From afd8844425dd761b06b78f20aa8ddfa92e5d8753 Mon Sep 17 00:00:00 2001
From: RAGHAV JUNEJA <raghavjuneja2391@gmail.com>
Date: Tue, 12 Mar 2024 15:39:16 +0530
Subject: [PATCH 1/3] Removed the blood sugar error

---
 11_chrun_prediction/churn.ipynb | 290 ++++++++++++++++----------------
 1 file changed, 141 insertions(+), 149 deletions(-)

diff --git a/11_chrun_prediction/churn.ipynb b/11_chrun_prediction/churn.ipynb
index 19001bd..3b4df65 100644
--- a/11_chrun_prediction/churn.ipynb
+++ b/11_chrun_prediction/churn.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -86,124 +86,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>6535</th>\n",
-       "      <td>0520-FDVVT</td>\n",
+       "      <th>2106</th>\n",
+       "      <td>4547-FZJWE</td>\n",
        "      <td>Male</td>\n",
        "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>55</td>\n",
+       "      <td>Yes</td>\n",
        "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>35</td>\n",
+       "      <td>DSL</td>\n",
        "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
        "      <td>No</td>\n",
-       "      <td>Fiber optic</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Yes</td>\n",
        "      <td>No</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
        "      <td>One year</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bank transfer (automatic)</td>\n",
-       "      <td>102.35</td>\n",
-       "      <td>3626.1</td>\n",
-       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Credit card (automatic)</td>\n",
+       "      <td>59.45</td>\n",
+       "      <td>3157</td>\n",
+       "      <td>No</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5527</th>\n",
-       "      <td>5985-BEHZK</td>\n",
-       "      <td>Female</td>\n",
+       "      <th>6451</th>\n",
+       "      <td>4868-AADLV</td>\n",
+       "      <td>Male</td>\n",
        "      <td>1</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>72</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>66</td>\n",
        "      <td>Yes</td>\n",
        "      <td>Yes</td>\n",
        "      <td>Fiber optic</td>\n",
-       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
        "      <td>...</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
        "      <td>Yes</td>\n",
        "      <td>Yes</td>\n",
        "      <td>One year</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Credit card (automatic)</td>\n",
-       "      <td>105.00</td>\n",
-       "      <td>7578.05</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>116.25</td>\n",
+       "      <td>7862.25</td>\n",
        "      <td>No</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>668</th>\n",
-       "      <td>3859-CVCET</td>\n",
-       "      <td>Female</td>\n",
+       "      <th>1717</th>\n",
+       "      <td>0404-AHASP</td>\n",
+       "      <td>Male</td>\n",
        "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
        "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>4</td>\n",
+       "      <td>72</td>\n",
        "      <td>Yes</td>\n",
        "      <td>No</td>\n",
-       "      <td>DSL</td>\n",
        "      <td>No</td>\n",
+       "      <td>No internet service</td>\n",
        "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>Two year</td>\n",
        "      <td>No</td>\n",
+       "      <td>Credit card (automatic)</td>\n",
+       "      <td>19.70</td>\n",
+       "      <td>1421.9</td>\n",
        "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Month-to-month</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Mailed check</td>\n",
-       "      <td>45.65</td>\n",
-       "      <td>191.05</td>\n",
-       "      <td>Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4822</th>\n",
-       "      <td>2664-XJZNO</td>\n",
-       "      <td>Male</td>\n",
+       "      <th>2371</th>\n",
+       "      <td>2712-SYWAY</td>\n",
+       "      <td>Female</td>\n",
        "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
        "      <td>Yes</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>72</td>\n",
-       "      <td>Yes</td>\n",
        "      <td>No</td>\n",
-       "      <td>Fiber optic</td>\n",
-       "      <td>Yes</td>\n",
+       "      <td>No internet service</td>\n",
        "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>Month-to-month</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Two year</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Credit card (automatic)</td>\n",
-       "      <td>104.90</td>\n",
-       "      <td>7559.55</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>25.70</td>\n",
+       "      <td>25.7</td>\n",
        "      <td>No</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6850</th>\n",
-       "      <td>0531-XBKMM</td>\n",
-       "      <td>Male</td>\n",
+       "      <th>1383</th>\n",
+       "      <td>3334-CTHOL</td>\n",
+       "      <td>Female</td>\n",
        "      <td>0</td>\n",
        "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>66</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
        "      <td>Yes</td>\n",
        "      <td>Yes</td>\n",
        "      <td>DSL</td>\n",
-       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
        "      <td>...</td>\n",
        "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>Two year</td>\n",
        "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
        "      <td>Bank transfer (automatic)</td>\n",
-       "      <td>65.70</td>\n",
-       "      <td>4378.9</td>\n",
-       "      <td>No</td>\n",
+       "      <td>49.95</td>\n",
+       "      <td>49.95</td>\n",
+       "      <td>Yes</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -212,44 +212,44 @@
       ],
       "text/plain": [
        "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
-       "6535  0520-FDVVT    Male              0      No         No      35   \n",
-       "5527  5985-BEHZK  Female              1     Yes         No      72   \n",
-       "668   3859-CVCET  Female              0      No         No       4   \n",
-       "4822  2664-XJZNO    Male              0     Yes        Yes      72   \n",
-       "6850  0531-XBKMM    Male              0      No        Yes      66   \n",
+       "2106  4547-FZJWE    Male              0     Yes        Yes      55   \n",
+       "6451  4868-AADLV    Male              1     Yes        Yes      66   \n",
+       "1717  0404-AHASP    Male              0     Yes         No      72   \n",
+       "2371  2712-SYWAY  Female              0      No         No       1   \n",
+       "1383  3334-CTHOL  Female              0      No         No       1   \n",
        "\n",
-       "     PhoneService MultipleLines InternetService OnlineSecurity  ...  \\\n",
-       "6535          Yes            No     Fiber optic             No  ...   \n",
-       "5527          Yes           Yes     Fiber optic             No  ...   \n",
-       "668           Yes            No             DSL             No  ...   \n",
-       "4822          Yes            No     Fiber optic            Yes  ...   \n",
-       "6850          Yes           Yes             DSL            Yes  ...   \n",
+       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...  \\\n",
+       "2106          Yes            No             DSL                  Yes  ...   \n",
+       "6451          Yes           Yes     Fiber optic                  Yes  ...   \n",
+       "1717          Yes            No              No  No internet service  ...   \n",
+       "2371          Yes           Yes              No  No internet service  ...   \n",
+       "1383          Yes           Yes             DSL                   No  ...   \n",
        "\n",
-       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
-       "6535              Yes          No         Yes             Yes        One year   \n",
-       "5527              Yes          No         Yes             Yes        One year   \n",
-       "668                No          No          No              No  Month-to-month   \n",
-       "4822              Yes         Yes         Yes             Yes        Two year   \n",
-       "6850               No         Yes          No              No        Two year   \n",
+       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
+       "2106                   No                   No                   No   \n",
+       "6451                  Yes                  Yes                  Yes   \n",
+       "1717  No internet service  No internet service  No internet service   \n",
+       "2371  No internet service  No internet service  No internet service   \n",
+       "1383                   No                   No                   No   \n",
        "\n",
-       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
-       "6535              Yes  Bank transfer (automatic)         102.35        3626.1   \n",
