This project focuses on predicting an individual's income category using machine learning techniques. The model is trained on demographic and economic features to classify whether a person earns above or below a certain income threshold.

• Python – Core programming language
• Pandas & NumPy – Data preprocessing and manipulation
• Scikit-Learn – Machine learning model development
• Matplotlib & Seaborn – Data visualization
• Jupyter Notebook – Experimentation and analysis

This project includes the following steps:

• Data Cleaning & Preprocessing: Handling missing values, encoding categorical variables, and feature scaling
• Exploratory Data Analysis (EDA): Understanding feature relationships and distributions
• Model Training & Evaluation: Implementing various machine learning algorithms
• Hyperparameter Tuning: Optimizing model performance
• Prediction & Insights: Analyzing model outputs and key influencing factors

• data/ → Contains raw and processed datasets
• notebooks/ → Jupyter Notebooks for analysis and experimentation
• models/ → Saved trained models
• scripts/ → Python scripts for data processing and training
• README.md → Project documentation and description

1. Clone the Repository:

   git clone https://github.com/Utkuersy/IncomePredictionMachineLearningProject.git

2. Install Dependencies:

   pip install -r requirements.txt

3. Run Jupyter Notebook (for exploration and training):

   jupyter notebook

4. Execute Python Scripts (for automated processing and training):

   python scripts/train_model.py