This repository contains a collection of comprehensive case studies designed to apply various data science techniques and concepts within different businesses. Each case study provides detailed explanations of the business case, data cleaning steps, data analysis and visualization using Tableau, and insights to address the business problem at hand using data science.
1. Personal Loan Marketing for Universal Bank
- Description: This case study analyzes data from the Universal bank to identify potential customers for personal loan marketing using predictive modeling techniques.
- Techniques Used: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting
- Data: Customer demographic and financial data
2. Customer Churn Prediction
- Description: This case study aims to predict customer churn using machine learning techniques to help telecommunication providers retain customers.
- Techniques Used: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting
- Data: Customer subscription data, usage patterns, and customer demographics
3. Employee Attrition Prediction
- Description: This case study focuses on predicting employee attrition using various machine learning techniques to help organizations retain valuable employees. The dataset used is from IBM HR Analytics.
- Techniques Used: Logistic Regression, Decision Trees, Support Vector Machines, Random Forest, Gradient Boosting, K-nearest Neighbors
- Data: Employee demographic data, job satisfaction, performance ratings, and other relevant factors
4. Fannie Mae Loan Performance
- Description: Predicting loan defaults to help Fannie Mae make informed lending decisions and maximize profitability.
- Techniques Used: Logistic Regression, Decision Trees, Support Vector Machines, Gradient Boosting
- Data: Loan performance data from Q1 2007
5. Potential Donor Prediction for NGOs
- Description: Developing a predictive model to identify potential donors for non-profit organizations by analyzing historical data from Paralyzed Veterans of America (PVA), including donor demographics and contribution history, to optimize resource allocation and enhance campaign effectiveness.
- Techniques Used: Gradient Boosting, Elastic Net, Generalized Additive Model, Neural Networks
- Data: Donor demographics and contribution history data from PVA
6. Real Estate Price Prediction
- Description: This case study aims to predict real estate prices using various regression techniques and understand the factors influencing property values.
- Techniques Used: Linear Regression, Gradient Boosting
- Data: Real estate transaction data, property characteristics, and location information
7. Term Deposit Marketing
- Description: Predicting customer propensity to subscribe to term deposit products using historical banking data to help optimize marketing strategies and improve campaign targeting.
- Techniques Used: Logistic Regression, Decision Trees, Random Forest
- Data: Customer demographic and contact data from the Portuguese bank