![]() ![]() 'spoken_languages', 'status', 'tagline', 'title', 'vote_average', 'production_countries', 'release_date', 'revenue', 'runtime', 'original_title', 'overview', 'popularity', 'production_companies', Index(['budget', 'genres', 'homepage', 'id', 'keywords', 'original_language', The code for the project can be found below. Finally, we deployed the model as a web application using Flask. We started by acquiring a dataset from Kaggle, then we preprocessed the data and trained a machine learning model. In this blog post, we have gone through the process of building an end-to-end machine learning project. The prediction result will be displayed on a new page. The prediction route will take the uploaded image, preprocess the image, and make a prediction using the trained model. The home route will display a simple HTML page with a form for uploading an image. Our Flask application will have two routes: a home route and a prediction route. We will use Flask to build a web application that takes an input image and predicts the class of the image using the trained model. We will save the trained model as a file so that we can load it later in our Flask application.įlask is a popular web framework for Python that allows us to build web applications quickly and easily. Pickle is a library that allows us to save Python objects in a binary format. It is used to calculate distance between vectors.Īfter training the model, we will serialize the model using the pickle library. We will use the cosine similarity for our project. Scikit-learn is a popular library that provides various machine learning algorithms and tools for model selection, evaluation, and preprocessing. In our project, we will use the scikit-learn library for machine learning. Once we have preprocessed the data, we can train a machine learning model. We will load the data into a pandas dataframe and perform various preprocessing tasks on the dataframe. Pandas is a powerful library that provides data structures and functions for data analysis. In our project, we will use pandas for data preprocessing. ![]() Preprocessing involves tasks such as cleaning the data, handling missing values, scaling the data, and encoding categorical features. After downloading the dataset, we can extract the files and load the data into our project.īefore we can train a machine learning model, we need to preprocess the data. However, some datasets might require us to accept a competition rule or agreement first. ![]() Once we have found a dataset, we can download it directly from Kaggle. To get data from Kaggle, we first need to create an account on Kaggle and join a competition or find a dataset of interest. Kaggle is a popular platform that hosts various datasets for machine learning. Finally, we will deploy the model as a web application using Flask. We will start by acquiring a dataset from Kaggle, then we will preprocess the data and train a machine learning model. ![]() In this blog post, we will go through the process of building an end-to-end machine learning project. End to End Movie Recommendation System with Flask app ![]()
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