customer churn predicton.ipynb

{
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   "id": "b8991f8a-2022-48ba-b685-7ec46008dcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    " # loadig the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f2e9a4a-3b03-477e-bf21-3c670fa29076",
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Customer_1</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Customer_2</td>\n",
       "      <td>62</td>\n",
       "      <td>Female</td>\n",
       "      <td>New York</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Customer_3</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Customer_4</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Customer_5</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>Customer_996</td>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Houston</td>\n",
       "      <td>23</td>\n",
       "      <td>56.34</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>Customer_997</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>Miami</td>\n",
       "      <td>5</td>\n",
       "      <td>44.00</td>\n",
       "      <td>357</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>Customer_998</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>2</td>\n",
       "      <td>78.07</td>\n",
       "      <td>484</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>Customer_999</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3</td>\n",
       "      <td>37.66</td>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>Customer_1000</td>\n",
       "      <td>55</td>\n",
       "      <td>Female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "      <td>79.43</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID           Name  Age  Gender     Location  \\\n",
       "0             1     Customer_1   63    Male  Los Angeles   \n",
       "1             2     Customer_2   62  Female     New York   \n",
       "2             3     Customer_3   24  Female  Los Angeles   \n",
       "3             4     Customer_4   36  Female        Miami   \n",
       "4             5     Customer_5   46  Female        Miami   \n",
       "..          ...            ...  ...     ...          ...   \n",
       "995         996   Customer_996   50    Male      Houston   \n",
       "996         997   Customer_997   27    Male        Miami   \n",
       "997         998   Customer_998   36    Male      Chicago   \n",
       "998         999   Customer_999   49    Male  Los Angeles   \n",
       "999        1000  Customer_1000   55  Female  Los Angeles   \n",
       "\n",
       "     Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \n",
       "0                            17         73.36             236      0  \n",
       "1                             1         48.76             172      0  \n",
       "2                             5         85.47             460      0  \n",
       "3                             3         97.94             297      1  \n",
       "4                            19         58.14             266      0  \n",
       "..                          ...           ...             ...    ...  \n",
       "995                          23         56.34              86      1  \n",
       "996                           5         44.00             357      1  \n",
       "997                           2         78.07             484      1  \n",
       "998                           3         37.66             342      1  \n",
       "999                          14         79.43             240      0  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('C:/Users/Harish/Music/customer_churn_large_dataset.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bbeb93ac-5dcf-456a-a3ed-0b76a8648754",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "96a6803c-b697-42d7-8b5d-537228867b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>43.756000</td>\n",
       "      <td>12.518000</td>\n",
       "      <td>64.273050</td>\n",
       "      <td>272.98900</td>\n",
       "      <td>0.519000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288.819436</td>\n",
       "      <td>14.923578</td>\n",
       "      <td>7.095775</td>\n",
       "      <td>20.211799</td>\n",
       "      <td>131.92326</td>\n",
       "      <td>0.499889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.020000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>250.750000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>46.460000</td>\n",
       "      <td>156.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>63.940000</td>\n",
       "      <td>272.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>750.250000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>82.102500</td>\n",
       "      <td>389.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>99.870000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        CustomerID          Age  Subscription_Length_Months  Monthly_Bill  \\\n",
       "count  1000.000000  1000.000000                 1000.000000   1000.000000   \n",
       "mean    500.500000    43.756000                   12.518000     64.273050   \n",
       "std     288.819436    14.923578                    7.095775     20.211799   \n",
       "min       1.000000    18.000000                    1.000000     30.020000   \n",
       "25%     250.750000    31.000000                    6.000000     46.460000   \n",
       "50%     500.500000    44.000000                   13.000000     63.940000   \n",
       "75%     750.250000    56.000000                   19.000000     82.102500   \n",
       "max    1000.000000    70.000000                   24.000000     99.870000   \n",
       "\n",
       "       Total_Usage_GB        Churn  \n",
       "count      1000.00000  1000.000000  \n",
       "mean        272.98900     0.519000  \n",
       "std         131.92326     0.499889  \n",
       "min          50.00000     0.000000  \n",
       "25%         156.00000     0.000000  \n",
       "50%         272.00000     1.000000  \n",
       "75%         389.00000     1.000000  \n",
       "max         500.00000     1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "617fb767-12d3-4319-95f9-aaf7b47d8da7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 9)"
      ]
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6b1cc893-4bf9-438a-b2b3-8fe327249dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## detecting outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1364ee19-d64e-4b8b-bc29-2190fb7b4a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "1.0"
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "max_thresold = df['Churn'].quantile(0.95)\n",
    "max_thresold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "be901a4a-4e94-4f8c-a724-cddb2639d582",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
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       "Empty DataFrame\n",
       "Columns: [CustomerID, Name, Age, Gender, Location, Subscription_Length_Months, Monthly_Bill, Total_Usage_GB, Churn]\n",
       "Index: []"
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     "metadata": {},
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   ],
   "source": [
    "df[df['Churn']>max_thresold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8aefa9e7-e7e5-42fa-b423-3ec2bc9309bf",
