Movie-Recommendation-System

Dataset : The Movies Dataset

About the Dataset :

movies_metadata.csv : Contains Informations like genres, release year, release date, budget, revenue etc. for 45000+ movies

keywords.csv : Contains keywords of reviews given by users for all movies in the movies_metadata.csv file

credits.csv : Contains Cast and Crew Information for all movies in the movies_metadata.csv file

ratings_small.csv : Contains 100 ratings from 700 users on 9,000 movies

links_small.csv : Contains IMDB and TMDB IDs of all movies featured in the ratings_small.csv file (About 9000 movies).

Final Dataset Obtained After Data Cleaning, Data Wrangling and Merging credits.csv, links_small.csv, keywords.csv with movies_metadata.csv:

MoviesData.csv : Contains all information about 9081 movies. The features of MoviesData.csv are as follows:

Data Analysis :

Genres Distribution in the Dataset :

Distribution of production countries in the Dataset :

Distribution of production house in the Dataset :

Release Year Distribution :

Release Day Distribution :

Most frequently appearing actors/actresses in the dataset :

Most frequently appearing directors in the dataset :

Correlation Between The Columns budget, profit, revenue, runtime, vote_count, vote_average, rating_count, mean_rating and release_year :

Top 20 movies based on the popularity column in the dataset :

Top 20 movies based on the vote_count column in the dataset :

Top 20 movies based on the number of users rated in the dataset :

Top 20 high Budget Movies :

Top 20 high Profit Movies :

Popularity-Based Recommendation System :

It is a type of recommendation system which works on the principle of popularity and or anything which is in trend. These systems check about the product or movie which are in trend or are most popular among the users and directly recommend those.

For example, if a product is often purchased by most people then the system will get to know that that product is most popular so for every new user who just signed it, the system will recommend that product to that user also and chances becomes high that the new user will also purchase that.