🎥 The Netflix Recommendation System

Recommend the similar movie(s) or TV show(s) based on Cosine Similarity algorithm.

✍ Description:

• Netflix is one of the most popular streaming platforms with a vast library of movies and TV shows spanning various genres. With so many options, making a decision can often feel overwhelming. This recommendation system helps users by suggesting similar content based on their favorite movies or shows. Using a combination of Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity, it finds content with the highest similarity to what the user likes, making selection easier and more enjoyable.
• The dataset 'netflix_data.csv' contains: 12 columns which describing the title, country, description,...and 8807 rows belong to each of the movies/TV show.

✪ Usage:

The project is designed to be completeted these following steps:

1. Data collecting: Download the dataset from Kaggle platform here

2. Data preparation:

• Take an overall look the dataset.
• Cleaning data: Fill the missing values

3. Exploration Data Analysis:

• Visualize the distribution of the contents regarding the movies/TV shows such as the rating, the countries to understand trends in content production and viewer preferences.
• Genarate the images of the common words in the description as well as the listed-in.

4. Feature Engineering:

Text cleaning is essential to standardize and simplify the input data, ensuring more accurate analysis during the TF-IDF transformation.

• Create a class TextCleaner combinding several steps to cleaning the text: a. Seperate text b. Remove space b. Remove punctuation characters
• Create the BAG OF WORDS of each the objects to calculate.

5. The TF-IDF and Cosine Similarity:

• After we generate a new dataframe containing 2 columns: Type and BagOfWords: a. Transforming the value BagOfWords into the matrix by using TfidfVectorizer() b. Calculating the similarity among the words of matrix by using Cosine-similarity formula. c. Save the results of the matrix, the cosine-similar into our files and the new dataframe we made from it.

6. Build the recommendation system:

• Create the class Netfixrec: sorting the values 'similarity' we calculated previously to yeild the list of the similar movie(s) or TV show(s).
• Output the result.

🗳️ The sample results:

The movie/Tv show title: dick johnson is dead Similar Movie(s) list:

1. How to Be a Player
2. End Game
3. Midnight Special
4. The Death and Life of Marsha P. Johnson
5. Small Soldiers
6. Woodshock
7. A Gray State
8. Win It All

Similar TV show(s) list:

1. New Girl
2. Barbie Dreamhouse Adventures

💻 Method:

• Content Based Filtering- They suggest similar items based on a particular item. This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations. The general idea behind these recommender systems is that if a person liked a particular item, he or she will also like an item that is similar to it.

🔖 Credits and Acknowledgements:

• Ashfak Yeafi: This project was inspired by Asfak Yeafi's work on Kaggle. His project provided valuable steps and served as a reference point in the development of this project. You can view his original work here.