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     "execution_count": 32,
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   "source": [
    "min_thresold = df['Churn'].quantile(0.05)\n",
    "min_thresold\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 33,
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       "Empty DataFrame\n",
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   "source": [
    "df[df['Churn']<min_thresold]"
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       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
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       "      <th>Churn</th>\n",
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       "Empty DataFrame\n",
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    "df[(df['Churn']<max_thresold) & (df['Churn']>min_thresold)]"
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       "      <th>CustomerID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>Customer_818</td>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>17</td>\n",
       "      <td>92.35</td>\n",
       "      <td>436</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>298</td>\n",
       "      <td>Customer_298</td>\n",
       "      <td>45</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>3</td>\n",
       "      <td>46.32</td>\n",
       "      <td>369</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>969</td>\n",
       "      <td>Customer_969</td>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Houston</td>\n",
       "      <td>14</td>\n",
       "      <td>78.99</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>411</td>\n",
       "      <td>Customer_411</td>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3</td>\n",
       "      <td>34.26</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>679</td>\n",
       "      <td>Customer_679</td>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>5</td>\n",
       "      <td>82.06</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>389</td>\n",
       "      <td>Customer_389</td>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>3</td>\n",
       "      <td>41.68</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>734</td>\n",
       "      <td>Customer_734</td>\n",
       "      <td>54</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>12</td>\n",
       "      <td>66.19</td>\n",
       "      <td>487</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>Customer_880</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>New York</td>\n",
       "      <td>21</td>\n",
       "      <td>49.90</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>587</td>\n",
       "      <td>Customer_587</td>\n",
       "      <td>52</td>\n",
       "      <td>Female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>13</td>\n",
       "      <td>39.79</td>\n",
       "      <td>363</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>983</td>\n",
       "      <td>Customer_983</td>\n",
       "      <td>33</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>8</td>\n",
       "      <td>66.38</td>\n",
       "      <td>474</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID          Name  Age  Gender     Location  \\\n",
       "817         818  Customer_818   32    Male  Los Angeles   \n",
       "297         298  Customer_298   45  Female      Chicago   \n",
       "968         969  Customer_969   41    Male      Houston   \n",
       "410         411  Customer_411   60    Male  Los Angeles   \n",
       "678         679  Customer_679   29    Male  Los Angeles   \n",
       "388         389  Customer_389   50  Female        Miami   \n",
       "733         734  Customer_734   54  Female        Miami   \n",
       "879         880  Customer_880   18    Male     New York   \n",
       "586         587  Customer_587   52  Female  Los Angeles   \n",
       "982         983  Customer_983   33  Female      Chicago   \n",
       "\n",
       "     Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \n",
       "817                          17         92.35             436      1  \n",
       "297                           3         46.32             369      0  \n",
       "968                          14         78.99             349      0  \n",
       "410                           3         34.26             132      1  \n",
       "678                           5         82.06             208      0  \n",
       "388                           3         41.68             160      1  \n",
       "733                          12         66.19             487      0  \n",
       "879                          21         49.90             164      1  \n",
       "586                          13         39.79             363      1  \n",
       "982                           8         66.38             474      1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d12ab133-94a5-4e5c-8c67-bd343c4047c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## detecting null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9d6c5c1f-1094-4fba-9f60-dc59840bbe98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID   Name    Age  Gender  Location  Subscription_Length_Months  \\\n",
       "0         False  False  False   False     False                       False   \n",
       "1         False  False  False   False     False                       False   \n",
       "2         False  False  False   False     False                       False   \n",
       "3         False  False  False   False     False                       False   \n",
       "4         False  False  False   False     False                       False   \n",
       "..          ...    ...    ...     ...       ...                         ...   \n",
       "995       False  False  False   False     False                       False   \n",
       "996       False  False  False   False     False                       False   \n",
       "997       False  False  False   False     False                       False   \n",
       "998       False  False  False   False     False                       False   \n",
       "999       False  False  False   False     False                       False   \n",
       "\n",
       "     Monthly_Bill  Total_Usage_GB  Churn  \n",
       "0           False           False  False  \n",
       "1           False           False  False  \n",
       "2           False           False  False  \n",
       "3           False           False  False  \n",
       "4           False           False  False  \n",
       "..            ...             ...    ...  \n",
       "995         False           False  False  \n",
       "996         False           False  False  \n",
       "997         False           False  False  \n",
       "998         False           False  False  \n",
       "999         False           False  False  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2274da6e-db2d-41a5-a5ed-b192b155c654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                    0\n",
       "Name                          0\n",
       "Age                           0\n",
       "Gender                        0\n",
       "Location                      0\n",
       "Subscription_Length_Months    0\n",
       "Monthly_Bill                  0\n",
       "Total_Usage_GB                0\n",
