TelecomSub

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":9142954,"sourceType":"datasetVersion","datasetId":5522218}],"dockerImageVersionId":30747,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"raw","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"code","source":"import pandas as pd\ndf=pd.read_excel('/kaggle/input/telecom/Telecom.xlsx', sheet_name='Telecom')","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:00.158892Z","iopub.execute_input":"2024-08-09T22:58:00.159548Z","iopub.status.idle":"2024-08-09T22:58:03.463994Z","shell.execute_reply.started":"2024-08-09T22:58:00.159513Z","shell.execute_reply":"2024-08-09T22:58:03.463210Z"},"trusted":true},"execution_count":140,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:03.465538Z","iopub.execute_input":"2024-08-09T22:58:03.465827Z","iopub.status.idle":"2024-08-09T22:58:03.489461Z","shell.execute_reply.started":"2024-08-09T22:58:03.465804Z","shell.execute_reply":"2024-08-09T22:58:03.488337Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":"   CustomerID  PlanTaken   Age    TypeofContact  CityTier  DurationOfPitch  \\\n0      200000          1  41.0     Self Enquiry         3              6.0   \n1      200001          0  49.0  Company Invited         1             14.0   \n2      200002          1  37.0     Self Enquiry         1              8.0   \n3      200003          0  33.0  Company Invited         1              9.0   \n4      200004          0   NaN     Self Enquiry         1              8.0   \n\n       Occupation  Gender  NumberOfPersons  NumberOfFollowups PlanPitched  \\\n0        Salaried  Female                3                3.0      Deluxe   \n1        Salaried    Male                3                4.0      Deluxe   \n2     Free Lancer    Male                3                4.0       Basic   \n3        Salaried  Female                2                3.0       Basic   \n4  Small Business    Male                2                3.0       Basic   \n\n   PreferredServiceStar MaritalStatus  NumberOfUpgrades  iPhone  \\\n0                   3.0        Single               1.0       1   \n1                   4.0      Divorced               2.0       0   \n2                   3.0        Single               7.0       1   \n3                   3.0      Divorced               2.0       1   \n4                   4.0      Divorced               1.0       0   \n\n   PitchSatisfactionScore  PhoneContract  NumberOfChildren Designation  \\\n0                       2              1               0.0     Manager   \n1                       3              1               2.0     Manager   \n2                       3              0               0.0   Executive   \n3                       5              1               1.0   Executive   \n4                       5              1               0.0   Executive   \n\n   MonthlyIncome  \n0        20993.0  \n1        20130.0  \n2        17090.0  \n3        17909.0  \n4        18468.0  ","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>CustomerID</th>\n      <th>PlanTaken</th>\n      <th>Age</th>\n      <th>TypeofContact</th>\n      <th>CityTier</th>\n      <th>DurationOfPitch</th>\n      <th>Occupation</th>\n      <th>Gender</th>\n      <th>NumberOfPersons</th>\n      <th>NumberOfFollowups</th>\n      <th>PlanPitched</th>\n      <th>PreferredServiceStar</th>\n      <th>MaritalStatus</th>\n      <th>NumberOfUpgrades</th>\n      <th>iPhone</th>\n      <th>PitchSatisfactionScore</th>\n      <th>PhoneContract</th>\n      <th>NumberOfChildren</th>\n      <th>Designation</th>\n      <th>MonthlyIncome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>200000</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>Self Enquiry</td>\n      <td>3</td>\n      <td>6.0</td>\n      <td>Salaried</td>\n      <td>Female</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>Deluxe</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>Manager</td>\n      <td>20993.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>200001</td>\n      <td>0</td>\n      <td>49.0</td>\n      <td>Company Invited</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>Salaried</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Deluxe</td>\n      <td>4.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>Manager</td>\n      <td>20130.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>200002</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>Self Enquiry</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>Free Lancer</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>7.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>Executive</td>\n      <td>17090.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>200003</td>\n      <td>0</td>\n      <td>33.0</td>\n      <td>Company Invited</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>Salaried</td>\n      <td>Female</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>Executive</td>\n      <td>17909.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>200004</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>Self Enquiry</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>Small Business</td>\n      <td>Male</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>Basic</td>\n      <td>4.0</td>\n      <td>Divorced</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>Executive</td>\n      <td>18468.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:03.491117Z","iopub.execute_input":"2024-08-09T22:58:03.491505Z","iopub.status.idle":"2024-08-09T22:58:03.561780Z","shell.execute_reply.started":"2024-08-09T22:58:03.491473Z","shell.execute_reply":"2024-08-09T22:58:03.560898Z"},"trusted":true},"execution_count":142,"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":"           CustomerID    PlanTaken          Age TypeofContact     CityTier  \\\ncount     4888.000000  4888.000000  4662.000000          4863  4888.000000   \nunique            NaN          NaN          NaN             2          NaN   \ntop               NaN          NaN          NaN  Self Enquiry          NaN   \nfreq              NaN          NaN          NaN          3444          NaN   \nmean    202443.500000     0.188216    37.622265           NaN     1.654255   \nstd       1411.188388     0.390925     9.316387           NaN     0.916583   \nmin     200000.000000     0.000000    18.000000           NaN     1.000000   \n25%     201221.750000     0.000000    31.000000           NaN     1.000000   \n50%     202443.500000     0.000000    36.000000           NaN     1.000000   \n75%     203665.250000     0.000000    44.000000           NaN     3.000000   \nmax     204887.000000     1.000000    61.000000           NaN     3.000000   \n\n        DurationOfPitch Occupation Gender  NumberOfPersons  NumberOfFollowups  \\\ncount       4637.000000       4888   4888      4888.000000        4843.000000   \nunique              NaN          4      3              NaN                NaN   \ntop                 NaN   Salaried   Male              NaN                NaN   \nfreq                NaN       2368   2916              NaN                NaN   \nmean          15.490835        NaN    NaN         2.905074           3.708445   \nstd            8.519643        NaN    NaN         0.724891           1.002509   \nmin            5.000000        NaN    NaN         1.000000           1.000000   \n25%            9.000000        NaN    NaN         2.000000           3.000000   \n50%           13.000000        NaN    NaN         3.000000           4.000000   \n75%           20.000000        NaN    NaN         3.000000           4.000000   \nmax          127.000000        NaN    NaN         5.000000           6.000000   \n\n       PlanPitched  PreferredServiceStar MaritalStatus  NumberOfUpgrades  \\\ncount         4888           4862.000000          4888       4748.000000   \nunique           5                   NaN             4               NaN   \ntop          Basic                   NaN       Married               NaN   \nfreq          1842                   NaN          2340               NaN   \nmean           NaN              3.581037           NaN          3.236521   \nstd            NaN              0.798009           NaN          1.849019   \nmin            NaN              3.000000           NaN          1.000000   \n25%            NaN              3.000000           NaN          2.000000   \n50%            NaN              3.000000           NaN          3.000000   \n75%            NaN              