For this project, I analysed a dataset provided by Rotten Tomatoes, which consisted of ratings and reviews of more than 15,000 movies that had been reviewed by the website Rotten tomatoes (source: Kaggle). Each record contains information about the movie (title, synopsis, rating, genre, cast, directors, studio, runtime, etc.), along with highlighted reviews, "Tomatometer" status, and audience ratings. The "Tomatometer" status is a unique feature of Rotten Tomatoes, which is a rating system that aggregates the reviews of professional critics to produce a score representing the percentage of positive reviews. The audience ratings, on the other hand, represent the average rating given by the general public. With this information, it is possible to conduct various analyses on the dataset. The dataset consist of 16,614 rows and 17 columns.

Overall, the dataset provides a rich and diverse collection of information about movies that can be used to explore various recommended analysis questions, such as the relationship between ratings and box office success, the impact of different genres on ratings, and the influence of cast and directors on movie ratings. The dataset also provides opportunities for developing predictive models to forecast the success of a movie based on its details and ratings.

RECOMMENDED ANALYSIS ON THE DATASET :

What does the distribution of films look like by rating? By primary genre?

What % of films received a Certified Fresh Tomatometer rating? What about Rotten?

Explore new film releases over time. How has the volume of releases by month trended over time? What year/month were the most new films released?

Compare average Tomatometer ratings by Studio. Which studios produce the highest rated films, on average? The lowest?

Compare the Tomatometer ratings against audience ratings. Which films showed the largest discrepancies between audiences and critics?

Explore the critics consensus rating: what language is used most often?

CONCLUSION :

The dataset provides a wealth of information that can be used to gain insights into the movie industry, and to develop predictive charts that can inform decision-making in the field. Therefore, the findings of such an analysis can have important implications for the movie industry and help inform decision-making related to movie production, marketing, and distribution.