Pandas Machine Learning Integration: Exercises and Solutions for Data Integrity

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Pandas Machine Learning Integration [17 exercises with solution]

Structure of data.csv:

ID	Name	  Age	Gender	Salary	 Target
1,Sara,25,Female,50000,0
2,Ophrah,30,Male,60000,1
3,Torben,22,Male,70000,0
4,Masaharu,35,Male,80000,1
5,Kaya,NaN,Female,55000,0
6,Abaddon,29,Male,NaN,1

Column Description:

ID: A unique identifier for each record (integer).

Name: The name of the individual (string).

Age: Age of the individual (numerical, may have missing values).

Gender: Gender of the individual (categorical: Male/Female).

Salary: The individual's salary (numerical, may have missing values).

Target: The target variable for binary classification (binary: 0 or 1).

1. Write a Pandas program that loads a Dataset from a CSV file.
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2. Write a Pandas program to check for missing values in a dataset.
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3. Write a Pandas program to drop rows with missing values from a dataset.
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4. Write a Pandas program that fills missing values with the Mean.
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5. Write a Pandas program that converts categorical variables into numerical values using label.
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6. Write a Pandas program to apply one-hot encoding to categorical variables.
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7. Write a Pandas program that normalizes numerical data using Min-Max scaling.
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8. Write a Pandas program to standardize numerical data using Z-Score scaling.
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9. Write a Pandas program that splits Dataset into Training and Testing sets.
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10. Write a Pandas program that removes outliers from a Dataset.
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11. Write a Pandas program that imputes missing values using K-Nearest neighbours.
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12. Write a Pandas program to select feature selection using variance threshold.
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13. Write a Pandas program to handling class imbalance using random oversampling.
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14. Write a Pandas program that applies Polynomial Features for feature expansion.
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15. Write a Pandas program to scale numerical features using Scikit-learn's RobustScaler.
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16. Write a Pandas program to save the processed Dataset to a CSV file.
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17. Write a Pandas program that applies Log Transformation to Skewed Data.
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