!pip install wordcloud
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from wordcloud import WordCloud
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
sns.set_style('darkgrid')
## reading dataset
df_movies = pd.read_csv(r'C:\Users\jgaur\Downloads\Gaurav 50\Data Science Project 4 - Movie Recommendation System\movies.csv')
df_rating = pd.read_csv(r'C:\Users\jgaur\Downloads\Gaurav 50\Data Science Project 4 - Movie Recommendation System\ratings.csv')
## displaying first 5 rows of movies dataset
df_movies.head()
## displaying last 5 rows of ratings dataset
df_rating.tail()
print("Movies data shape: ", df_movies.shape)
print("Ratings data shape: ", df_rating.shape)
Movies data shape: (10329, 3)
Ratings data shape: (105339, 4)
## checking null values in movies dataset
df_movies.isnull().sum()
## checking null values in ratings dataset
df_rating.isnull().sum()
df_movies.genres.unique()
total_genres = []
for g in df_movies.genres:
g = g.split('|') ## spliting every generes from '|'
## checking if the is not present in the list
if g not in total_genres:
total_genres.append(g)
total_genres
df_movies.title.head()
df_movies.title[0][:-7]
year, title = [], []
for t in df_movies.title:
title.append(t[:-7])
year.append(t[-7:])
year[:5], title[:5]
word_cloud_genre=WordCloud(width=1500,height=800,background_color='black',min_font_size=2 ,
min_word_length=3).generate(str(total_genres))
word_cloud_title=WordCloud(width=1500,height=800,background_color='cyan',min_font_size=2 ,
min_word_length=3).generate(str(title))
plt.figure(figsize=(20,10))
plt.axis('off')
plt.title('Word Cloud for Movies Genre',fontsize=30)
plt.imshow(word_cloud_genre);
plt.figure(figsize=(20,10))
plt.axis('off')
plt.title('Word Cloud for Movies Title',fontsize=30)
plt.imshow(word_cloud_title);
final_df = pd.merge(df_rating, df_movies, how='left',on='movieId')
final_df.head()
title_rating = final_df.groupby(['title'])[['rating']].sum()
title_rating = title_rating.nlargest(10,'rating')
title_rating.head()
plt.figure(figsize=(10, 10))
plt.xticks(rotation=75);
sns.barplot(title_rating.index, title_rating['rating']);
plt.ylabel('Title');
plt.xlabel('Count');
C:\Users\jgaur\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
warnings.warn(
tfidf = TfidfVectorizer()
tfidf_matrix = tfidf.fit_transform(df_movies['genres'])
movie_user = final_df.pivot_table(index='userId',columns='title',values='rating')
movie_user.head()
cos_similarity = linear_kernel(tfidf_matrix, tfidf_matrix)
indices=pd.Series(df_movies.index,index=df_movies['title'])
titles=df_movies['title']
def recom_sys(title):
idx = indices[title]
similarity_scores = list(enumerate(cos_similarity[idx]))
similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)
similarity_scores = similarity_scores[1:21]
movie_indices = [i[0] for i in similarity_scores]
return titles.iloc[movie_indices]
recom_sys('Grumpier Old Men (1995)')
titles