An automated machine learning pipeline that performs data preprocessing, applies multiple algorithms, evaluates their performance, and selects the best-performing model.

This project is designed to streamline the typical ML workflow by eliminating manual steps such as data cleaning, feature handling, model selection, and evaluation. It is especially useful for rapid experimentation and benchmarking across different datasets.

🚀 Features 🔄 Automated Data Cleaning Handles missing values Encodes categorical variables Normalizes/scales features 📊 Multi-Algorithm Execution Runs multiple ML algorithms (e.g., regression, classification models) 🧪 Performance Evaluation Compares models using relevant metrics (accuracy, F1-score, RMSE, etc.) 🏆 Best Model Selection Automatically identifies and outputs the top-performing algorithm 📁 File-Based Input Accepts dataset files (CSV, etc.) for processing ⚡ Fully Automated Pipeline Minimal user input required 🛠️ Tech Stack Language: Python Libraries: pandas, numpy (data processing) scikit-learn (ML models & evaluation) (Optional: matplotlib / seaborn for visualization) 📂 How It Works Load dataset from file Automatically clean and preprocess data Split into training and testing sets Run multiple machine learning algorithms Evaluate each model Return the best-performing model with metrics ▶️ Usage python main.py --file data.csv 📈 Example Output Best Model: Random Forest Classifier Accuracy: 91.2% F1 Score: 0.89 🎯 Use Cases Rapid ML prototyping Model benchmarking Educational purposes Automating repetitive ML workflows 🔮 Future Improvements Add deep learning models Hyperparameter tuning (GridSearch / Optuna) Web UI for easier interaction Support for larger datasets & parallel execution