Mastering Marketing Data Science

EXERCISE 5.1: CHURN PREDICTION MODEL

Objective: Use the churn_data to train a logistic regression model that predicts customer churn.

Tasks:

1. Split the “churn_data.csv” dataset into training and validation sets.
2. Train a logistic regression model to predict the binary dependent variable churn.
3. Make predictions and evaluate the model.

Steps:

1. Importing Required Libraries:

   1. import pandas as pd
   2. from sklearn.model_selection import train_test_split
   3. from sklearn.linear_model import LogisticRegression
   4. from sklearn.metrics import classification_report

   • pandas is used for data manipulation and analysis.
   • train_test_split from sklearn.model_selection is a utility to split datasets into training and test sets.
   • LogisticRegression from sklearn.linear_model is a machine learning model for classification tasks.
   • classification_report from sklearn.metrics provides a way to evaluate the quality of predictions from a classification algorithm.
2. Loading the Dataset:

   5. churn_data = pd.read_csv('/data/churn_data.csv')

   • We load the churn dataset from a CSV file into a pandas DataFrame. The dataset contains features that describe customer behavior and a target variable that indicates whether the customer has churned.
3. Defining Features and Target:

   6. X = churn_data.drop('churn', axis=1)
   7. y = churn_data['churn']

   • We separate the features (X) and the target (y). The features include all columns except the target column ‘churn’, which we want to predict. The target is the ‘churn’ column, ...