Task 1: Data Cleaning and Preprocessing

🎯 Objective

The goal of this task is to clean and prepare a raw dataset that may contain:

• Missing values
• Duplicate entries
• Inconsistent formats
  so that the dataset is ready for analysis or modeling.

The Netflix dataset was chosen for this task.

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🛠 Steps Performed

1. Handle Missing Values

• Filled director column with "Unknown" where data was missing.
• Filled cast column with "Not Available" where data was missing.
• Filled country column with "Unknown" where data was missing.

2. Remove Duplicate Rows

• Checked for duplicate entries.
• Removed duplicate rows using Pandas .drop_duplicates().

3. Standardize Text Values

• Standardized country names (e.g., converted to title case like United States, India).
• Stripped extra spaces.

4. Convert Date Formats

• Converted date_added column to datetime format for consistency.

5. Rename Columns

• Changed all column headers to lowercase with underscores instead of spaces.
  Example: Date Added → date_added

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📊 Final Dataset

• Shape: 200 rows × 12 columns
• All missing values handled.
• No duplicate rows remain.
• Data is consistent and ready for analysis.

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📁 Files Included

• Netflix_Cleaned.csv → Cleaned dataset (ready for analysis)
• README.md → This summary file