NNTI Assignment 1

Overview

This repository contains the implementation for Assignment 1 of the Neural Networks: Theory and Implementation (NNTI) course. The assignment covers key concepts of data generation, visualization, and classification using Python libraries such as NumPy, Matplotlib, and scikit-learn.

Topics Covered

1. Data Generation:

   • Creating linearly separable 2D clusters.
   • Generating an XOR dataset with logical XOR operation.

2. Visualization:

   • Visualizing clusters and XOR datasets.
   • Plotting decision boundaries for classification models.

3. Classification:

   • Using sklearn.svm.LinearSVC to classify datasets.
   • Exploring the impact of hyperparameters and noise on decision boundaries.

4. Analysis:

   • Observing decision boundary differences between linearly separable and XOR datasets.
   • Evaluating the influence of outliers on model performance.

Files and Structure

• assignment1_1_1.py: Functions for generating 2D clusters and shuffling data.
• assignment1_1_2.py: Functions for creating an XOR dataset.
• assignment1_1_3.py: Functions to visualize datasets using scatter plots.
• assignment1_2_1.py: Implementation of a linear SVM model using LinearSVC.
• assignment1_2_2.py: Functions to plot decision boundaries for classification models.
• assignment1_2_3.py: Functions for hyperparameter tuning using GridSearchCV and RandomizedSearchCV.
• main.py: The main script that combines and executes the above functionalities.

Dependencies

The following Python libraries are required:

• NumPy
• Matplotlib
• scikit-learn

You can install them using:

pip install numpy matplotlib scikit-learn