       "Churn                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "caed0f26-658b-466f-9033-23216dcced10",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = df[['CustomerID', 'Name', 'Age', 'Gender', 'Location', 'Subscription_Length_Months', 'Monthly_Bill', 'Total_Usage_GB', 'Churn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c1cb5b27-836e-432d-8691-fbc175b6d851",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Name</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Customer_1</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Customer_2</td>\n",
       "      <td>62</td>\n",
       "      <td>Female</td>\n",
       "      <td>New York</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Customer_3</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Customer_4</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Customer_5</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID        Name  Age  Gender     Location  \\\n",
       "0           1  Customer_1   63    Male  Los Angeles   \n",
       "1           2  Customer_2   62  Female     New York   \n",
       "2           3  Customer_3   24  Female  Los Angeles   \n",
       "3           4  Customer_4   36  Female        Miami   \n",
       "4           5  Customer_5   46  Female        Miami   \n",
       "\n",
       "   Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \n",
       "0                          17         73.36             236      0  \n",
       "1                           1         48.76             172      0  \n",
       "2                           5         85.47             460      0  \n",
       "3                           3         97.94             297      1  \n",
       "4                          19         58.14             266      0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5a14784-9d30-4856-a396-da50112bf510",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = pd.get_dummies(final_dataset, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bf8b91d9-ff47-4e0b-9276-731791efbe61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Location_Houston</th>\n",
       "      <th>Location_Los Angeles</th>\n",
       "      <th>Location_Miami</th>\n",
       "      <th>Location_New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</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>1</th>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>2</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>4</th>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1010 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Age  Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  \\\n",
       "0           1   63                          17         73.36             236   \n",
       "1           2   62                           1         48.76             172   \n",
       "2           3   24                           5         85.47             460   \n",
       "3           4   36                           3         97.94             297   \n",
       "4           5   46                          19         58.14             266   \n",
       "\n",
       "   Churn  Name_Customer_10  Name_Customer_100  Name_Customer_1000  \\\n",
       "0      0             False              False               False   \n",
       "1      0             False              False               False   \n",
       "2      0             False              False               False   \n",
       "3      1             False              False               False   \n",
       "4      0             False              False               False   \n",
       "\n",
       "   Name_Customer_101  ...  Name_Customer_995  Name_Customer_996  \\\n",
       "0              False  ...              False              False   \n",
       "1              False  ...              False              False   \n",
       "2              False  ...              False              False   \n",
       "3              False  ...              False              False   \n",
       "4              False  ...              False              False   \n",
       "\n",
       "   Name_Customer_997  Name_Customer_998  Name_Customer_999  Gender_Male  \\\n",
       "0              False              False              False         True   \n",
       "1              False              False              False        False   \n",
       "2              False              False              False        False   \n",
       "3              False              False              False        False   \n",
       "4              False              False              False        False   \n",
       "\n",
       "   Location_Houston  Location_Los Angeles  Location_Miami  Location_New York  \n",
       "0             False                  True           False              False  \n",
       "1             False                 False           False               True  \n",
       "2             False                  True           False              False  \n",
       "3             False                 False            True              False  \n",
       "4             False                 False            True              False  \n",
       "\n",
       "[5 rows x 1010 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "79e3557e-4ce8-4d42-a5aa-5ff511cbc722",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preparaton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1e68a4af-1a9d-4b9b-9eef-ef49c19f4792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "995    1\n",
       "996    1\n",
       "997    1\n",
       "998    1\n",
       "999    0\n",
       "Name: Churn, Length: 1000, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = final_dataset['Churn']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4947b217-8c8d-4dda-866d-2a4c153ad8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CustomerID</th>\n",
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       "      <th>Subscription_Length_Months</th>\n",
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       "      <th>Total_Usage_GB</th>\n",
       "      <th>Name_Customer_10</th>\n",
       "      <th>Name_Customer_100</th>\n",
       "      <th>Name_Customer_1000</th>\n",
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       "      <th>Name_Customer_102</th>\n",
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       "      <th>Location_Los Angeles</th>\n",
       "      <th>Location_Miami</th>\n",
       "      <th>Location_New York</th>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
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       "      <td>240</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1009 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Age  Subscription_Length_Months  Monthly_Bill  \\\n",
       "0             1   63                          17         73.36   \n",
       "1             2   62                           1         48.76   \n",
       "2             3   24                           5         85.47   \n",
       "3             4   36                           3         97.94   \n",
       "4             5   46                          19         58.14   \n",
       "..          ...  ...                         ...           ...   \n",
       "995         996   50                          23         56.34   \n",
       "996         997   27                           5         44.00   \n",
