Linear Regression in Python| Implementing Linear Regression| Python Machine Learning Tutorial Part 2

Linear Regression in Python| Implementing Linear Regression| Python Machine Learning Tutorial Part 2
Hello and welcome back to Machine learning tutorial part 2 powered by Acadgild. In the previous session we learned, what is linear regression, what is simple linear regression, what is multiple linear regression, what is regression analysis, advantages and disadvantages of linear regression etc. In this tutorial you will be to learn, Implementation of simple linear regression on a sample problem using Python. Before that, if you have missed the previous session, kindly click the following for the better understanding.

Steps to Implement Linear Regression in Python:
• Import the necessary libraries
• Load the data set
• Select predictor and target variables
• Split the data into train and test data
• Fit the model to the data
• Evaluate model

The important libraries to be imported:
• Sklearn: for scientific computing
• Pandas: for data managing and processing
• Matplotlib: for plotting
• Numpy: for the base in array calculation and data summarization

Loading Data set: Taxi Data Set

R-Squared:
• Also called as the coefficient of determination
• A statistical measure of how close the data is to the filter regression line.
• Between 0 and 100%
• 0% - Model explains none of the variability of the response data around its mean
• 100% - Model explains all of the variability of the response data around its mean

In the next video, we will look at the implementation of multiple linear regression in Python. Please subscribe and stay tuned for more such videos.
#linearRegression #MachineLearning, #Simplelinearregression, #linear, #regression, #Implementation

For more updates on courses and tips follow us on:

[Acadgild]

If you have any questions on Linear Aggression, please shootout your queries in the comment section and our experts will get in touch with you.

[jyothsnayenugu]

i could not download the data set that you are using