Recommendation Engine

Importing Necessary Libraries 

Firstly, we will load some basic libraries:-

(i) Numpy - for linear algebra. 

(ii) Pandas - for data analysis. 

(iii) Seaborn - for data visualization.

(iv) Matplotlib - for data visualisation.  

import numpy as np  import pandas as pd  import seaborn as sns from matplotlib import pyplot as plt

 

Loading Data

Firstly, we will create a few columns for the dataset, namely, User_ID, Item_ID, Rating, and Timestamp. 

User_ID: ID of the unique user.

Item_ID: Item of the unique movie.

Rating: Rating given to the movie by a particular user.

Timestamp: Timestamp of the movie rating. 

# Creating columns for the Dataset  columns = ['User_ID', 'Item_id', 'Rating', 'Timestamp']   df = pd.read_csv('file (1).tsv', sep='\t', names=columns)   #printing the dataset  df.head(n=10)

 

Output

 

 

movies = pd.read_csv('Movie_Id_Titles.csv') movies.head(n=10)

 

Output

 

 

We will merge the two datasets to build the movie recommendation engine. We will have the following features as part of the final dataset:-

→ User_ID - unique ID of the user. 

→ item_id - unique ID of the item. 

→ Rating - rating of the movie assigned by a unique user. 

→ Timestamp - timestamp of the movie rating. 

→ Title - the title of the movie. 

#merging both the datasets  data = pd.merge(df, movies, on='item_id') data.head(n=10)

 

Output

 

 

Visualization 

Firstly, let us calculate the average ratings of all the movies for better visualization. 

# Calculating mean rating of all movies in ascending order  Mean_Ratings=data.groupby('title')['Rating'].mean().sort_values(ascending=True).head() Mean_Ratings

 

Output

 

Secondly, let us calculate the number of ratings assigned to all movies.

# Calculating count rating of all movies Total_Ratings= counts = data.groupby('title')['Rating'].count().sort_values(ascending=False).head() Total_Ratings

 

Output

 

Now, let us create a data frame to list each movie with its respective average rating and total ratings. 

#lets create a dataframe of each movie with its number of ratings and avg rating  ratings = pd.DataFrame(data.groupby('title')['Rating'].mean()) ratings['Total Ratings'] = pd.DataFrame(data.groupby('title')['Rating'].count()) ratings.head()

 

Output

 

 

Finally, let us plot a graph of the Average Rating vs. Number of Ratings for different movies. 

# plot graph sns.set_style('darkgrid') plt.figure(figsize =(10, 5)) plt.xlabel('Average Rating') plt.ylabel('Number of Ratings') ratings['Rating'].hist(bins = 70)

 

Output

 

Recommendations 

Now, let us sort movies according to the number of ratings. 

#Sorting movies according to the number of ratings  movie_sort = data.pivot_table(index ='User_ID',columns ='title', values ='Rating')   movie_sort.head()   ratings.sort_values('Total Ratings', ascending = False).head(10)

 

Output

 

So, now we get the total number of ratings assigned to each movie in descending order. We observe that 'Star Wars' has got the highest ratings. 

So, let us try to find the correlation of 'Star Wars' with other movies and generate similar movies for the user. This will be the goal of our recommendation engine, i.e., to suggest movies similar to 'Star Wars' for the user. 

# analysing correlation with similar movies similar_to_starwars = movie_sort.corrwith(starwars_user_ratings) #correlation of star wars with other movies, high correlation indicating higher similarity  corr_starwars = pd.DataFrame(similar_to_starwars, columns =['Correlation']) corr_starwars.dropna(inplace = True)   corr_starwars.head()

 

Output

 

 

Now, let us find movies similar to star wars with the condition that the movie has greater than 200 ratings. We are doing this to make user preferences more accurate. 

#movies similar to Star Wars  corr_starwars.sort_values('Correlation', ascending = False).head(10) corr_starwars = corr_starwars.join(ratings['Total Ratings'])   corr_starwars.head()   corr_starwars[corr_starwars['Total Ratings']>200].sort_values('Correlation', ascending = False).head()

 

Output

 

 Finally, we find the top 5 movies (>200 ratings) that are most similar to ‘Star Wars’:-

→ Empire, Strikes Back, The (1980)

→ Return of the Jedi (1983)

→ Raiders of the Lost Ark (1981) 

→ Sting, The (1973) 

Hence, we can predict user preference based on his liking.

Frequently Asked Questions  

Q1. What is a Recommendation Engine?

A Recommendation Engine is a system that helps to filter preferences based on the user. These are used in varied domains such as movies, books, music, and more. 

 

Q2. What are the two ways of building a Recommendation Engine?

The two ways of building a recommendation engine are:-

(i) Collaborative Filtering 

(ii) Content-Based Filtering 

 

Q3. What is the importance of Data Visualization?

Data visualization helps to gain insights into data and, in turn, figure out patterns and identify errors. 

Key Takeaways 

Congratulations on making it this far. This blog discussed a fundamental overview of building a Recommendation Engine !!

We learned about Data Loading, Data Visualisation, and finally, generating Recommendations using correlation. 

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