Machine Learning and Pattern Recognition

This repository contains all coursework, mini-projects, assignments, and experiments completed for the subject Machine Learning and Pattern Recognition. Each project demonstrates core ML concepts, algorithms, and practical applications.

📂 Repository Structure

📁 Machine-Learning-and-Pattern-Recognition/
├── 📁 Datasets/
├── Project_1.ipynb
├── Project_2.ipynb
├── Project_3.ipynb
├──     .
├──     .
├──     .
├── requirments.txt
└── readme.md

Each dataset is stored in Datasets folder and the ones too large to push are given here

Project 1 | Project 2 | Project 3 | Project 4 | Project 5 | Project 6 | Project 7 | Project 8 | Project 9

🧠 Topics Covered

• Supervised Learning
  • Linear & Logistic Regression
  • Decision Trees
  • K-Nearest Neighbors
  • Support Vector Machines
• Unsupervised Learning
  • K-Means Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction (PCA, LDA)
• Probabilistic Models
  • Naive Bayes Classifier
  • Hidden Markov Models
• Neural Networks (Basics)
• Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC/AUC
• Pattern Recognition Techniques and Applications

⚙️ Requirements

All code is written in Python and uses common libraries such as:

• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn
• jupyter (for notebooks)

Install dependencies with:

pip install -r requirements.txt

📝 Usage

Each subfolder contains:

• Python scripts or Jupyter notebooks
• Input datasets (or links to datasets)
• Result plots or output files
• A brief README.md or code comments describing the work

Clone this repo:

git clone https://github.com/KavyaJP/Machine-Learning-and-Pattern-Recognition.git
cd Machine-Learning-and-Pattern-Recognition

Explore any folder and run the scripts or notebooks.

📩 Contact

For any questions or suggestions, feel free to reach out:

Kavya Prajapati
📧 [kavya31052005@gmail.com]

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This repository is maintained for academic and reference purposes. Feel free to fork and use the material with proper credit.