https://github.com/manishkr1754/loan_eligibility_prediction_python
Predict Loan Eligibility for a Finance company
https://github.com/manishkr1754/loan_eligibility_prediction_python
bivariate-analysis data-visualization decision-trees evaluation-metrics exploratory-data-analysis feature-engineering hypothesis-generation logistic-regression matplotlib model-building numpy pandas random-forest seaborn sklearn univariate-analysis xgboost
Last synced: 6 months ago
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Predict Loan Eligibility for a Finance company
- Host: GitHub
- URL: https://github.com/manishkr1754/loan_eligibility_prediction_python
- Owner: manishkr1754
- Created: 2023-01-30T08:20:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-04T18:06:44.000Z (over 2 years ago)
- Last Synced: 2025-02-05T08:15:31.910Z (8 months ago)
- Topics: bivariate-analysis, data-visualization, decision-trees, evaluation-metrics, exploratory-data-analysis, feature-engineering, hypothesis-generation, logistic-regression, matplotlib, model-building, numpy, pandas, random-forest, seaborn, sklearn, univariate-analysis, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 377 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Loan Eligibility Prediction
## Problem Statement
Predict Loan Eligibility for a Finance company
## Approach
Performed crucial data science steps from **Hypothesis Generation, Data Preparation to Exploratory Data Analysis and Model Building**
## Outcome
Achieved **accuracy of 80%** in loan eligibility prediction using Logistic Regression model
## Process Flow
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