SwiftML - Accelerating ML Journeys

Welcome to SwiftML, your all-in-one AI assistant that accelerates your machine learning workflow! No coding required—just upload your data, choose your tasks, and let SwiftML do the magic. Whether you're a beginner or a pro, SwiftML empowers you to process data, build models, and visualize results in no time! ⚡

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Key Features

1. Data Preprocessing - Clean & Prepare Like a Pro

• Missing Values Handling: Say goodbye to NaN! Choose how to fill missing values: mean, median, mode, or interpolate. 💧
• Automatic Data Cleaning: Stripped, sanitized, and ready to go! We clean numeric and string columns automatically. 🧽
• Feature Scaling: Scale your data using Standard or MinMax to get your features in top shape! 📏
• Train-Test Split: Automatically split your dataset into training and testing sets for seamless machine learning workflows. 📊

2. Data Ingestion - Easy Upload & Quick Access

• CSV, Excel, JSON: Upload your datasets in multiple formats and have them ready for processing in no time.
• Instant Preview: See a preview of your dataset to ensure it's ready for the next steps. 🔍

3. Model Selection - Choose the Best ML Model

• Automatic Model Selection: Choose between Regression and Classification tasks, and let SwiftML suggest the best model for your dataset. 🏆
• Compare & Evaluate: See how different models perform on your data and choose the one with the best results! 📈

4. Clustering - Convert Unsupervised to Supervised

• KMeans Clustering: Want to group your data into meaningful clusters? Use KMeans to identify hidden patterns and label your data! 🔍
• Supervised Data Conversion: Transform your unsupervised data into a supervised format with cluster labels added automatically. 🔄

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How It Works

1. Upload Your Data: Start by uploading your dataset (CSV, Excel, JSON, etc.). 📂
2. Preprocess the Data: Clean the dataset, handle missing values, scale the features, and split into training and test sets. 🔧
3. Choose a Task: Select whether you want to perform Regression or Classification.
4. Run Model Selection: Let SwiftML pick and evaluate the best machine learning model for your task. 🏅
5. Visualize & Download: View the results, compare models, and download your processed data and predictions. 📊💾

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📌 Features Coming Soon

• Model Deployment: Deploy your models and make predictions in real-time.
• Advanced Visualizations: Get deeper insights with more visualization options!
• Automated Hyperparameter Tuning: Let SwiftML tune models for optimal performance!

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Technologies Used

• Streamlit: Fast web app development framework for Python. 💻
• PyCaret: AutoML library for easy and quick machine learning model training and evaluation. 🤖
• Scikit-learn: Classic machine learning library for model selection and clustering. 📚
• Pandas & NumPy: Powerful data manipulation and analysis tools. 📊
• KMeans: Clustering algorithm for unsupervised learning. 🔍

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💬 How to Run Locally

1. Clone the repository:

   git clone https://github.com/your-username/AutoML.git
   cd AutoML

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

4. Open your browser at http://localhost:8501 and start exploring!

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##Contact

If you have any questions or need help, feel free to open an issue on the repository or reach out via email!

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🎉 Happy ML-ing!