π€ Machine Learning Automation App
One-click ML magic β from raw data to model accuracy leaderboard!
π About the Project Tired of cleaning data manually and applying ML models one by one? This app does it all for you, so you can sip your coffee while the machine does the learning.
What it does:
π Explores your data (shape, columns, summary stats, unique values)
π§Ή Cleans your data (removes nulls, drops messy columns, fills gaps)
π¨ Visualizes the data
βοΈ Applies ML algorithms automatically
π§ Finds the best model based on accuracy
π Suggests the top-performing model
All with just a few clicks. No coding. No fuss. Just results.
π Live App π Check out the app here: Machine Learning Automation
π¦ Features Feature Description 𧬠Data Inspection View shape, column names, head of the dataset π Summary Stats Mean, median, std, min, max per column π§½ Data Cleaning Handles nulls, drops columns with >50% missing data π Unique Value Checker See unique values per column π Data Visualization Visual plots to understand trends & patterns π€ Auto ML Applies multiple ML models π§ Model Suggestion Recommends the model with highest accuracy βοΈ One-click Experience No code required, just upload and explore!
π§ Machine Learning Models Applied The app tests multiple algorithms like:
Logistic Regression
Decision Tree
Random Forest
Support Vector Machine
K-Nearest Neighbors
Naive Bayes (...and more coming soon!)
π οΈ How It's Built π Python
π pandas, scikit-learn, matplotlib, seaborn
π§Ό Streamlit for UI
π§ Machine Learning with scikit-learn
π§ Still in Progress Like all great tools, this oneβs still evolving. Upcoming features:
Auto hyperparameter tuning π§
Model interpretability (SHAP, LIME) π
Downloadable report π
π€ Contributing Feel free to fork the project and raise PRs if you have feature ideas, bug fixes, or want to help with optimization!
π¬ Contact Created by: Shubham Chitaguppe π§ Reach out on LinkedIn π¬ Suggestions and feedback always welcome!
β If You Like It... Give it a star π on GitHub and share it with your fellow data nerds!