This repository contains the solutions to five machine-learning tasks. Each task focuses on a specific problem and several small tasks and provides a solution using machine learning techniques. The tasks included in this repository are as follows:
In this task, we explore the Boston Housing dataset and apply various regression models to predict the median value of owner-occupied homes. The solution includes data preprocessing, model training, and evaluation.
This task involves analyzing the relationship between the temperature and the chirping rate of ground crickets. We build a regression model to predict the temperature based on the chirping rate using linear regression.
In this task, we investigate the correlation between the brain weight and body weight of different species. We employ a linear regression model to predict the brain weight based on the body weight.
This task deals with analyzing salary data to identify potential gender-based salary discrimination. We apply classification algorithms to detect discriminatory patterns and evaluate the model's performance.
In this task, we analyze car attributes and their corresponding prices. We develop a regression model to predict the price of a car based on its characteristics using various machine learning techniques.
Each assignment in this repository includes a detailed explanation of the problem statement, the dataset used, the methodology employed, and the code implementation. Additionally, the solutions are provided in Python using popular machine-learning libraries.
Feel free to explore each task and the corresponding solution code to gain insights into machine learning techniques and their application to real-world problems.
Note: Please refer to the individual task folders for each assignment's specific datasets and code files.
Happy learning and exploring the world of machine learning!