Regression and Prediction with scikit learn python (numeric response and feature variables)
In this video, I would like to explore performing regression and prediction using python scikit learn. Here I will be focusing on numeric variables as both my response and feature variables.
The content is solely for educational purposes and is based on my personal experience.
Links:
Dataset - https://www.kaggle.com/datasets/zynic...
Code - https://github.com/maddyhyc/Regressio...
See other links to sites that I used to hone my skills below. I may receive commission from them.
BE SURE TO CHECK THEM OUT!
Datacamp signup and learn for free - https://datacamp.pxf.io/c/3053810/161...
Datacamp student - https://datacamp.pxf.io/c/3053810/161...
Datacamp business - https://datacamp.pxf.io/c/3053810/154...
Canva - https://partner.canva.com/FwDbyMaddy
Timestamps:
00:00 Introduction
00:12 Askchatgpt
01:11 Import packages and read in dataset
01:50 Remove NAs
02:21 Data preparation for scikit learn
03:11 Plot scatter plot
03:22 Fit linear regression
03:44 Analyse model coefficients
04:11 Prepare second wine dataset for prediction
05:06 Predict price based on points in second wine dataset
05:18 Plot scatter plot of original and predicted prices using points in second wine dataset
05:27 Evaluate model performance - r-squared
06:06 Evaluate model performance - residual standard error
06:42 Final thoughts
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