Python, Machine Learning.
This project implements a content-based filtering recommendation system to recommend movies to users based on their preferences. The recommendation system analyzes the content (genres, ratings, year, etc.) of movies and suggests similar movies that match the user's preferences.
Project Overview
The project consists of the following components:
- Data Preparation: The movie and rating datasets are loaded and preprocessed to extract relevant information such as movie titles, genres, and ratings.
- Content-Based Filtering: The recommendation system uses content-based filtering techniques to analyze the movie content and identify similar movies based on user preferences.
- User Input: Users provide their movie preferences, including movie titles and ratings, which are used as input for the recommendation system.
- Recommendation Generation: The recommendation system generates a list of movie recommendations based on the user's preferences and the content-based filtering algorithm.
Usage
To use the recommendation system, follow these steps:
- Run the project code to load the movie dataset and preprocess the data.
- Provide your movie preferences by specifying movie titles and ratings.
- The recommendation system will analyze your preferences and generate a list of movie recommendations based on the content-based filtering algorithm.
- Dataset The project utilizes a movie dataset that contains information about various movies, including titles, genres, and ratings. The dataset is used to train the content-based filtering recommendation system. data source: GroupLens. Data source
- Future Enhancements The project can be further enhanced in the following ways:
- Incorporate collaborative filtering techniques to improve the accuracy of movie recommendations by considering user behavior and preferences.
- Implement a user interface to make it more user-friendly and interactive.
- Explore additional features such as movie directors, actors, and release years to enhance the recommendation system.
import pandas as pd
from math import sqrt
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# We will use onehot encoding technique to convert the list of genres to a vector where each column corresponds to one possible value of the feature.
# lets copy the dataframe into a new df
moviesWithGenres_df = movies_df.copy()
#For every row in the dataframe, iterate through the list of genres and place a 1 into the corresponding column
for index, row in movies_df.iterrows():
for genre in row['genres']:
moviesWithGenres_df.at[index, genre] = 1
#Filling in the NaN values with 0 to show that a movie doesn't have that column's genre
moviesWithGenres_df = moviesWithGenres_df.fillna(0)
moviesWithGenres_df.head()
Conclusion
The content-based filtering recommendation system provides personalized movie recommendations based on user preferences. By analyzing the content of movies, the system suggests similar movies that align with the user's interests. This project serves as a foundation for building more advanced recommendation systems and can be extended to other domains beyond movies.