Movie Recommendation System

Hello, I have create Movie Recommendation project with my project partner Parimal and Jay. This project is basically work on Machine learning Techniques and concepts of Recommendation. In this blog you can see all details about our project. So first we understand about the recommendation.

A movie recommendation system is a ML based approach to recommend the movies to particular user’s based on their past experiences. Let’s take example if we like to watch action movie then the action movies, they recommend like that. The core concept of any recommendation system is users and items. For movie prediction items are movies itself.

The primary Goal of recommendation system is to filter and predict only those movies that corresponding to user most likely to watch. The ML model uses these data and based on that do predictions.

Recommender system helps to enhance user’s experience through suggesting because there is number of shows or movies listed on website so from those shows or movies user will find their interest of movies or shows.

Types of Recommendation system :

• Content Based   
• Collaborative Filtering
• Hybrid         

This project we use content-based filtering where we recommend movies based on content watched by a user. The TMDB 5000 Movies Dataset we used to build a model, by using this dataset we create the tags for particular movies using overview, genres, keywords, cast, crew fields and convert that tags into vectors called text vectorization to do that we use bag of word technique, in this technique we combine all the tags and find the most frequent words. After that find the count of that words in particular movies tags an create a vector matrix and calculate the distance between vectors, if distance high similarity low so to do that we use Cosine similarity. At last deploy the recommender system on web using Streamlit python framework.

Technologies :

In this project we used Pickle which is used serializing and deserializing a Python object structure. it's the process of converting a Python object into a byte stream to store it in a file/database, maintain program state across sessions, or transport data over the network.

Streamlit is an open-source app framework in Python language. To creates web apps for data science and machine learning in a short time it helps. It is compatible with major Python libraries such as scikit-learn, Keras, PyTorch, SymPy(latex), NumPy, pandas, Matplotlib etc. it is more structured and focused more on simplicity.

In data science/data analysis and machine learning tasks, the Pandas which is open-source Python package that is most widely used for, it is built on top of another package named Numpy, which provides support for multi-dimensional arrays.

You can check the souce code of this project on Github with below link :

https://github.com/JayVekariya/Movie-Recommendation-System

So this all about the movie recommendation system. We hope that you would find and gain something new from this blog.