🚀 InsightML Studio

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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🌟 Overview

InsightML Studio is an all-in-one Machine Learning and Computer Vision platform built with Python, Streamlit, Scikit-Learn, OpenCV, and MediaPipe.

It enables users to perform complete end-to-end ML workflows—from uploading raw datasets to generating predictions, visualizations, explainability reports, business insights, and computer vision analysis—within a modern interactive web interface.

Whether you're a student, researcher, data scientist, or developer, InsightML Studio provides an intuitive environment to explore data, build models, analyze results, and generate actionable insights.

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✨ Features

📂 Dataset Explorer

• Upload CSV datasets
• Automatic dataset profiling
• Data type detection
• Missing value analysis
• Duplicate detection
• Dataset statistics
• Memory usage
• Column summaries

📊 Automated EDA

Automatically generates:

• Distribution plots
• Correlation heatmaps
• Missing value visualization
• Outlier detection
• Class distribution
• Feature statistics
• Pairwise relationships
• Numerical summaries

⚙ Data Preprocessing

Supports:

• Missing value handling
• Label encoding
• One-hot encoding
• Feature scaling
• Feature selection
• Duplicate removal
• Data cleaning

🧠 Feature Engineering

Automatically creates useful features such as:

• Banking domain features
• Ratio features
• Log transformations
• Balance change metrics
• Credit/Debit ratios
• Variance-based feature selection

🤖 Machine Learning

Supports:

• Classification
• Regression

Automatically trains multiple models:

• Logistic Regression
• Linear Regression
• Decision Tree
• Random Forest
• Extra Trees
• Gradient Boosting
• AdaBoost
• KNN
• Support Vector Machine
• Naive Bayes
• XGBoost
• LightGBM
• CatBoost
• MLP Neural Network

🏆 Model Comparison

Automatically compares models using cross-validation and displays:

• Accuracy
• Precision
• Recall
• F1 Score
• ROC AUC
• MAE
• RMSE
• R² Score
• Training time
• Cross validation score

📈 Hyperparameter Optimization

Supports:

• Cross validation
• Grid search
• Randomized search
• Best model selection

🎯 Prediction

Make predictions using trained models:

• Single prediction
• Batch prediction
• CSV upload prediction

📦 Batch Prediction

Upload a CSV with unseen data and generate:

• Prediction CSV
• Downloadable results
• Prediction summary

🧠 Explainable AI (XAI)

Understand model decisions using:

• SHAP values
• Feature importance
• Global explainability
• Local explainability
• Decision interpretation

📈 Business Insights

Automatically generates business-friendly insights:

• Customer segmentation
• Risk analysis
• Revenue opportunities
• Important feature highlights
• Strategic recommendations

📥 Downloads

Generate downloadable:

• Processed dataset
• Predictions
• Reports
• Model leaderboard
• Feature importance
• Evaluation results

👁 Vision AI

Includes a computer vision module for image analysis.

👤 Human Analysis

Using MediaPipe, detects:

• Face
• Eyes
• Nose
• Lips
• Ears
• Face mesh
• Facial landmarks

🚗 Object Detection

Detects objects like:

• Cars
• Bikes
• Trucks
• Buses
• People
• Animals

🌳 Scene Analysis

Analyzes:

• Nature
• Forest
• Mountain
• Beach
• City
• Indoor
• Sky
• Sunset

🖼 Image Analysis

Automatically computes:

• Resolution
• Brightness
• Contrast
• Sharpness
• Blur score

📄 Vision Report

Generate complete image analysis reports including:

• Human detection
• Object detection
• Scene analysis
• Image quality
• Confidence scores

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📁 Project Structure

InsightML-Studio/
├── app.py
├── config.py
├── requirements.txt
├── README.md
├── LICENSE
├── artifacts/
├── data/
├── models/
│   └── vision/
├── notebooks/
├── pages/
└── utils/

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🛠 Technologies Used

• Python
• Streamlit
• Pandas
• NumPy
• Scikit-Learn
• XGBoost
• LightGBM
• CatBoost
• OpenCV
• MediaPipe
• Plotly
• SHAP
• Joblib

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🚀 Installation

git clone https://github.com/codedbydollys10/InsightML-Studio.git
cd InsightML-Studio
python -m venv venv

Activate virtual environment

Windows:

venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Run the app:

streamlit run app.py

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🔮 Future Enhancements

• AutoML
• Time series forecasting
• NLP module
• OCR support
• Video analysis
• Audio analysis
• Live webcam detection
• Model monitoring
• Cloud deployment
• REST API support

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Author

Dolly Sharma
GitHub: github.com/codedbydollys10

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⭐ If you found this project helpful, consider giving it a star on GitHub!