       "997         998   36                           2         78.07   \n",
       "998         999   49                           3         37.66   \n",
       "999        1000   55                          14         79.43   \n",
       "\n",
       "     Total_Usage_GB  Name_Customer_10  Name_Customer_100  Name_Customer_1000  \\\n",
       "0               236             False              False               False   \n",
       "1               172             False              False               False   \n",
       "2               460             False              False               False   \n",
       "3               297             False              False               False   \n",
       "4               266             False              False               False   \n",
       "..              ...               ...                ...                 ...   \n",
       "995              86             False              False               False   \n",
       "996             357             False              False               False   \n",
       "997             484             False              False               False   \n",
       "998             342             False              False               False   \n",
       "999             240             False              False                True   \n",
       "\n",
       "     Name_Customer_101  Name_Customer_102  ...  Name_Customer_995  \\\n",
       "0                False              False  ...              False   \n",
       "1                False              False  ...              False   \n",
       "2                False              False  ...              False   \n",
       "3                False              False  ...              False   \n",
       "4                False              False  ...              False   \n",
       "..                 ...                ...  ...                ...   \n",
       "995              False              False  ...              False   \n",
       "996              False              False  ...              False   \n",
       "997              False              False  ...              False   \n",
       "998              False              False  ...              False   \n",
       "999              False              False  ...              False   \n",
       "\n",
       "     Name_Customer_996  Name_Customer_997  Name_Customer_998  \\\n",
       "0                False              False              False   \n",
       "1                False              False              False   \n",
       "2                False              False              False   \n",
       "3                False              False              False   \n",
       "4                False              False              False   \n",
       "..                 ...                ...                ...   \n",
       "995               True              False              False   \n",
       "996              False               True              False   \n",
       "997              False              False               True   \n",
       "998              False              False              False   \n",
       "999              False              False              False   \n",
       "\n",
       "     Name_Customer_999  Gender_Male  Location_Houston  Location_Los Angeles  \\\n",
       "0                False         True             False                  True   \n",
       "1                False        False             False                 False   \n",
       "2                False        False             False                  True   \n",
       "3                False        False             False                 False   \n",
       "4                False        False             False                 False   \n",
       "..                 ...          ...               ...                   ...   \n",
       "995              False         True              True                 False   \n",
       "996              False         True             False                 False   \n",
       "997              False         True             False                 False   \n",
       "998               True         True             False                  True   \n",
       "999              False        False             False                  True   \n",
       "\n",
       "     Location_Miami  Location_New York  \n",
       "0             False              False  \n",
       "1             False               True  \n",
       "2             False              False  \n",
       "3              True              False  \n",
       "4              True              False  \n",
       "..              ...                ...  \n",
       "995           False              False  \n",
       "996            True              False  \n",
       "997           False              False  \n",
       "998           False              False  \n",
       "999           False              False  \n",
       "\n",
       "[1000 rows x 1009 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = final_dataset.drop('Churn', axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9ae9ae6b-1a7e-42ef-8560-acc1fa87f88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "718fc013-9fbe-4644-a767-5d16d02ed626",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3ff65539-8da0-4a5e-a29c-459ff2315af4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>521</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 1009 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Age  Subscription_Length_Months  Monthly_Bill  \\\n",
       "675         676   32                          21         36.51   \n",
       "358         359   47                          13         35.97   \n",
       "159         160   26                          23         87.42   \n",
       "533         534   63                          23         56.31   \n",
       "678         679   29                           5         82.06   \n",
       "..          ...  ...                         ...           ...   \n",
       "855         856   28                          11         68.52   \n",
       "871         872   40                           2         74.54   \n",
       "835         836   21                          16         93.53   \n",
       "792         793   27                           2         56.60   \n",
       "520         521   43                          10         54.62   \n",
       "\n",
       "     Total_Usage_GB  Name_Customer_10  Name_Customer_100  Name_Customer_1000  \\\n",
       "675              56             False              False               False   \n",
       "358             224             False              False               False   \n",
       "159             261             False              False               False   \n",
       "533             383             False              False               False   \n",
       "678             208             False              False               False   \n",
       "..              ...               ...                ...                 ...   \n",
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       "835             204             False              False               False   \n",
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       "520             442             False              False               False   \n",