-       "5527               No    Credit card (automatic)         105.00       7578.05   \n",
-       "668                No               Mailed check          45.65        191.05   \n",
-       "4822              Yes    Credit card (automatic)         104.90       7559.55   \n",
-       "6850               No  Bank transfer (automatic)          65.70        4378.9   \n",
+       "          StreamingMovies        Contract PaperlessBilling  \\\n",
+       "2106                  Yes        One year               No   \n",
+       "6451                  Yes        One year              Yes   \n",
+       "1717  No internet service        Two year               No   \n",
+       "2371  No internet service  Month-to-month              Yes   \n",
+       "1383                   No  Month-to-month              Yes   \n",
        "\n",
-       "     Churn  \n",
-       "6535   Yes  \n",
-       "5527    No  \n",
-       "668    Yes  \n",
-       "4822    No  \n",
-       "6850    No  \n",
+       "                  PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
+       "2106    Credit card (automatic)          59.45          3157    No  \n",
+       "6451           Electronic check         116.25       7862.25    No  \n",
+       "1717    Credit card (automatic)          19.70        1421.9    No  \n",
+       "2371           Electronic check          25.70          25.7    No  \n",
+       "1383  Bank transfer (automatic)          49.95         49.95   Yes  \n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 252,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
@@ -308,7 +308,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 254,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -336,7 +336,7 @@
        "      dtype=object)"
       ]
      },
-     "execution_count": 255,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -354,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -364,13 +364,13 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32mpandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mlib.pyx:2391\u001b[0m, in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[1;34m()\u001b[0m\n",
       "\u001b[1;31mValueError\u001b[0m: Unable to parse string \" \"",
       "\nDuring handling of the above exception, another exception occurred:\n",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-256-06ba430a4ba5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTotalCharges\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\tools\\numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[1;34m(arg, errors, downcast)\u001b[0m\n\u001b[0;32m    150\u001b[0m         \u001b[0mcoerce_numeric\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 152\u001b[1;33m             values = lib.maybe_convert_numeric(\n\u001b[0m\u001b[0;32m    153\u001b[0m                 \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce_numeric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoerce_numeric\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m             )\n",
-      "\u001b[1;32mpandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[1;34m()\u001b[0m\n",
+      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTotalCharges\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Charanjeet Juneja\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\tools\\numeric.py:232\u001b[0m, in \u001b[0;36mto_numeric\u001b[1;34m(arg, errors, downcast, dtype_backend)\u001b[0m\n\u001b[0;32m    230\u001b[0m coerce_numeric \u001b[38;5;241m=\u001b[39m errors \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    231\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 232\u001b[0m     values, new_mask \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaybe_convert_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[call-overload]\u001b[39;49;00m\n\u001b[0;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcoerce_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoerce_numeric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    236\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_to_masked_nullable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype_backend\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mno_default\u001b[49m\n\u001b[0;32m    237\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues_dtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mStringDtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    238\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mvalues_dtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpyarrow_numpy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    239\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    240\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m    241\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+      "File \u001b[1;32mlib.pyx:2433\u001b[0m, in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[1;34m()\u001b[0m\n",
       "\u001b[1;31mValueError\u001b[0m: Unable to parse string \" \" at position 488"
      ]
     }
@@ -388,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -408,7 +408,7 @@
        "Name: TotalCharges, Length: 7043, dtype: bool"
       ]
      },
-     "execution_count": 257,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -419,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -790,7 +790,7 @@
        "6754  Bank transfer (automatic)           61.90                 No  "
       ]
      },
-     "execution_count": 258,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -801,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -810,7 +810,7 @@
        "(7043, 20)"
       ]
      },
-     "execution_count": 259,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -830,7 +830,7 @@
        "' '"
       ]
      },
-     "execution_count": 260,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -841,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -850,7 +850,7 @@
        "(7032, 20)"
       ]
      },
-     "execution_count": 261,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -868,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -877,7 +877,7 @@
        "(7032, 20)"
       ]
      },
-     "execution_count": 262,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -889,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
@@ -920,7 +920,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 263,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -931,19 +931,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\dhava\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\generic.py:5159: SettingWithCopyWarning: \n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\973151263.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  self[name] = value\n"
+      "  df1.TotalCharges = pd.to_numeric(df1.TotalCharges)\n"
      ]
     }
    ],
@@ -953,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 265,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -962,7 +962,7 @@
        "array([  29.85, 1889.5 ,  108.15, ...,  346.45,  306.6 , 6844.5 ])"
       ]
      },
-     "execution_count": 265,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -973,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 266,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
@@ -1362,7 +1362,7 @@
        "[5163 rows x 20 columns]"
       ]
      },
-     "execution_count": 266,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1380,29 +1380,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2181d04b700>"
+       "<matplotlib.legend.Legend at 0x1fdf054fa10>"
       ]
      },
-     "execution_count": 271,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1414,8 +1412,6 @@
     "plt.ylabel(\"Number Of Customers\")\n",
     "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
     "\n",
-    "blood_sugar_men = [113, 85, 90, 150, 149, 88, 93, 115, 135, 80, 77, 82, 129]\n",
-    "blood_sugar_women = [67, 98, 89, 120, 133, 150, 84, 69, 89, 79, 120, 112, 100]\n",
     "\n",
     "plt.hist([tenure_churn_yes, tenure_churn_no], rwidth=0.95, color=['green','red'],label=['Churn=Yes','Churn=No'])\n",
     "plt.legend()"
@@ -1423,29 +1419,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 272,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2181d15fac0>"
+       "<matplotlib.legend.Legend at 0x1fdf04753d0>"
       ]
      },
-     "execution_count": 272,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1457,8 +1451,6 @@
     "plt.ylabel(\"Number Of Customers\")\n",
     "plt.title(\"Customer Churn Prediction Visualiztion\")\n",
     "\n",
-    "blood_sugar_men = [113, 85, 90, 150, 149, 88, 93, 115, 135, 80, 77, 82, 129]\n",
-    "blood_sugar_women = [67, 98, 89, 120, 133, 150, 84, 69, 89, 79, 120, 112, 100]\n",
     "\n",
     "plt.hist([mc_churn_yes, mc_churn_no], rwidth=0.95, color=['green','red'],label=['Churn=Yes','Churn=No'])\n",
     "plt.legend()"
@@ -2970,7 +2962,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x504 with 2 Axes>"
       ]
