeature_selection_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Imbalance treatment method is:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimbalance_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'feature_selection_var' is not defined"
-     ]
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+       "      <td>0.93</td>\n",
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+       "           Model  Accuracy  Precision  Recall  F1-score\n",
+       "0  random_forest      0.93        0.0     0.0       0.0"
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+       "           Model  True Negatives  False Positives  False Negatives  \\\n",
+       "0  random_forest             366                0               26   \n",
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+       "   True Positives  \n",
+       "0               0  "
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",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [