Predict Loan Eligibility for a Finance company

Performed crucial data science steps from Hypothesis Generation, Data Preparation to Exploratory Data Analysis and Model Building

Achieved accuracy of 80% in loan eligibility prediction using Logistic Regression model

1. Understanding Problem Statement
2. Hypothesis Generation
3. Getting the system ready and loading the data
4. Understanding the data
5. Exploratory Data Analysis (EDA)
   • Univariate Analysis
   • Bivariate Analysis
6. Missing Value and Outlier Treatment
7. Evaluation Metrics for classification problems
8. Model Building Part-I
9. Logistics regression using stratified k-folds cross validation
10. Feature Engineering
11. Model Building Part-II

• Logistic Regression
• Decision Tree
• Random Forest
• XGBoost