Data Science project! Most people can understand the visualizations, as 40% of the people can answer basic questions about the information provided on the record visualizations. Therefore, when providing information in the form of charts, people show a good understanding of the plotsand provide accurate forecasts in this project.
https://github.com/usamnet000/Data-Science-Blog-Post-Movies
https://dev.to/usamnet000/reasons-for-success-and-failure-in-the-movie-industry-25m2
This data is from Movie Database (TMDb) which contains info about different movies.The data contains several details about different parameters of the movie such as user ratings and revenue,budget,revenue,original_title,cast etc.This project is associated with using this dataset as input and draw meaningful observations.Finally communicating the observations to the people.
The major libraries which have been used are:- pandas- for reading and processing the data_frames numpy- for numerical manipulations csv- reading the csv file seaborn- for plotting matplotlib.pyplot- for making plots sklearn - for LinearRegression Model Machine Leanring
The repo contains:- Data Science Blog Post - Movies.ipynb file- It is jupyternote book generated file where all the data processing has been done tmdb-movies.csv- Contains the raw data to be processed.
The process starts with business understanding. Posing Question- All the relevant question are posted Preparing data- In this section of the report, we will load in the data, check for cleanliness, and then trim and clean the dataset for analysis. Analysing Modelling and Visualizing- Here we analyse,model and finally make plots to visulize them. Conclusions- Finally all the conclusions are made
By identifying the big companies that have modern equipment and have big capital for the film industry, they achieve great revenues, while the average companies achieve small revenues and accordingly we can find out the reasons for the success of these companies or the failure of other companies.
Voting clearly plays an important role in determining movie revenues, as some votes can say something about the nature of the movie and can restrict the film market.
Most of the time, we found that the strength of the directors, production budgets and sequences contributed positively to the film's revenue.
Special effects and computer technology have come a long way in the past ten years, and may have contributed to changing consumers' tastes and preferences for certain types of movies.
Better quality films will be more successful.
If a movie is released in the holiday season, it is expected to see an increase in revenue, while the summer release will bring an expected increase in views.
Comedies tend to experience positive success in the supply market, although the influence of other genres is inconclusive.
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