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Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.

In the dataset, each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

• Customers who left within the last month – the column is called Churn

• Services that each customer has signed up for :

  • phone,

  • multiple lines,

  • Internet,

  • online security,

  • online backup,

  • device protection,

  • tech support, and

  • streaming TV and movies

• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges

• Demographic info about customers – gender, age range, and if they have partners and dependents

• Perform initial data preparation by converting the 'TotalCharges' column to numeric values and filling missing values with 0.
• Convert the 'Churn' column to binary values, where 'No' is mapped to 0 and 'Yes' is mapped to 1.- - Split the data into an 80-20 train-test split with a random state of “1”. Select these features:
  categorical = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService','OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies','Contract', 'PaperlessBilling', 'PaymentMethod'] numerical = ['tenure', 'MonthlyCharges', 'TotalCharges']

• The numerical features should be scaled using StandardScaler, convert the output back to a dataframe and put back the column names.
• The categorical features are one-hot encoded using OneHotEncoder(set sparse_output to false), convert the output back to a dataframe and put back the column names.
• Combine scaled numerical and one-hot encoded categorical features into train and test set dataframes (use pd.concat)
• Use scikit learn to train a random forest and extra trees classifier, and use xgboost and lightgbm to train an extreme boosting model and a light gradient boosting model. Use random_state = 1 for training all models and evaluate on the test set. Answer the following questions:

• Kaggle

• IBM -Telco dataset