This is a project for the "Introduction to Machine Learning" course, where we explore how machine learning models work internally and how to implement different ones.
We should implement Linear Regression, Logistic Regression, K-Means, and a simple Neural Network using the Python programming language. The detailed requirements for each model are included in their respective files.
Different datasets are used for various machine learning models in this assignment. Each dataset serves a specific purpose in building and evaluating different types of models.
This dataset from Cardekho consists of 302 instances with 8 features each. It is used for linear regression, aiming to predict a single target variable.
This dataset contains passenger information from the ill-fated RMS Titanic voyage of 1912. It is used for logistic regression, primarily for binary classification tasks like predicting survival.
This dataset consists of measurements of sepal and petal length for three different iris species (Setosa, Versicolour, and Virginica). It contains 150 data points and is suitable for K-means clustering exercises.
This dataset is used for binary classification tasks related to breast cancer diagnosis. It provides various features relevant to the diagnostic process.
This repository is under the MIT license.