Skip to content

SHUBHAM-max449/Machine_Learning_Automation

Repository files navigation

πŸ€– Machine Learning Automation App

One-click ML magic β€” from raw data to model accuracy leaderboard!

πŸš€ 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:

πŸ“Š Explores your data (shape, columns, summary stats, unique values)

🧹 Cleans your data (removes nulls, drops messy columns, fills gaps)

🎨 Visualizes the data

βš™οΈ Applies ML algorithms automatically

🧠 Finds the best model based on accuracy

πŸ† Suggests the top-performing model

All with just a few clicks. No coding. No fuss. Just results.

πŸ”— Live App πŸ‘‰ Check out the app here: Machine Learning Automation

πŸ“¦ Features Feature Description 🧬 Data Inspection View shape, column names, head of the dataset πŸ“‰ Summary Stats Mean, median, std, min, max per column 🧽 Data Cleaning Handles nulls, drops columns with >50% missing data πŸ” Unique Value Checker See unique values per column πŸ“Š Data Visualization Visual plots to understand trends & patterns πŸ€– Auto ML Applies multiple ML models 🧠 Model Suggestion Recommends the model with highest accuracy βš™οΈ One-click Experience No code required, just upload and explore!

🧠 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!)

πŸ› οΈ How It's Built 🐍 Python

πŸ“š pandas, scikit-learn, matplotlib, seaborn

🧼 Streamlit for UI

🧠 Machine Learning with scikit-learn

🚧 Still in Progress Like all great tools, this one’s still evolving. Upcoming features:

Auto hyperparameter tuning πŸ”§

Model interpretability (SHAP, LIME) πŸ”

Downloadable report πŸ“

🀝 Contributing Feel free to fork the project and raise PRs if you have feature ideas, bug fixes, or want to help with optimization!

πŸ“¬ Contact Created by: Shubham Chitaguppe πŸ“§ Reach out on LinkedIn πŸ’¬ Suggestions and feedback always welcome!

⭐ If You Like It... Give it a star 🌟 on GitHub and share it with your fellow data nerds!

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages