Which of the following are indicators of good data visualizations? [ Select all that apply]

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).

You are given a dataset  on purchases with the following information.

Match the analytics questions in Table 1 with their appropriate descriptive analytics

Table 1

Table 2

Which of the following features cannot be engineered using information on an individual’s address

Which of the following is NOT possible to figure out by filtering the data 

Which of the following is NOT possible to figure out by sorting the data?