Description:
Please download the Oscar_2000_2018.csv dataset provided.
This dataset amounts to a total of 1,235 movies from 2000 to 2018, where each film has 100+ features including:
It sports 20 categorical, 56 numeric, 42 items, and 1 DateTime field totaling 119 fields giving you plenty of details about various aspects of the past nominees and winners.
The dataset is organized such that each record represents a unique movie identified by the field movie_id.
The first 17 fields have to do with the metadata associated with each movie e.g., release_date, genre, synopsis, duration, metascore.
Tasks:
Part 1: EDA
1.Using a scatterplot or a pair plot show the relationship between features user_reviews and critic_reviews. Find the Pearson’s correlation coefficient(r) between the 2 features.
2.Plot the average duration per certificate feature. In other words, x-axis would be certificate and the y-axes would be the average duration.
3.Plot a histogram for the genre feature. Note that the field genre needs to be split first to find the frequency for each individual genre type; Comedy, Romance, Action etc. (Hint: Functions like strsplit in R or split in Python can be used)
Part 2: Model Building
1.You are going to predict Oscar_Best_Picture_won feature; this will be your target variable. Remove all of the features which has the convention Oscar_Best_XXX_won except for the target variable Oscar_Best_Picture_won.
2.Convert the target variables type to a numerical type by doing the transformation, Yes = 1, No = 0.
3.Remove columns with high cardinality, i.e., for every column that has a unique value frequency of 70% or higher, remove them from the dataset.
4.Perform a time split and create a training dataset spanning the period 2000-2017 and a test dataset for the movies released in 2018 – use year feature for the data split
5.Create a tree-based model to predict the target Oscar_Best_Picture_won
6.Use the model to predict the test dataset and find the maximum predicted value
Optional: Go back to the initial dataset and find the movie in 2018 that is associated with the maximum predicted value.
