Final Report Exploratory Analysis of movies from IMDb and Prediction of Movie Rating

 Dataset Description In the dataset that we have scrapped from IMDB movies has 1000 rows and 18 columns with 12 numeric columns and 6 categorical columns. Column Names Description Range of Columns Id Movie Id 0-999 Movie_Name Name of the movie - Year_of_Release The year in which the movie is released 1920-2022 Watch_Time The total time the movie runs 45-321 Movie Rating Rating for a movie given by IMDB 7.6-9.3 Meatscore_of_movie Score given to a movie by average reviews 28-100 Votes Number of votes a movie got 25547-2655610 Gross An amount that a movie earned 0-936.66 Description A short description of movies - Rank It is the rank given to each movie 1-1000 No_Of_Reviews Total number of reviews a movie got 14-25 Positive_Reviews Total number of positive reviews 4-25 Negative_Reviews Total number of negative reviews 0-21 Certificate Certificate given to each movie - Top250 Ranked the top 250 movies 1-250 Director Name of the directors - Stars Name of the stars - Genre Different genres names -  Predicted Attribute As seen in the Rating distribution plot, 6.10% of the Movies is greater and equal 8.5. Most of the Movies in the data set have a rating lower than 8.5.  Distribution of Attributes (From slide ~15-23)  Numerical attributes The following three boxplots show the distribution of Runtime (Watch time), Gross and Number of votes for the two groups of rating. As seen on the plot, for those movies with a rating greater and equal to 8.5 (Green), the mentioned attributes have a higher average. The movies with the highest rating have on average around 150 minutes of runtime, $100,000 gross and around 1,000,000 votes. And the bar plot shows the top 5 meta scores of the movies and their corresponding rating. Meta Score and Movie Ratings need not be proportional but from the plot we can say that the Meta Score 96 has a lesser rating than in comparison to Meta score 94.