@@ -3184,7 +3176,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

From 88e74dd3b769eaa654859de6b701e3bd4c899276 Mon Sep 17 00:00:00 2001
From: RAGHAV JUNEJA <raghavjuneja2391@gmail.com>
Date: Tue, 12 Mar 2024 15:42:18 +0530
Subject: [PATCH 2/3] Removed the numpy to tensor error

---
 11_chrun_prediction/churn.ipynb | 946 ++++++++++++++------------------
 1 file changed, 418 insertions(+), 528 deletions(-)

diff --git a/11_chrun_prediction/churn.ipynb b/11_chrun_prediction/churn.ipynb
index 3b4df65..c126d56 100644
--- a/11_chrun_prediction/churn.ipynb
+++ b/11_chrun_prediction/churn.ipynb
@@ -1465,7 +1465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1477,7 +1477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 21,
    "metadata": {
     "scrolled": true
    },
@@ -1519,7 +1519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 22,
    "metadata": {
     "scrolled": false
    },
@@ -1528,11 +1528,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\dhava\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\frame.py:4373: SettingWithCopyWarning: \n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\2045096646.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  return super().replace(\n"
+      "  df1.replace('No internet service','No',inplace=True)\n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\2045096646.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df1.replace('No phone service','No',inplace=True)\n"
      ]
     }
    ],
@@ -1543,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 23,
    "metadata": {
     "scrolled": false
    },
@@ -1585,18 +1590,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\dhava\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\series.py:4563: SettingWithCopyWarning: \n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\1648037665.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  df1[col].replace({'Yes': 1,'No': 0},inplace=True)\n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\1648037665.py:4: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "  df1[col].replace({'Yes': 1,'No': 0},inplace=True)\n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\1648037665.py:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  return super().replace(\n"
+      "  df1[col].replace({'Yes': 1,'No': 0},inplace=True)\n"
      ]
     }
    ],
@@ -1609,7 +1623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1649,16 +1663,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\698335744.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  df1['gender'].replace({'Female':1,'Male':0},inplace=True)\n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\698335744.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "  df1['gender'].replace({'Female':1,'Male':0},inplace=True)\n",
+      "C:\\Users\\Charanjeet Juneja\\AppData\\Local\\Temp\\ipykernel_9132\\698335744.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df1['gender'].replace({'Female':1,'Male':0},inplace=True)\n"
+     ]
+    }
+   ],
    "source": [
     "df1['gender'].replace({'Female':1,'Male':0},inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1667,7 +1702,7 @@
        "array([1, 0], dtype=int64)"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1685,7 +1720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1703,7 +1738,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1715,7 +1750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1764,124 +1799,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>6127</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56</td>\n",
+       "      <th>2531</th>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>71</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1293</th>\n",
+       "      <th>5258</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>67</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>69</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1958</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>1</td>\n",
+       "      <th>5049</th>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5161</th>\n",
+       "      <th>4128</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>57</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3968</th>\n",
-       "      <td>1</td>\n",
+       "      <th>1097</th>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>72</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
+       "      <td>25</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1890,58 +1925,58 @@
       ],
       "text/plain": [
        "      gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
-       "6127       1              0        1           0      56             1   \n",
-       "1293       0              0        1           1      67             1   \n",
-       "1958       0              0        0           0      24             1   \n",
-       "5161       0              0        0           1      23             1   \n",
-       "3968       1              0        1           0      72             1   \n",
+       "2531       1              0        1           0      71             1   \n",
+       "5258       0              0        0           0      69             1   \n",
+       "5049       1              0        0           0       1             1   \n",
+       "4128       0              0        0           0      57             0   \n",
+       "1097       0              0        1           1      25             1   \n",
        "\n",
        "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
-       "6127              1               1             1                 1  ...   \n",
-       "1293              1               0             1                 1  ...   \n",
-       "1958              1               0             1                 1  ...   \n",
-       "5161              0               0             1                 0  ...   \n",
-       "3968              0               0             0                 0  ...   \n",
+       "2531              1               0             0                 0  ...   \n",
+       "5258              1               0             1                 0  ...   \n",
+       "5049              0               0             0                 0  ...   \n",
+       "4128              0               0             1                 0  ...   \n",
+       "1097              0               0             0                 0  ...   \n",
        "\n",
        "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
-       "6127                    0                            1                   0   \n",
-       "1293                    0                            1                   0   \n",
-       "1958                    0                            1                   0   \n",
-       "5161                    1                            0                   0   \n",
-       "3968                    0                            0                   1   \n",
+       "2531                False                        False                True   \n",
+       "5258                False                         True               False   \n",
+       "5049                False                        False                True   \n",
+       "4128                 True                        False               False   \n",
+       "1097                False                         True               False   \n",
        "\n",
        "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
-       "6127                        0                  1                  0   \n",
-       "1293                        0                  0                  1   \n",
-       "1958                        1                  0                  0   \n",
-       "5161                        1                  0                  0   \n",
-       "3968                        0                  0                  1   \n",
+       "2531                    False              False               True   \n",
+       "5258                    False               True              False   \n",
+       "5049                     True              False              False   \n",
+       "4128                     True              False              False   \n",
+       "1097                    False               True              False   \n",
        "\n",
        "      PaymentMethod_Bank transfer (automatic)  \\\n",
-       "6127                                        1   \n",
-       "1293                                        1   \n",
-       "1958                                        1   \n",
-       "5161                                        0   \n",
-       "3968                                        1   \n",
+       "2531                                    False   \n",
+       "5258                                     True   \n",
+       "5049                                     True   \n",
+       "4128                                    False   \n",
