Multiple Linear Regression & Prediction with #scikitlearn #python Part 1 #onehotencoder #regression

In this video, I would like to explore performing multi regression and prediction using python scikit learn where my response is a numeric variable and feature variables are both numeric and categorical. The content is solely for educational purposes and is based on my personal experience. Links: Dataset - https://www.kaggle.com/datasets/zynicide/wine-reviews Code - https://github.com/maddyhyc/Multiple-Linear-Regression-and-Prediction-with-scikit-learn-python-Part-1 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/3053810/1611872/13294 Datacamp student - https://datacamp.pxf.io/c/3053810/1611874/13294 Datacamp business - https://datacamp.pxf.io/c/3053810/1545190/13294 Canva - https://partner.canva.com/FwDbyMaddy Timestamps: 00:00 Introduction 00:17 Ask ChatGPT 01:25 Coding starts - importing packages, read in and explore data, check missing values 02:37 Investigate country categorical variable, check counts, and values, create top 10 countries, data manipulation 04:19 Create Response and Feature variables using .loc call 05:10 Start preprocessing with OneHotEncoder and StandardScaler 05:35 Covert categorical country variable to binary variables, determining, binary columns 06:31 Normalize numeric points variable, how to interpret coefficient, reasons for normalizing numeric points 08:02 Split train/testing datasets, fit Linear Regression 08:33 Evaluate model's performance, coefficients, intercept 09:16 R-squared, and Residuals (for points and country model) 10:06 Investigate taster_name categorical variable 10:43 Preprocessing using OneHotEncoder and StandardScaler 11:12 Fitting linear regression, coefficients, intercept 11:21 R-squared, and Residuals (for points and taster_name model) 12:00 Compare models and closing final thoughts