IMDb (Internet Movie Database) is a popular online database of information related to films, television programs,
and video games, including cast, production crew, plot summaries, trivia, and user ratings.
How IMDb Platform Works ?
1.User Ratings -- IMDb ratings are primarily based on the ratings submitted by registered users of the IMDb website or app. Users can rate movies on a scale from 1 to 10, with 10 being the highest rating. These ratings are aggregated to calculate the overall rating for each movie.
2.Weighted Average -- IMDb uses a weighted average formula to calculate the overall rating for a movie. The formula takes into account factors such as the number of ratings received and the distribution of those ratings.
3.Ratings Distribution -- IMDb considers the distribution of ratings when calculating the overall rating for a movie. Movies with a more uniform distribution of ratings may receive a higher overall rating compared to movies with a more polarized distribution.
4.User Reviews -- In addition to numerical ratings, IMDb also allows users to submit written reviews and comments for movies. These user reviews can provide additional context and insight into the quality of a movie.
5.Top Rated Lists -- IMDb also provides curated lists of top-rated movies based on user ratings.These lists highlight the highest-rated movies across various genres, decades, and other categories.
❑ Purpose of the Project -- Firstly , find the meaningful insights from the data. Secondly , understand the Trend or pattern between different types of variable in a dataset. My main purpose is to visualize the data to uncover trends and relationships which would have been difficult to identify from raw data alone.
❑ Tools & Techniques – Advance Excel , Pivot table .
❑ Content
| Overview of Data | Understand the theoretical part of data for strong foundation. |
| Limitation of Project | Understand some limitation of the project. |
| Investigate About Data | Find Basic insights about data to getting comfortable with dataset. |
| Find Complex Answers | Showcase the power of pivot table to solve complex answers. |
| Torture the Data | Find Trends or pattern with the help of advance charts like Scatter plot. |
| Conclusion | Discussion about outcomes which we get through this analysis for future decision making. |
• Data Scope - The analysis might be limited to the data available in the IMDb database, which may not cover all movies or have complete information for each title.
• Limitations Depth - The project uses tools like Excel and pivot tables, which are powerful but may not offer the same depth of analysis as more advanced statistical software.
• Timeframe - The project seems to cover data up to a certain point, which means recent movies or trends might not be included.
• Data Accuracy - The accuracy of the data could be a limitation. Since the data is user-generated, there may be errors or inaccuracies that could affect the results of the analysis. Users may submit
incorrect information or make subjective judgments about movie ratings, which could introduce biases into the analysis.
• Through this analysis, We have build good theoretical knowledge about movies and their terms like what is the difference between genre & rating, etc.
• With the help of this analysis , We find insights related to some important movies terms like contribution of genre(%) ,gross revenue ,budget ,director, actors.
• In this analysis, we also discover some time patterns like which month movies release the most. This analysis help director to decide release month.
• Through this analysis, We understand the power of pivot table that how pivot table solve complex questions easily.
• Through cost analysis, We understand the very important concept of profit margin & cost. As a producer , we must should focus on cost or budget of a movie because profit margin is inversely proportional to Cost.
• With the help of analysis of time duration, the director decide how many mins of movie should he make for get best IMDb range.