4.000000           NaN          4.000000   \nmax            NaN              5.000000           NaN         22.000000   \n\n             iPhone  PitchSatisfactionScore  PhoneContract  NumberOfChildren  \\\ncount   4888.000000             4888.000000    4888.000000       4822.000000   \nunique          NaN                     NaN            NaN               NaN   \ntop             NaN                     NaN            NaN               NaN   \nfreq            NaN                     NaN            NaN               NaN   \nmean       0.290917                3.078151       0.620295          1.187267   \nstd        0.454232                1.365792       0.485363          0.857861   \nmin        0.000000                1.000000       0.000000          0.000000   \n25%        0.000000                2.000000       0.000000          1.000000   \n50%        0.000000                3.000000       1.000000          1.000000   \n75%        1.000000                4.000000       1.000000          2.000000   \nmax        1.000000                5.000000       1.000000          3.000000   \n\n       Designation  MonthlyIncome  \ncount         4888    4655.000000  \nunique           5            NaN  \ntop      Executive            NaN  \nfreq          1842            NaN  \nmean           NaN   23619.853491  \nstd            NaN    5380.698361  \nmin            NaN    1000.000000  \n25%            NaN   20346.000000  \n50%            NaN   22347.000000  \n75%            NaN   25571.000000  \nmax            NaN   98678.000000  ","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>CustomerID</th>\n      <th>PlanTaken</th>\n      <th>Age</th>\n      <th>TypeofContact</th>\n      <th>CityTier</th>\n      <th>DurationOfPitch</th>\n      <th>Occupation</th>\n      <th>Gender</th>\n      <th>NumberOfPersons</th>\n      <th>NumberOfFollowups</th>\n      <th>PlanPitched</th>\n      <th>PreferredServiceStar</th>\n      <th>MaritalStatus</th>\n      <th>NumberOfUpgrades</th>\n      <th>iPhone</th>\n      <th>PitchSatisfactionScore</th>\n      <th>PhoneContract</th>\n      <th>NumberOfChildren</th>\n      <th>Designation</th>\n      <th>MonthlyIncome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4662.000000</td>\n      <td>4863</td>\n      <td>4888.000000</td>\n      <td>4637.000000</td>\n      <td>4888</td>\n      <td>4888</td>\n      <td>4888.000000</td>\n      <td>4843.000000</td>\n      <td>4888</td>\n      <td>4862.000000</td>\n      <td>4888</td>\n      <td>4748.000000</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4822.000000</td>\n      <td>4888</td>\n      <td>4655.000000</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Self Enquiry</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Salaried</td>\n      <td>Male</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Basic</td>\n      <td>NaN</td>\n      <td>Married</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Executive</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3444</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2368</td>\n      <td>2916</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1842</td>\n      <td>NaN</td>\n      <td>2340</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1842</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>202443.500000</td>\n      <td>0.188216</td>\n      <td>37.622265</td>\n      <td>NaN</td>\n      <td>1.654255</td>\n      <td>15.490835</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.905074</td>\n      <td>3.708445</td>\n      <td>NaN</td>\n      <td>3.581037</td>\n      <td>NaN</td>\n      <td>3.236521</td>\n      <td>0.290917</td>\n      <td>3.078151</td>\n      <td>0.620295</td>\n      <td>1.187267</td>\n      <td>NaN</td>\n      <td>23619.853491</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1411.188388</td>\n      <td>0.390925</td>\n      <td>9.316387</td>\n      <td>NaN</td>\n      <td>0.916583</td>\n      <td>8.519643</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.724891</td>\n      <td>1.002509</td>\n      <td>NaN</td>\n      <td>0.798009</td>\n      <td>NaN</td>\n      <td>1.849019</td>\n      <td>0.454232</td>\n      <td>1.365792</td>\n      <td>0.485363</td>\n      <td>0.857861</td>\n      <td>NaN</td>\n      <td>5380.698361</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>200000.000000</td>\n      <td>0.000000</td>\n      <td>18.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>201221.750000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>9.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>20346.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>202443.500000</td>\n      <td>0.000000</td>\n      <td>36.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>13.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>22347.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>203665.250000</td>\n      <td>0.000000</td>\n      <td>44.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>20.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>NaN</td>\n      <td>25571.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>204887.000000</td>\n      <td>1.000000</td>\n      <td>61.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>127.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.000000</td>\n      <td>6.000000</td>\n      <td>NaN</td>\n      <td>5.000000</td>\n      <td>NaN</td>\n      <td>22.000000</td>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>98678.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:03.563561Z","iopub.execute_input":"2024-08-09T22:58:03.563837Z","iopub.status.idle":"2024-08-09T22:58:03.570274Z","shell.execute_reply.started":"2024-08-09T22:58:03.563815Z","shell.execute_reply":"2024-08-09T22:58:03.569205Z"},"trusted":true},"execution_count":143,"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":"(4888, 20)"},"metadata":{}}]},{"cell_type":"code","source":"missing_values=df.isnull().sum()\nmissing_values","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:03.571803Z","iopub.execute_input":"2024-08-09T22:58:03.572124Z","iopub.status.idle":"2024-08-09T22:58:03.589241Z","shell.execute_reply.started":"2024-08-09T22:58:03.572094Z","shell.execute_reply":"2024-08-09T22:58:03.588427Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":"CustomerID                  0\nPlanTaken                   0\nAge                       226\nTypeofContact              25\nCityTier                    0\nDurationOfPitch           251\nOccupation                  0\nGender                      0\nNumberOfPersons             0\nNumberOfFollowups          45\nPlanPitched                 0\nPreferredServiceStar       26\nMaritalStatus               0\nNumberOfUpgrades          140\niPhone                      0\nPitchSatisfactionScore      0\nPhoneContract               0\nNumberOfChildren           66\nDesignation                 0\nMonthlyIncome             233\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df['Age'] = df['Age'].fillna(df['Age'].median())\n","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:03.590198Z","iopub.execute_input":"2024-08-09T22:58:03.590464Z","iopub.status.idle":"2024-08-09T22:58:03.596882Z","shell.execute_reply.started":"2024-08-09T22:58:03.590434Z","shell.execute_reply":"2024-08-09T22:58:03.595880Z"},"trusted":true},"execution_count":145,"outputs":[]},{"cell_type":"code","source":"df['TypeofContact'] = df['TypeofContact'].fillna(df['TypeofContact'].