+       "1097                                    False   \n",
        "\n",
        "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
-       "6127                                      0                               0   \n",
-       "1293                                      0                               0   \n",
-       "1958                                      0                               0   \n",
-       "5161                                      0                               1   \n",
-       "3968                                      0                               0   \n",
+       "2531                                   True                           False   \n",
+       "5258                                  False                           False   \n",
+       "5049                                  False                           False   \n",
+       "4128                                  False                            True   \n",
+       "1097                                  False                            True   \n",
        "\n",
        "      PaymentMethod_Mailed check  \n",
-       "6127                           0  \n",
-       "1293                           0  \n",
-       "1958                           0  \n",
-       "5161                           0  \n",
-       "3968                           0  \n",
+       "2531                       False  \n",
+       "5258                       False  \n",
+       "5049                       False  \n",
+       "4128                       False  \n",
+       "1097                       False  \n",
        "\n",
        "[5 rows x 27 columns]"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1952,7 +1987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 30,
    "metadata": {
     "scrolled": true
    },
@@ -1977,20 +2012,20 @@
        "MonthlyCharges                             float64\n",
        "TotalCharges                               float64\n",
        "Churn                                        int64\n",
-       "InternetService_DSL                          uint8\n",
-       "InternetService_Fiber optic                  uint8\n",
-       "InternetService_No                           uint8\n",
-       "Contract_Month-to-month                      uint8\n",
-       "Contract_One year                            uint8\n",
-       "Contract_Two year                            uint8\n",
-       "PaymentMethod_Bank transfer (automatic)      uint8\n",
-       "PaymentMethod_Credit card (automatic)        uint8\n",
-       "PaymentMethod_Electronic check               uint8\n",
-       "PaymentMethod_Mailed check                   uint8\n",
+       "InternetService_DSL                           bool\n",
+       "InternetService_Fiber optic                   bool\n",
+       "InternetService_No                            bool\n",
+       "Contract_Month-to-month                       bool\n",
+       "Contract_One year                             bool\n",
+       "Contract_Two year                             bool\n",
+       "PaymentMethod_Bank transfer (automatic)       bool\n",
+       "PaymentMethod_Credit card (automatic)         bool\n",
+       "PaymentMethod_Electronic check                bool\n",
+       "PaymentMethod_Mailed check                    bool\n",
        "dtype: object"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2001,7 +2036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2014,7 +2049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2049,16 +2084,16 @@
       "MonthlyCharges: [0.11542289 0.38507463 0.35422886 ... 0.44626866 0.25820896 0.60149254]\n",
       "TotalCharges: [0.0012751  0.21586661 0.01031041 ... 0.03780868 0.03321025 0.78764136]\n",
       "Churn: [0 1]\n",
-      "InternetService_DSL: [1 0]\n",
-      "InternetService_Fiber optic: [0 1]\n",
-      "InternetService_No: [0 1]\n",
-      "Contract_Month-to-month: [1 0]\n",
-      "Contract_One year: [0 1]\n",
-      "Contract_Two year: [0 1]\n",
-      "PaymentMethod_Bank transfer (automatic): [0 1]\n",
-      "PaymentMethod_Credit card (automatic): [0 1]\n",
-      "PaymentMethod_Electronic check: [1 0]\n",
-      "PaymentMethod_Mailed check: [0 1]\n"
+      "InternetService_DSL: [ True False]\n",
+      "InternetService_Fiber optic: [False  True]\n",
+      "InternetService_No: [False  True]\n",
+      "Contract_Month-to-month: [ True False]\n",
+      "Contract_One year: [False  True]\n",
+      "Contract_Two year: [False  True]\n",
+      "PaymentMethod_Bank transfer (automatic): [False  True]\n",
+      "PaymentMethod_Credit card (automatic): [False  True]\n",
+      "PaymentMethod_Electronic check: [ True False]\n",
+      "PaymentMethod_Mailed check: [False  True]\n"
      ]
     }
    ],
@@ -2076,7 +2111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2089,7 +2124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -2098,7 +2133,7 @@
        "(5625, 26)"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2109,7 +2144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 35,
    "metadata": {
     "scrolled": true
    },
@@ -2120,7 +2155,7 @@
        "(1407, 26)"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2129,9 +2164,14 @@
     "X_test.shape"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2192,16 +2232,16 @@
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>101</th>\n",
@@ -2216,16 +2256,16 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2621</th>\n",
@@ -2240,16 +2280,16 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>392</th>\n",
@@ -2264,16 +2304,16 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1327</th>\n",
@@ -2288,16 +2328,16 @@
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3607</th>\n",
@@ -2312,16 +2352,16 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2773</th>\n",
@@ -2336,16 +2376,16 @@
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1936</th>\n",
@@ -2360,16 +2400,16 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5387</th>\n",
@@ -2384,16 +2424,16 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4331</th>\n",
@@ -2408,16 +2448,16 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2450,69 +2490,69 @@
        "4331              1               0             0                 0  ...   \n",
        "\n",
        "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
-       "5664                    0                            1                   0   \n",
-       "101                     0                            0                   1   \n",
-       "2621                    1                            0                   0   \n",
-       "392                     1                            0                   0   \n",
-       "1327                    0                            1                   0   \n",
-       "3607                    1                            0                   0   \n",
-       "2773                    1                            0                   0   \n",
-       "1936                    1                            0                   0   \n",
-       "5387                    1                            0                   0   \n",
-       "4331                    0                            0                   1   \n",
+       "5664                False                         True               False   \n",
+       "101                 False                        False                True   \n",
+       "2621                 True                        False               False   \n",
+       "392                  True                        False               False   \n",
+       "1327                False                         True               False   \n",
+       "3607                 True                        False               False   \n",
+       "2773                 True                        False               False   \n",
+       "1936                 True                        False               False   \n",
+       "5387                 True                        False               False   \n",
+       "4331                False                        False                True   \n",
        "\n",
        "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
-       "5664                        1                  0                  0   \n",
-       "101                         1                  0                  0   \n",
-       "2621                        0                  0                  1   \n",
-       "392                         1                  0                  0   \n",
-       "1327                        0                  1                  0   \n",
-       "3607                        0                  1                  0   \n",
-       "2773                        1                  0                  0   \n",
-       "1936                        0                  1                  0   \n",
