Welcome to this comprehensive "Linear Regression with Python Tutorial" for beginners! In this video, we will cover the basics of linear regression, a fundamental concept in data science and machine learning. With this tutorial, you will learn how to perform data analysis using linear regression in Python. This tutorial is perfect for anyone who is new to the field of data science and machine learning and wants to get started with a real-world data science project.

We will start by introducing the concepts of statistical learning and statistics, and then move on to applying these concepts in Python to perform linear regression. You will learn how to use Python libraries such as NumPy and Pandas for data preprocessing, and then use scikit-learn for building and evaluating a linear regression model.

By the end of this tutorial, you will have a solid understanding of linear regression and how to apply it in Python for your own data science projects. So, if you're ready to dive into the world of data science and machine learning, make sure to watch this tutorial till the end. Don't forget to like and subscribe for more tutorials like this!

👉 Check out the code: [ Ссылка ]
👉 Dataset: [ Ссылка ]

Errata
30:30 Intersection with the Y axis*

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Timestamps:
00:00 Intro
02:10 Importing the dataset
08:14 Exploratory Data Analysis
11:31 Pairplot of all numerical variables
14:34 Quick Linear Regression Explanation
20:10 Split the data using Scikit-Learn
27:05 Train a model using Scikit-Learn
29:08 Interpreting the Coefficients
33:09 Create Predictions
35:20 Graphical Evaluation of the Predictions
37:40 Analytical Evaluation of the Errors
42:19 Residual Analysis
48:37 Outro

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