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:03.638799Z","iopub.execute_input":"2024-08-09T22:58:03.639050Z","iopub.status.idle":"2024-08-09T22:58:03.646165Z","shell.execute_reply.started":"2024-08-09T22:58:03.639027Z","shell.execute_reply":"2024-08-09T22:58:03.645111Z"},"trusted":true},"execution_count":146,"outputs":[]},{"cell_type":"code","source":"missing_values=df.isnull().sum()\nmissing_values","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:04.522934Z","iopub.execute_input":"2024-08-09T22:58:04.523633Z","iopub.status.idle":"2024-08-09T22:58:04.534854Z","shell.execute_reply.started":"2024-08-09T22:58:04.523601Z","shell.execute_reply":"2024-08-09T22:58:04.533990Z"},"trusted":true},"execution_count":147,"outputs":[{"execution_count":147,"output_type":"execute_result","data":{"text/plain":"CustomerID                  0\nPlanTaken                   0\nAge                         0\nTypeofContact               0\nCityTier                    0\nDurationOfPitch           251\nOccupation                  0\nGender                      0\nNumberOfPersons             0\nNumberOfFollowups          45\nPlanPitched                 0\nPreferredServiceStar       26\nMaritalStatus               0\nNumberOfUpgrades          140\niPhone                      0\nPitchSatisfactionScore      0\nPhoneContract               0\nNumberOfChildren           66\nDesignation                 0\nMonthlyIncome             233\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df['DurationOfPitch']=df['DurationOfPitch'].fillna(df['DurationOfPitch'].median)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:05.752783Z","iopub.execute_input":"2024-08-09T22:58:05.753392Z","iopub.status.idle":"2024-08-09T22:58:05.759359Z","shell.execute_reply.started":"2024-08-09T22:58:05.753346Z","shell.execute_reply":"2024-08-09T22:58:05.758445Z"},"trusted":true},"execution_count":148,"outputs":[]},{"cell_type":"code","source":"df['NumberOfFollowups'] = df['NumberOfFollowups'].fillna(df['NumberOfFollowups'].median())\ndf['NumberOfUpgrades'] = df['NumberOfUpgrades'].fillna(df['NumberOfUpgrades'].median())\ndf['NumberOfChildren'] = df['NumberOfChildren'].fillna(df['NumberOfChildren'].median())\ndf['MonthlyIncome'] = df['MonthlyIncome'].fillna(df['MonthlyIncome'].median())\ndf['PreferredServiceStar'] = df['PreferredServiceStar'].fillna(df['PreferredServiceStar'].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:06.519550Z","iopub.execute_input":"2024-08-09T22:58:06.519899Z","iopub.status.idle":"2024-08-09T22:58:06.531757Z","shell.execute_reply.started":"2024-08-09T22:58:06.519867Z","shell.execute_reply":"2024-08-09T22:58:06.530443Z"},"trusted":true},"execution_count":149,"outputs":[]},{"cell_type":"code","source":"missing_values=df.isnull().sum()\nmissing_values","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:08.160305Z","iopub.execute_input":"2024-08-09T22:58:08.161148Z","iopub.status.idle":"2024-08-09T22:58:08.172561Z","shell.execute_reply.started":"2024-08-09T22:58:08.161115Z","shell.execute_reply":"2024-08-09T22:58:08.171597Z"},"trusted":true},"execution_count":150,"outputs":[{"execution_count":150,"output_type":"execute_result","data":{"text/plain":"CustomerID                0\nPlanTaken                 0\nAge                       0\nTypeofContact             0\nCityTier                  0\nDurationOfPitch           0\nOccupation                0\nGender                    0\nNumberOfPersons           0\nNumberOfFollowups         0\nPlanPitched               0\nPreferredServiceStar      0\nMaritalStatus             0\nNumberOfUpgrades          0\niPhone                    0\nPitchSatisfactionScore    0\nPhoneContract             0\nNumberOfChildren          0\nDesignation               0\nMonthlyIncome             0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:10.146749Z","iopub.execute_input":"2024-08-09T22:58:10.147335Z","iopub.status.idle":"2024-08-09T22:58:10.168879Z","shell.execute_reply.started":"2024-08-09T22:58:10.147303Z","shell.execute_reply":"2024-08-09T22:58:10.167902Z"},"trusted":true},"execution_count":151,"outputs":[{"execution_count":151,"output_type":"execute_result","data":{"text/plain":"   CustomerID  PlanTaken   Age    TypeofContact  CityTier DurationOfPitch  \\\n0      200000          1  41.0     Self Enquiry         3             6.0   \n1      200001          0  49.0  Company Invited         1            14.0   \n2      200002          1  37.0     Self Enquiry         1             8.0   \n3      200003          0  33.0  Company Invited         1             9.0   \n4      200004          0  36.0     Self Enquiry         1             8.0   \n\n       Occupation  Gender  NumberOfPersons  NumberOfFollowups PlanPitched  \\\n0        Salaried  Female                3                3.0      Deluxe   \n1        Salaried    Male                3                4.0      Deluxe   \n2     Free Lancer    Male                3                4.0       Basic   \n3        Salaried  Female                2                3.0       Basic   \n4  Small Business    Male                2                3.0       Basic   \n\n   PreferredServiceStar MaritalStatus  NumberOfUpgrades  iPhone  \\\n0                   3.0        Single               1.0       1   \n1                   4.0      Divorced               2.0       0   \n2                   3.0        Single               7.0       1   \n3                   3.0      Divorced               2.0       1   \n4                   4.0      Divorced               1.0       0   \n\n   PitchSatisfactionScore  PhoneContract  NumberOfChildren Designation  \\\n0                       2              1               0.0     Manager   \n1                       3              1               2.0     Manager   \n2                       3              0               0.0   Executive   \n3                       5              1               1.0   Executive   \n4                       5              1               0.0   Executive   \n\n   MonthlyIncome  \n0        20993.0  \n1        20130.0  \n2        17090.0  \n3        17909.0  \n4        18468.0  ","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>CustomerID</th>\n      <th>PlanTaken</th>\n      <th>Age</th>\n      <th>TypeofContact</th>\n      <th>CityTier</th>\n      <th>DurationOfPitch</th>\n      <th>Occupation</th>\n      <th>Gender</th>\n      <th>NumberOfPersons</th>\n      <th>NumberOfFollowups</th>\n      <th>PlanPitched</th>\n      <th>PreferredServiceStar</th>\n      <th>MaritalStatus</th>\n      <th>NumberOfUpgrades</th>\n      <th>iPhone</th>\n      <th>PitchSatisfactionScore</th>\n      <th>PhoneContract</th>\n      <th>NumberOfChildren</th>\n      <th>Designation</th>\n      <th>MonthlyIncome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>200000</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>Self Enquiry</td>\n      <td>3</td>\n      <td>6.0</td>\n      <td>Salaried</td>\n      <td>Female</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>Deluxe</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>Manager</td>\n      <td>20993.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>200001</td>\n      <td>0</td>\n      <td>49.0</td>\n      <td>Company Invited</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>Salaried</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Deluxe</td>\n      <td>4.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>Manager</td>\n      <td>20130.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>200002</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>Self Enquiry</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>Free Lancer</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>7.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>Executive</td>\n      <td>17090.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>200003</td>\n      <td>0</td>\n      <td>33.0</td>\n      <td>Company Invited</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>Salaried</td>\n      <td>Female</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>Executive</td>\n      <td>17909.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>200004</td>\n      <td>0</td>\n      <td>36.0</td>\n      <td>Self Enquiry</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>Small Business</td>\n      <td>Male</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>Basic</td>\n      <td>4.0</td>\n      <td>Divorced</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>Executive</td>\n      <td>18468.