Recommendation-System-using-AI

RECOMMENDATION SYSTEM I create a simple recommendation system that suggests items to users based on their preferences. I used the techniques like collaborative filtering to recommend movies to users.

import numpy as np import pandas as pd import difflib from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity

loading the data from the csv file to apandas dataframe

movies_data = pd.read_csv('/content/movies.csv')

printing the first 5 rows of the dataframe

movies_data.head() #number of rows and columns in the data frame

movies_data.shape

selecting the relevant features for recommendation

selected_features = ['genres','keywords','tagline','cast','director'] print(selected_features)

replacing the null valuess with null string

for feature in selected_features: movies_data[feature] = movies_data[feature].fillna('')

combining all the 5 selected features

combined_features = movies_data['genres']+' '+movies_data['keywords']+' '+movies_data['tagline']+' '+movies_data['cast']+' '+movies_data['director'] print(combined_features)

converting the text data to feature vectors

vectorizer = TfidfVectorizer() feature_vectors = vectorizer.fit_transform(combined_features) print(feature_vectors)

getting the similarity scores using cosine similarity

similarity = cosine_similarity(feature_vectors) print(similarity) print(similarity.shape)

getting the movie name from the user

movie_name = input(' Enter your favourite movie name : ')

creating a list with all the movie names given in the dataset

list_of_all_titles = movies_data['title'].tolist() print(list_of_all_titles)

finding the close match for the movie name given by the user

find_close_match = difflib.get_close_matches(movie_name, list_of_all_titles) print(find_close_match) close_match = find_close_match[0] print(close_match)

finding the index of the movie with title

index_of_the_movie = movies_data[movies_data.title == close_match]['index'].values[0] print(index_of_the_movie)

getting a list of similar movies

similarity_score = list(enumerate(similarity[index_of_the_movie])) print(similarity_score) len(similarity_score) #sorting the movies based on their similarity score

sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True) print(sorted_similar_movies) print the name of similar movies based on the index

print('Movies suggested for you : \n')

i = 1

for movie in sorted_similar_movies: index = movie[0] title_from_index = movies_data[movies_data.index==index]['title'].values[0] if (i<30): print(i, '.',title_from_index) i+=1 movie_name = input(' Enter your favourite movie name : ')

list_of_all_titles = movies_data['title'].tolist()

find_close_match = difflib.get_close_matches(movie_name, list_of_all_titles)

close_match = find_close_match[0]

index_of_the_movie = movies_data[movies_data.title == close_match]['index'].values[0]

similarity_score = list(enumerate(similarity[index_of_the_movie]))

sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True)

print('Movies suggested for you : \n')

i = 1

for movie in sorted_similar_movies: index = movie[0] title_from_index = movies_data[movies_data.index==index]['title'].values[0] if (i<30): print(i, '.',title_from_index) i+=1