-       "5387                        1                  0                  0   \n",
-       "4331                        0                  0                  1   \n",
+       "5664                     True              False              False   \n",
+       "101                      True              False              False   \n",
+       "2621                    False              False               True   \n",
+       "392                      True              False              False   \n",
+       "1327                    False               True              False   \n",
+       "3607                    False               True              False   \n",
+       "2773                     True              False              False   \n",
+       "1936                    False               True              False   \n",
+       "5387                     True              False              False   \n",
+       "4331                    False              False               True   \n",
        "\n",
        "      PaymentMethod_Bank transfer (automatic)  \\\n",
-       "5664                                        0   \n",
-       "101                                         0   \n",
-       "2621                                        0   \n",
-       "392                                         0   \n",
-       "1327                                        1   \n",
-       "3607                                        0   \n",
-       "2773                                        0   \n",
-       "1936                                        1   \n",
-       "5387                                        0   \n",
-       "4331                                        1   \n",
+       "5664                                    False   \n",
+       "101                                     False   \n",
+       "2621                                    False   \n",
+       "392                                     False   \n",
+       "1327                                     True   \n",
+       "3607                                    False   \n",
+       "2773                                    False   \n",
+       "1936                                     True   \n",
+       "5387                                    False   \n",
+       "4331                                     True   \n",
        "\n",
        "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
-       "5664                                      1                               0   \n",
-       "101                                       0                               1   \n",
-       "2621                                      1                               0   \n",
-       "392                                       0                               1   \n",
-       "1327                                      0                               0   \n",
-       "3607                                      0                               0   \n",
-       "2773                                      0                               1   \n",
-       "1936                                      0                               0   \n",
-       "5387                                      0                               1   \n",
-       "4331                                      0                               0   \n",
+       "5664                                   True                           False   \n",
+       "101                                   False                            True   \n",
+       "2621                                   True                           False   \n",
+       "392                                   False                            True   \n",
+       "1327                                  False                           False   \n",
+       "3607                                  False                           False   \n",
+       "2773                                  False                            True   \n",
+       "1936                                  False                           False   \n",
+       "5387                                  False                            True   \n",
+       "4331                                  False                           False   \n",
        "\n",
        "      PaymentMethod_Mailed check  \n",
-       "5664                           0  \n",
-       "101                            0  \n",
-       "2621                           0  \n",
-       "392                            0  \n",
-       "1327                           0  \n",
-       "3607                           1  \n",
-       "2773                           0  \n",
-       "1936                           0  \n",
-       "5387                           0  \n",
-       "4331                           0  \n",
+       "5664                       False  \n",
+       "101                        False  \n",
+       "2621                       False  \n",
+       "392                        False  \n",
+       "1327                       False  \n",
+       "3607                        True  \n",
+       "2773                       False  \n",
+       "1936                       False  \n",
+       "5387                       False  \n",
+       "4331                       False  \n",
        "\n",
        "[10 rows x 26 columns]"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2523,7 +2563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -2532,7 +2572,7 @@
        "26"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2541,243 +2581,93 @@
     "len(X_train.columns)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Build a model (ANN) in tensorflow/keras**"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4822 - accuracy: 0.7623\n",
-      "Epoch 2/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4269 - accuracy: 0.8000\n",
-      "Epoch 3/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4182 - accuracy: 0.7984\n",
-      "Epoch 4/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4153 - accuracy: 0.8046\n",
-      "Epoch 5/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4127 - accuracy: 0.8078\n",
-      "Epoch 6/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4108 - accuracy: 0.8073\n",
-      "Epoch 7/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4084 - accuracy: 0.8057\n",
-      "Epoch 8/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4070 - accuracy: 0.8108\n",
-      "Epoch 9/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4059 - accuracy: 0.8107\n",
-      "Epoch 10/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4043 - accuracy: 0.8107\n",
-      "Epoch 11/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4037 - accuracy: 0.8110\n",
-      "Epoch 12/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.4020 - accuracy: 0.8114\n",
-      "Epoch 13/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3996 - accuracy: 0.8128\n",
-      "Epoch 14/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3992 - accuracy: 0.8132\n",
-      "Epoch 15/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3982 - accuracy: 0.8119\n",
-      "Epoch 16/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3973 - accuracy: 0.8105\n",
-      "Epoch 17/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3955 - accuracy: 0.8128\n",
-      "Epoch 18/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3939 - accuracy: 0.8126\n",
-      "Epoch 19/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3936 - accuracy: 0.8149\n",
-      "Epoch 20/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3930 - accuracy: 0.8155\n",
-      "Epoch 21/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3920 - accuracy: 0.8151\n",
-      "Epoch 22/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3912 - accuracy: 0.8148\n",
-      "Epoch 23/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3896 - accuracy: 0.8162\n",
-      "Epoch 24/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3897 - accuracy: 0.8162\n",
-      "Epoch 25/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3876 - accuracy: 0.8174\n",
-      "Epoch 26/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3864 - accuracy: 0.8187\n",
-      "Epoch 27/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3864 - accuracy: 0.8172\n",
-      "Epoch 28/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3846 - accuracy: 0.8181\n",
-      "Epoch 29/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3846 - accuracy: 0.8172\n",
-      "Epoch 30/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3834 - accuracy: 0.8187\n",
-      "Epoch 31/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3812 - accuracy: 0.8197\n",
-      "Epoch 32/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3815 - accuracy: 0.8180\n",
-      "Epoch 33/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3811 - accuracy: 0.8199\n",
-      "Epoch 34/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3806 - accuracy: 0.8178\n",
