⚡ Stop Rewriting Scikit-Learn Pipelines. Need a fast machine learning baseline? Spend hours handling NaNs, encoding categories, and writing data-preprocessing scripts just to see if a model works?

RapidModels is a production-ready Python ML boilerplate that turns tedious setup into a 2-minute benchmark. Built for data scientists, students, and Kaggle competitors who want to ingest messy CSVs and get immediate mathematical clarity.

🛠️ What’s Inside the Framework?

• Smart Preprocessing Pipeline: Auto-handles missing values (Median/Most-Frequent) and applies Standard Scaling without data leakage.

• Dual-Engine Algorithms: One toggle switches between 5 Classification (Random Forest, SVM, Logistic, etc.) & 5 Regression models.

• Instant Leaderboard: Clean, ranked terminal output of your model accuracy and metrics in seconds.

🔬 Built for Real-World Data Science Whether you are prepping for technical interviews, entering a Kaggle competition, or processing complex research datasets (like medical metrics or industrial sensors), this scikit-learn template ensures a mathematically sound baseline.

📦 The Download Package (.zip)

• main_pipeline.py (Core Engine)

• requirements.txt (One-click setup)

• README.md (Cheat Sheet to interpret results)

• sample_data.csv (Instant testing)

Get the baseline. Save your afternoon. Download the script instantly.