EDA with Python & Pandas (4/6): Goodness of Fit, R^2, Calculating RMSE скачать в хорошем качестве

EDA with Python & Pandas (4/6): Goodness of Fit, R^2, Calculating RMSE

Exploratory Data Analysis Python Tutorial Series on Vienna Hotels - Part 4 of 6 In this video, we continue our journey into parameterized linear regression. We'll build upon our previous model and delve into novel techniques to improve our predictions and interpretations. 🎓 For the blog post + code snippets from this video, visit: https://bit.ly/eda-python-tutorial-ho... We'll start by summarizing our model results and discussing the importance of visualizing predictions versus actual values using scatter plots instead of line charts. This approach helps identify underfitting and the goodness of fit, which we explore through concepts like R-squared and RMSE. We then move on to calculating RMSE and the R-squared formula by hand, offering a detailed explanation of these metrics and their significance in regression analysis. Using the statsmodels library, we'll fit an ordinary least squares model and interpret the coefficients. Additionally, we'll compare our manual computations with results from the scikit-learn library to verify accuracy. Finally, we introduce multiple linear regression, discussing the inclusion of additional explanatory variables, dummy variables, and data transformations to enhance model performance. This sets the stage for our next video, where we'll dive deeper into multiple linear regression, filtering, and data transformations to achieve more accurate and interpretable models. See the full EDA with Python Tutorial Series Part 1:    • EDA with Python & Pandas (1/6): Explorator...   Part 2:    • EDA with Python & Pandas (2/6): Regression...   Part 3:    • EDA with Python & Pandas (3/6): Build a Si...   Part 4:    • EDA with Python & Pandas (4/6): Goodness o...   Part 5:    • EDA with Python & Pandas (5/6): Create Pan...   Part 6:    • EDA with Python & Pandas (6/6): Build a Mu...   Timestamps 00:00 - Introduction and Recap 00:06 - Visualizing Predictions vs. Actual Values 01:02 - Goodness of Fit and R-Squared 02:15 - Computing R-Squared by Hand 03:45 - RMSE Explained and Calculating RMSE 06:45 - Calculating RMSE with Scikit-Learn 09:00 - Introduction to Multiple Linear Regression 12:00 - Adding Explanatory Variables and Dummy Variables 16:00 - Preparing for the Next Session If you found this video helpful, please like and subscribe to our channel! Leave a comment if there's a topic you'd like us to cover next. 🎓 Visit https://codingnomads.com for more resources and to become a coding pro. See you in the next video!