https://github.com/aditya-ranjan1234/interactive-salary-prediction-with-machine-learning
A Streamlit web application for exploring the UCI Census Income dataset, training machine learning models, and predicting employee salaries.
https://github.com/aditya-ranjan1234/interactive-salary-prediction-with-machine-learning
data-science machine-learning prediction python scikit-learn streamlit xgboost
Last synced: about 9 hours ago
JSON representation
A Streamlit web application for exploring the UCI Census Income dataset, training machine learning models, and predicting employee salaries.
- Host: GitHub
- URL: https://github.com/aditya-ranjan1234/interactive-salary-prediction-with-machine-learning
- Owner: Aditya-Ranjan1234
- Created: 2025-07-28T20:59:19.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-28T21:21:20.000Z (3 months ago)
- Last Synced: 2025-07-28T23:22:17.504Z (3 months ago)
- Topics: data-science, machine-learning, prediction, python, scikit-learn, streamlit, xgboost
- Language: Jupyter Notebook
- Homepage: https://interactive-salary-prediction-with-machine-learning.streamlit.app/
- Size: 687 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Employee Salary Prediction Dashboard
A Streamlit web application that lets you:
1. **Explore** the UCI Census Income dataset (`adult 3.csv`).
2. **Train** multiple classification algorithms (Logistic Regression, Random Forest, Gradient Boosting) on the cleaned data.
3. **Predict** whether a new employee earns `>50K` using the trained models via an interactive form.
---
## 📂 Project Structure
```
.
├── salary_dashboard/
│ ├── __init__.py
│ ├── app.py # Streamlit entry-point
│ ├── data_loader.py # Dataset loading helper
│ ├── preprocessing.py # Cleaning & preprocessing utilities
│ ├── models.py # Training, saving, loading models
│ ├── artifacts/ # Auto-generated trained model files
│ └── pages/
│ ├── 1_EDA_Dataset.py # Dataset preview & EDA page
│ ├── 2_Model_Training.py # Model training page
│ └── 3_Predict_Salary.py # Prediction UI page
├── adult 3.csv # Raw dataset (ensure this stays in project root)
├── requirements.txt
└── README.md
```
---
## 🚀 Quick Start
1. **Install dependencies**
```bash
pip install -r requirements.txt
```
2. **Launch the dashboard**
```bash
streamlit run salary_dashboard/app.py
```
The browser will open automatically (or visit the displayed URL) with three sidebar pages: Dataset preview, Model training, and Predict Salary.
---
## 🔧 Usage Tips
* Train the models once on the *Model Training* page; trained pipelines are saved to `salary_dashboard/artifacts/`.
* After training, the *Predict Salary* page loads the saved models for instant predictions.
* You can retrain at any time using different `test_size` or `random_state` values; artifacts are overwritten.
---
## 📊 Algorithms Implemented
| Algorithm | Library | Notes |
|-----------|---------|-------|
| Logistic Regression | scikit-learn | baseline linear classifier |
| Random Forest | scikit-learn | ensemble of decision trees |
| Gradient Boosting | scikit-learn | additive ensemble, handles non-linearities |
| Support Vector Machine | scikit-learn | RBF kernel with probability estimates |
| K-Nearest Neighbors | scikit-learn | K=10, non-parametric |
| XGBoost | xgboost | gradient-boosted decision trees (requires `xgboost` wheel) |
During training the dashboard reports Accuracy, Precision, Recall, F1 and shows Confusion-Matrix heatmaps for each model.
---
## 🖼️ Visualisations
* Interactive Altair histograms for numeric columns.
* Category count bars for categorical columns.
* Metric bar-chart comparing model scores.
* Seaborn heatmap confusion matrices.
---
## 🛠️ Tech Stack
* Python ≥ 3.9
* Streamlit
* Pandas, NumPy
* scikit-learn
---
## 🤝 Contributing
Pull requests are welcome! Open an issue first to discuss any major changes.
---
## 📄 License
This project is provided for educational purposes and comes with no warranty.