eda

Exploratory Data Analysis (EDA)：

• Data Inventory: Successfully ingested raw dataset containing 10,866 rows and 21 observations.

• Visual Verification: Initial profiling via Table View no significant inconsistencies, spelling or formatting errors detected.

• Missing values :

  • High missing rate: homepage (73% ), tagline (26% ) and keywords (13%) identified, do no directly related to the object, removed.

  • Minor missing rate: genres, product_company and cast, requiring imputation.

• Outlier : Box Plot reveals significant outliers in revenue and budget.

Financial Analysis

• Variable Isolation: Refined the dataset to focus exclusively on core financial metrics (budget, revenue) and their inflation-adjusted counterparts to ensure historical comparability.

• Correlation: Confirmed a positive trend between budget and revenue.

• Distribution: Validated a heavily right-skewed via Histogram; confirmed that the majority of films earn under $500e.

• Final Output: Exported clean dataset for financial analysis.

Genre Analysis

• Isolation: Used Column Filter to extract only id and genres.

• Normalization: Applied Cell Splitter followed by Ungroup to transform multi-valued genre strings into a list, ensuring each movie is correctly indexed under every category.

• Aggregation: Utilized Group By on the split genre results with a Count aggregation on id to determine the total volume of films produced per category.

• Visualization: The Bar Chart reveals that Drama is the most frequent genre in the dataset ( 1,500 ), followed closely by Comedy, Thriller and Action.

• Final Output: Exported the dataset via CSV Writer for final reporting.

Rating Analysis

• Isolation: Used a Column Filter to isolate popularity, vote_average, and vote_count for analysis.

• Distribution :The scatter plot: no strong linear correlation between popularity and vote_average. high scores do not drive high revenue directly.

• Outlier:Box Plot, identified significant outliers in vote_count, indicating a small subset of films receive disproportionately higher audience engagement.

• Final Output: Exported the dataset via CSV Writer for final reporting.

Data cleaning：

• Integrity Enforcement: Applied Duplicate Row Filter to remove redundant records and ensure the uniqueness of each movie .

• Text Normalization:

  • Utilized String Cleaner to strip unnecessary space.

  • Used String Manipulation (Multi Column) to capitalize categories and maintain consistency across text-based fields.

• Temporal Formatting: Converted the release_date column from string format to a standardized Date&Time format.

• Dimensionality Reduction: Implemented a Column Filter to remove low-relevance features such as imdb_id, homepage, and overview to focus on analytical objects.

• Constraint Filtering: Applied a Row Filter to exclude records with unrealistic financial data, specifically targeting movies with budget and revenue below $10,000.

• Missing values: Applied Missing Value node, using median imputation for runtime and mean imputation for vote_average to maintain statistical distribution.

• Correlation Verification: Validated the cleaned dataset through Linear Correlation and Heatmap nodes, confirming strong relationships between revenue, budget, and vote.

Basic Information

• Isolation: Used a Column Filter to select core descriptive variables, including runtime, release_year, release_date, production_companies, original_title, id, director

• Purpose: This step isolates essential identification and contextual attributes of each movie, separating descriptive information from financial and engagement metrics.

• Final Output: Exported the dataset via CSV Writer for final reporting.