Customer Behavior Prediction

Project Objective:
The goal of this machine learning project is to predict customer behavior (likely in the context of marketing or credit risk) using advanced classification techniques. Special emphasis is placed on ensemble learning to boost model performance.

🔍 Overview of Methodology:
Data Processing:

Imported and preprocessed customer-related data.

Cleaned missing or inconsistent values.

Applied label encoding and feature scaling to prepare the data for model training.

Base Models Trained:

Multiple machine learning classifiers such as:

Logistic Regression

K-Nearest Neighbors (KNN)

Naive Bayes

Support Vector Machine (SVM)

Neural Networks

Evaluated each base model using metrics like Accuracy, Precision, Recall, F1-score, and ROC-AUC.

Stacking Ensemble:

Combined predictions from base models into a meta-model to leverage strengths of each.

The final stacked model uses a meta-classifier to make the final prediction based on base model outputs.