       "\n",
       "     Name_Customer_101  Name_Customer_102  ...  Name_Customer_995  \\\n",
       "675              False              False  ...              False   \n",
       "358              False              False  ...              False   \n",
       "159              False              False  ...              False   \n",
       "533              False              False  ...              False   \n",
       "678              False              False  ...              False   \n",
       "..                 ...                ...  ...                ...   \n",
       "855              False              False  ...              False   \n",
       "871              False              False  ...              False   \n",
       "835              False              False  ...              False   \n",
       "792              False              False  ...              False   \n",
       "520              False              False  ...              False   \n",
       "\n",
       "     Name_Customer_996  Name_Customer_997  Name_Customer_998  \\\n",
       "675              False              False              False   \n",
       "358              False              False              False   \n",
       "159              False              False              False   \n",
       "533              False              False              False   \n",
       "678              False              False              False   \n",
       "..                 ...                ...                ...   \n",
       "855              False              False              False   \n",
       "871              False              False              False   \n",
       "835              False              False              False   \n",
       "792              False              False              False   \n",
       "520              False              False              False   \n",
       "\n",
       "     Name_Customer_999  Gender_Male  Location_Houston  Location_Los Angeles  \\\n",
       "675              False        False             False                 False   \n",
       "358              False         True              True                 False   \n",
       "159              False         True             False                 False   \n",
       "533              False        False             False                  True   \n",
       "678              False         True             False                  True   \n",
       "..                 ...          ...               ...                   ...   \n",
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       "871              False        False             False                 False   \n",
       "835              False        False              True                 False   \n",
       "792              False         True             False                 False   \n",
       "520              False        False             False                 False   \n",
       "\n",
       "     Location_Miami  Location_New York  \n",
       "675           False              False  \n",
       "358           False              False  \n",
       "159           False               True  \n",
       "533           False              False  \n",
       "678           False              False  \n",
       "..              ...                ...  \n",
       "855           False              False  \n",
       "871           False              False  \n",
       "835           False              False  \n",
       "792           False              False  \n",
       "520           False              False  \n",
       "\n",
       "[800 rows x 1009 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4febd99c-4f6c-4482-bcf8-a43c59ed2cf6",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>425</td>\n",
       "      <td>62</td>\n",
       "      <td>18</td>\n",
       "      <td>61.32</td>\n",
       "      <td>385</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>63</td>\n",
       "      <td>22</td>\n",
       "      <td>52.75</td>\n",
       "      <td>425</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>685</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>85.12</td>\n",
       "      <td>411</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>62</td>\n",
       "      <td>21</td>\n",
       "      <td>63.67</td>\n",
       "      <td>360</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>110</th>\n",
       "      <td>111</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>72.78</td>\n",
       "      <td>304</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>72.09</td>\n",
       "      <td>270</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>77.48</td>\n",
       "      <td>444</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 1009 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Age  Subscription_Length_Months  Monthly_Bill  \\\n",
       "249         250   29                          15         92.34   \n",
       "353         354   48                           3         37.17   \n",
       "537         538   52                          14         33.07   \n",
       "424         425   62                          18         61.32   \n",
       "564         565   63                          22         52.75   \n",
       "..          ...  ...                         ...           ...   \n",
       "684         685   23                          22         85.12   \n",
       "644         645   62                          21         63.67   \n",
       "110         111   60                          10         72.78   \n",
       "28           29   31                           5         72.09   \n",
       "804         805   38                          18         77.48   \n",
       "\n",
       "     Total_Usage_GB  Name_Customer_10  Name_Customer_100  Name_Customer_1000  \\\n",
       "249             263             False              False               False   \n",
       "353             243             False              False               False   \n",
       "537             217             False              False               False   \n",
       "424             385             False              False               False   \n",
       "564             425             False              False               False   \n",
       "..              ...               ...                ...                 ...   \n",
       "684             411             False              False               False   \n",
       "644             360             False              False               False   \n",
       "110             304             False              False               False   \n",
       "28              270             False              False               False   \n",
       "804             444             False              False               False   \n",
       "\n",
       "     Name_Customer_101  Name_Customer_102  ...  Name_Customer_995  \\\n",
       "249              False              False  ...              False   \n",
       "353              False              False  ...              False   \n",
       "537              False              False  ...              False   \n",
       "424              False              False  ...              False   \n",
