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| # -*- coding: utf-8 -*- | |
| """Movie_Recommender_System.ipynb | |
| import os | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| """## Loading Data""" | |
| movies = pd.read_csv('tmdb_5000_movies.csv') | |
| credits = pd.read_csv('tmdb_5000_credits.csv') | |
| movies.head(5) | |
| movies.info() | |
| credits.info() | |
| credits.head(5) | |
| """## Merging both dataframes : Movies & Credits""" | |
| movies = movies.merge(credits,on='title') | |
| movies.shape | |
| movies.head(1) | |
| """## Data Pre-Processing""" | |
| #> important columns to be used in recommendation system : | |
| # genres | |
| # id | |
| # keywords | |
| # title | |
| # overview | |
| # cast | |
| # crew | |
| movies = movies[['movie_id','title','overview','genres','cast','keywords','crew']] | |
| movies.head(5) | |
| movies.isnull().sum() | |
| movies.dropna(inplace=True) | |
| movies.isnull().sum() | |
| movies.duplicated().sum() | |
| movies.iloc[0].genres | |
| import ast | |
| # extracting genres from raw data for the creation of tags | |
| def convert(obj): | |
| L = [] | |
| for i in ast.literal_eval(obj): | |
| L.append(i['name']) | |
| return L | |
| movies['genres'] = movies['genres'].apply(convert) | |
| movies['keywords'] = movies['keywords'].apply(convert) | |
| movies.head(5) | |
| #function for extracting top 3 actors from the movie | |
| def convert3(obj): | |
| L = [] | |
| counter = 0 | |
| for i in ast.literal_eval(obj): | |
| if counter !=3: | |
| L.append(i['name']) | |
| counter+=1 | |
| else: | |
| break | |
| return L | |
| movies['cast'] = movies['cast'].apply(convert3) | |
| movies.head(5) | |
| #function to fetch the director of movie from crew column | |
| def fetch_director(obj): | |
| L = [] | |
| for i in ast.literal_eval(obj): | |
| if i['job'] == 'Director': | |
| L.append(i['name']) | |
| break | |
| return L | |
| movies['crew'] = movies['crew'].apply(fetch_director) | |
| movies.head(5) | |
| movies['overview'] = movies['overview'].apply(lambda x:x.split()) | |
| movies.head() | |
| # applying a transformation to remove spaces between words | |
| movies['genres'] = movies['genres'].apply(lambda x:[i.replace(" ","") for i in x]) | |
| movies['keywords'] = movies['keywords'].apply(lambda x:[i.replace(" ","") for i in x]) | |
| movies['cast'] = movies['cast'].apply(lambda x:[i.replace(" ","") for i in x]) | |
| movies['crew'] = movies['crew'].apply(lambda x:[i.replace(" ","") for i in x]) | |
| movies.head() | |
| movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew'] | |
| movies.head() | |
| new_df = movies[['movie_id','title','tags']] | |
| new_df['tags'] = new_df['tags'].apply(lambda x:" ".join(x)) | |
| new_df.head() | |
| new_df['tags'][0] | |
| new_df['tags'] = new_df['tags'].apply(lambda x:x.lower()) | |
| new_df['tags'][0] | |
| """## Text Vectorization""" | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| cv = CountVectorizer(max_features=5000,stop_words='english') | |
| vectors = cv.fit_transform(new_df['tags']).toarray() | |
| ## Most frequent 5000 words | |
| # cv.get_feature_names() | |
| """## Applying Stemming Process""" | |
| import nltk #for stemming process | |
| from nltk.stem.porter import PorterStemmer | |
| ps = PorterStemmer() | |
| #defining the stemming function | |
| def stem(text): | |
| y=[] | |
| for i in text.split(): | |
| y.append(ps.stem(i)) | |
| return " ".join(y) | |
| stem('In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization. Action Adventure Fantasy ScienceFiction cultureclash future spacewar spacecolony society spacetravel futuristic romance space alien tribe alienplanet cgi marine soldier battle loveaffair antiwar powerrelations mindandsoul 3d SamWorthington ZoeSaldana SigourneyWeaver JamesCameron') | |
| new_df['tags'] = new_df['tags'].apply(stem) | |
| """## Similarity Measures""" | |
| # For calculating similarity, the cosine distance between different vectors will be used. | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| similarity = cosine_similarity(vectors) | |
| """## Making the recommendation function""" | |
| def recommend(movie): | |
| movie_index = new_df[new_df['title'] == movie].index[0] | |
| distances = similarity[movie_index] | |
| movies_list = sorted(list(enumerate(distances)),reverse=True, key=lambda x:x[1])[1:6] | |
| for i in movies_list: | |
| print(new_df.iloc[i[0]].title) | |
| """## Recommendation""" | |
| recommend('Batman Begins') #enter movies only which are in the dataset, otherwise it would result in error | |
| new_df.iloc[1216] | |
| """## Exporting the Model""" | |
| import pickle | |
| pickle.dump(new_df,open('movies.pkl','wb')) | |
| pickle.dump(new_df.to_dict(),open('movie_dict.pkl','wb')) | |
| pickle.dump(similarity,open('similarity.pkl','wb')) |