Prediction using supervised ML

Overview

This is a simple task. It was assigned to me from the Sparks Foundation organiztion to implement linear regression technique, So we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.

Problem statement

In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied.

Description

I solved this task and made some predictions using data that was allowed to me to use it for the task, so you will see:

1. How to impelement simple linear regression using the Python Scikit-Learn library ?
2. What are the required Packages for linear regression ?
3. Simple explaination of each essential Package I used.
4. What are five basic steps to be considered when you’re implementing linear regression ?
5. Show how to implement each of these five steps practically in this notebook.
6. What are metrics used to evaluate linear regression model ?

What are the technologies usedin this Notebook ?

1. Scikit-Learn
2. Pandas
3. Matplotlib
4. Seaborn
5. Numpy

What is upcoming ?

I may try to write clean code with depth explaination of each cell, and may try to make this notebook to be as a reference to revision linear regression and answer questions like

1. What is machine learning ?
2. What are types of machine learning ?
3. What is the differece between classification and regression ?