Best Donor Prediction Web Application Using Machine Learning

Donor Prediction Web Application

A simple project on Donor Prediction Using Income Level helps in understanding how machine learning models can be integrated with a web application to provide real-time predictions. This project is built using Streamlit and utilizes the XGBoost algorithm trained on an income dataset. The primary aim of the project is to predict the likelihood of an individual’s income being greater than 50K or less than or equal to 50K, which can then be used as an indirect indicator for predicting potential donors.

The project is lightweight, student-friendly, and demonstrates the complete flow of a data science project: data preprocessing, model training, model deployment, and web integration. From a learning perspective, this project is highly valuable because it allows students to see how theoretical concepts of machine learning are practically applied through coding.

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Project Overview

Available Features

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Based on the implementation, the following features are present in the project:

• Sidebar Navigation: A simple sidebar that allows users to switch between Home and About sections.
• Prediction Form: On the Home page, the user provides input details such as Age, Workclass, Education Level, Marital Status, Occupation, Relationship, Race, Sex, Capital Gain, Capital Loss, Hours per Week, and Native Country.
• Real-Time Prediction: On clicking the Predict button, the system uses the trained XGBoost model to analyze the inputs.
• Result Display: The prediction result is shown as either Income Level: Greater than 50K or Income Level: Less than or equal to 50K.
• About Section: A simple informational page that explains the project’s purpose.

These features make the project straightforward yet effective for understanding how user data flows into a machine learning model for producing meaningful outputs.

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Installation Guide (For VS Code)

Follow the steps below to set up and run this project in Visual Studio Code (VS Code):

Step 1: Install Python

Ensure that Python 3.x is installed on your system. You can verify by running:

python --version

Step 2: Open Project in VS Code

1. Launch VS Code.
2. Open the extracted project folder Donar-Prediction-Web-App-main.

Step 3: Create a Virtual Environment

Inside VS Code terminal, create a virtual environment:

python -m venv venv

Activate the environment:

• On Windows: venv\Scripts\activate
• On macOS/Linux: source venv/bin/activate

Step 4: Install Dependencies

Install the required libraries listed in requirements.txt:

pip install -r requirements.txt

Step 5: Run the Application

Run the Streamlit app with:

streamlit run app.py

This will launch the web application in your default browser. You can now use the prediction system.

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Usage

This project is designed in a very simple way so that students and beginners can easily understand its workflow. The usage can be explained as follows:

1. Home Section

• In this section, the user fills in the form with all the required inputs such as Age, Workclass, Education, Marital Status, Occupation, Relationship, Race, Sex, Capital Gain, Capital Loss, Hours per Week, and Native Country.
• After filling the inputs, clicking on the Predict button will trigger the model to process the information.
• The system will then display the predicted income level as either Greater than 50K or Less than or equal to 50K.

2. About Section

• Provides details about the purpose of the application and how it works.
• This helps users understand the intent behind predicting income levels and its role in identifying potential donors.

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Student’s Perspective: Why This Project is Useful

From a student’s point of view, this project is extremely beneficial for learning multiple concepts in an integrated way:

1. Understanding Machine Learning Models
   The project uses XGBoost, which is a popular gradient boosting algorithm. Students get a chance to learn how machine learning models are trained, saved as .pkl files, and reused for predictions in real-world applications.
2. Hands-On Web Development with Streamlit
   Many times, students focus only on model training in Jupyter Notebooks. This project goes one step further by teaching how to deploy that model using Streamlit, which transforms the machine learning code into an interactive web app with minimal effort.
3. Practical Input Handling
   The project demonstrates how to handle multiple user inputs (both categorical and numerical), preprocess them, and feed them into the model. This directly mirrors real-life scenarios where diverse user data is collected through forms.
4. Interpreting Prediction Results
   The prediction outcome is straightforward: whether income is greater than or less than 50K. This binary classification is easy for beginners to understand but also demonstrates a real-world use case of classifying individuals for targeted decision-making, such as donor prediction or eligibility screening.
5. End-to-End Learning
   Students not only learn about training and deploying models but also about installing dependencies, setting up environments, and running applications locally in VS Code. This prepares them for larger projects where a full workflow is required.
6. Real-Life Application
   Income level is often considered a critical factor when predicting the likelihood of individuals contributing as donors. Although simplified, this project shows how such predictive systems can be applied in fields like fundraising, healthcare donation drives, and social research.
7. Project Readiness
   Since the project is already structured with separate files for the model, requirements, and main application, students can easily understand how to organize professional machine learning projects. This is valuable for both academic learning and job readiness.

This project, Donor Prediction Using Income Level, is an excellent example of combining data science with real-time web applications. It gives students the chance to practice machine learning, web deployment, and prediction analysis in a single project. The simplicity of the features ensures that beginners can follow along without confusion, while the real-world relevance of donor prediction makes it meaningful.

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