Section 1.1: Setting Up Your Environment

Before diving into the code, make sure you have the necessary libraries installed. You can set up your environment with the following command:

pip install pandas scikit-learn

Section 1.2: Coding the Churn Prediction Model

Now, let’s proceed with the actual implementation of the churn prediction model. Here’s a basic outline of the code you’ll need:

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# Step 1: Load and prepare the dataset (Ensure you have a CSV file with features and target column) # Replace 'dataset.csv' with the path to your actual dataset data = pd.read_csv('dataset.csv')

# Assuming the 'churn' column is your target (1 for churned customers, 0 for retained) X = data.drop('churn', axis=1) y = data['churn']

# Step 2: Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Initialize and train the Logistic Regression model model = LogisticRegression() model.fit(X_train, y_train)

# Step 4: Make predictions on the test set y_pred = model.predict(X_test)

# Step 5: Evaluate the model's performance accuracy = accuracy_score(y_test, y_pred) confusion_mat = confusion_matrix(y_test, y_pred) classification_rep = classification_report(y_test, y_pred)

print(f"Accuracy: {accuracy:.2f}") print("Confusion Matrix:") print(confusion_mat) print("Classification Report:") print(classification_rep)

Ensure you modify 'dataset.csv' to reflect the actual path to your dataset. Your dataset should include relevant features that may influence customer churn, with the 'churn' column indicating whether a customer has left (1) or remained (0).