🤖 Machine Learning: Basic to Advanced

Welcome to the Machine Learning: Basic to Advanced repository by Girijaray07 – a well-structured, progressive learning resource covering the essentials and deeper insights into machine learning using Python. The notebooks are built for learning, exploration, and practical implementation.

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📚 Repository Structure

Machine-Learning-Basic-to-Advanced/
├── Files/datasets/                    # Datasets used in notebooks
├── Basic-understanding-of-Data.ipynb  # Understanding and analyzing datasets
├── Basic-Graphs-of-Data.ipynb         # Visualizing datasets with basic plots
├── Categorical-Graphs-of-Data.ipynb   # Graphs for categorical data
├── Plots-Categorical-&-Numerical.ipynb# Mixed variable plotting
├── Scaled-vs-unScaled.ipynb           # Difference between scaled and unscaled data
├── Ydata-Profiling.ipynb              # Dataset profiling using Ydata
├── requirements.txt                   # Python dependencies
└── .gitignore                         # Git ignored files

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✨ Highlights

• 📊 Data Visualization: Line, bar, pie, histograms, and scatter plots
• 📂 Data Understanding: Types, distributions, correlations
• 📈 Categorical vs Numerical Analysis: Custom plots for both
• ⚖️ Scaling Demonstrations: Scaled vs unscaled datasets
• 📋 Automated Profiling: Leveraging Ydata-Profiling for quick EDA
• ✅ Ready-to-run Notebooks: Structured in Jupyter (.ipynb) format

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🧪 Technologies Used

• Language: Python 3

• Libraries:

  • pandas, numpy, matplotlib, seaborn, scikit-learn
  • ydata-profiling, requests, beautifulsoup4
  • (Full list available in requirements.txt)

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🔧 Installation & Usage

1. Clone the Repository

git clone https://github.com/Girijaray07/Machine-Learning-Basic-to-Advanced.git
cd Machine-Learning-Basic-to-Advanced

2. Create Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

3. Install Dependencies

pip install -r requirements.txt

Open any notebook from the list to explore concepts interactively.

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🚀 Use Cases

• 📘 Learning & Practice
• 🧠 Concept Reinforcement
• 📂 Project Reference
• 💼 Interview Preparation

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📌 Contribution

This repository is actively growing. Contributions and feedback are welcome!

• 🛠 Add new notebooks
• 🧪 Improve visualizations or analytics
• 📥 Suggest ideas via issues

If you like this project, don't forget to ⭐️ it!

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📄 License

This repository is under the MIT License. Feel free to use, modify, and share.

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Author

Girija Shankar Ray

Passionate about machine learning, data visualization, and building tools that help others learn.

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Start learning ML from the basics and keep growing! 🚀