The data are contained in six files. 本文的介绍主要基于ratings.csv 和 movies.csv.
The output will be a CSV file 'movie_metadata.csv' (1.5MB) "movie_title" "color" "num_critic_for_reviews" "movie_facebook_likes" "duration" "director_name" "director_facebook_likes" "actor_3_name" "actor_3_facebook_likes" "actor_2_name" "actor_2_facebook_likes" A series of assignment questions is included and the accompanying Instructor’s Manual provides representative solutions. Dates are provided for all time series values. Genres that a movie belongs to (eg. Stable benchmark dataset. Available are collections of movie-review documents labeled with respect to their overall (e.g., "two and a half stars") and sentences labeled with respect to their This dataset contains faceted metadata describing contemporary American films, along with relevant judgements by actual human users. The Movie dataset contains weekend and daily per theater box office receipt data as well as total U.S. gross receipts for a set of 49 movies. MovieLens 20M movie ratings. Second, after presenting the Confusion Matrix where the True Positives are 110 and False Positives are 19757, you report a Precision score of 0.6873 (!!!).
Sci-Fi and Comedy movies also get low average ratings.● Musical, Animation and Romance movies get the highest average ratings.● Sci-Fi and Animation movies show very similar trends, they again become popular during 2009-2013.● Trends in the average ratings of Romance and Horror movies show positive association between them.In this project, we aim to build machine learning models to automatically detect frauds in credit card transactions.
README.txt; ml-20m.zip (size: 190 MB, checksum) First, you build a model (lets take the SVM for example) which does a very good job in finding True Positives (110 out of 120) but at the same time it misclassifies another whooping 19757 observations as Positives when they should be Negatives.
The following problems are taken from the projects / assignments in the edX course Python for Data Science and the coursera course Applied Machine Learning in Python (UMich).● The IMDB Movie Dataset (MovieLens 20M) is used for the analysis.● This will give us an insight about how the people’s liking for the different movie genres change over time and about the strength of association between trends in between different movie genres, insights possibly useful for the critics.The answer to the following research questions will be searched for, usingThe input tables are pre-processed using the following code to get the data in the desired format, ready for the analysis.The next figure shows the trends of the average ratings by users for different genres across different years. Motivation Understand the trend in average ratings for different movie genres over years (from 1995 to 2015) and Correlation between the trends for different genres (8 different genres are considered: Animation, Comedy, Romance, Thriller, Horror, Sci-Fi and Musical). across the movies belonging to the same genre).The next figure shows the trends of the ratings (averaged over users and movies for each genre) for different genres across different years.The next figures show the trends for different genres for different sub-windows of time and with the variations (inter-quartile ranges) in average ratings for each genre.● There is a decreasing trend in the average ratings for all 8 genres during 1995-98, then the ratings become stable during 1999-2007, then again increase.● Horror movies always have the lowest average ratings. Each user is represented by an id, and no other information is provided.
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