Linear Regression Practical Example | Tutorial 3 | Linear Regression for Beginners

If you are new to #python and #machinelearning, this video should help you. If you have any question you can email me [email protected] . We can learn together. Hope you find this video helpful. Let me know your thoughts in the comment section below. If you like this video, please don’t forget to subscribe. Happy Learning!#multiplelinearregression #python #jupyter #jupyternotebook #beginnerinmachinelearning #machinelearning #pythontutorial #regression #scikitlearn #sklearn In this session, I am covering practical aspects of #linear #regression, including #exploratorydataanalysis (EDA) and #feature #selection. We will start by exploring the data and gaining insights through EDA techniques. This involves examining the distributions of variables, identifying patterns or trends, and checking for any missing or outlier values. EDA helps us understand the relationships between variables and determine the suitability of linear regression for modeling the data. After EDA, we will move on to feature selection. This step involves identifying the most relevant features that have the strongest impact on the target variable. Code :- https://github.com/mukherjeekoushiklearning/Linear-Regression/blob/main/Univariant%20%26%20Multivariant%20Analysis%20with%20Feature%20Selection%20On%20Linear%20Regression.ipynb For any question you can email me [email protected]