Problem Statement: https://github.com/sanand0/tools-in-data-science-public/blob/tds-2024-t3/project-2-automated-analysis.md This is one sample readme generated by the openai llm
The dataset contains detailed information about movies, focusing on their performance metrics and characteristics which include:
- Date of Release
- Language
- Type
- Title
- People Involved
- Overall Ratings
- Quality Ratings
- Repeatability Ratings
The dataset includes numerous entries, with notable distributions in ratings across languages, as well as trends over time in average quality ratings.
Several inferences can be drawn from the dataset profile:
- Language Influence: Ratings tend to vary significantly based on the language of the movie. Films in certain languages appear to be rated more favorably.
- Quality Trends: There are observable trends in the average quality ratings over time, which may indicate shifts in movie production quality and audience expectations.
- Repeatability Correlation: Initial analyses suggest there is no substantial correlation between repeatability scores and overall ratings, hinting that repeat viewing may not be a strong predictor of movie quality.
The following preprocessing steps were applied to ensure data integrity and usability:
- Handling Missing Values:
- The 'by' column had missing entries filled with 'Unknown' to avoid bias in the analysis.
- For the numeric columns ('overall', 'quality', 'repeatability'), the median was utilized to replace any missing values to mitigate the effect of outliers.
- The date column utilized a forward-fill method to maintain time series continuity.
- Columns with more than 50% missing entries, such as the 'by' column when applicable, were dropped from the analysis.
The following analyses were performed to derive insights from the dataset:
- Group Analysis: To explore how the overall rating of movies varies by language.
- Time Series Analysis: To assess the trends of average quality ratings over time, capturing the evolution of movie quality ratings.
- Correlation Analysis: To investigate the relationship between repeatability and overall ratings, determining if one might influence the other.
- Chart Name: Average Movie Ratings by Language
- Description: This bar chart displays the average overall movie ratings categorized by language.
- Purpose: To analyze how movie ratings vary across different languages.
Analysis Result:
- Insight Discovered: French movies had the highest average ratings, while Telugu and Tamil movies were rated the lowest.
- Answer to Question: The overall rating of movies varies significantly by language, with higher ratings in French compared to others.
- Implication of Findings: Filmmakers might consider focusing on higher-rated languages to enhance audience engagement and investor interest.
- Chart Name: Trend of Average Quality Ratings Over Time
- Description: A line chart depicting the trend of average quality ratings aggregated by year.
- Purpose: To illustrate the fluctuations in movie quality ratings over time.
Analysis Result:
- Insight Discovered: Quality ratings showed significant peaks in 2010 and 2015, with a gradual increase noted from 2020 to 2024.
- Answer to Question: A general upward trend in average quality ratings is observed, especially since 2020.
- Implication of Findings: Recent practices in quality management might be contributing to the rise, suggesting that further exploration of these practices could yield beneficial insights for the movie industry.
- Chart Name: Scatter Plot of Repeatability vs Overall Ratings
- Description: This scatter plot displays the relationship between repeatability scores and overall ratings.
- Purpose: To evaluate if there is any correlation between repeatability and overall ratings.
Analysis Result:
- Insight Discovered: The analysis revealed no visible correlation; all overall ratings clustered at the higher end regardless of repeatability scores.
- Answer to Question: No significant relationship between repeatability and overall ratings is confirmed.
- Implication of Findings: Future analyses could focus on different variables that may impact ratings. Exploring rating determinants could provide more context to the audience's response to movies.
This README provides a comprehensive overview of the movie ratings analysis project, covering dataset insights, preprocessing steps, analytical types, and results, all structured to facilitate understanding and storytelling around the data analysis conducted.