Exploratory Data Analysis (EDA) and Model Evaluation

Exploratory Data Analysis (EDA) is the process of examining data to identify patterns, trends, and relationships before building a predictive model. Visualization and summary statistics help understand churn behavior.

In this project, EDA helps identify patterns such as customer segments with higher churn tendencies and relationships between tenure, charges, and churn status.

Trend Identification: Understands how churn varies across different customer groups.

Category Comparison: Compares churn rates across contract types and payment methods.

Relationship Analysis: Identifies correlations between numerical variables and churn.

After EDA, model evaluation ensures the predictive system performs effectively. The model is assessed using:

1. Accuracy
2. Precision
3. Recall
4. F1-score
5. Confusion Matrix

These evaluation metrics help measure how well the model distinguishes between customers who churn and those who remain.

By combining data cleaning, feature engineering, encoding, EDA, and evaluation, this project transforms raw telecom customer data into a structured and reliable churn prediction system.