Applied-Machine-Learning

Showcase of various Applied Machine Learning techniques

Topics

1. Fire Data Visualization
2. Brightness Temperature Analysis
3. House Price Prediction
4. Credit Risk Classification
5. Breast Cancer Clustering
6. Fisher Linear Discriminant Analysis
7. Bias-Variance Decomposition
8. Image Compression using PCA
9. Name Classification using Naive Bayes

Getting Started

To get started with any of the assignments, navigate to the respective directory and follow the instructions provided in the README files.

Requirements

• Python 3.x
• Jupyter Notebook
• Required Python packages (listed in each topic's README)

How to Run

1. Clone the repository:

git clone https://github.com/yourusername/Applied-Machine-Learning.git

2. Navigate to the assignment directory:

cd Applied-Machine-Learning/<topic>

3. Install the required packages:

pip install <requirements>

4. Run the Jupyter Notebook:

jupyter notebook <DesiredNotebookname.ipynb>