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https://github.com/adityabhamare10/aiml-hackathon

Smart Credit Risk Solutions
https://github.com/adityabhamare10/aiml-hackathon

chatgpt confusion-matrix css ejs flask git github html javascript joblib jupyter-notebook logistic-regression matplotlib numpy pandas pycharm python seaborn sklearn stremlit

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Smart Credit Risk Solutions

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README 

        
# TrustFinance  



Smart Credit Risk Evolution is a web application that evaluates the credit risk of loan applicants. Using a machine learning model, it predicts whether a loan application should be approved and provides the probability of approval based on various applicant details.



## Table of Contents



- [Project Overview](#project-overview)

- [Features](#features)

- [Installation](#installation)

- [Usage](#usage)

- [Model Training](#model-training)

- [API Endpoints](#api-endpoints)

- [Technologies Used](#technologies-used)

- [Contributing](#contributing)

- [License](#license)



## Project Overview



Smart Credit Risk Evolution leverages a logistic regression model to assess the risk associated with loan applicants. The system provides a frontend for users to input their details and receive a prediction on their loan application status along with the probability of approval.



## Features



- Input personal and financial information through a web interface.

- Receive real-time predictions and approval probabilities.

- User-friendly interface built with Streamlit.

- Backend powered by Flask and a logistic regression model.



## Installation



### Prerequisites



- Python 3.8+

- pip (Python package installer)



### Setup



1. Clone the repository:



    ```sh

    git clone https://github.com/yourusername/smart-credit-risk-evolution.git

    cd smart-credit-risk-evolution

    ```



2. Install dependencies:



    ```sh

    pip install -r requirements.txt

    ```



3. Prepare the Model:



    Ensure you have the dataset (`Loan_default.csv`) in the appropriate directory. Run the Jupyter Notebook (`model_training.ipynb`) to train and save the model:



    ```sh

    jupyter notebook model_training.ipynb

    ```



    This will generate a `prediction_model.pkl` file in the specified directory.



4. Configure the Flask server:



    Update the `model_path` in the Flask script to the location of your `prediction_model.pkl`.



    ```python

    model_path = r'C:\Users\hp\Documents\AILML Hackathon\prediction_model.pkl'

    ```



5. Run the Flask server:



    ```sh

    python flask_server.py

    ```



6. Run the Streamlit app:



    ```sh

    streamlit run streamlit_app.py

    ```



## Usage



- Open the Streamlit app in your web browser. By default, it runs at [http://localhost:8501](http://localhost:8501).

- Input your personal and financial information into the provided form.

- Click the "Predict" button to receive your loan approval prediction and probability.



## Model Training



The model is trained using a logistic regression algorithm on a dataset of loan applications. The training process involves:



- Preprocessing the data by converting categorical variables to numerical values.

- Splitting the data into training and testing sets.

- Training the logistic regression model.

- Saving the trained model for use in the Flask server.



### Example Code for Model Training



```python

import pandas as pd

import numpy as np

import joblib

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression



# Load the dataset

df = pd.read_csv("Loan_default.csv")



# Preprocess the dataset

df = df.drop(columns=['LoanID'])

df.replace({"Education": {"Bachelor's": 0, 'High School': 1, "Master's": 2, 'PhD': 3}}, inplace=True)

df.replace({"EmploymentType": {'Part-time': 0, 'Unemployed': 1, 'Self-employed': 2, 'Full-time': 3}}, inplace=True)

df.replace({"MaritalStatus": {'Married': 0, 'Divorced': 1, 'Single': 2}}, inplace=True)

df.replace({"HasMortgage": {'No': 0, 'Yes': 1}}, inplace=True)

df.replace({"HasDependents": {'No': 0, 'Yes': 1}}, inplace=True)

df.replace({"LoanPurpose": {'Business': 0, 'Home': 1, 'Education': 2, 'Other': 3, 'Auto': 4}}, inplace=True)

df.replace({"HasCoSigner": {'No': 0, 'Yes': 1}}, inplace=True)



# Split the data

X = df.drop(columns=['Default'], axis=1)

Y = df['Default']

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify=Y, random_state=2)



# Train the model

model = LogisticRegression()

model.fit(X_train, Y_train)



# Save the model

joblib.dump(model, 'prediction_model.pkl')

```

# API Endpoints

## POST /predict-model

**Description:** Receives applicant data and returns the prediction and probability.

**Request Body:** JSON object containing applicant features.



```json

{

    "age": 30,

    "income": 50000,

    "loanAmount": 20000,

    "creditScore": 700,

    "monthsEmployed": 36,

    "numCreditLines": 5,

    "interestRate": 5,

    "loanTerm": 60,

    "dtiRatio": 0.4,

    "education": 1,

    "employmentType": 3,

    "maritalStatus": 0,

    "hasMortgage": 1,

    "hasDependents": 0,

    "loanPurpose": 1,

    "hasCoSigner": 0

}

{

    "prediction": "Approved",

    "probability": 0.85

}



```



# Technologies Used

* Frontend: Streamlit

* Backend: Flask

* Machine Learning: scikit-learn, joblib

* Data Processing: pandas, numpy



# Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure that your code adheres to the project's coding standards and includes appropriate tests.