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https://github.com/sureshmrd/loan-prediction-ml
https://github.com/sureshmrd/loan-prediction-ml
fastapi ml python3
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/sureshmrd/loan-prediction-ml
- Owner: sureshmrd
- Created: 2024-09-08T07:08:17.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-09-08T08:40:44.000Z (5 months ago)
- Last Synced: 2024-10-17T03:27:16.139Z (4 months ago)
- Topics: fastapi, ml, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 232 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Loan Approval Prediction using Machine Learning
## Project Overview
This project focuses on predicting loan approval outcomes using machine learning techniques. The prediction model aims to assist lenders in assessing whether an applicant is eligible for a loan based on certain factors. The project follows a structured approach starting from data analysis to deploying the model using FastAPI.
## Table of Contents
1. [Project Overview](#project-overview)
2. [Technologies Used](#technologies-used)
3. [Steps Involved](#steps-involved)
- [1. Data Analysis](#1-data-analysis)
- [2. Feature Engineering](#2-feature-engineering)
- [3. Training ML Models](#3-training-ml-models)
- [4. FastAPI App Creation](#4-fastapi-app-creation)
4. [How to Run the Project](#how-to-run-the-project)
5. [Results](#results)
## Technologies Used
- Python
- Pandas
- Scikit-learn
- FastAPI
- NumPy
- Matplotlib / Seaborn for visualization
## Steps Involved
### 1. Data Analysis
- The dataset contains features related to applicants, such as their Gender(Male/Female) ,Education(Graduate/Not),income, loan amount, credit history, etc.
- Exploratory data analysis (EDA) was conducted to understand patterns and trends in the data.
- Various visualization techniques were used to highlight key relationships between the features and the target variable.
### 2. Feature Engineering
- Feature selection and transformation were performed to enhance the model's predictive power.
- Missing values were handled appropriately.
- Categorical variables were encoded using techniques such Label Encoder.
### 3. Training ML Models
- Several machine learning algorithms were evaluated, including Logistic Regression, SVM, KNearestNeighbors.
- The model was trained on a split of training and test datasets.
- Hyperparameter tuning was performed to optimize the models using techniques like GridSearchCV.
- The best-performing model was selected based on evaluation metrics like accuracy.
### 4. FastAPI App Creation
- A FastAPI web application was created to serve the loan approval model as an API.
- Users can send a request with loan-related details and get an immediate prediction on whether the loan will be approved or not.
- The API can handle both GET and POST requests.
## Run the Project
### Install the Requirements.txt file:
```bash
pip install -r requirements.txt
```
### Run the FastAPI App:
```
uvicorn app:app --reload
```
#### Access the API: Open your browser and go to http://127.0.0.1:8000/docs to see the interactive API documentation.
## Results
- Accuracy of SVM ---> 87.09677419354838
- Accuracy of Logistic Regression ---> 87.09677419354838
- Accuracy of KNN model ---> 80.64516129032258