Customer Relationship Management (CRM) - Customer Churn Predictor

 

Notes:

• Problem Solved: Predicts which customers are likely to stop using your product/service.

• Customization Benefits: Train the model with your own data for improved accuracy.

• Further Adoption: Trigger proactive retention campaigns based on churn scores.

Python Code:

import pandas as pd

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

df = pd.read_csv("customer_data.csv")  # Must include 'churned' column (1=yes, 0=no)

features = df.drop(columns=['customer_id', 'churned'])

target = df['churned']

X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2)

model = RandomForestClassifier()

model.fit(X_train, y_train)

# Predict churn

df['churn_probability'] = model.predict_proba(features)[:, 1]

print(df[['customer_id', 'churn_probability']].sort_values(by='churn_probability', ascending=False))