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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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hey, can i get the data file ?
is it possible to get the powerpoint presentation? please
Great class.
Keep up the good work.
Thank You,
Natasha Samuel
Thank you soooo much for such an amazing video on linear regression, +1 sub !!!!!
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.
Great video! can i get the csv file thanks
can u provide us the dataset which ur using in the video
csv file kindly
How could I convert more than one categorical variable to numerical? Thanks!!
Can I get the dataset?
Thanks for the video! please send me the data file, thanks a lot!!
I just got to know of simplilearn and good to know it's worth sharing
The detailed explanation is spot on
Can I have the dataset used please?
I would like to get access to this dataset.
Loved this so much. Understood the contents perfectly. Thank you
Provide those csv file
Superb, thanks
TypeError: __init__() got an unexpected keyword argument 'categorical_features'
getting this error while executing encoding part. How to solve?
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!
Hi Sir /Ma'am,
Why avoided R&D Spend column from model.
I think this is the indipendent variable so why not considering in model. Please explain
No way to express my gratitude. Amazing explanation with code. I don't how I missed this video for long time
why did u edit only the 3rd row using LabelEncoder?
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.
Fantastic
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Hi! May I know how you got 3 in the equation Y=m*X+c; Y=0.6*3+2.2? Thanks.
one question how does the equation to find M at 11.04 work? I tried to pass it through python but the result is never 0.6
Really great course. Can I get the code used in this tutorial.
Thank you so much for this video and I would like to get the python code used in this tutorial.
Crystal clear. Thanks.
lets paste this
Hello. When I did sns.heatmap(companies.corr()), I have error: ValueError: could not convert string to float: 'New York'. I followed all the steps. Thanks