🐍 Python Data Science & Machine Learning Collection

This repository contains a comprehensive collection of educational materials for data science and machine learning with Python. You'll find Jupyter notebooks covering various topics from beginner to intermediate level, along with sample datasets for hands-on practice.

📚 Contents

🔤 Python Fundamentals

• Basics.ipynb - Python fundamental concepts
• Class_and_OOP.ipynb - Classes and Object-Oriented Programming
• Common_Functions.ipynb - Commonly used functions
• File_Operations.ipynb - File operations

🔢 NumPy

• Numpy_Intro.ipynb - Introduction to NumPy
• Numpy_basics.ipynb - NumPy basic operations
• Various data files and examples

🐼 Pandas

• Pandas_Intro.ipynb - Introduction to Pandas
• Pandas_Basics.ipynb - Basic Pandas operations
• Pandas_Data_Types_and_Missing_Values.ipynb - Data types and missing values
• Pandas_Grouping_and_Sorting.ipynb - Grouping and sorting
• Pandas_Summary_Func_and_Maps.ipynb - Summary functions and mapping
• Pandas_Remaing_and_Combining.ipynb - Renaming and combining

📊 Data Visualization

Matplotlib

• Matplotlib_Intro.ipynb - Introduction to Matplotlib
• Matplotlib_Basics.ipynb - Basic plotting operations
• Matplotlib_Customizing.ipynb - Plot customization
• Matplotlib_Animations.ipynb - Animated plots
• Matplotlib_Legend.ipynb - Legend usage
• Matplotlib_Tight_Layout.ipynb - Layout arrangements
• Matplotlib_Constrained_Layout.ipynb - Constrained layout
• Matplotlib_Arranging_Axes.ipynb - Axes arrangements
• Matplotlib_Autoscale.ipynb - Automatic scaling
• Matplotlib_Blitting.ipynb - Blitting techniques
• Matplotlib_Imshow_Extent.ipynb - Image display
• Matplotlib_Path_Effects.ipynb - Path effects
• Matplotlib_Paths.ipynb - Path plotting
• Matplotlib_Transformations.ipynb - Transformations

Seaborn

• Seaborn_Intro.ipynb - Introduction to Seaborn and basic usage

🤖 Machine Learning

Supervised Learning

Linear Models:

• Linear_Models.ipynb - Linear models overview
• Linear_Regression_Example.ipynb - Linear regression example
• L1_Penalty_and_Sparsity_in_Logistic_Regression.ipynb - L1 penalty and logistic regression
• MNIST_Classification_Using_Multinominal_Logistic_+_L1.ipynb - MNIST classification
• Multiclass_Sparse_Logistic_REgression_on_20newgroups.ipynb - Multiclass sparse logistic regression
• Non_Negative_Least_Squares_Example.ipynb - Non-negative least squares
• Plot_Classification_Probality.ipynb - Classification probabilities
• Plot_Ridge.ipynb - Ridge regression
• Regularization_Path_of_L1-Logistic_Regression.ipynb - L1 regularization path
• Compressive_Sensing_Tomography_Reconstruction_with_L1_Prior.ipynb - Compressive sensing

Support Vector Machines:

• Support_Vector_Machines.ipynb - SVM overview
• Plot_Different_SVM_Classifires_in_the_Iris_Dataset.ipynb - SVM classifiers on Iris dataset
• RBF_SVM_Parameters.ipynb - RBF SVM parameters
• SVM-Anova_ SVM_with_Univariate_Feature_Selection.ipynb - SVM with feature selection
• SVM_Maximum_Margin_Separating_Hyperplane.ipynb - Maximum margin separating hyperplane
• SVM_Separating_Hyperlane_for_unbalanced_classes.ipynb - SVM for unbalanced classes
• SVM_Weighted_Samples.ipynb - SVM with weighted samples
• Scaling_the_REgularization_Parameter_for_SVCs.ipynb - SVC regularization parameter scaling
• Support_Vector_Regression_(SRV)_Using_Linear_and_non-linear_Kernels.ipynb - Support vector regression

TO BE CONTINUED

Scikit-Learn

• Scikit_Learn_Intro.ipynb - Introduction to Scikit-Learn

📁 Datasets

The repository includes various sample datasets:

• CAvideos.csv - Canada YouTube videos
• GBvideos.csv - Great Britain YouTube videos
• IMDB-Movie-Data.csv - IMDB movie data
• winemag-data-130k-v2.csv - Wine magazine review data

🚀 Installation

1. Clone the repository:

git clone https://github.com/KurKigal/Data-Science
cd Data-Science

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# or
.venv\Scripts\activate     # Windows

3. Install required packages:

pip install jupyter numpy pandas matplotlib seaborn scikit-learn

4. Launch Jupyter Notebook:

jupyter notebook

📋 Requirements

• Python 3.7+
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-Learn

🎯 Target Audience

• Python beginners
• Those interested in getting started with data science
• People who want to practice machine learning
• Anyone looking to learn visualization techniques

📖 Usage

Each notebook can be run independently. You can follow the notebooks sequentially to learn topics in depth and practice by running code examples.

🤝 Contributing

If you'd like to contribute to this repository:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Create a Pull Request

📄 License

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

📞 Contact

Feel free to open an issue for questions or contact us directly.

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