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:15.170357Z","iopub.execute_input":"2024-08-09T22:58:15.170866Z","iopub.status.idle":"2024-08-09T22:58:15.235452Z","shell.execute_reply.started":"2024-08-09T22:58:15.170834Z","shell.execute_reply":"2024-08-09T22:58:15.234563Z"},"trusted":true},"execution_count":152,"outputs":[{"execution_count":152,"output_type":"execute_result","data":{"text/plain":"           CustomerID    PlanTaken          Age TypeofContact     CityTier  \\\ncount     4888.000000  4888.000000  4888.000000          4888  4888.000000   \nunique            NaN          NaN          NaN             2          NaN   \ntop               NaN          NaN          NaN  Self Enquiry          NaN   \nfreq              NaN          NaN          NaN          3469          NaN   \nmean    202443.500000     0.188216    37.547259           NaN     1.654255   \nstd       1411.188388     0.390925     9.104795           NaN     0.916583   \nmin     200000.000000     0.000000    18.000000           NaN     1.000000   \n25%     201221.750000     0.000000    31.000000           NaN     1.000000   \n50%     202443.500000     0.000000    36.000000           NaN     1.000000   \n75%     203665.250000     0.000000    43.000000           NaN     3.000000   \nmax     204887.000000     1.000000    61.000000           NaN     3.000000   \n\n        DurationOfPitch Occupation Gender  NumberOfPersons  NumberOfFollowups  \\\ncount            4888.0       4888   4888      4888.000000        4888.000000   \nunique             35.0          4      3              NaN                NaN   \ntop                 9.0   Salaried   Male              NaN                NaN   \nfreq              483.0       2368   2916              NaN                NaN   \nmean                NaN        NaN    NaN         2.905074           3.711129   \nstd                 NaN        NaN    NaN         0.724891           0.998271   \nmin                 NaN        NaN    NaN         1.000000           1.000000   \n25%                 NaN        NaN    NaN         2.000000           3.000000   \n50%                 NaN        NaN    NaN         3.000000           4.000000   \n75%                 NaN        NaN    NaN         3.000000           4.000000   \nmax                 NaN        NaN    NaN         5.000000           6.000000   \n\n       PlanPitched  PreferredServiceStar MaritalStatus  NumberOfUpgrades  \\\ncount         4888           4888.000000          4888       4888.000000   \nunique           5                   NaN             4               NaN   \ntop          Basic                   NaN       Married               NaN   \nfreq          1842                   NaN          2340               NaN   \nmean           NaN              3.577946           NaN          3.229746   \nstd            NaN              0.797005           NaN          1.822769   \nmin            NaN              3.000000           NaN          1.000000   \n25%            NaN              3.000000           NaN          2.000000   \n50%            NaN              3.000000           NaN          3.000000   \n75%            NaN              4.000000           NaN          4.000000   \nmax            NaN              5.000000           NaN         22.000000   \n\n             iPhone  PitchSatisfactionScore  PhoneContract  NumberOfChildren  \\\ncount   4888.000000             4888.000000    4888.000000       4888.000000   \nunique          NaN                     NaN            NaN               NaN   \ntop             NaN                     NaN            NaN               NaN   \nfreq            NaN                     NaN            NaN               NaN   \nmean       0.290917                3.078151       0.620295          1.184738   \nstd        0.454232                1.365792       0.485363          0.852323   \nmin        0.000000                1.000000       0.000000          0.000000   \n25%        0.000000                2.000000       0.000000          1.000000   \n50%        0.000000                3.000000       1.000000          1.000000   \n75%        1.000000                4.000000       1.000000          2.000000   \nmax        1.000000                5.000000       1.000000          3.000000   \n\n       Designation  MonthlyIncome  \ncount         4888    4888.000000  \nunique           5            NaN  \ntop      Executive            NaN  \nfreq          1842            NaN  \nmean           NaN   23559.179419  \nstd            NaN    5257.862921  \nmin            NaN    1000.000000  \n25%            NaN   20485.000000  \n50%            NaN   22347.000000  \n75%            NaN   25424.750000  \nmax            NaN   98678.000000  ","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>CustomerID</th>\n      <th>PlanTaken</th>\n      <th>Age</th>\n      <th>TypeofContact</th>\n      <th>CityTier</th>\n      <th>DurationOfPitch</th>\n      <th>Occupation</th>\n      <th>Gender</th>\n      <th>NumberOfPersons</th>\n      <th>NumberOfFollowups</th>\n      <th>PlanPitched</th>\n      <th>PreferredServiceStar</th>\n      <th>MaritalStatus</th>\n      <th>NumberOfUpgrades</th>\n      <th>iPhone</th>\n      <th>PitchSatisfactionScore</th>\n      <th>PhoneContract</th>\n      <th>NumberOfChildren</th>\n      <th>Designation</th>\n      <th>MonthlyIncome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888</td>\n      <td>4888.000000</td>\n      <td>4888.0</td>\n      <td>4888</td>\n      <td>4888</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888</td>\n      <td>4888.000000</td>\n      <td>4888</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888.000000</td>\n      <td>4888</td>\n      <td>4888.000000</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>35.0</td>\n      <td>4</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Self Enquiry</td>\n      <td>NaN</td>\n      <td>9.0</td>\n      <td>Salaried</td>\n      <td>Male</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Basic</td>\n      <td>NaN</td>\n      <td>Married</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Executive</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3469</td>\n      <td>NaN</td>\n      <td>483.0</td>\n      <td>2368</td>\n      <td>2916</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1842</td>\n      <td>NaN</td>\n      <td>2340</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1842</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>202443.500000</td>\n      <td>0.188216</td>\n      <td>37.547259</td>\n      <td>NaN</td>\n      <td>1.654255</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.905074</td>\n      <td>3.711129</td>\n      <td>NaN</td>\n      <td>3.577946</td>\n      <td>NaN</td>\n      <td>3.229746</td>\n      <td>0.290917</td>\n      <td>3.078151</td>\n      <td>0.620295</td>\n      <td>1.184738</td>\n      <td>NaN</td>\n      <td>23559.179419</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1411.188388</td>\n      <td>0.390925</td>\n      <td>9.104795</td>\n      <td>NaN</td>\n      <td>0.916583</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.724891</td>\n      <td>0.998271</td>\n      <td>NaN</td>\n      <td>0.797005</td>\n      <td>NaN</td>\n      <td>1.822769</td>\n      <td>0.454232</td>\n      <td>1.365792</td>\n      <td>0.485363</td>\n      <td>0.852323</td>\n      <td>NaN</td>\n      <td>5257.862921</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>200000.000000</td>\n      <td>0.000000</td>\n      <td>18.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>201221.750000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>20485.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>202443.500000</td>\n      <td>0.000000</td>\n      <td>36.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>22347.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>203665.250000</td>\n      <td>0.000000</td>\n      <td>43.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>NaN</td>\n      <td>25424.750000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>204887.000000</td>\n      <td>1.000000</td>\n      <td>61.000000</td>\n      <td>NaN</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.000000</td>\n      <td>6.000000</td>\n      <td>NaN</td>\n      <td>5.000000</td>\n      <td>NaN</td>\n      <td>22.000000</td>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>98678.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nnumerical_columns=df.select_dtypes(include=['float64', 'int64']).columns \ndf[numerical_columns].hist(figsize=(15,10), bins=15)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:17.228126Z","iopub.execute_input":"2024-08-09T22:58:17.228550Z","iopub.status.idle":"2024-08-09T22:58:19.839422Z","shell.execute_reply.started":"2024-08-09T22:58:17.228517Z","shell.execute_reply":"2024-08-09T22:58:19.838524Z"},"trusted":true},"execution_count":153,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 16 Axes>","image/png":"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,"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":"Index(['TypeofContact', 'DurationOfPitch', 'Occupation', 'Gender',\n       'PlanPitched', 'MaritalStatus', 'Designation'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"df2=df.drop('CustomerID', axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:58:45.292761Z","iopub.execute_input":"2024-08-09T22:58:45.293118Z","iopub.status.idle":"2024-08-09T22:58:45.300088Z","shell.execute_reply.started":"2024-08-09T22:58:45.293088Z","shell.execute_reply":"2024-08-09T22:58:45.299112Z"},"trusted":true},"execution_count":156,"outputs":[]},{"cell_type":"code","source":"numeric_df = df2.select_dtypes(include=['int64', 'float64'])\nnumeric_df","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:59:31.425448Z","iopub.execute_input":"2024-08-09T22:59:31.426240Z","iopub.status.idle":"2024-08-09T22:59:31.449116Z","shell.execute_reply.started":"2024-08-09T22:59:31.426106Z","shell.execute_reply":"2024-08-09T22:59:31.448282Z"},"trusted":true},"execution_count":163,"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":"      PlanTaken   Age  CityTier  NumberOfPersons  NumberOfFollowups  \\\n0             1  41.0         3                3                3.0   \n1             0  49.0         1                3                4.0   \n2             1  37.0         1                3                4.0   \n3             0  33.0         1                2                3.0   \n4             0  36.0         1                2                3.0   \n...         ...   ...       ...              ...                ...   \n4883          1  49.0         3                3                5.0   \n4884          1  28.0         1                4                5.0   \n4885          1  52.0         3                4                4.0   \n4886          1  19.0         3                3                4.0   \n4887          1  36.0         1                4                4.0   \n\n      PreferredServiceStar  NumberOfUpgrades  iPhone  PitchSatisfactionScore  \\\n0                      3.0               1.0       1                       2   \n1                      4.0               2.0       0                       3   \n2                      3.0               7.0       1                       3   \n3                      3.0               2.0       1                       5   \n4                      4.0               1.0       0                       5   \n...                    ...               ...     ...                     ...   \n4883                   4.0               2.0       1                       1   \n4884                   3.0               3.0       1                       3   \n4885                   4.0               7.0       0                       1   \n4886                   3.0               3.0       0                       5   \n4887                   4.0               3.0       1                       3   \n\n      PhoneContract  NumberOfChildren  MonthlyIncome  \n0                 1               0.0        20993.0  \n1                 1               2.0        20130.0  \n2                 0               0.0        17090.0  \n3                 1               1.0        17909.0  \n4                 1               0.0        18468.0  \n...             ...               ...            ...  \n4883              1               1.0        26576.0  \n4884              1               2.0        21212.0  \n4885              1               3.0        31820.0  \n4886              0               2.0        20289.0  \n4887              1               2.0        24041.0  \n\n[4888 rows x 12 columns]","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>PlanTaken</th>\n      <th>Age</th>\n      <th>CityTier</th>\n      <th>NumberOfPersons</th>\n      <th>NumberOfFollowups</th>\n      <th>PreferredServiceStar</th>\n      <th>NumberOfUpgrades</th>\n      <th>iPhone</th>\n      <th>PitchSatisfactionScore</th>\n      <th>PhoneContract</th>\n      <th>NumberOfChildren</th>\n      <th>MonthlyIncome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>41.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>20993.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>49.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>20130.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>7.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>17090.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>33.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>17909.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>36.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>18468.0</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    </tr>\n    <tr>\n      <th>4883</th>\n      <td>1</td>\n      <td>49.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>26576.0</td>\n    </tr>\n    <tr>\n      <th>4884</th>\n      <td>1</td>\n      <td>28.0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>21212.0</td>\n    </tr>\n    <tr>\n      <th>4885</th>\n      <td>1</td>\n      <td>52.0</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>31820.0</td>\n    </tr>\n    <tr>\n      <th>4886</th>\n      <td>1</td>\n      <td>19.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>20289.0</td>\n    </tr>\n    <tr>\n      <th>4887</th>\n      <td>1</td>\n      <td>36.0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>24041.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4888 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:57:08.684885Z","iopub.execute_input":"2024-08-09T22:57:08.685554Z","iopub.status.idle":"2024-08-09T22:57:08.715371Z","shell.execute_reply.started":"2024-08-09T22:57:08.685521Z","shell.execute_reply":"2024-08-09T22:57:08.714407Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"      CustomerID  PlanTaken   Age    TypeofContact  CityTier DurationOfPitch  \\\n0         200000          1  41.0     Self Enquiry         3             6.0   \n1         200001          0  49.0  Company Invited         1            14.0   \n2         200002          1  37.0     Self Enquiry         1             8.0   \n3         200003          0  33.0  Company Invited         1             9.0   \n4         200004          0  36.0     Self Enquiry         1             8.0   \n...          ...        ...   ...              ...       ...             ...   \n4883      204883          1  49.0     Self Enquiry         3             9.0   \n4884      204884          1  28.0  Company Invited         1            31.0   \n4885      204885          1  52.0     Self Enquiry         3            17.0   \n4886      204886          1  19.0     Self Enquiry         3            16.0   \n4887      204887          1  36.0     Self Enquiry         1            14.0   \n\n          Occupation  Gender  NumberOfPersons  NumberOfFollowups PlanPitched  \\\n0           Salaried  Female                3                3.0      Deluxe   \n1           Salaried    Male                3                4.0      Deluxe   \n2        Free Lancer    Male                3                4.0       Basic   \n3           Salaried  Female                2                3.0       Basic   \n4     Small Business    Male                2                3.0       Basic   \n...              ...     ...              ...                ...         ...   \n4883  Small Business    Male                3                5.0      Deluxe   \n4884        Salaried    Male                4                5.0       Basic   \n4885        Salaried  Female                4                4.0    Standard   \n4886  Small Business    Male                3                4.0       Basic   \n4887        Salaried    Male                4                4.0       Basic   \n\n      PreferredServiceStar MaritalStatus  NumberOfUpgrades  iPhone  \\\n0                      3.0        Single               1.0       1   \n1                      4.0      Divorced               2.0       0   \n2                      3.0        Single               7.0       1   \n3                      3.0      Divorced               2.0       1   \n4                      4.0      Divorced               1.0       0   \n...                    ...           ...               ...     ...   \n4883                   4.0     Unmarried               2.0       1   \n4884                   3.0        Single               3.0       1   \n4885                   4.0       Married               7.0       0   \n4886                   3.0        Single               3.0       0   \n4887                   4.0     Unmarried               3.0       1   \n\n      PitchSatisfactionScore  PhoneContract  NumberOfChildren     Designation  \\\n0                          2              1               0.0         Manager   \n1                          3              1               2.0         Manager   \n2                          3              0               0.0       Executive   \n3                          5              1               1.0       Executive   \n4                          5              1               0.0       Executive   \n...                      ...            ...               ...             ...   \n4883                       1              1               1.0         Manager   \n4884                       3              1               2.0       Executive   \n4885                       1              1               3.0  Senior Manager   \n4886                       5              0               2.0       Executive   \n4887                       3              1               2.0       Executive   \n\n      MonthlyIncome  \n0           20993.0  \n1           20130.0  \n2           17090.0  \n3           17909.0  \n4           18468.0  \n...             ...  \n4883        26576.0  \n4884        21212.0  \n4885        31820.0  \n4886        20289.0  \n4887        24041.0  \n\n[4888 rows x 20 columns]","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>CustomerID</th>\n      <th>PlanTaken</th>\n      <th>Age</th>\n      <th>TypeofContact</th>\n      <th>CityTier</th>\n      <th>DurationOfPitch</th>\n      <th>Occupation</th>\n      <th>Gender</th>\n      <th>NumberOfPersons</th>\n      <th>NumberOfFollowups</th>\n      <th>PlanPitched</th>\n      <th>PreferredServiceStar</th>\n      <th>MaritalStatus</th>\n      <th>NumberOfUpgrades</th>\n      <th>iPhone</th>\n      <th>PitchSatisfactionScore</th>\n      <th>PhoneContract</th>\n      <th>NumberOfChildren</th>\n      <th>Designation</th>\n      <th>MonthlyIncome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>200000</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>Self Enquiry</td>\n      <td>3</td>\n      <td>6.0</td>\n      <td>Salaried</td>\n      <td>Female</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>Deluxe</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>Manager</td>\n      <td>20993.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>200001</td>\n      <td>0</td>\n      <td>49.0</td>\n      <td>Company Invited</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>Salaried</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Deluxe</td>\n      <td>4.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>Manager</td>\n      <td>20130.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>200002</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>Self Enquiry</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>Free Lancer</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>7.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>Executive</td>\n      <td>17090.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>200003</td>\n      <td>0</td>\n      <td>33.0</td>\n      <td>Company Invited</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>Salaried</td>\n      <td>Female</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>Executive</td>\n      <td>17909.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>200004</td>\n      <td>0</td>\n      <td>36.0</td>\n      <td>Self Enquiry</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>Small Business</td>\n      <td>Male</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>Basic</td>\n      <td>4.0</td>\n      <td>Divorced</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>Executive</td>\n      <td>18468.0</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    </tr>\n    <tr>\n      <th>4883</th>\n      <td>204883</td>\n      <td>1</td>\n      <td>49.0</td>\n      <td>Self Enquiry</td>\n      <td>3</td>\n      <td>9.0</td>\n      <td>Small Business</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>5.0</td>\n      <td>Deluxe</td>\n      <td>4.0</td>\n      <td>Unmarried</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>Manager</td>\n      <td>26576.0</td>\n    </tr>\n    <tr>\n      <th>4884</th>\n      <td>204884</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>Company Invited</td>\n      <td>1</td>\n      <td>31.0</td>\n      <td>Salaried</td>\n      <td>Male</td>\n      <td>4</td>\n      <td>5.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>Executive</td>\n      <td>21212.0</td>\n    </tr>\n    <tr>\n      <th>4885</th>\n      <td>204885</td>\n      <td>1</td>\n      <td>52.0</td>\n      <td>Self Enquiry</td>\n      <td>3</td>\n      <td>17.0</td>\n      <td>Salaried</td>\n      <td>Female</td>\n      <td>4</td>\n      <td>4.0</td>\n      <td>Standard</td>\n      <td>4.0</td>\n      <td>Married</td>\n      <td>7.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>Senior Manager</td>\n      <td>31820.0</td>\n    </tr>\n    <tr>\n      <th>4886</th>\n      <td>204886</td>\n      <td>1</td>\n      <td>19.0</td>\n      <td>Self Enquiry</td>\n      <td>3</td>\n      <td>16.0</td>\n      <td>Small Business</td>\n      <td>Male</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Basic</td>\n      <td>3.0</td>\n      <td>Single</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>Executive</td>\n      <td>20289.0</td>\n    </tr>\n    <tr>\n      <th>4887</th>\n      <td>204887</td>\n      <td>1</td>\n      <td>36.0</td>\n      <td>Self Enquiry</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>Salaried</td>\n      <td>Male</td>\n      <td>4</td>\n      <td>4.0</td>\n      <td>Basic</td>\n      <td>4.0</td>\n      <td>Unmarried</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>Executive</td>\n      <td>24041.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4888 rows × 20 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"Occupation\nPlanPitched\nMaritalStatus\nDesignation\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#binary and ordered features label encoding\n# Label Encoding for binary categories or ordered categories\ndf['Gender'] = df['Gender'].map({'Female': 1, 'Male': 0})\ndf['TypeofContact'] = df['TypeofContact'].map({'Company Invited': 0, 'Self Enquiry': 1})\n","metadata":{"execution":{"iopub.status.busy":"2024-08-09T23:04:08.657131Z","iopub.execute_input":"2024-08-09T23:04:08.657845Z","iopub.status.idle":"2024-08-09T23:04:08.668801Z","shell.execute_reply.started":"2024-08-09T23:04:08.657813Z","shell.execute_reply":"2024-08-09T23:04:08.667958Z"},"trusted":true},"execution_count":167,"outputs":[]},{"cell_type":"code","source":"#Onehot encoding\ndf = pd.get_dummies(df, columns=['Occupation', 'PlanPitched', 'MaritalStatus', 'Designation'], drop_first=True)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T23:04:09.352830Z","iopub.execute_input":"2024-08-09T23:04:09.353571Z","iopub.status.idle":"2024-08-09T23:04:09.443517Z","shell.execute_reply.started":"2024-08-09T23:04:09.353540Z","shell.execute_reply":"2024-08-09T23:04:09.442386Z"},"trusted":true},"execution_count":168,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","Cell \u001b[0;32mIn[168], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#Onehot encoding\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_dummies\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mOccupation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPlanPitched\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMaritalStatus\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDesignation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop_first\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/reshape/encoding.py:169\u001b[0m, in \u001b[0;36mget_dummies\u001b[0;34m(data, prefix, prefix_sep, dummy_na, columns, sparse, drop_first, dtype)\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput must be a list-like for parameter `columns`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 169\u001b[0m     data_to_encode \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;66;03m# validate prefixes and separator to avoid silently dropping cols\u001b[39;00m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcheck_len\u001b[39m(item, name: \u001b[38;5;28mstr\u001b[39m):\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4106\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   4107\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 4108\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/indexes/base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6198\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m   6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m   6204\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/indexes/base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[1;32m   6248\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[0;32m-> 6249\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   6251\u001b[0m     not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m   6252\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['Occupation', 'PlanPitched', 'MaritalStatus', 'Designation'], dtype='object')] are in the [columns]\""],"ename":"KeyError","evalue":"\"None of [Index(['Occupation', 'PlanPitched', 'MaritalStatus', 'Designation'], dtype='object')] are in the [columns]\"","output_type":"error"}]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2024-08-09T23:04:11.635929Z","iopub.execute_input":"2024-08-09T23:04:11.636914Z","iopub.status.idle":"2024-08-09T23:04:11.667129Z","shell.execute_reply.started":"2024-08-09T23:04:11.636866Z","shell.execute_reply":"2024-08-09T23:04:11.666276Z"},"trusted":true},"execution_count":169,"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"      CustomerID  PlanTaken   Age  TypeofContact  CityTier DurationOfPitch  \\\n0         200000          1  41.0            NaN       NaN             6.0   \n1         200001          0  49.0            NaN       NaN            14.0   \n2         200002          1  37.0            NaN       NaN             8.0   \n3         200003          0  33.0            NaN       NaN             9.0   \n4         200004          0  36.0            NaN       NaN             8.0   \n...          ...        ...   ...            ...       ...             ...   \n4883      204883          1  49.0            NaN       NaN             9.0   \n4884      204884          1  28.0            NaN       NaN            31.0   \n4885      204885          1  52.0            NaN       NaN            17.0   \n4886      204886          1  19.0            NaN       NaN            16.0   \n4887      204887          1  36.0            NaN       NaN            14.0   \n\n      Gender  NumberOfPersons  NumberOfFollowups  PreferredServiceStar  ...  \\\n0        NaN                3                3.0                   3.0  ...   \n1        NaN                3                4.0                   4.0  ...   \n2        NaN                3                4.0                   3.0  ...   \n3        NaN                2                3.0                   3.0  ...   \n4        NaN                2                3.0                   4.0  ...   \n...      ...              ...                ...                   ...  ...   \n4883     NaN                3                5.0                   4.0  ...   \n4884     NaN                4                5.0                   3.0  ...   \n4885     NaN                4                4.0                   4.0  ...   \n4886     NaN                3                4.0                   3.0  ...   \n4887     NaN                4                4.0                   4.0  ...   \n\n      PlanPitched_King  PlanPitched_Standard  PlanPitched_Super Deluxe  \\\n0                False                 False                     False   \n1                False                 False                     False   \n2                False                 False                     False   \n3                False                 False                     False   \n4                False                 False                     False   \n...                ...                   ...                       ...   \n4883             False                 False                     False   \n4884             False                 False                     False   \n4885             False                  True                     False   \n4886             False                 False                     False   \n4887             False                 False                     False   \n\n      MaritalStatus_Married  MaritalStatus_Single  MaritalStatus_Unmarried  \\\n0                     False                  True                    False   \n1                     False                 False                    False   \n2                     False                  True                    False   \n3                     False                 False                    False   \n4                     False                 False                    False   \n...                     ...                   ...                      ...   \n4883                  False                 False                     True   \n4884                  False                  True                    False   \n4885                   True                 False                    False   \n4886                  False                  True                    False   \n4887                  False                 False                     True   \n\n      Designation_Executive  Designation_Manager  Designation_Senior Manager  \\\n0                     False                 True                       False   \n1                     False                 True                       False   \n2                      True                False                       False   \n3                      True                False                       False   \n4                      True                False                       False   \n...                     ...                  ...                         ...   \n4883                  False                 True                       False   \n4884                   True                False                       False   \n4885                  False                False                        True   \n4886                   True                False                       False   \n4887                   True                False                       False   \n\n      Designation_VP  \n0              False  \n1              False  \n2              False  \n3              False  \n4              False  \n...              ...  \n4883           False  \n4884           False  \n4885           False  \n4886           False  \n4887           False  \n\n[4888 rows x 30 columns]","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>CustomerID</th>\n      <th>PlanTaken</th>\n      <th>Age</th>\n      <th>TypeofContact</th>\n      <th>CityTier</th>\n      <th>DurationOfPitch</th>\n      <th>Gender</th>\n      <th>NumberOfPersons</th>\n      <th>NumberOfFollowups</th>\n      <th>PreferredServiceStar</th>\n      <th>...</th>\n      <th>PlanPitched_King</th>\n      <th>PlanPitched_Standard</th>\n      <th>PlanPitched_Super Deluxe</th>\n      <th>MaritalStatus_Married</th>\n      <th>MaritalStatus_Single</th>\n      <th>MaritalStatus_Unmarried</th>\n      <th>Designation_Executive</th>\n      <th>Designation_Manager</th>\n      <th>Designation_Senior Manager</th>\n      <th>Designation_VP</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>200000</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>...</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      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>200001</td>\n      <td>0</td>\n      <td>49.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>4.0</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>2</th>\n      <td>200002</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>...</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      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>200003</td>\n      <td>0</td>\n      <td>33.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.0</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>3.0</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>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>200004</td>\n      <td>0</td>\n      <td>36.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>4.0</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>True</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>4883</th>\n      <td>204883</td>\n      <td>1</td>\n      <td>49.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>5.0</td>\n      <td>4.0</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>4884</th>\n      <td>204884</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>31.0</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>...</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      <td>False</td>\n    </tr>\n    <tr>\n      <th>4885</th>\n      <td>204885</td>\n      <td>1</td>\n      <td>52.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>17.0</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>...</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4886</th>\n      <td>204886</td>\n      <td>1</td>\n      <td>19.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>16.0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>...