Census Income Prediction

This project aims to predict whether an individual earns more than $50K annually based on demographic and employment-related attributes from the UCI Adult dataset. We compare and evaluate multiple machine learning models to determine the most effective approach.

📊 Dataset

The dataset used is the Adult Income Dataset from the UCI Machine Learning Repository:

• 32,561 training instances and 16,281 test instances
• Features include age, workclass, education, marital status, occupation, race, sex, hours-per-week, and more
• Target variable: >50K or <=50K

🛠️ Features Used

• Categorical: workclass, education, marital-status, occupation, relationship, race, sex, native-country
• Numerical: age, fnlwgt, education-num, capital-gain, capital-loss, hours-per-week

📦 Models Implemented

Model	Description
Logistic Regression	Baseline linear model
Random Forest	Ensemble model with decision trees
Multi-layer Perceptron (MLP)	Neural network for classification
Support Vector Machine (SVM)	Kernel-based classification

⚙️ Preprocessing

• Handled missing values (?) by removal or imputation
• Label encoding and one-hot encoding for categorical features
• Feature scaling for numerical values (where required)
• Train-test split (if not already provided)

📈 Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC

🔍 Results

Model	Accuracy
Logistic Regression	76.80%
Random Forest	93.90%
MLP	82.80%
SVM	89.80%

🚀 How to Run

1. Clone the repository:

git clone https://github.com/SanyamBK/ML-Project-Census-Income-Prediction.git
cd ML-Project-Census-Income-Prediction

2. Install dependencies:

pip install -r requirements.txt

3. Run the Jupyter notebooks to train and evaluate models.

📌 Future Work

• Hyperparameter tuning
• Feature selection and dimensionality reduction
• Handling class imbalance
• Deployment using FastAPI or Streamlit

📄 License

This project is for academic use and learning purposes.

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