-      "Epoch 35/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3799 - accuracy: 0.8219\n",
-      "Epoch 36/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3787 - accuracy: 0.8185\n",
-      "Epoch 37/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3775 - accuracy: 0.8236\n",
-      "Epoch 38/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3783 - accuracy: 0.8212\n",
-      "Epoch 39/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3769 - accuracy: 0.8229\n",
-      "Epoch 40/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3760 - accuracy: 0.8224\n",
-      "Epoch 41/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3757 - accuracy: 0.8199\n",
-      "Epoch 42/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3749 - accuracy: 0.8260\n",
-      "Epoch 43/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3738 - accuracy: 0.8238\n",
-      "Epoch 44/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3727 - accuracy: 0.8228\n",
-      "Epoch 45/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3725 - accuracy: 0.8242\n",
-      "Epoch 46/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3722 - accuracy: 0.8245\n",
-      "Epoch 47/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3718 - accuracy: 0.8252\n",
-      "Epoch 48/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3716 - accuracy: 0.8244\n",
-      "Epoch 49/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3706 - accuracy: 0.8240\n",
-      "Epoch 50/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3703 - accuracy: 0.8224\n",
-      "Epoch 51/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3682 - accuracy: 0.8279\n",
-      "Epoch 52/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3695 - accuracy: 0.8233\n",
-      "Epoch 53/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3678 - accuracy: 0.8251\n",
-      "Epoch 54/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3671 - accuracy: 0.8261\n",
-      "Epoch 55/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3666 - accuracy: 0.8251\n",
-      "Epoch 56/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3656 - accuracy: 0.8251\n",
-      "Epoch 57/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3650 - accuracy: 0.8263\n",
-      "Epoch 58/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3643 - accuracy: 0.8268\n",
-      "Epoch 59/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3646 - accuracy: 0.8284\n",
-      "Epoch 60/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3641 - accuracy: 0.8252\n",
-      "Epoch 61/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3639 - accuracy: 0.8228\n",
-      "Epoch 62/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3630 - accuracy: 0.8299\n",
-      "Epoch 63/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3617 - accuracy: 0.8277\n",
-      "Epoch 64/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3622 - accuracy: 0.8284\n",
-      "Epoch 65/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3615 - accuracy: 0.8276\n",
-      "Epoch 66/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3623 - accuracy: 0.8263\n",
-      "Epoch 67/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3603 - accuracy: 0.8281\n",
-      "Epoch 68/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3600 - accuracy: 0.8284\n",
-      "Epoch 69/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3602 - accuracy: 0.8293\n",
-      "Epoch 70/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3596 - accuracy: 0.8288\n",
-      "Epoch 71/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3587 - accuracy: 0.8276\n",
-      "Epoch 72/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3585 - accuracy: 0.8290\n",
-      "Epoch 73/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3581 - accuracy: 0.8277\n",
-      "Epoch 74/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3582 - accuracy: 0.8311\n",
-      "Epoch 75/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3573 - accuracy: 0.8272\n",
-      "Epoch 76/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3575 - accuracy: 0.8277\n",
-      "Epoch 77/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3573 - accuracy: 0.8306\n",
-      "Epoch 78/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3564 - accuracy: 0.8288\n",
-      "Epoch 79/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3550 - accuracy: 0.8313\n",
-      "Epoch 80/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3550 - accuracy: 0.8324\n",
-      "Epoch 81/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3548 - accuracy: 0.8284\n",
-      "Epoch 82/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3552 - accuracy: 0.8329\n",
-      "Epoch 83/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3556 - accuracy: 0.8279\n",
-      "Epoch 84/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3534 - accuracy: 0.8331\n",
-      "Epoch 85/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3533 - accuracy: 0.8299\n",
-      "Epoch 86/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3536 - accuracy: 0.8332\n",
-      "Epoch 87/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3536 - accuracy: 0.8325\n",
-      "Epoch 88/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3505 - accuracy: 0.8356\n",
-      "Epoch 89/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3517 - accuracy: 0.8311\n",
-      "Epoch 90/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3513 - accuracy: 0.8313\n",
-      "Epoch 91/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3525 - accuracy: 0.8309\n",
-      "Epoch 92/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3495 - accuracy: 0.8334\n",
-      "Epoch 93/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3506 - accuracy: 0.8270\n",
-      "Epoch 94/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3495 - accuracy: 0.8350\n",
-      "Epoch 95/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3497 - accuracy: 0.8327\n",
-      "Epoch 96/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3500 - accuracy: 0.8338\n",
-      "Epoch 97/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3484 - accuracy: 0.8343\n",
-      "Epoch 98/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3504 - accuracy: 0.8325\n",
-      "Epoch 99/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3490 - accuracy: 0.8325\n",
-      "Epoch 100/100\n",
-      "176/176 [==============================] - 0s 1ms/step - loss: 0.3486 - accuracy: 0.8368\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x21818af0f10>"
+       "gender                                       int64\n",
+       "SeniorCitizen                                int64\n",
+       "Partner                                      int64\n",
+       "Dependents                                   int64\n",
+       "tenure                                     float64\n",
+       "PhoneService                                 int64\n",
+       "MultipleLines                                int64\n",
+       "OnlineSecurity                               int64\n",
+       "OnlineBackup                                 int64\n",
+       "DeviceProtection                             int64\n",
+       "TechSupport                                  int64\n",
+       "StreamingTV                                  int64\n",
+       "StreamingMovies                              int64\n",
+       "PaperlessBilling                             int64\n",
+       "MonthlyCharges                             float64\n",
+       "TotalCharges                               float64\n",
+       "InternetService_DSL                           bool\n",
+       "InternetService_Fiber optic                   bool\n",
+       "InternetService_No                            bool\n",
+       "Contract_Month-to-month                       bool\n",
+       "Contract_One year                             bool\n",
+       "Contract_Two year                             bool\n",
+       "PaymentMethod_Bank transfer (automatic)       bool\n",
+       "PaymentMethod_Credit card (automatic)         bool\n",
+       "PaymentMethod_Electronic check                bool\n",
+       "PaymentMethod_Mailed check                    bool\n",
+       "dtype: object"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "X_train.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CHANGE HERE\n",
+    "# DUE TO THE NEW TF VERSION , instead of uid8 they will be boolean which our model cant recognize\n",
+    "'''\n",
+    "The added code will add a astype notation to maintain a consistent dtype along all x-trains so that model s trained successfuly without error\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train[['InternetService_DSL', 'InternetService_Fiber optic', 'InternetService_No', 'Contract_Month-to-month',\n",
+    "         'Contract_One year', 'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
+    "         'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check']] = \\\n",
