1. Introduction
2. Course Contents
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1. Import Data (6:39)
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2. visualizing missing data in a dataset (2:12)
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3. calculating statistical information (3:47)
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4. checking for duplicate rows in the DataFrame (2:06)
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5. calculating the number of distinct values in each column (3:45)
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6. checking for missing or null values in the DataFrame (2:21)
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7. Cleaning the data (4:39)
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8. creating a new column called -Label- in the DataFrame (5:03)
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9. creating a histogram plot (7:55)
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10. displaying the distribution of the data using a box plot (6:26)
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11. displaying the distribution of the data by the different categories (9:05)
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12. visualize the relationship between two variables with jointplot (8:40)
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13. calculating the correlation matrix of the DataFrame (5:33)
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14. creating a mask using NumPy (3:48)
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15. creating a color map using seaborn (2:20)
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16. creating a heatmap using seaborn (5:42)
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17. calculates the number of outliers (14:38)
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18. standardizing features (3:47)
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19. Hypothesis testing (2:27)
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20. Normalization (9:00)
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21. split the data into training and testing sets (9:57)
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22. Start traning SVC and Learn Hyperparameters (4:33)
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23. find the best hyperparameter (6:55)
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24. make predictions on the test data and avaluate the model (9:20)
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25. Train RandomForestClassifier (6:49)
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26. Train XGBClassifier (4:19)
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27. Train KNeighborsClassifier (3:22)
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28. Train LGBMClassifier (3:57)
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29. calculate the (ROC) curve and the (AUC) score (7:03)
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30. Supporting Files
