Analysing Netflix Data Cleaning

A data cleaning and preprocessing project focused on preparing the Netflix dataset for analysis using Python, Pandas, and Jupyter Notebook.

This project demonstrates real-world data cleaning workflows including handling missing values, fixing mixed data types, and transforming raw data into analysis-ready datasets.

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Project Overview

Raw datasets are rarely clean.
This project focuses on transforming messy Netflix content data into a structured and usable format suitable for analytics and visualization.

Key objectives:

• Identify and handle missing values
• Fix inconsistent/mixed-type columns
• Convert date columns into datetime format
• Create derived analytical features
• Prepare clean dataset for further analysis

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Tech Stack

• Python
• Pandas
• Matplotlib
• Jupyter Notebook

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Project Structure

Analysing-Netflix-Data-Cleaning/
│
├── data/
│ ├── netflix_titles.csv
│ └── cleaned-data.csv
│
├── notebook/
│ └── netflix_data_cleaning.ipynb
│
└── README.md

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Data Cleaning Workflow

flowchart LR
    A[Raw Netflix Dataset] --> B[Data Inspection]
    B --> C[Handle Missing Values]
    C --> D[Fix Mixed-Type Columns]
    D --> E[Convert Date Columns]
    E --> F[Feature Engineering]
    F --> G[Cleaned Dataset Ready]

    style A fill:#1f77b4,color:#fff
    style B fill:#9467bd,color:#fff
    style C fill:#2ca02c,color:#fff
    style D fill:#ff7f0e,color:#fff
    style E fill:#17becf,color:#fff
    style F fill:#e377c2,color:#fff
    style G fill:#d62728,color:#fff

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Cleaning Steps Performed

1️Missing Values Handling

• Filled categorical columns using "Unknown" or mode values
• Verified null counts column-wise
• Ensured dataset consistency after imputation

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Mixed-Type Column Fix

• Cleaned the duration column by splitting into:
  • Numeric duration value
  • Duration type (Minutes / Seasons)
• Standardized data types for analysis readiness

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Datetime Conversion

• Converted date_added into proper datetime format
• Extracted new analytical features:
  • year_added
  • month_added
  • month_name

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Data Validation

• Verified column datatypes
• Removed inconsistencies and formatting issues
• Saved a fully cleaned dataset for downstream analysis

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Output

A cleaned and structured dataset ready for:

• Exploratory Data Analysis (EDA)
• Data Visualization
• Dashboard Creation
• Business Insights

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Project Source

This project is inspired by the learning project from roadmap.sh:

</> https://roadmap.sh/projects/cleaning-netflix-dataset

Implementation and analysis were completed independently as part of learning real-world data analytics workflows.

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Future Improvements

• Content trend analysis
• Genre popularity insights
• Dashboard using Power BI / Tableau
• Time-series visualization

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Learning Outcome

This project strengthened understanding of:

• Real-world data preprocessing
• Pandas data transformation
• Analytical thinking
• Structuring analytics projects for GitHub portfolios

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Connect

If you have feedback or suggestions, feel free to connect or open an issue in this repository!

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