Telecom Customer Churn Analysis using Python, NumPy, pandas, seaborn, matplotlib, scikit-learn

As the Data Scientist at "Neo," a telecom company, I conducted an analysis to understand and prevent customer churn. The goal was to derive insights from the data and develop strategies to retain customers who were switching to competitors.

• Python
• NumPy
• pandas
• seaborn
• matplotlib
• scikit-learn

The dataset used for analysis is stored in customer_churn.csv. It contains information about customers, including demographics, services subscribed, and churn status.

• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest

• The logistic regression model achieved an accuracy of 73.6%.
• The decision tree model achieved an accuracy of 74.7%.
• The random forest model achieved an accuracy of 73.2%.

These models can be further fine-tuned and used to develop strategies to retain customers and reduce churn.