课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
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Creating the linear regression model and model summary: Part 1 - SQL教程
课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
Creating the linear regression model and model summary: Part 1
- [Instructor] Welcome to step 15, where you create your linear regression model and dive deep into your model's performance. Now, everything that you've done before was preparation for the steps you're about to take. So we now waste no time. Let's get right to it. Here's the code that creates your model. You'll use the OLS method from the Python Statsmodel Library to perform linear regression. Now, let's break down this OLS regression results table, step by step. First, you have the dependent variable, which is the log-transformed home value, Home_Value_Log. This is what you're trying to predict. Next, you have the model, OLS, which stands for ordinary least squares. It's the linear regression method you're using. In the context of linear regression, OLS or ordinary least squares, is the fundamental method you use to build your predictive model. Think of it as the tried-and-true technique that helps you find…
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Creating the linear regression model and model summary: Part 19 分钟 33 秒
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Creating the linear regression model and model summary: Part 27 分钟 16 秒
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Creating the linear regression model and model summary: Part 35 分钟 33 秒
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Dropping insignificant variables and re-creating the model7 分钟 57 秒
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Checking assumptions for linear regression3 分钟 18 秒
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Assumption 1: Checking for mean residuals2 分钟 47 秒
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Assumption 2: Checking homoscedasticity3 分钟 13 秒
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Assumption 3: Checking linearity2 分钟 12 秒
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Assumption 4: Checking normality of error terms3 分钟 24 秒
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Q-Q plot for checking the normality of error terms3 分钟 14 秒
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Model performance comparison on train and test data6 分钟 7 秒
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Applying cross-validation and evaluation4 分钟 40 秒
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Challenge: Model building48 秒
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Solution: Model building1 分钟 16 秒
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