💼 Salary Prediction — Adult Dataset

Binary classification project predicting whether an individual's annual income exceeds $50K based on demographic and employment attributes from the UCI Adult Census dataset.

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📑 Table of Contents

• Overview
• Dataset
• Project Workflow
• Tech Stack
• Project Structure
• Installation
• Usage
• Exploratory Data Analysis
• Modeling
• Results
• Model Evaluation
• Conclusion
• Future Improvements
• Author
• License

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📌 Overview

This project applies supervised machine learning to predict whether a person earns more than $50K per year using the well-known UCI Adult Census Income dataset. The pipeline covers data cleaning, exploratory data analysis (EDA), feature engineering, model training, hyperparameter tuning, and evaluation — all packaged in a reproducible workflow suitable for portfolio and internship presentation.

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📊 Dataset

• Source: UCI Machine Learning Repository — Adult Dataset
• Records: ~48,842 instances
• Features: 14 demographic and employment attributes
• Target: income — <=50K or >50K

Key Features

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

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🔁 Project Workflow

1. Data Loading & Inspection
2. Data Cleaning — handling missing values & duplicates
3. Exploratory Data Analysis (EDA)
4. Feature Engineering & Encoding
5. Train / Test Split & Scaling
6. Model Training (multiple algorithms)
7. Hyperparameter Tuning
8. Model Evaluation & Comparison
9. Model Persistence (.pkl)
10. Conclusion & Insights

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🛠 Tech Stack

Category	Tools
Language	Python 3.9+
Data Handling	pandas, numpy
Visualization	matplotlib, seaborn
Machine Learning	scikit-learn
Model Persistence	joblib
Environment	Jupyter Notebook

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📁 Project Structure

salary-prediction/
│
├── data/
│   └── adult.csv
│
├── notebooks/
│   └── salary_prediction.ipynb
│
├── models/
│   └── best_model.pkl
│
├── images/
│   └── eda_plots/
│       ├── correlation_heatmap.png
│       ├── feature_importance.png
│       ├── confusion_matrix.png
│       └── roc_curve.png
│
├── requirements.txt
├── README.md
├── LICENSE
└── .gitignore

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⚙️ Installation

1. Clone the repository

git clone https://github.com/ParvathyM155/Salary_Prediction_Using_Machine_Learning.git
cd Salary_Prediction_Using_Machine_Learning

2. Create and activate a virtual environment

python -m venv venv
source venv/bin/activate        # macOS / Linux
venv\Scripts\activate           # Windows

3. Install dependencies

pip install -r requirements.txt

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▶️ Usage

Launch the notebook:

jupyter notebook notebooks/salary_prediction.ipynb

Or load the saved model directly in Python:

import joblib
model = joblib.load("models/best_model.pkl")
prediction = model.predict(new_data)

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🔍 Exploratory Data Analysis

Key insights uncovered during EDA:

• Strong correlation between education level and income.
• Hours-per-week and age significantly affect earning probability.
• Marital status and occupation are powerful categorical predictors.
• The dataset is imbalanced (~76% <=50K, ~24% >50K).

🔥 Correlation Heatmap

Visualizes pairwise relationships between numerical features and the target variable.

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🤖 Modeling

The following classifiers were trained and compared:

• Logistic Regression
• Decision Tree
• Random Forest
• Gradient Boosting
• K-Nearest Neighbors
• Support Vector Machine

Hyperparameter tuning was performed using GridSearchCV with cross-validation.

🌟 Feature Importance

Top features driving the Gradient Boosting model's predictions.

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🏆 Results

Model	Accuracy	Precision	Recall	F1-Score	ROC-AUC
Logistic Regression	0.85	0.74	0.60	0.66	0.90
Random Forest	0.86	0.76	0.63	0.69	0.91
Gradient Boosting	0.87	0.79	0.65	0.71	0.92

✅ Gradient Boosting achieved the best overall performance and was selected as the final model.

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📈 Model Evaluation

Evaluation techniques applied:

• Confusion Matrix
• Classification Report
• ROC Curve & AUC
• Precision–Recall Curve
• K-Fold Cross-Validation
• Feature Importance Analysis

🧩 Confusion Matrix

Breakdown of correct vs incorrect predictions for each income class.

📉 ROC Curve

Trade-off between true-positive and false-positive rates across thresholds.

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✅ Conclusion

The final Gradient Boosting model reliably predicts income brackets with ~87% accuracy and a 0.92 ROC-AUC, demonstrating strong generalization. Education, age, hours-per-week, and capital gain emerged as the most influential features — aligning with real-world economic intuition.

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🚀 Future Improvements

• Address class imbalance using SMOTE or class weighting
• Experiment with XGBoost and LightGBM
• Deploy the model as a Streamlit or Flask web app
• Add MLflow for experiment tracking
• Build a CI pipeline with GitHub Actions

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👤 Author

Parvathy M

• 🌐 Portfolio: yourwebsite.com
• 💼 LinkedIn: linkedin.com/in/parvathym155
• 🐙 GitHub: @ParvathyM155

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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