Machine Learning Examples

This repository contains various machine learning examples implemented in Python. Each example demonstrates a different machine learning algorithm and its application to real-world problems.

Examples

1. Linear Regression with Gradient Descent

   • Description: Fits a linear model to predict housing prices based on the size of the house.
   • File: linear_regression/gradient_descent.py
   • Usage: Run the script to see gradient descent in action.

2. Logistic Regression for Diabetes Classification

   • Description: Predicts whether a person has diabetes based on health measurements using logistic regression.
   • File: logistic_regression/diabetes_classification.py
   • Usage: Run the script to train and evaluate a logistic regression model on the Pima Indians Diabetes dataset.

3. K-Means Clustering for Customer Segmentation

   • Description: Segments customers based on their purchasing behavior using K-Means clustering.
   • File: clustering/customer_segmentation.py
   • Usage: Run the script to see customer segmentation based on synthetic data.

Requirements

• Python 3.6 or later
• Required Python packages are listed in each script and can be installed using pip.

Running the Scripts

1. Clone the repository:

   git clone https://github.com/yourusername/machine_learning_examples.git
   cd machine_learning_examples