Data Science and Analytics Tools - Customer Churn Prediction using Logistic Regression

 Notes:

• What problem does it solve?
  Predicts which customers are likely to churn, helping businesses proactively intervene with retention strategies.

• How can businesses or users benefit from customizing the code?
  Customizations can be made for specific customer attributes, churn behaviors, and features.

• How can businesses or users adopt the solution further, if needed?
  This can be integrated into CRM systems, alerting teams when customers are at risk.

Actual Python Code:

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

# Load customer data (assumed to have 'Churn' and various features)

data = pd.read_csv('customer_data.csv')

# Prepare the features and target variable

X = data[['Age', 'Annual_Income', 'Service_Usage', 'Customer_Satisfaction']]

y = data['Churn']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train a logistic regression model

model = LogisticRegression()

model.fit(X_train, y_train)

# Make predictions and evaluate the model

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print(f'Accuracy: {accuracy * 100:.2f}%')

print('Confusion Matrix:')

print(confusion_matrix(y_test, y_pred))