+    "    X_train[['InternetService_DSL', 'InternetService_Fiber optic', 'InternetService_No', 'Contract_Month-to-month',\n",
+    "             'Contract_One year', 'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
+    "             'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check']].astype(int)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Build a model (ANN) in tensorflow/keras**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",

From b125c7337f5ca2169be08549820ccc4997736e48 Mon Sep 17 00:00:00 2001
From: RAGHAV JUNEJA <raghavjuneja2391@gmail.com>
Date: Tue, 12 Mar 2024 15:45:52 +0530
Subject: [PATCH 3/3] Removed this error in x_test as well

---
 11_chrun_prediction/churn.ipynb | 383 ++++++++++++++++++++++++++++----
 1 file changed, 341 insertions(+), 42 deletions(-)

diff --git a/11_chrun_prediction/churn.ipynb b/11_chrun_prediction/churn.ipynb
index c126d56..c35a8f5 100644
--- a/11_chrun_prediction/churn.ipynb
+++ b/11_chrun_prediction/churn.ipynb
@@ -2667,7 +2667,234 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Charanjeet Juneja\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Charanjeet Juneja\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Charanjeet Juneja\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n",
+      "Epoch 1/100\n",
+      "WARNING:tensorflow:From c:\\Users\\Charanjeet Juneja\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Charanjeet Juneja\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
+      "\n",
+      "176/176 [==============================] - 2s 3ms/step - loss: 0.5324 - accuracy: 0.7364\n",
+      "Epoch 2/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4318 - accuracy: 0.7957\n",
+      "Epoch 3/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4214 - accuracy: 0.8032\n",
+      "Epoch 4/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.4170 - accuracy: 0.8044\n",
+      "Epoch 5/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.4143 - accuracy: 0.8028\n",
+      "Epoch 6/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4129 - accuracy: 0.8057\n",
+      "Epoch 7/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.4104 - accuracy: 0.8059\n",
+      "Epoch 8/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4087 - accuracy: 0.8075\n",
+      "Epoch 9/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4065 - accuracy: 0.8096\n",
+      "Epoch 10/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4059 - accuracy: 0.8096\n",
+      "Epoch 11/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4023 - accuracy: 0.8108\n",
+      "Epoch 12/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4035 - accuracy: 0.8116\n",
+      "Epoch 13/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4014 - accuracy: 0.8139\n",
+      "Epoch 14/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.4003 - accuracy: 0.8128\n",
+      "Epoch 15/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3979 - accuracy: 0.8151\n",
+      "Epoch 16/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3983 - accuracy: 0.8124\n",
+      "Epoch 17/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3967 - accuracy: 0.8108\n",
+      "Epoch 18/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3957 - accuracy: 0.8139\n",
+      "Epoch 19/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3940 - accuracy: 0.8156\n",
+      "Epoch 20/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3940 - accuracy: 0.8172\n",
+      "Epoch 21/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3939 - accuracy: 0.8126\n",
+      "Epoch 22/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3913 - accuracy: 0.8153\n",
+      "Epoch 23/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3917 - accuracy: 0.8158\n",
+      "Epoch 24/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3903 - accuracy: 0.8153\n",
+      "Epoch 25/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3910 - accuracy: 0.8162\n",
+      "Epoch 26/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3888 - accuracy: 0.8165\n",
+      "Epoch 27/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3878 - accuracy: 0.8178\n",
+      "Epoch 28/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3874 - accuracy: 0.8132\n",
+      "Epoch 29/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3873 - accuracy: 0.8164\n",
+      "Epoch 30/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3870 - accuracy: 0.8190\n",
+      "Epoch 31/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3866 - accuracy: 0.8197\n",
+      "Epoch 32/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3861 - accuracy: 0.8178\n",
+      "Epoch 33/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3862 - accuracy: 0.8201\n",
+      "Epoch 34/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3849 - accuracy: 0.8180\n",
+      "Epoch 35/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3836 - accuracy: 0.8229\n",
+      "Epoch 36/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3833 - accuracy: 0.8176\n",
+      "Epoch 37/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3827 - accuracy: 0.8192\n",
+      "Epoch 38/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3832 - accuracy: 0.8197\n",
+      "Epoch 39/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3815 - accuracy: 0.8194\n",
+      "Epoch 40/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3815 - accuracy: 0.8158\n",
+      "Epoch 41/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3806 - accuracy: 0.8196\n",
+      "Epoch 42/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3803 - accuracy: 0.8183\n",
+      "Epoch 43/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3807 - accuracy: 0.8192\n",
+      "Epoch 44/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3797 - accuracy: 0.8204\n",
+      "Epoch 45/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3790 - accuracy: 0.8240\n",
+      "Epoch 46/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3783 - accuracy: 0.8219\n",
+      "Epoch 47/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3775 - accuracy: 0.8251\n",
+      "Epoch 48/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3780 - accuracy: 0.8213\n",
+      "Epoch 49/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3764 - accuracy: 0.8240\n",
+      "Epoch 50/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3769 - accuracy: 0.8212\n",
+      "Epoch 51/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3760 - accuracy: 0.8220\n",
+      "Epoch 52/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3772 - accuracy: 0.8240\n",
+      "Epoch 53/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3751 - accuracy: 0.8204\n",
+      "Epoch 54/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3748 - accuracy: 0.8203\n",
+      "Epoch 55/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3768 - accuracy: 0.8215\n",
+      "Epoch 56/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3737 - accuracy: 0.8226\n",
+      "Epoch 57/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3743 - accuracy: 0.8226\n",
+      "Epoch 58/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8247\n",
+      "Epoch 59/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3733 - accuracy: 0.8240\n",
+      "Epoch 60/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3715 - accuracy: 0.8263\n",
+      "Epoch 61/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3713 - accuracy: 0.8247\n",
+      "Epoch 62/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3707 - accuracy: 0.8252\n",
+      "Epoch 63/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3700 - accuracy: 0.8263\n",
+      "Epoch 64/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3695 - accuracy: 0.8267\n",
+      "Epoch 65/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3696 - accuracy: 0.8236\n",
+      "Epoch 66/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3708 - accuracy: 0.8258\n",
+      "Epoch 67/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3689 - accuracy: 0.8256\n",
+      "Epoch 68/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3671 - accuracy: 0.8290\n",
+      "Epoch 69/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3680 - accuracy: 0.8254\n",
+      "Epoch 70/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3672 - accuracy: 0.8274\n",
+      "Epoch 71/100\n",
+      "176/176 [==============================] - 0s 3ms/step - loss: 0.3666 - accuracy: 0.8274\n",
+      "Epoch 72/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3673 - accuracy: 0.8252\n",
+      "Epoch 73/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3669 - accuracy: 0.8251\n",
+      "Epoch 74/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3649 - accuracy: 0.8277\n",
+      "Epoch 75/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3647 - accuracy: 0.8284\n",
+      "Epoch 76/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3645 - accuracy: 0.8288\n",
