https://github.com/aishwaryagade02/loan-funnel-optimization-analysis
Tracks how loan applications move through each stage, helps spot where people drop off, and gives clear insights to improve approval strategies and overall performance.
https://github.com/aishwaryagade02/loan-funnel-optimization-analysis
ab-testing data-analysis data-creation hypothesis-testing python reporting sql statistical-methods streamlit
Last synced: about 1 month ago
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Tracks how loan applications move through each stage, helps spot where people drop off, and gives clear insights to improve approval strategies and overall performance.
- Host: GitHub
- URL: https://github.com/aishwaryagade02/loan-funnel-optimization-analysis
- Owner: AishwaryaGade02
- Created: 2025-04-29T16:53:01.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-05-06T19:57:09.000Z (about 1 year ago)
- Last Synced: 2025-05-07T18:13:23.922Z (about 1 year ago)
- Topics: ab-testing, data-analysis, data-creation, hypothesis-testing, python, reporting, sql, statistical-methods, streamlit
- Language: Python
- Homepage:
- Size: 1.43 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 Loan Funnel Analysis
This is a full end-to-end data analytics project simulating a **loan application funnel** at a fintech or lending company. It includes:
✅ Funnel performance monitoring
✅ Automated KPI reporting and alerting
✅ Advanced data insights and cohort analysis
✅ A/B testing to evaluate underwriting strategy changes
✅ A deployed interactive dashboard (via Streamlit)
---
## 🚀 Project Overview
### 🎯 Objective
To simulate, analyze, and optimize the loan funnel journey — from application to funding — and identify areas of improvement using metrics, insights, and A/B experimentation.
---
## 📦 Features
| Category | Description |
|:--|:--|
| 🔁 **Synthetic Data Generation** | Realistic data for 10,000 applicants with credit score, income, loan amounts, funnel stages, approval, and default outcomes |
| 📊 **Funnel Analysis** | Stage-wise conversion rates, approval and funding rates, weekly application trends |
| 📈 **Advanced Insights** | Analyze how age, income, and credit score influence approvals. Cohort analysis by credit bands and income brackets |
| 🧪 **A/B Testing** | Compare approval and default rates for different underwriting strategies. Perform Z-tests for statistical significance |
| 🚨 **Automated Reporting** | Scheduled KPI monitoring and alerting if metrics fall below defined thresholds |
| 📊 **Interactive Dashboard** | Deployed with Streamlit to visualize KPIs, test results, cohort breakdowns, and alerts |
---
## 🧰 Tools & Technologies
- **Python**, **Pandas**, **SQLite**
- **Faker** for synthetic data generation
- **Statsmodels** for A/B testing (Z-test for proportions)
- **Streamlit** for dashboard deployment
- **SQL** for query-based analysis (via SQLite)
---
---
## ✅ How to Run Locally
1. Clone the repo
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Generate data and SQLite DB:
```bash
python src/generate_data.py
```
4. Launch the dashboard:
```bash
streamlit run dashboard/app.py
```