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The aim of this project is to perform exploratory data analysis on features of high-grossing movies using web-scraping, Pandas, and Matplotlib. The dataset consists of 200 high-grossing movies, and we will analyze various features such as the genre, runtime, budget, rating, and revenue of these movies.

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v1

Movie analysis Project.

Author : Yussuf Hersi

Overview

  • As part of my work with Microsoft's new movie studio, I was tasked with exploring the current landscape of the movie industry to determine what types of films are performing the best at the box office. To achieve this, I gathered data on movie budgets and worldwide box office grosses, and analyzed this data using descriptive statistics and visualizations.

  • My analysis has shown that there is a strong correlation between movie budgets and worldwide box office grosses. Specifically, movies with larger budgets tend to perform better at the box office than those with smaller budgets. Based on this finding, I recommend that Microsoft allocate at least 150 million dollars to produce a movie in order to maximize its chances of success at the box office.

Business Problem.

I have been informed that Microsoft wants a piece of the multi-billion dollar movie-making industry, but that they are unsure of where to begin. The challenge for their new movie studio is that they are ready to jump into the industry but do not have the necessary knowledge to move forward. To assist them with this goal, I have been looking at the movies that performed highest in worldwide box office . By analyzing the movies that have been most successful recently, I can make recommendations about attributes that Microsoft's movies should have in order to achieve the highest revenue. I have based my analysis on four main factors:

  • Movie Type (Genre): What types of movie content are currently most successful?

  • Release Month: When is the most lucrative time of year to release a movie?

  • Production Budget: What budget amount tends to achieve the highest box office gross?

  • Additional Attributes: Based on these findings, what else do top-grossing movies have in common?

Data

  • In order to gain insights into the movie industry and provide recommendations to Microsoft's new movie studio executives, I have taken several important steps. Firstly, I have acquired and cleaned two datasets: one containing information about movies' box office gross, and the other containing database about movie budgets. This process of data cleaning involves identifying and correcting errors, inconsistencies, and missing data in order to ensure that the data is accurate and reliable.

  • Now that I have the merged dataset, I began to analyze the data using statistical techniques and visualizations. By exploring the data in this way, I could identify patterns and relationships that may exist between different variables, such as the movie's genre, release month, budget, and box office gross. This analysis helped me draw conclusions about the industry and provide recommendations to Microsoft's new movie studio executives about how to maximize their chances of success.

  • Overall, the process of acquiring, cleaning, merging, and analyzing datasets is critical to gaining insights into complex industries like the movie industry. By taking these steps, I could make informed decisions and provide valuable recommendations to key Microsoft.

Methods

  • I removed unnecessary data such as duplicates and irrelevant columns. I filled null values.. I utilized descriptive statistics as well as visualizations to illuminate trends in the data and isolate key factors for making a successful movie. This approach was appropriate for analyzing trends in the movie industry and common attributes of high-grossing movies, so that I could make informed recommendations.

Results

The highest grossing films were of the genre action science fiction adventures.

The runtime for movies in the top 250 spot was on average between 113 to 166 minutes of runtime.

runtimegraph

The runtime of movies per genre pie chart.

genresbargraph

A scatter plot of relationships between production budget and worldwide gross. positivecorr

The genre Action adventure sci_fi had 44.0 percent of the genres distribution the dataset pie3

Conclusion

  • After conducting a thorough analysis of the film industry and the current state of technology, I strongly recommend that Microsoft invest in developing a new studio that specializes in producing high-budget action, science fiction, and adventure movies. By doing so, Microsoft can position itself as a major player in the entertainment industry and capitalize on the growing demand for these types of films.

  • One of the main advantages of investing in a new studio is the ability to leverage Microsoft's existing technology and resources. With its expertise in software development, cloud computing, and artificial intelligence, Microsoft can create cutting-edge special effects and CGI that will bring these films to life in a way that has never been seen before. This will help to differentiate Microsoft's movies from the competition and make them more appealing to audiences.

  • Furthermore, by investing in higher-budget productions, Microsoft can attract top talent in the film industry, including directors, actors, and writers, who are drawn to the prospect of working on big-budget projects with cutting-edge technology. This will help to ensure that Microsoft's films are of the highest quality and appeal to a wide range of audiences.

  • In conclusion, I strongly recommend that Microsoft invest in a new studio to produce high-budget action, science fiction, and adventure movies. This move will allow Microsoft to leverage its technology and resources, tap into a growing market, and attract top talent in the film industry. By doing so, Microsoft can achieve significant growth and performance in the box office space and establish itself as a major player in the entertainment industry.

About

The aim of this project is to perform exploratory data analysis on features of high-grossing movies using web-scraping, Pandas, and Matplotlib. The dataset consists of 200 high-grossing movies, and we will analyze various features such as the genre, runtime, budget, rating, and revenue of these movies.

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