So, let us now preprocess our data! Text Preprocessing with NLP Print('nMissing values: ', df.isnull().sum().values.sum())Ī short summary of statistics- df.describe().T Now, let’s check the missing values and print a short summary of statistics- # type of entries, how many missing values/null fields Print(df.nunique()) Output- Rows x Columns : 250 x 5įeatures: Print('Rows x Columns : ', df.shape, 'x', df.shape) Let’s have a quick overview of our dataset- # data overview df = pd.read_csv('IMDB_Top250Engmovies2_OMDB_Detailed.csv') Now let us load and print out our dataframe. Warnings.simplefilter(action='ignore', category=DeprecationWarning)įrom import cosine_similarityįrom sklearn.feature_extraction.text import CountVectorizer Warnings.simplefilter(action='ignore', category=FutureWarning)
So, let us begin by importing the necessary libraries first! #ignore warnings So, let’s begin! Importing the Dependencies and Loading the Data And there you go! You are ready for the project.
NETFLIX MOVIE SUGGESTER INSTALL
Then open a fresh terminal, navigate to the target directory, and type in ‘pip install requirements.txt’. Note – Before starting with this section, check if you have the dependencies installed! If not, create a file named ‘requirements.txt’ (in the same directory where you have your data and code) and paste the dependencies given below. Training and Testing Our Recommendation Engine Vectorizing Words and Creating the Similarity Matrixĥ. Generating Word Representations using Bag Of WordsĤ. Importing the Dependencies and Loading the Dataģ. Let us have a short overlook at what we will be covering-ġ. In this article, we will develop a Content-Based Movie Recommendation System with the IMDB top 250 English Movies dataset. Content-Based filtration is mainly focused on recommending similar products to the user based on their history.Ī Collaborative Recommender System, on the other hand, does not take an individual user at a time but a cluster of similar or alike users (here, users with almost likely taste in movies), and based on those users’ ratings, recommends similar products to those group or cluster of users. There are generally two types of Recommendation Systems-Ī Content-Based Recommender System is one that follows a content-based filtration method to generate recommendations to the user. A recommendation system predicts and filters user preferences after learning about the user’s past choices. Have you ever imagined how Amazon Prime, Netflix, and Google predict your taste in movies so easily? It is no rocket science, that after completing one movie/series you loved, and rated it on these platforms, a few more adds up to the suggested or ‘You May Like this ‘ section in seconds! It is Machine Learning. This article was published as a part of the Data Science Blogathon.
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