       "564              False              False  ...              False   \n",
       "..                 ...                ...  ...                ...   \n",
       "684              False              False  ...              False   \n",
       "644              False              False  ...              False   \n",
       "110              False              False  ...              False   \n",
       "28               False              False  ...              False   \n",
       "804              False              False  ...              False   \n",
       "\n",
       "     Name_Customer_996  Name_Customer_997  Name_Customer_998  \\\n",
       "249              False              False              False   \n",
       "353              False              False              False   \n",
       "537              False              False              False   \n",
       "424              False              False              False   \n",
       "564              False              False              False   \n",
       "..                 ...                ...                ...   \n",
       "684              False              False              False   \n",
       "644              False              False              False   \n",
       "110              False              False              False   \n",
       "28               False              False              False   \n",
       "804              False              False              False   \n",
       "\n",
       "     Name_Customer_999  Gender_Male  Location_Houston  Location_Los Angeles  \\\n",
       "249              False         True              True                 False   \n",
       "353              False         True             False                 False   \n",
       "537              False        False             False                  True   \n",
       "424              False        False             False                 False   \n",
       "564              False         True             False                 False   \n",
       "..                 ...          ...               ...                   ...   \n",
       "684              False         True             False                 False   \n",
       "644              False        False             False                 False   \n",
       "110              False         True              True                 False   \n",
       "28               False         True             False                 False   \n",
       "804              False        False             False                 False   \n",
       "\n",
       "     Location_Miami  Location_New York  \n",
       "249           False              False  \n",
       "353            True              False  \n",
       "537           False              False  \n",
       "424           False              False  \n",
       "564           False              False  \n",
       "..              ...                ...  \n",
       "684            True              False  \n",
       "644            True              False  \n",
       "110           False              False  \n",
       "28             True              False  \n",
       "804           False               True  \n",
       "\n",
       "[200 rows x 1009 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6251dde4-6000-470d-8dbb-75884215a391",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cf842f26-a303-4086-9326-b41f17375854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "083382e9-bbae-4637-85a3-35b10cd5563f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.07183992 0.0744556  0.06899306 ... 0.01226086 0.00417765 0.01378682]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "983c9239-3d04-4c64-a592-e12c506dd0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Building using  Random forest classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3802bd51-2800-49f5-ac4e-e1a3f76c992d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_features=5)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "rf = RandomForestClassifier(max_features=5, n_estimators=100)\n",
    "rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "af4dd55f-d1bc-4144-95e6-81301c3c97c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.465"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ba7e25f2-3e8b-4290-8647-3ede2df34362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.465"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_rf_train_pred = rf.predict(x_train)\n",
    "y_rf_test_pred = rf.predict(x_test)\n",
    "\n",
    "accuracy_score(y_rf_test_pred,  y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cc8f75c5-6cfa-49bf-9bed-e5683bad8a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "        1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "        0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
       "        0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "        1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "        1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "        0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "        0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0,\n",
       "        0, 0], dtype=int64),\n",
       " 249    1\n",
       " 353    0\n",
       " 537    0\n",
       " 424    0\n",
       " 564    1\n",
       "       ..\n",
       " 684    1\n",
       " 644    0\n",
       " 110    1\n",
       " 28     0\n",
       " 804    1\n",
       " Name: Churn, Length: 200, dtype: int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_rf_test_pred,  y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5db00244-639f-4505-a8fb-6165472771cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "rf_train_mse = mean_squared_error(y_train, y_rf_train_pred)\n",
    "rf_train_r2 = r2_score(y_train, y_rf_train_pred)\n",
    "\n",
    "rf_test_mse = mean_squared_error(y_test, y_rf_test_pred)\n",
    "rf_test_r2 = r2_score(y_test, y_rf_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a3d1e8b4-3e39-41e1-b245-fd771161b281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Training MSE</th>\n",
       "      <th>Training R2</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Test R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.535</td>\n",
       "      <td>-1.211658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method Training MSE Training R2 Test MSE   Test R2\n",
       "0  Random Forest          0.0         1.0    0.535 -1.211658"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_result = pd.DataFrame(['Random Forest', rf_train_mse, rf_train_r2, rf_test_mse, rf_test_r2]).transpose()\n",
    "rf_result.columns = ['Method', 'Training MSE', 'Training R2', 'Test MSE', 'Test R2' ]\n",
    "rf_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0108d634-b112-4e02-9e7d-95fc7e3a364b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model using LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "89428aa0-2d8f-40b7-b245-cadf6bee4a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9c12996c-8421-4037-801e-77f1a82adf8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.18428790428444786"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d3c05f62-ce2e-4d4c-80c6-bcdc5484ef07",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_lr_train_pred = lr.predict(x_train)\n",
