Project Title: Binary Linear Classifier and Linear Regression with Gradient Descent

Overview:

This project implements a binary linear classifier and performs linear regression using the gradient descent algorithm. Two separate tasks are performed:

1. Binary Linear Classifier: The project constructs a binary linear classifier for each class against the rest using the Iris dataset. It utilizes a simple linear regression-based approach with a threshold to classify samples into two classes.

2. Linear Regression with Gradient Descent: The project implements linear regression using the gradient descent optimization algorithm. It fits a linear equation to noisy data points generated from a linear function and calculates the mean squared error to evaluate the model's performance.

Requirements:

• Python 3.x

Installation and Setup:

1. Clone the repository or download the project files.

2. Navigate to the project directory:

   cd path_to_your_project_directory

3. Create a virtual environment:

   • For Linux/Mac:

     python3 -m venv venv_name

   • For Windows:

     python -m venv venv_name

4. Activate the virtual environment:

   • For Linux/Mac:

     source venv_name/bin/activate

   • For Windows:

     venv_name\Scripts\activate

5. Install the required libraries using requirements.txt:

   pip install -r requirements.txt

6. Run the project:

   python main.py

Code Structure:

• main.py: Main script containing the implementation of binary linear classifier and linear regression with gradient descent.
• README.md: This file providing an overview of the project.
• requirements.txt: File containing the list of required libraries.

Binary Linear Classifier:

• The fit() function constructs binary linear classifiers for each class against the rest using the Iris dataset.
• The predict() function predicts the class label for test data using the constructed classifiers.
• Accuracy scores for each class model are printed.

Linear Regression with Gradient Descent:

• The project generates noisy data points from a linear function and performs linear regression using gradient descent.
• The linear_eq() function defines the linear equation.
• The mean_squared_error() function calculates the mean squared error between predicted and true values.
• The gradient_descent() function implements the gradient descent optimization algorithm to fit the best line to the data points.
• The best fit line equation and mean squared error are printed.
• A scatter plot with the training data and the best fit line is displayed.

License:

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