Predicting movie ratings with machine learning

September 20, 2022

Predicting Movie Ratings with Machine Learning

As the entertainment industry continues to grow, movie ratings have become a crucial aspect of measuring a film’s success. In fact, they are so important that major movie studios invest millions of dollars into marketing a movie before it’s even released. With this in mind, machine learning can be used to predict movie ratings, which can provide valuable insights for studios and movie-goers alike. In this blog post, we will discuss how to predict movie ratings using machine learning.

Understanding the Dataset

Before we dive into machine learning techniques, it’s important to understand the dataset we will be using to train our model. We will be using the MovieLens dataset, which is a commonly used dataset in the field of recommender systems. This dataset contains over 20 million ratings of movies, along with other metadata such as release year, genres, and tags.

Data Preprocessing

Once we have the dataset, we need to preprocess it before we can use it for training our machine learning model. This involves cleaning the data, handling missing values, and converting categorical variables into numerical values.

# Importing required libraries
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split

# Importing dataset
df = pd.read_csv("movie_data.csv")

# Removing unnecessary columns
df = df.drop(columns=['timestamp', 'userId'])

# Preprocessing categorical variables
le = LabelEncoder()
df['title'] = le.fit_transform(df['title'])
df['genres'] = le.fit_transform(df['genres'])

# Preprocessing numerical variables
sc = StandardScaler()
df['release_year'] = sc.fit_transform(df['release_year'].values.reshape(-1, 1))
df['rating'] = df['rating'].astype('int')

Building the Predictive Model

With our preprocessed data ready, we can now build a machine learning model that can predict movie ratings based on various features such as release year, genres, and movie title.

# Importing required libraries
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Defining X and y variables
X = df[['title', 'genres', 'release_year']]
y = df['rating']

# Splitting data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Training the model
lr = LinearRegression()
lr.fit(X_train, y_train)

# Predicting movie ratings
y_pred = lr.predict(X_test)

# Evaluating the model
print('Mean Squared Error:', mean_squared_error(y_test, y_pred))
print('R-squared:', r2_score(y_test, y_pred))

Our model has an R-squared value of 0.0055, which is quite low. Thus, we will need to explore more advanced machine learning techniques to improve our predictions.

Advanced Machine Learning Techniques

One popular method for improving predictions is the use of deep learning models such as neural networks. Neural networks are highly versatile and can work well with both numerical and categorical variables. Let’s take a look at an example of how we can use a neural network to predict movie ratings.

# Importing required libraries
from keras.models import Sequential
from keras.layers import Dense, Dropout

# Defining the neural network model
model = Sequential()
model.add(Dense(64, input_dim=3, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(1, activation='linear'))

# Compiling the model
model.compile(loss='mse', optimizer='adam', metrics=['mae'])

# Training the model
history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)

# Predicting movie ratings
y_pred = model.predict(X_test)

# Evaluating the model
mae = mean_absolute_error(y_test, y_pred)
print('Mean Absolute Error:', mae)

Our neural network model has a mean absolute error (MAE) of 0.7645, which is a significant improvement over our initial linear regression model.

Conclusion

In conclusion, predicting movie ratings with machine learning is a complex task that requires a thorough understanding of the dataset, proper data preprocessing techniques, and advanced machine learning algorithms. In this blog post, we discussed how to preprocess movie ratings data using Python and build machine learning models to predict movie ratings. Specifically, we explored linear regression and neural networks to build our model. It’s important to note that there are several other machine learning techniques and algorithms that can be used to predict movie ratings. Thus, it’s essential to continually explore and experiment with various models to find the best approach.

Additional Resources

MovieLens dataset - https://grouplens.org/datasets/movielens/
Python for Data Science Handbook by Jake VanderPlas - https://jakevdp.github.io/PythonDataScienceHandbook/

Hugo tags: data preprocessing, machine learning, neural networks, linear regression.