+      "Epoch 77/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3642 - accuracy: 0.8283\n",
+      "Epoch 78/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3629 - accuracy: 0.8283\n",
+      "Epoch 79/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3636 - accuracy: 0.8299\n",
+      "Epoch 80/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3616 - accuracy: 0.8304\n",
+      "Epoch 81/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3619 - accuracy: 0.8324\n",
+      "Epoch 82/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3629 - accuracy: 0.8320\n",
+      "Epoch 83/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3623 - accuracy: 0.8313\n",
+      "Epoch 84/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3606 - accuracy: 0.8302\n",
+      "Epoch 85/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8324\n",
+      "Epoch 86/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3605 - accuracy: 0.8331\n",
+      "Epoch 87/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3600 - accuracy: 0.8322\n",
+      "Epoch 88/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3594 - accuracy: 0.8336\n",
+      "Epoch 89/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3612 - accuracy: 0.8309\n",
+      "Epoch 90/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3565 - accuracy: 0.8322\n",
+      "Epoch 91/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3583 - accuracy: 0.8302\n",
+      "Epoch 92/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3586 - accuracy: 0.8327\n",
+      "Epoch 93/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3572 - accuracy: 0.8359\n",
+      "Epoch 94/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3570 - accuracy: 0.8293\n",
+      "Epoch 95/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3568 - accuracy: 0.8364\n",
+      "Epoch 96/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3560 - accuracy: 0.8309\n",
+      "Epoch 97/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3565 - accuracy: 0.8313\n",
+      "Epoch 98/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3547 - accuracy: 0.8324\n",
+      "Epoch 99/100\n",
+      "176/176 [==============================] - 0s 2ms/step - loss: 0.3548 - accuracy: 0.8357\n",
+      "Epoch 100/100\n",
+      "176/176 [==============================] - 1s 3ms/step - loss: 0.3544 - accuracy: 0.8334\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x1fd843573d0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
@@ -2690,7 +2917,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test[['InternetService_DSL', 'InternetService_Fiber optic', 'InternetService_No', 'Contract_Month-to-month',\n",
+    "         'Contract_One year', 'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
+    "         'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check']] = \\\n",
+    "    X_test[['InternetService_DSL', 'InternetService_Fiber optic', 'InternetService_No', 'Contract_Month-to-month',\n",
+    "             'Contract_One year', 'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
+    "             'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check']].astype(int)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender                                       int64\n",
+       "SeniorCitizen                                int64\n",
+       "Partner                                      int64\n",
+       "Dependents                                   int64\n",
+       "tenure                                     float64\n",
+       "PhoneService                                 int64\n",
+       "MultipleLines                                int64\n",
+       "OnlineSecurity                               int64\n",
+       "OnlineBackup                                 int64\n",
+       "DeviceProtection                             int64\n",
+       "TechSupport                                  int64\n",
+       "StreamingTV                                  int64\n",
+       "StreamingMovies                              int64\n",
+       "PaperlessBilling                             int64\n",
+       "MonthlyCharges                             float64\n",
+       "TotalCharges                               float64\n",
+       "InternetService_DSL                          int32\n",
+       "InternetService_Fiber optic                  int32\n",
+       "InternetService_No                           int32\n",
+       "Contract_Month-to-month                      int32\n",
+       "Contract_One year                            int32\n",
+       "Contract_Two year                            int32\n",
+       "PaymentMethod_Bank transfer (automatic)      int32\n",
+       "PaymentMethod_Credit card (automatic)        int32\n",
+       "PaymentMethod_Electronic check               int32\n",
+       "PaymentMethod_Mailed check                   int32\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
    "metadata": {
     "scrolled": true
    },
@@ -2699,16 +2986,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "44/44 [==============================] - 0s 1ms/step - loss: 0.4932 - accuracy: 0.7754\n"
+      "44/44 [==============================] - 0s 2ms/step - loss: 0.4890 - accuracy: 0.7783\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[0.4931727349758148, 0.7754086852073669]"
+       "[0.48899081349372864, 0.778251588344574]"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2717,22 +3004,36 @@
     "model.evaluate(X_test, y_test)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again I am having error faileed to convert numpy array to a tensor so both x and y train must be converted into consistent data types"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "44/44 [==============================] - 0s 2ms/step\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "array([[0.25819573],\n",
-       "       [0.4437274 ],\n",
-       "       [0.00808946],\n",
-       "       [0.7649808 ],\n",
-       "       [0.35091308]], dtype=float32)"
+       "array([[0.54374355],\n",
+       "       [0.4629597 ],\n",
+       "       [0.01331834],\n",
+       "       [0.83174217],\n",
+       "       [0.41367024]], dtype=float32)"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2744,7 +3045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2758,27 +3059,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0, 0, 0, 1, 0, 1, 0, 0, 0, 0]"
+       "[1, 0, 0, 1, 0, 1, 0, 1, 0, 0]"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "y_pred[:10]"
+    "y_pred[:10]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -2797,7 +3098,7 @@
        "Name: Churn, dtype: int64"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2808,7 +3109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -2817,8 +3118,8 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.83      0.86      0.85       999\n",
-      "           1       0.63      0.56      0.59       408\n",
+      "           0       0.82      0.87      0.85       999\n",
+      "           1       0.64      0.54      0.59       408\n",
       "\n",
       "    accuracy                           0.78      1407\n",
       "   macro avg       0.73      0.71      0.72      1407\n",
@@ -2835,7 +3136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 51,
    "metadata": {
     "scrolled": false
    },
@@ -2843,23 +3144,21 @@
     {
      "data": {
       "text/plain": [
-       "Text(69.0, 0.5, 'Truth')"
+       "Text(95.72222222222221, 0.5, 'Truth')"
       ]
      },
-     "execution_count": 222,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x504 with 2 Axes>"
+       "<Figure size 1000x700 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -2875,7 +3174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -2884,7 +3183,7 @@
        "(1407,)"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2902,7 +3201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 53,
    "metadata": {
     "scrolled": true
    },
@@ -2913,7 +3212,7 @@
        "0.78"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2931,7 +3230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 54,
    "metadata": {
     "scrolled": true
    },
@@ -2942,7 +3241,7 @@
        "0.83"
       ]
      },
-     "execution_count": 240,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2960,7 +3259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 55,
    "metadata": {
     "scrolled": true
    },
@@ -2971,7 +3270,7 @@
        "0.63"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2989,7 +3288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -2998,7 +3297,7 @@
        "0.86"
       ]
      },
-     "execution_count": 243,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3009,7 +3308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 57,
    "metadata": {
     "scrolled": true
    },
@@ -3020,7 +3319,7 @@
        "0.56"
       ]
      },
-     "execution_count": 244,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }