movie_recommender.py

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

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")
# print(df.head())
# print(df.columns)


##Step 2: Select Features
features = ['keywords','cast','genres','director']


##Step 3: Create a column in DF which combines all selected features
# to clean dataset/fill null values with empty string
for feature in features:
	df[feature] = df[feature].fillna('')

def combine_features(row):
	try:
		return row['keywords'] +" "+row['cast']+" "+row["genres"]+" "+row["director"]
	except:
		print ("Error:", row)	

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

# print (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 = "Captain America: The First Avenger"


## 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]))


## 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 20 movies
i=0
for element in sorted_similar_movies:
		print(get_title_from_index(element[0]))
		i=i+1
		if i>20:
			break

#END