Linear Regression Model implemented in Python for Prediction Problem

Linear Regression Model

In this article, we dive into a linear regression model.
In this post, I am going to walk you through how you can load data, build, predict, evaluate and implement Linear Regression Model in python.
I will build and evaluate the model on the Boston Housing dataset using scikit-learn.

Philadelphia Housing Dataset:

Dataset is from Philadelphia, PA and includes average house sales price in a number of neighborhoods. The attributes of each neighborhood we have include the crime rate ('CrimeRate'), miles from Center City ('MilesPhila'), town name ('Name'), and county name ('County').

Here are the topics I’m going to cover with implementation in this post.

• Load the Data
• Explore the Data
• Build Linear Regression Model
• Predict on Test Data with Model
• Evaluation of the Prediction of the Model
• Visualizations 

These topics are implemented in Jupyter Notebook So go through this notebook to understand the implementation of the linear regression model and its performance on predicting problems.