🤖 My Machine Learning Portfolio

Welcome to my Machine Learning (ML) Portfolio!
This repository showcases various ML projects I've undertaken, encompassing a range of algorithms, models, and datasets. Each project is documented with Jupyter Notebooks, providing a comprehensive walkthrough of the methodologies and analyses.

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📚 Repository Overview

This repository includes:

• 📓 Jupyter Notebooks: Detailed implementations and analyses of various ML algorithms.
• 📁 Datasets: Data utilized for training and evaluating models.
• 📊 Model Implementations:
  • Supervised Learning: K-Nearest Neighbors (KNN), Random Forest Classifier
  • Unsupervised Learning: K-Means Clustering
  • Deep Learning: Convolutional Neural Networks (CNNs)
  • Model Optimization: Hyperparameter Tuning

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🚀 Projects Included

Project	Description	Status
🖋️ K-Nearest Neighbors (KNN)	Implementation of KNN algorithm for classification tasks.	✅ Completed
🌳 Random Forest Classifier	Utilizing Random Forests for robust classification.	✅ Completed
🔍 K-Means Clustering	Applying K-Means for unsupervised data segmentation.	✅ Completed
🧠 Convolutional Neural Networks (CNNs)	Building CNNs for image recognition tasks.	✅ Completed
⚙️ Hyperparameter Tuning	Techniques to optimize model performance.	✅ Completed
🧠 Mental Health Data Analysis	Exploring and modeling mental health datasets.	🔄 In Progress

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🏗️ Project Structure

📂 MyML
│
├── 📄 README.md                # Project documentation
├── 📁 CNN/                    # Convolutional Neural Networks project
│   └── CNN.ipynb              # Jupyter Notebook for CNN implementation
├── 📁 HyperParametreTunning/   # Hyperparameter Tuning project
│   └── HyperParametreTunning.ipynb  # Notebook for hyperparameter tuning
├── 📁 Mental Health Data/      # Mental Health Data Analysis project
│   └── Mental_Health_Analysis.ipynb # Notebook for mental health data analysis
├── 📄 KNN.ipynb                # K-Nearest Neighbors implementation
├── 📄 RandomForest.ipynb       # Random Forest Classifier implementation
├── 📄 digit_kcross.ipynb       # K-Fold Cross-Validation on digit dataset
├── 📄 iris.kmeans.ipynb        # K-Means Clustering on Iris dataset
├── 📄 iris_kcross.ipynb        # K-Fold Cross-Validation on Iris dataset
└── 📄 kmeanscl.ipynb           # General K-Means Clustering implementation

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📦 Requirements

To replicate the analyses and run the notebooks, install the necessary Python libraries:

pip install -r requirements.txt

Key libraries include:

• numpy
• pandas
• matplotlib
• scikit-learn
• tensorflow / keras
• seaborn

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💻 How to Use

1. Clone the repository:

   git clone https://github.com/paratha14/MyML.git
   cd MyML

2. Install dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

4. Explore the notebooks:

   • Navigate to the desired project folder.
   • Open the corresponding .ipynb file.
   • Execute the cells to run the code and visualize the results.

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📊 Key Observations

• KNN and Random Forest: Effective for classification tasks with interpretable results.
• K-Means Clustering: Useful for uncovering hidden patterns in unlabeled data.
• CNNs: Powerful in handling image data, capturing spatial hierarchies effectively.
• Hyperparameter Tuning: Essential for enhancing model performance and generalization.

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🔍 Future Enhancements

• 📌 Incorporate more advanced deep learning models, such as Recurrent Neural Networks (RNNs).
• 📌 Expand the Mental Health Data Analysis project with more datasets and insights.
• 📌 Implement model deployment strategies using platforms like Flask or FastAPI.

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📜 License

This project is open-source and available under the MIT License.

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🙌 Acknowledgements

• Scikit-Learn Documentation
• TensorFlow Documentation
• Keras Documentation
• UCI Machine Learning Repository

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Feel free to explore the projects and provide feedback or contributions! 🚀