This project aims to build a machine learning model to predict whether an individual's income exceeds $50K annually based on demographic and job-related data. The project includes the following components:
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Data Preprocessing: Data preprocessing techniques are applied to clean and prepare the dataset for model training.
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Model Training: The predictive model is trained using a decision tree classifier algorithm.
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Evaluation: The trained model is evaluated to assess its performance and accuracy in predicting income levels.
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Deployment: The model is deployed as a simple web application using Flask, allowing users to input their demographic and job-related information to obtain income predictions.
The goal of this project is to provide a practical tool for predicting income levels and to demonstrate the application of machine learning techniques in solving real-world problems.
To run this project locally, follow these steps:
- Clone the repository to your local machine:
git clone https://github.com/your-username/income-predictor.git- Navigate to the project directory:
cd income-predictor- Create a virtual environment (optional but recommended):
python -m venv venv- Activate the virtual environment:
On Windows:
venv\Scripts\activateOn macOS and Linux:
source venv/bin/activate- Install the required dependencies:
pip install -r requirements.txt- Run the application:
python script.py- Open your web browser and go to http://localhost:5000 to access the application.