</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      <td>False</td>\n    </tr>\n    <tr>\n      <th>4887</th>\n      <td>204887</td>\n      <td>1</td>\n      <td>36.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>4.0</td>\n      <td>4.0</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  </tbody>\n</table>\n<p>4888 rows × 30 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"PlanTaken\nCityTier\niPhone\nPhoneContract\nGender","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Correlation matrix\nplt.figure(figsize=(15, 10))\ncorrelation_matrix = numeric_df.corr()\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')\nplt.title('Correlation Matrix')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:18:15.470642Z","iopub.execute_input":"2024-08-09T22:18:15.470968Z","iopub.status.idle":"2024-08-09T22:18:16.209438Z","shell.execute_reply.started":"2024-08-09T22:18:15.470944Z","shell.execute_reply":"2024-08-09T22:18:16.208593Z"},"trusted":true},"execution_count":81,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:10:47.362037Z","iopub.execute_input":"2024-08-09T22:10:47.362862Z","iopub.status.idle":"2024-08-09T22:10:48.327895Z","shell.execute_reply.started":"2024-08-09T22:10:47.362819Z","shell.execute_reply":"2024-08-09T22:10:48.326738Z"},"trusted":true},"execution_count":56,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[categorical_columns] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcategorical_columns\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   6637\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   6638\u001b[0m         ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m   6639\u001b[0m     ]\n\u001b[1;32m   6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6642\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6643\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6644\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m   6645\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m    428\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    435\u001b[0m \u001b[43m    \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    361\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 363\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    364\u001b[0m     result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m    366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[1;32m    755\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    756\u001b[0m     values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :]  \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[0;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m    762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    234\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 237\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m    240\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[1;32m    241\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    179\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:133\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m    132\u001b[0m     \u001b[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001b[39;00m\n\u001b[0;32m--> 133\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n","\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Self Enquiry'"],"ename":"ValueError","evalue":"could not convert string to float: 'Self Enquiry'","output_type":"error"}]},{"cell_type":"code","source":"# Assuming your DataFrame is named df\ndf['Gender'] = df['Gender'].replace('Fe Male', 'Female')","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:08:24.744131Z","iopub.execute_input":"2024-08-09T22:08:24.744460Z","iopub.status.idle":"2024-08-09T22:08:24.750634Z","shell.execute_reply.started":"2024-08-09T22:08:24.744434Z","shell.execute_reply":"2024-08-09T22:08:24.749327Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nplt.figure(figsize=(15,20))\nfor i, col in enumerate(categorical_columns, 1):\n    plt.subplot(len(categorical_columns), 1, i)\n    sns.countplot(y=df[col], palette='Set2')\n    plt.title(f'Distribution of {col}')\n    plt.tight_layout()\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:08:27.068186Z","iopub.execute_input":"2024-08-09T22:08:27.068595Z","iopub.status.idle":"2024-08-09T22:08:29.148226Z","shell.execute_reply.started":"2024-08-09T22:08:27.068561Z","shell.execute_reply":"2024-08-09T22:08:29.147299Z"},"trusted":true},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x2000 with 7 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Correlation matrix\nplt.figure(figsize=(15, 10))\ncorrelation_matrix = df.corr()\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')\nplt.title('Correlation Matrix')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:10:16.663999Z","iopub.execute_input":"2024-08-09T22:10:16.664692Z","iopub.status.idle":"2024-08-09T22:10:17.075040Z","shell.execute_reply.started":"2024-08-09T22:10:16.664661Z","shell.execute_reply":"2024-08-09T22:10:17.073704Z"},"trusted":true},"execution_count":54,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[54], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Correlation matrix\u001b[39;00m\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m15\u001b[39m, \u001b[38;5;241m10\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m correlation_matrix \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(correlation_matrix, annot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoolwarm\u001b[39m\u001b[38;5;124m'\u001b[39m, fmt\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCorrelation Matrix\u001b[39m\u001b[38;5;124m'\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/frame.py:11049\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[0;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[1;32m  11047\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m  11048\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m> 11049\u001b[0m mat \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m  11051\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m  11052\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/frame.py:1993\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1992\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[0;32m-> 1993\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[1;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(result, dtype\u001b[38;5;241m=\u001b[39mdtype)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:1694\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1692\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   1693\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1694\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_interleave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1695\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[1;32m   1696\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[1;32m   1698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:1753\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[0;34m(self, dtype, na_value)\u001b[0m\n\u001b[1;32m   1751\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1752\u001b[0m         arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n\u001b[0;32m-> 1753\u001b[0m     \u001b[43mresult\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m arr\n\u001b[1;32m   1754\u001b[0m     itemmask[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1756\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask\u001b[38;5;241m.\u001b[39mall():\n","\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Manager'"],"ename":"ValueError","evalue":"could not convert string to float: 'Manager'","output_type":"error"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 0 Axes>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Boxplots for outlier detection\nplt.figure(figsize=(15, 10))\nfor i, col in enumerate(numerical_columns, 1):\n    plt.subplot(4, 4, i)\n    sns.boxplot(y=df[col], palette='Set2')\n    plt.title(f'Boxplot of {col}')\n    plt.tight_layout()\n\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-08-09T22:09:47.927115Z","iopub.execute_input":"2024-08-09T22:09:47.927810Z","iopub.status.idle":"2024-08-09T22:09:51.276253Z","shell.execute_reply.started":"2024-08-09T22:09:47.927775Z","shell.execute_reply":"2024-08-09T22:09:51.275341Z"},"trusted":true},"execution_count":53,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 13 Axes>","image/png":"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