Analyze Box Office Data with Plotly and Python
Welcome to this project-based course on Analyzing Box Office Data with Plotly 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) and feature engineering. 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. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Data Visualization (DataViz)
由 AS 提供Jun 26, 2020
Great Course to get a foundation of plotly . One will also get a good knowledge of dealing with time series data
由 KG 提供Jun 20, 2020
i raise a query during class time but not get resolved maybe it happen due to some technical issue.please fix this.thanks for the guided projects.
由 VP 提供May 27, 2020
It was very nice experience of lerning the subject from such a excillent professor
由 YS 提供May 3, 2020
The guided project was very nicely explained and gave me a hands on experience with Feature Engineering and Data Visualization.