Sklearn: Sklearn is a free machine learning library for Python. I used this library to work with various text in the dataset. Nltk: NLTK is a leading platform for building Python programs to work with human language data. It provides a high-level interface for drawing attractive and informative statistical graphics.
Seaborn: Seaborn is a Python data visualization library based on matplotlib. Most of the visualizations are done using this library.
It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits. Matplotlib: Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. All data analysis operations are performed using the pandas library. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays. Pandas: Pandas is an open-source Python package that is most widely used for data science/data analysis and machine learning tasks. All the array operations in this project are done using the numpy library. It also has functions for working in the domain of linear algebra, Fourier transform, and matrices. Numpy: Numpy is a Python library used for working with arrays. The data set I used contains 7787 records and 12 columns. The recommender system uses Cosine Similarity along with some interesting visualizations using python.
#Kate netflix similar movies tv
I am using the Netflix Movies and TV Shows dataset from Kaggle to do this project. This project recommends movies and shows on the Netflix streaming platform depending upon what you have watched.
#Kate netflix similar movies movie
Or if I have watched one good movie and if I want to watch a movie similar to it how can I know it? To come up with a solution, I decided to do a recommender system of movies and TV shows on Netflix based on anything I want to watch or based on a movie I have previously watched. One of the hardest things while deciding to binge-watch Netflix is ‘what to watch?’.
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