movieRecommendationSystem.py

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
###### helper functions. Use them when needed #######
def get_title_from_index(index):
	return df[df.index == index]["title"].values[0]

def get_index_from_title(title):
	return df[df.title == title]["index"].values[0]
##################################################

##Step 1: Read CSV File
df=pd.read_csv("movie_dataset.csv") # kindly import according to your local file name
#print (df.head())

##Step 2: Select Features
features = ['keywords', 'cast', 'genres', 'director']
##Step 3: Create a column in DF which combines all selected features
for feature in features:
	df[feature]= df[feature].fillna('')
def combine_features (row):
	try:
		return row['keywords'] +" "+row['cast']+" "+ row['genres']+" "+row['directors']
	except:
		print("Error:", row)

df["combined_features"]=df.apply(combine_features,axis=1)

#print("combined features:",df["combined_features"].head())

##Step 4: Create count matrix from this new combined column
cv = CountVectorizer()
count_matrix = cv.fit_transform(df["combined_features"])

##Step 5: Compute the Cosine Similarity based on the count_matrix
cosine_sim=cosine_similarity(count_matrix)
movie_user_likes = "Avatar"

## Step 6: Get index of this movie from its title
movie_index=get_index_from_title(movie_user_likes)
similar_movies=list(enumerate(cosine_sim[movie_index]))
sorted_similar_movies=sorted(similar_movies,key=lambda x:x[1],reverse=True)

## Step 7: Get a list of similar movies in descending order of similarity score
sorted_similar_movies=sorted(similar_movies,key=lambda x:x[1],reverse=True)

## Step 8: Print titles of first 50 movies
i=0
for movie in sorted_similar_movies:
	print(get_title_from_index(movie[0]))
	i=i+1
	if(i>50):
		break