e-customer-insights-analysis

Comprehensive data analysis and predictive modeling to uncover customer behavior patterns, optimize engagement strategies, and enhance business outcomes.

Customer Insights Analysis

This repository contains a data analysis and predictive modeling project aimed at uncovering customer behavior patterns and providing actionable insights to optimize engagement strategies and drive better business outcomes.

Features

• Data Exploration: In-depth exploration and visualization of customer data to identify trends and key drivers.
• Feature Engineering: Creation of advanced features to enhance model performance and interpretability.
• Predictive Modeling: Implementation and evaluation of machine learning models to predict customer behaviors and outcomes.
• Actionable Insights: Recommendations based on findings to support strategic decision-making.

Files

• customer_insights_analysis.ipynb: Main notebook containing the full analysis, from data preprocessing to modeling and insights.
• data/: Directory for datasets (not included for confidentiality reasons).
• models/: Directory for saved models and evaluation reports.

Getting Started

Prerequisites

• Python 3.8 or higher
• Required libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn
  • additional libraries specified in the notebook

Install dependencies with:

pip install -r requirements.txt