    "y_lr_test_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4634eaac-3886-453e-9ea1-07231a520678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  6.66133815e-16,\n",
       "        1.00000000e+00, -8.43769499e-15,  4.76285678e-14,  4.77395901e-15,\n",
       "        1.00000000e+00, -7.32747196e-15,  1.00000000e+00, -1.29896094e-14,\n",
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       "        2.69784195e-14,  3.60822483e-14,  1.00000000e+00,  4.66293670e-15,\n",
       "        1.00000000e+00,  1.00000000e+00,  9.43689571e-15, -1.92068583e-14,\n",
       "        1.00000000e+00,  1.00000000e+00, -4.10782519e-15,  1.89848137e-14,\n",
       "       -3.33066907e-16,  2.33146835e-15,  1.00000000e+00,  1.00000000e+00,\n",
       "       -1.14352972e-14, -1.36557432e-14, -1.65423231e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  3.45279361e-14, -4.76285678e-14,  1.00000000e+00,\n",
       "       -5.60662627e-14, -2.22044605e-16,  2.78665979e-14, -1.98729921e-14,\n",
       "        9.65894031e-15,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        4.00790512e-14,  3.40838469e-14,  2.44249065e-15,  1.00000000e+00,\n",
       "       -5.29576383e-14,  1.00000000e+00, -4.54081217e-14,  1.00000000e+00,\n",
       "       -2.65343303e-14,  1.98729921e-14,  1.00000000e+00, -1.86517468e-14,\n",
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       "       -7.21644966e-15,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
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       "        1.00000000e+00,  1.00000000e+00, -4.80726570e-14,  1.00000000e+00,\n",
       "       -2.48689958e-14,  9.43689571e-15,  1.00000000e+00,  1.00000000e+00,\n",
       "       -2.18713936e-14, -5.70654635e-14, -5.67323966e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.40998324e-14, -1.12132525e-14,  2.35367281e-14,\n",
       "        1.65423231e-14,  1.00000000e+00,  1.00000000e+00,  4.57411886e-14,\n",
       "        1.00000000e+00,  1.00000000e+00, -4.89608354e-14,  2.65343303e-14,\n",
       "        1.00000000e+00,  4.98490138e-14,  7.88258347e-15,  1.00000000e+00,\n",
       "        1.00000000e+00,  3.33066907e-16, -2.27595720e-14,  3.05311332e-14,\n",
       "        2.77555756e-15,  1.00000000e+00,  1.00000000e+00, -1.11022302e-15,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.83186799e-14,  1.00000000e+00,\n",
       "        1.90958360e-14,  1.00000000e+00,  7.43849426e-15,  5.88418203e-15,\n",
       "       -5.60662627e-14, -4.35207426e-14, -2.09832152e-14,  1.00000000e+00,\n",
       "       -7.66053887e-15,  1.00000000e+00, -3.05311332e-14, -1.82076576e-14,\n",
       "        1.00000000e+00,  1.75415238e-14,  3.24185123e-14,  1.92068583e-14,\n",
       "        1.72084569e-14, -3.54161145e-14,  2.08721929e-14,  1.00000000e+00,\n",
       "       -5.32907052e-15,  8.99280650e-15, -5.16253706e-14,  1.00000000e+00,\n",
       "       -1.66533454e-14,  3.33066907e-16, -2.08721929e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.77635684e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -2.09832152e-14, -2.20934382e-14,  1.00000000e+00, -4.20774526e-14,\n",
       "        1.00000000e+00,  1.00000000e+00, -4.56301663e-14,  5.68434189e-14,\n",
       "        1.00000000e+00, -3.96349620e-14,  2.32036612e-14, -1.27675648e-14,\n",
       "        1.00000000e+00, -1.43218770e-14,  1.00000000e+00,  7.66053887e-15,\n",
       "        1.00000000e+00,  1.95399252e-14,  1.00000000e+00, -1.05471187e-14,\n",
       "        3.03090886e-14,  1.00000000e+00, -6.99440506e-15, -5.08482145e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00, -1.11022302e-14,\n",
       "       -3.15303339e-14,  1.00000000e+00,  1.00000000e+00,  1.01030295e-14,\n",
       "        1.00000000e+00,  1.00000000e+00, -4.25215418e-14,  2.66453526e-15,\n",
       "       -2.44249065e-15,  1.00000000e+00, -2.83106871e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  3.99680289e-15,  1.11022302e-16,\n",
       "        9.43689571e-15, -5.81756865e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  3.87467836e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  2.66453526e-14,  1.00000000e+00,  7.21644966e-15,\n",
       "        2.83106871e-14, -2.45359288e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.24344979e-14,  1.00000000e+00,  4.07451850e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  6.55031585e-15,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  3.54161145e-14,  3.53050922e-14,\n",
       "        1.00000000e+00,  1.00000000e+00, -2.10942375e-15, -9.99200722e-16,\n",
       "        1.00000000e+00,  8.21565038e-15, -5.88418203e-15,  1.00000000e+00,\n",
       "        3.60822483e-14,  1.00000000e+00,  1.00000000e+00,  1.63202785e-14,\n",
       "       -2.78665979e-14, -9.99200722e-16,  4.27435864e-14, -5.49560397e-14,\n",
       "        1.39888101e-14,  1.27675648e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -4.99600361e-15, -7.77156117e-16,  2.43138842e-14, -4.05231404e-14,\n",
       "        3.43058915e-14,  1.00000000e+00, -7.32747196e-15,  1.00000000e+00,\n",
       "        2.46469511e-14,  1.00000000e+00,  1.00000000e+00, -4.35207426e-14,\n",
       "       -5.12923037e-14,  1.00000000e+00, -4.16333634e-14,  1.00000000e+00,\n",
       "        1.68753900e-14,  1.00000000e+00,  1.00000000e+00, -5.73985304e-14,\n",
       "       -1.12132525e-14,  1.00000000e+00, -6.43929354e-15,  1.00000000e+00,\n",
       "       -5.18474152e-14, -1.78745907e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -1.37667655e-14,  7.54951657e-15,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00, -2.14273044e-14,\n",
       "        2.40918396e-14,  1.00000000e+00, -2.18713936e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  3.71924713e-14,\n",
       "        3.63042929e-14,  5.55111512e-14,  5.88418203e-15,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  3.60822483e-14, -2.56461519e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  3.13082893e-14,  1.00000000e+00,\n",
       "        4.21884749e-15,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "       -3.98570066e-14,  1.00000000e+00,  1.00000000e+00, -8.21565038e-15,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00, -1.53210777e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  5.55111512e-15,\n",
       "        2.27595720e-14,  1.00000000e+00,  4.90718577e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00, -4.99600361e-15,  1.00000000e+00,\n",
       "        1.00000000e+00, -6.11732887e-14,  1.33226763e-14,  1.00000000e+00,\n",
       "       -3.74145159e-14,  1.00000000e+00,  2.57571742e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00, -1.60982339e-14, -2.22044605e-14,\n",
       "       -2.07611706e-14,  1.00000000e+00, -1.33226763e-15,  1.00000000e+00,\n",
       "        1.00000000e+00, -1.52100554e-14,  1.32116540e-14,  6.21724894e-15,\n",
       "        1.00000000e+00, -1.55431223e-15,  1.89848137e-14,  1.63202785e-14,\n",
       "        3.76365605e-14,  1.00000000e+00,  4.79616347e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00, -4.99600361e-14,  2.88657986e-14,\n",
       "        1.00000000e+00,  6.10622664e-15, -1.52100554e-14, -1.55431223e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  5.38458167e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00, -2.36477504e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -1.69864123e-14,  3.45279361e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00, -2.04281037e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -5.25135491e-14,  1.00000000e+00, -3.55271368e-14,  7.10542736e-15,\n",
