Machine Learning Practice 2024

This repository contains my journey and practice work in machine learning for the year 2024. As a promise to myself, I committed to learning and implementing a wide range of machine learning algorithms and concepts. Here's a summary of the topics I have completed:

Data Visualization

Topics Covered

Data Preprocessing

1. Data Cleaning
2. Feature Scaling
3. Encoding Categorical Data

Regression

1. Linear Regression
2. Multiple Linear Regression
3. Polynomial Linear Regression
4. Support Vector Regression (SVR)
5. Decision Tree Regression
6. Random Forest Regression

Evaluation Metrics (Regression)

1. R-Square
2. Adjusted R-Square
3. Other Evaluation Metrics

Classification

1. Logistic Regression
2. K-Nearest Neighbors (KNN) Classification
3. Support Vector Classification (SVC)
4. Naive Bayes Theorem
5. Decision Tree Classifier
6. Decision Tree Classifier by Gini Index
7. Random Forest Classifier

Evaluation Metrics (Classification)

1. Confusion Matrix
2. Precision, Recall, F1-Score
3. ROC Curve

Clustering

1. K-Means Clustering
2. Hierarchical Clustering
3. Density-Based Clustering (DBSCAN)

Association Rule Learning

1. Apriori Algorithm
2. Eclat Algorithm
3. FP Growth Algorithm

Reinforcement Learning

1. Markov Decision Process (MDP)
2. Hidden Markov Model (HMM)
3. Multi-Armed Bandit Algorithm
4. Thompson Sampling for Multi-Armed Bandit Problem

Dimensionality Reduction

1. Principal Component Analysis (PCA)
2. Other Dimensionality Reduction Techniques

Advanced Algorithms

1. XGBoost

Repository Structure

• datasets/: Contains datasets used for practice.
• notebooks/: Jupyter notebooks for each topic.
• scripts/: Python scripts for implemented algorithms.
• results/: Outputs, visualizations, and results of different models.

How to Use

1. Clone the repository:

   git clone https://github.com/yourusername/ml-practice-2024.git

2. Navigate to the directory and explore the topics:

   cd ml-practice-2024

3. Install the required dependencies:

   pip install -r requirements.txt

Dependencies

Ensure you have Python 3.7+ installed. Install the dependencies listed in requirements.txt.

License

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

Acknowledgments

• Inspired by self-learning and online resources.
• Special thanks to the machine learning community for providing open resources and inspiration.

Contact

Feel free to reach out if you have questions or suggestions:

• Developer: Hashaam Zahid
• GitHub: Hashaam Zahid