Data Science Foundations and Machine Learning Labs

A foundational data science repository containing exploratory data analysis (EDA), statistical modeling, machine learning implementations, and Python numerical programming exercises.

Repository Structure

Data_Science/
├── BootCamp/                          # Machine learning algorithms, classification, and regression notebooks
├── Python Revision For Data Science/  # Python syntax revision, NumPy arrays, Pandas DataFrames, and Matplotlib
├── travel-translator-master/          # Multi-lingual travel translation tool and NLP exercises
├── .gitignore                         # Jupyter checkpoints and system ignore rules
├── LICENSE                            # MIT License
└── README.md                          # Curriculum and topic overview

Key Topics Covered

1. Python for Numerical Computation:
   • Advanced list comprehensions, functional constructs (map, filter, reduce), and object-oriented design.
   • NumPy vectorization, multi-dimensional array slicing, broadcasting, and matrix algebra.
2. Exploratory Data Analysis (EDA) & Wrangling:
   • Pandas DataFrame manipulation, missing value imputation, group-by aggregations, and feature engineering.
   • Data visualization with Matplotlib and Seaborn (correlation matrices, histograms, scatter plots).
3. Supervised & Unsupervised Machine Learning:
   • Linear and Logistic Regression, Decision Trees, K-Nearest Neighbors (KNN), and Support Vector Machines.
   • Unsupervised clustering via K-Means and Principal Component Analysis (PCA) dimensionality reduction.

License

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