An intuitive, user-friendly machine learning application that simplifies the entire ML workflow from data analysis to model deployment. Built with Streamlit, this tool provides a comprehensive suite of ML capabilities accessible through a modern web interface.

• Interactive data upload and preview
• Automated data type detection and quality analysis
• Missing value visualization and handling
• Feature correlation analysis
• Automated data preprocessing pipeline

• Traditional Models

  • Ridge Regression
  • Lasso Regression
  • Random Forest
  • Gradient Boosting
  • SVR (Support Vector Regression)

• Advanced Models

  • XGBoost
  • LightGBM
  • Prophet (Time Series)
  • SARIMA (Time Series)

• Features

  • Automated feature preprocessing
  • Model performance metrics
  • Cross-validation support
  • Feature importance analysis
  • Interactive parameter tuning

• Feature distribution plots
• Correlation heatmaps
• Model performance comparisons
• Prediction vs Actual plots
• Time series forecasting plots

• Step-by-step guidance
• Best practices
• Troubleshooting tips
• Real-world examples
• Advanced topics

• Python 3.8 or higher
• pip package manager

1. Clone the repository:

git clone https://github.com/yourusername/ml-swiss-army-knife.git
cd ml-swiss-army-knife

2. Create and activate a virtual environment:

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python -m venv venv
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Launch the application:

streamlit run app.py

1. Data Upload

# Example CSV structure
date,feature1,feature2,target
2024-01-01,23.5,high,100
2024-01-02,24.1,low,95

2. Data Preprocessing

• Select features for encoding
• Handle missing values
• Scale numerical features

3. Model Training

# Example model configuration
model_params = {
    "n_estimators": 100,
    "learning_rate": 0.1,
    "max_depth": 5
}

4. Evaluation & Prediction

• View performance metrics
• Analyze feature importance
• Make predictions on new data

# Example Prophet configuration
prophet_params = {
    "yearly_seasonality": True,
    "weekly_seasonality": True,
    "daily_seasonality": False
}

• Parameter tuning
• Cross-validation
• Feature selection

• RAM: 8GB minimum (16GB recommended)
• Storage: 1GB free space
• Processor: Multi-core processor recommended

# Optional configuration
STREAMLIT_SERVER_PORT=8501
STREAMLIT_SERVER_ADDRESS=localhost

ml-swiss-army-knife/
├── .streamlit/               # Streamlit configuration
│   └── config.toml          # Theme and settings
├── app.py                   # Main application
├── requirements.txt         # Dependencies
├── README.md               # Documentation
├── styles.py               # Custom styling
└── modules/               # Application modules
    ├── data_analysis.py    # Data analysis functionality
    ├── time_series.py      # Time series analysis
    ├── model_training.py   # Model training functionality
    ├── predictions.py      # Prediction functionality
    └── tutorial.py         # Tutorial content

1. Start the application
2. Upload your data (CSV format)
3. Explore data insights automatically
4. Train models with guided selection
5. Make predictions with trained models

Located in .streamlit/config.toml:

[theme]
primaryColor = "#7C3AED"
backgroundColor = "#FFFFFF"
secondaryBackgroundColor = "#F3F4F6"
textColor = "#111827"
font = "sans serif"

# Sample data structure
sales_data = {
    'date': ['2024-01-01', '2024-01-02'],
    'sales': [1000, 1200],
    'promotion': ['yes', 'no']
}

# Sample categorical features
category_data = {
    'feature1': ['A', 'B', 'C'],
    'feature2': [1, 2, 3],
    'target': ['cat1', 'cat2', 'cat1']
}

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch:

git checkout -b feature/AmazingFeature

3. Commit changes:

git commit -m 'Add AmazingFeature'

4. Push to branch:

git push origin feature/AmazingFeature

5. Open a Pull Request

1. Installation Problems

# If you encounter SSL errors
pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org -r requirements.txt

2. Memory Issues

# Reduce memory usage
import pandas as pd
pd.read_csv('large_file.csv', nrows=1000)  # Load subset for testing

1. Large Datasets

• Use chunked processing
• Implement memory optimization
• Consider data sampling

2. Model Training

• Start with simple models
• Use cross-validation
• Monitor resource usage

• v1.0.0 (2024-01-01)

  • Initial release
  • Basic model support
  • Data preprocessing

• v1.1.0 (2024-02-01)

  • Added time series support
  • Improved visualization
  • Bug fixes

• Create an issue on GitHub
• Email: [email protected]
• Documentation: [Wiki Link]

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

• Streamlit team
• Scikit-learn community
• All contributors

Made with ❤️ by [Mlawali]