A collection of end-to-end Supervised Machine Learning projects covering both Regression and Classification algorithms. Each project demonstrates the complete Machine Learning workflow, from data preprocessing and model training to evaluation and deployment through an interactive Streamlit web application.
This repository serves as a portfolio of practical Machine Learning implementations built using Python and Scikit-learn.
- 10 End-to-End Machine Learning Projects
- Regression and Classification Algorithms
- Interactive Streamlit Applications
- Model Serialization using Pickle
- Data Preprocessing and Feature Engineering
- Model Evaluation
- Clean Project Structure
- Individual Documentation for Every Project
- Python
- Scikit-learn
- Streamlit
- Pandas
- NumPy
- Matplotlib
- Pickle
| Project | Algorithm | Type | Status |
|---|---|---|---|
| House Price Prediction | Linear Regression | Regression | Complete |
| Breast Cancer Prediction | Logistic Regression | Classification | Complete |
| Heart Attack Prediction | KNN Classification | Classification | Complete |
| Student Performance Prediction | KNN Regression | Regression | Complete |
| Walmart Sales Prediction | Decision Tree Regression | Regression | Complete |
| Wine Quality Prediction | Decision Tree Classification | Classification | Complete |
| Insurance Cost Prediction | Random Forest Regression | Regression | Complete |
| Banknote Authentication | Random Forest Classification | Classification | Complete |
| Fish Weight Prediction | Support Vector Regression | Regression | Complete |
| Dry Bean Classification | Support Vector Classification | Classification | Complete |
Supervised_ML_Models
│
├── House-Price-Prediction (Linear Regression LR)
├── Breast-Cancer-Prediction (Logistic Regression)
├── Heart-attack (KNN-C)
├── Student_performance (KNN-R)
├── Walmart Sales (DTR)
├── Wine-Quality (DTC)
├── Insurance (RFR)
├── banknote_authentication (RFC)
├── Fish_Weight (SVR)
└── Dry_bean_dataset (SVC)
This project predicts residential house prices using various property features through a Linear Regression model deployed with Streamlit.
- Real-time prediction
- Interactive Streamlit dashboard
- Download prediction report
- Model evaluation
- Data visualization
Live Application
Try the application here:
https://housepricepredictionmodel-fjcshg9alxgfnzcehbvfmd.streamlit.app/
house-price-prediction.mp4
Every project in this repository follows a similar pipeline:
- Dataset Collection
- Exploratory Data Analysis (EDA)
- Data Cleaning
- Data Preprocessing
- Feature Engineering / Encoding
- Feature Scaling (where applicable)
- Train-Test Split
- Model Training
- Model Evaluation
- Model Serialization using Pickle
- Streamlit Deployment
- Data Preprocessing
- Exploratory Data Analysis
- Feature Engineering
- Feature Scaling
- Regression
- Classification
- Model Evaluation
- Streamlit Development
- Pickle Serialization
- End-to-End Machine Learning Pipeline
- Deep Learning implementations
- Ensemble Learning techniques
- Hyperparameter Optimization
- Cross Validation
- Explainable AI (SHAP/LIME)
- Docker deployment
- CI/CD pipeline
- Cloud deployment
- MLOps integration
Clone the repository
git clone https://github.com/adiba0-0/Supervised_ML_Models.gitMove into the repository
cd Supervised_ML_ModelsNavigate to any project
cd "House-Price-Prediction (Linear Regression LR)"Create a virtual environment
python -m venv venvActivate it
venv\Scripts\activateInstall dependencies
pip install -r requirements.txtRun the Streamlit application
streamlit run app.py- 10 Supervised Machine Learning Projects
- 5 Regression Algorithms
- 5 Classification Algorithms
- 10 Interactive Streamlit Applications
- 10 Trained Machine Learning Models
Machine Learning • Artificial Intelligence • Computer Vision • Deep Learning