       "        7.66053887e-15, -1.83186799e-14,  1.16573418e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "       -1.36557432e-14,  1.00000000e+00,  1.00000000e+00,  2.65343303e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  2.66453526e-14,\n",
       "        2.80886425e-14,  1.00000000e+00,  4.61852778e-14, -3.67483821e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "       -3.99680289e-15,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  3.35287353e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  5.44009282e-15, -4.10782519e-15,  4.32986980e-15,\n",
       "        3.18634008e-14,  2.47579734e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -1.76525461e-14,  1.00000000e+00, -6.32827124e-15,  1.00000000e+00,\n",
       "        5.10702591e-14, -9.88098492e-15,  1.00000000e+00, -5.21804822e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "       -5.32907052e-15, -2.47579734e-14,  1.00000000e+00,  3.18634008e-14,\n",
       "        1.00000000e+00,  8.32667268e-15,  1.00000000e+00,  1.00000000e+00,\n",
       "        4.38538095e-14,  1.00000000e+00,  1.00000000e+00, -3.33066907e-15,\n",
       "        1.00000000e+00,  1.00000000e+00, -2.54241073e-14,  3.00870440e-14,\n",
       "        2.52020627e-14,  2.43138842e-14, -1.97619698e-14, -6.66133815e-15,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "       -4.77395901e-15,  3.90798505e-14,  3.28626015e-14,  1.00000000e+00,\n",
       "        7.10542736e-15,  1.00000000e+00,  2.32036612e-14,  1.21014310e-14,\n",
       "       -3.07531778e-14,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.37667655e-14, -6.00630656e-14,  1.00000000e+00,  3.29736238e-14,\n",
       "        7.99360578e-15, -5.82867088e-14,  3.66373598e-15,  1.00000000e+00,\n",
       "       -4.64073224e-14,  1.00000000e+00, -9.99200722e-16, -5.35127498e-14,\n",
       "       -4.65183447e-14, -2.02060590e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -2.99760217e-15,  1.00000000e+00,  3.66373598e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  2.93098879e-14, -8.88178420e-15,\n",
       "       -1.76525461e-14, -1.14352972e-14,  2.89768209e-14,  1.98729921e-14,\n",
       "        1.34336986e-14, -2.32036612e-14,  1.64313008e-14,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.89848137e-14,  1.45439216e-14,  1.00000000e+00,\n",
       "       -3.03090886e-14,  2.57571742e-14,  4.03010958e-14,  4.27435864e-14,\n",
       "        4.06341627e-14, -7.77156117e-16,  1.00000000e+00,  1.00000000e+00,\n",
       "        7.32747196e-15, -6.16173779e-14,  1.00000000e+00,  4.92939023e-14,\n",
       "        1.00000000e+00,  1.00000000e+00,  4.58522109e-14,  5.63993297e-14,\n",
       "        1.00000000e+00,  2.25375274e-14,  1.00000000e+00, -4.72955008e-14,\n",
       "        6.27276009e-14,  2.88657986e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -1.32116540e-14,  1.00000000e+00, -2.54241073e-14, -9.43689571e-15,\n",
       "        1.00000000e+00,  8.43769499e-15,  1.00000000e+00,  6.10622664e-15,\n",
       "        1.00000000e+00,  2.07611706e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "       -2.68673972e-14,  4.37427872e-14, -2.14273044e-14,  2.63122857e-14,\n",
       "        1.00000000e+00, -9.65894031e-15,  1.00000000e+00,  3.27515792e-14,\n",
       "        9.43689571e-15,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.02140518e-14,  1.00000000e+00,\n",
       "        1.00000000e+00, -1.69864123e-14,  5.05151476e-14, -2.44249065e-15,\n",
       "        1.00000000e+00, -7.43849426e-15, -7.88258347e-15, -2.47579734e-14,\n",
       "        1.00000000e+00, -4.05231404e-14,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.00000000e+00,  1.00000000e+00,\n",
       "        1.00000000e+00,  1.00000000e+00,  1.79856130e-14,  1.00000000e+00])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_lr_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "670d2cb3-5ae7-4639-bdda-ce98fa01bd48",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "lr_train_mse = mean_squared_error(y_train, y_lr_train_pred)\n",
    "lr_train_r2 = r2_score(y_train, y_lr_train_pred)\n",
    "\n",
    "lr_test_mse = mean_squared_error(y_test, y_lr_test_pred)\n",
    "lr_test_r2 = r2_score(y_test, y_lr_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5c08a767-ab1d-433c-8020-f8386f4a0a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Training MSE</th>\n",
       "      <th>Training R2</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Test R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286479</td>\n",
       "      <td>-0.184288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Method Training MSE Training R2  Test MSE   Test R2\n",
       "0  LinearRegression          0.0         1.0  0.286479 -0.184288"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_result = pd.DataFrame(['LinearRegression', lr_train_mse, lr_train_r2, lr_test_mse, lr_test_r2]).transpose()\n",
    "lr_result.columns = ['Method', 'Training MSE', 'Training R2', 'Test MSE', 'Test R2' ]\n",
    "lr_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c63a628c-ffe5-4f15-9ba3-191ebd907e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cc419d32-63d9-47bd-8c40-aaef4348cecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Training MSE</th>\n",
       "      <th>Training R2</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Test R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.535</td>\n",
       "      <td>-1.211658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286479</td>\n",
       "      <td>-0.184288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Method Training MSE Training R2  Test MSE   Test R2\n",
       "0     Random Forest          0.0         1.0     0.535 -1.211658\n",
       "0  LinearRegression          0.0         1.0  0.286479 -0.184288"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset_models = pd.concat([rf_result, lr_result], axis= 0)\n",
    "final_dataset_models.reset_index()\n",
    "final_dataset_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8bccdfd2-eab1-4fe1-a5ae-7049f2612d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Visualizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "36beddf5-9ec2-43d4-a2af-229965d7acb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x= y_train, y= y_rf_train_pred)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf1bfd2c-b047-4c9f-8c0b-8e4d53727da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c57e3dc-afe9-441a-8fca-7e60dbed4279",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "Classifier = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "57adbdc9-ea63-4dc3-8bc3-679f47a128cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'customer churn trained model.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2caff4ee-c587-487e-a7a1-39a61d7e6e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(Classifier, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf288343-3ffd-4c58-acba-410341ec73d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open('customer churn trained model.sav', 'rb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}