We have refined our approach to create a more precise and reliable movie dataset. Here's a concise overview of the steps we took to construct this new dataset.
- Original Plan: Initially, we integrated four datasets: "The Movies Industry Dataset," "IMDb 5000 Movies Multiple Genres Dataset," "IMDb 5000 Movies Dataset," and "Top 500 Movies Budget," as defined in the README for the old dataset.
- Challenges: This approach led to suboptimal performance in our predictive models due to forced integration and potential confusion from movies with similar titles.
- Re-evaluation: We paused to re-evaluate our methodology, focusing on a deeper analysis of the data to improve model accuracy.
- New Approach: We opted for a refined dataset derived solely from "The Movies Industry Dataset," excluding entries with missing values.
- Performance Issues: The initial dataset's performance was hindered by lower accuracy rates due to inadequate variable selection and forced dataset integration.
- Complexities: Merging datasets with similar movie titles added unnecessary complexity and noise.
- Enhanced Accuracy: The new dataset includes additional input variables such as Year, Production Company, and Votes, which enhance the accuracy of our machine learning model in predicting movie revenues.
- Single Source: Sourcing the dataset from a single origin eliminated extraneous noise and improved data integrity.
- Reliability: By eliminating entries with null values, we ensured the dataset's reliability before subjecting it to our predictive models.
- Input Features:
name,rating,genre,year,released,score,votes,director,writer,star,country,budget,company,runtime - Output Feature:
gross
Our refined approach has resulted in a more robust and accurate dataset, better suited for predicting movie revenues and providing valuable insights into the cinematic world. 🎬📊