PDC4S:\IT\DATA SCIENCE AND MACHINE LEARNING\[365 Data Science] Programming for Data Science\18. SQL + Tableau + Python\4. Applying Machine Learning to the Preprocessed Data

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NameSizeDate Modified
1. Exploring the Problem from a Machine Learning Point of View.mp426,115 KB12/12/2021 3:34 AM
10. Interpreting the (Important) Coefficients.mp428,790 KB12/12/2021 3:34 AM
11. Simplifying the Model (Backward Elimination).mp438,783 KB12/12/2021 3:34 AM
12. Testing the Logistic Regression Model.mp442,446 KB12/12/2021 3:34 AM
13. Saving the Logistic Regression Model.mp431,133 KB12/12/2021 3:34 AM
14. Creating a module for later use of the model.mp450,805 KB12/12/2021 3:34 AM
2. Creating the Targets for the Regression.mp435,214 KB12/12/2021 3:34 AM
3. Selecting the Inputs for the Regression.mp412,553 KB12/12/2021 3:34 AM
4. Standardizing the Dataset for Better Results.mp415,996 KB12/12/2021 3:34 AM
5. Train-Test Split.mp440,881 KB12/12/2021 3:34 AM
6. Training and evaluating the model.mp433,435 KB12/12/2021 3:34 AM
7. Extracting the Intercept and Coefficients.mp434,193 KB12/12/2021 3:34 AM
8. Interpreting the Coefficients.mp447,589 KB12/12/2021 3:34 AM
9. Creating a Custom Scaler to Standardize Only Numerical Features.mp434,679 KB12/12/2021 3:34 AM