Data Visualization with Plotly and Seaborn: Analyzing Worldwide Box Office Revenue (Part 2)

In this project, we will be working with the TMDB Box Office Revenue Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? We try to answer these questions by way of exploratory data analysis (EDA) in this project.

We will primarily use Plotly for data visualization. Plotly Python which is Plotly’s Python graphing library makes interactive, publication-quality graphs ready for both online and offline use. Built on top of the Plotly JavaScript library (plotly.js), enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash.

Available On Coursera
Data Visualization with Plotly and Seaborn: Analyzing Worldwide Box Office Revenue (Part 2)

Task List

We will cover the following tasks in 44 minutes:

Analyzing Movie Release Dates

In Part 1 of this project we focused on exploratory data analysis. In this task, we identify the release_date column as ripe for feature engineering.

Preprocessing Features

Before we can create new features based on release_date, we need to define a function to process the dates and convert them to a standard Datetime format. We will perform data imputation to account for missing values, after which we will apply our processing on the training and test sets.

Create Features Based on Release Date

Now that we have standardized the date format, we will define a function to create new columns for the year, weekday, month, week of the year, day, and quarter.

Using Plotly to Visualize the Number of Films Per Year

We will use Plotly to create an interactive visualization of the number of films released per year in both the training and test sets.

We use the generic go.Scatter function from plotly.graph_objects, and specify the mode argument to choose between markers or lines.

Number of Films and Revenue Per Year

In this task, we will visualize both the number of films and total revenue per year, and the number of films vs the average revenue per year. We will be able to compare and contrast trends we observe to that of the previous task.

Do Release Days Impact Revenue?

Is it reasonable to assume that movies released on weekends will gross higher revenues? Let’s put this assumption to the test in this task by creating a categorical plot of the day of the week on the x-axis and revenue on the y-axis. Are you surprised by the results? Why or why not?

Relationship between Runtime and Revenue

We will create two plots in this task. The first describes the distribution of the duration of films. The second plots revenue against duration. Let’s find out if the data illustrates the optimal duration of a movie to maximize revenue.

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Snehan Kekre

About the Host (Snehan Kekre)

Snehan Kekre is a Machine Learning and Data Science Instructor at Coursera. He studied Computer Science and Artificial Intelligence at Minerva Schools at KGI, based in San Francisco. His interests include AI safety, EdTech, and instructional design. He recognizes that building a deep, technical understanding of machine learning and AI among students and engineers is necessary in order to grow the AI safety community. This passion drives him to design hands-on, project-based machine learning courses on Rhyme.

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