Jupyter Notebooks of Regression models

Basic syntax and concepts involved in Python using Jupyter notebook.

This notebook is focused on predicting bike rental counts using various regression models.

This repository contains an academic project developed in jupyter notebook using python language and machine learning algorithms.

In this notebook, we want to create a machine learning model from scratch to predict car prices using independent variables.

Implementation of multiple linear regression (MLR) completed using the Gradient Descent Algorithm and Normal Equations Method in a Jupyter Notebook.

This project involves the prediction of energy output in a Combined Cycle Power Plant (CCPP) using Multiple Linear Regression in Jupyter Notebook. The dataset contains features such as temperature, pressure, humidity, and exhaust vacuum, which are used to predict the net hourly electrical energy output.

This repository contains the Lab practices of Machine Learning performed in Jupyter Notebook using python language. This repo consists of Simple and Multiple Linear regression models to perform regression on the given datasets.

This repository concerns Machine Learning concepts Contents: Written by Brian Lesko, the repository contains Python Notebooks demonstrating Statistical Machine Learning theories largely originating from the book, An Introduction to Statistical Learning, by Gareth James.

In this project is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same.

Depository related with "Investigating player attributes" project. It includes datasets used for the research, Jupyter Notebook file with the code of data cleaning, data manipulations and multiple linear regressions that were performed in order to achieve the project's goals.

This notebook investigates **multiple linear regression** and **polynomial regression** techniques for modeling and predicting relationships between variables in a dataset. The goal is to understand, apply, and compare these regression approaches in practice, analyze results, and reflect on common issues and learning points.

In this series of notebooks, we will dive into each step of the data analysis process of a data set with some information about a list of cars and several attibutes, including their prices. So essentially we will develop a model to predict cars price.

Portfolio of Jupyter Notebooks demonstrating various ML models/concepts learned and developed during my graduate machine learning course and independently post-grad. Generally, a bottom-up modeling approach with Numpy is used to showcase grasp of mathematical foundation. Higher-level libraries (scikit-learn) used for optimized algo implementations.