Add 'Implementing the model'

forecasting_logistic_regression.ipynb

@@ -342,7 +342,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/fj/ycln97zn6b1ckstg6ksdmgl80000gp/T/ipykernel_60413/2225886973.py:1: FutureWarning: \n",
+      "/var/folders/fj/ycln97zn6b1ckstg6ksdmgl80000gp/T/ipykernel_62188/2225886973.py:1: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
@@ -1399,7 +1399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1544,6 +1544,76 @@
     "print(rfe.ranking_)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols=['euribor3m', 'job_blue-collar', 'job_housemaid', 'marital_unknown', 'education_illiterate', 'default_no', 'default_unknown', \n",
+    "      'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', \n",
+    "      'month_may', 'month_nov', 'month_oct', \"poutcome_failure\", \"poutcome_success\"] \n",
+    "X=os_data_X[cols]\n",
+    "y=os_data_y['y']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.442547\n",
+      "         Iterations 7\n",
+      "                           Results: Logit\n",
+      "=====================================================================\n",
+      "Model:                Logit             Method:            MLE       \n",
+      "Dependent Variable:   y                 Pseudo R-squared:  0.362     \n",
+      "Date:                 2024-04-06 21:11  AIC:               45298.4477\n",
+      "No. Observations:     51134             BIC:               45475.2918\n",
+      "Df Model:             19                Log-Likelihood:    -22629.   \n",
+      "Df Residuals:         51114             LL-Null:           -35443.   \n",
+      "Converged:            1.0000            LLR p-value:       0.0000    \n",
+      "No. Iterations:       7.0000            Scale:             1.0000    \n",
+      "---------------------------------------------------------------------\n",
+      "                      Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
+      "---------------------------------------------------------------------\n",
+      "euribor3m            -0.9276   0.0104 -89.3986 0.0000 -0.9479 -0.9072\n",
+      "job_blue-collar       0.2944   0.0276  10.6790 0.0000  0.2403  0.3484\n",
+      "job_housemaid         0.3491   0.0719   4.8587 0.0000  0.2083  0.4900\n",
+      "marital_unknown       0.7634   0.2170   3.5179 0.0004  0.3381  1.1887\n",
+      "education_illiterate  1.8983   0.3669   5.1738 0.0000  1.1792  2.6175\n",
+      "default_no            0.7756   0.0479  16.2049 0.0000  0.6818  0.8694\n",
+      "default_unknown       0.8449   0.0455  18.5609 0.0000  0.7556  0.9341\n",
+      "contact_cellular     -0.8155   0.0511 -15.9708 0.0000 -0.9156 -0.7154\n",
+      "contact_telephone     0.3087   0.0498   6.1957 0.0000  0.2110  0.4063\n",
+      "month_apr             1.6227   0.0459  35.3165 0.0000  1.5326  1.7127\n",
+      "month_aug             2.8544   0.0543  52.5638 0.0000  2.7479  2.9608\n",
+      "month_dec             1.8739   0.1338  14.0063 0.0000  1.6117  2.1362\n",
+      "month_jul             3.2465   0.0537  60.4650 0.0000  3.1412  3.3517\n",
+      "month_jun             2.0927   0.0497  42.0806 0.0000  1.9952  2.1902\n",
+      "month_mar             2.6971   0.0833  32.3795 0.0000  2.5338  2.8603\n",
+      "month_may             1.0958   0.0416  26.3248 0.0000  1.0143  1.1774\n",
+      "month_nov             2.7162   0.0544  49.9173 0.0000  2.6096  2.8229\n",
+      "month_oct             2.8007   0.0794  35.2615 0.0000  2.6450  2.9564\n",
+      "poutcome_failure     -0.3545   0.0344 -10.3019 0.0000 -0.4220 -0.2871\n",
+      "poutcome_success      1.2213   0.0649  18.8104 0.0000  1.0941  1.3486\n",
+      "=====================================================================\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "logit_model=sm.Logit(y,X.astype(float))\n",
+    "result=logit_model.fit()\n",
+    "print(result.summary2())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,