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Learner Reviews & Feedback for Analyze Box Office Data with Seaborn and Python by Coursera Project Network
180 ratings
About the Course
Welcome to this project-based course on Analyzing Box Office Data with Seaborn and Python. In this course, you will be working with the The
Movie Database (TMDB) Box Office 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 and the next. The statistical data
visualization libraries Seaborn and Plotly will be our workhorses to generate interactive, publication-quality graphs. By the en...
Movie Database (TMDB) Box Office 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 and the next. The statistical data
visualization libraries Seaborn and Plotly will be our workhorses to generate interactive, publication-quality graphs. By the en...
Top reviews
AD
Jun 6, 2020
Everything taught was understood. Well explained. Looking for more projects from the instructor! Thank you! It was a great experience and I learnt a lot !
MA
Dec 23, 2020
Mr. Kekre was elaborative, clear, neat, and direct in illustrating the project, this is not overpraising; I would like to attend more projects for him.
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26 - 28 of 28 Reviews for Analyze Box Office Data with Seaborn and Python
By Amal N L
•Jul 23, 2020
Need some more of content and wanted to download and try out in my applocation
By Павлова С
•Mar 7, 2020
ничего нового
By Damir
•Jun 2, 2020
Alright course, nothing too impressive. And Resource section where I am supposed to find information about something is missing.