To complete this assignment please refer to the below given dataset link.

Customer_Survey_Dataset

🔧 Part A: Hands-On Data Cleaning Tasks

Dataset: Use any simple CSV file (e.g., customer information, survey data) with numeric and categorical columns.

1. Remove Duplicates

• Identify and drop duplicate rows from the dataset.

• Show the number of rows before and after.

2. Handle Missing Values

• Find all missing values.

• Replace missing numerical values with mean and categorical values with mode.

3. Detect and Handle Data Errors

• Manually introduce one incorrect age (e.g., -5 or 135).

• Detect and handle the error using logical reasoning.

4. Correct Formatting Issues

• Format a column with inconsistent date formats (e.g., "2024-01-01", "01/01/2024").

• Standardize all dates to YYYY-MM-DD.

5. Compare Two Data Sources

• Create two mini datasets with a record mismatch (e.g., salary of the same person differs).

• Detect the discrepancy and choose which value to retain, providing justification.

🧮 Part B: Hands-On Transformation Tasks

1. Standardization (Z-score Normalization)

• Apply standardization to a numeric column.

• Show the original mean and standard deviation, and verify the new column’s mean is ~0 and std dev is ~1.

2. Min-Max Scaling

• Apply Min-Max scaling to a numeric column.

• Confirm values are scaled between 0 and 1.

3. Log Transformation

• Apply log transformation to a skewed column (add +1 if there are zeros).

• Plot histogram before and after to visualize distribution improvement.

4. Categorical Encoding

• Use one-hot encoding for a gender column.

• Use label encoding for a color column.

• Use ordinal encoding for a satisfaction rating column (Low, Medium, High).