Tree-based model

  Q1. Give brief explanations for the questions below 1. Importance of activation functions in neural networks and state 3 commonly used such activation functions 2. Explain “Bagging” ensemble method (Eg: used in Random Forests) Q2. Predicting the Oscars 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”.