Income Predictor

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:

1. Data Preprocessing: Data preprocessing techniques are applied to clean and prepare the dataset for model training.

2. Model Training: The predictive model is trained using a decision tree classifier algorithm.

3. Evaluation: The trained model is evaluated to assess its performance and accuracy in predicting income levels.

4. 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.

Installation

To run this project locally, follow these steps:

1. Clone the repository to your local machine:

git clone https://github.com/your-username/income-predictor.git

2. Navigate to the project directory:

cd income-predictor

3. Create a virtual environment (optional but recommended):

python -m venv venv

4. Activate the virtual environment:

On Windows:

venv\Scripts\activate

On macOS and Linux:

source venv/bin/activate

5. Install the required dependencies:

pip install -r requirements.txt

6. Run the application:

python script.py

7. Open your web browser and go to http://localhost:5000 to access the application.