05. Modelling

Customer Behavior and Sales Analysis

2. Improving Customer Retention and Lifetime Value

Modeling techniques considered:

• Logistic Regression
• Decision Tree
• Random Forest

Model Selection criteria

Since objective is to predict and identify customers likely to churn (binary classification) and churn values were imbalanced, best model was selected based on its F1 score, ensuring that both false positives and false negatives are minimized.

Detailed description of the chosen model

Random Forest model had the highest F1 score among the 3 models. RandomizedSearchCV was then ran to find the best hyperparameters, which are:

• n_estimators : 300
• min_samples_split : 2
• min_samples_leaf : 1
• max_depth : 10
• bootstrap : True

Model Performance metrics and interpretation:

• Test Accuracy: 86.83%. While accuracy provides an overall measure of the model's performance, it may not fully represent its effectiveness in predicting the minority class (churned customers) due to the class imbalance in our dataset.

• Classification Report:

  • Precision for class 1 (churn) is 0.73, indicating that 73% of the customers the model predicted as churned were indeed correct.
  • Recall for class 1 is 0.63, meaning the model successfully identified 63% of actual churned customers.
  • F1-Score for class 1 is 0.68, balancing both precision and recall, making it a useful metric given the importance of accurately identifying churned customers.

Inventory Management and Pricing Optimization