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Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn



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This Linear Regression Analysis video will help you understand the basics of linear regression algorithm. You will learn how Simple Linear Regression works with solved examples, look at the applications of Linear Regression and Multiple Linear Regression model. In the end, we will implement a use case on profit estimation of companies using Linear Regression in Python.

Dataset Link – https://drive.google.com/drive/folders/1BNAsNI6cbwX8I81Wf42xzNtoOzDLw9to

Below topics are covered in this Linear Regression Analysis Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Usecase – Profit estimation of companies

What is Linear Regression Analysis?
Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Linear regression is a statistical model used to predict the relationship between independent and dependent variables by examining two factors:
Which variables, in particular, are significant predictors of the outcome variable?
How significant is the regression line in terms of making predictions with the highest possible accuracy?

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31 thoughts on “Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn
  1. Hi, i have to say Amazing video, I had seen a lot of videos looking for a easy way to learn, but this video is the best, I can get it! I think you should update the code because the part "categorical_features" doesn't work.

    thanks for shared this information and learn us about ML.

  2. Hello! This video is very helpful to understand the basics of Linear Regression but can you update the code where you import sklearn.preprocessing to transform the State column? Since the latest sklearn library removed categorical_features and hence we are getting errors as "TypeError: __init__() got an unexpected keyword argument 'categorical_features", Thank you!

  3. So my question is when doing predictions, are always given a guide (My lecturer calls it a lab) to follow or we have to come with the formats ourselves. Because I believe different predictions come with different times depending on the CSV contents.

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