Financial Risk Detection

Course Code: CSL2010
Mentor: Dr. Avinash Sharma

---

Description

This project aims to build a machine learning-based Financial Risk Assessment System to classify individuals or entities into different risk categories (e.g., low, medium, high).

The workflow includes:

• Extensive Exploratory Data Analysis (EDA) to uncover key patterns and understand the dataset.
• Preprocessing steps to clean and transform the data for optimal model performance.

Key Highlights

• Utilizes machine learning algorithms such as:

  • Logistic Regression
  • Support Vector Classifier (SVC)
  • Decision Trees
  • Random Forest
  • XGBoost

• Provides feature importance analysis to identify critical risk factors, delivering actionable insights for financial institutions.

---

Deployment

Follow the instructions below to set up and deploy the project locally:

1. Set Up a Folder in VS Code

• Create a new folder named Deployment in VS Code.
• Save the following files inside the folder:
  • app_final.py
  • financial_risk_assessment.csv
  • Project_Code_final.py

2. Run the Project Code

• In the Deployment folder, run the file Project_Code_final.py to set up necessary dependencies and models.

3. Run the Application

• After successfully running the project code, execute the file app_final.py to launch the web application.

4(a). Open the Folder on Your Computer

• Navigate to the folder location on your computer (outside of VS Code). OR

4(b). Open the Terminal in the Folder

• Open the terminal in the same folder where the project files are stored.

5. Run the Streamlit Application

• Type the following command in the terminal:

  streamlit run app_final.py