noopurdhawan / movie_genre_classification

Movie Genre Classification Service

This service uses machine learning to predict the genre of a movie based on its overview. It provides an API that accepts a movie overview and returns the predicted genre.

Prerequisites

• Python 3
• Jupyter
• Docker

About Jupyter Notebook

Movie_Genre_Classification_Assignment.ipynb : The notebooks contain the Exploratory Data Analysis(EDA), Preprocess the Data and Train the Model.

To run the file locally install pip install jupyter-lab and on terminal command jupyter-lab to start jupyter notebook.

* Run the command `jupyter notebook` on the terminal window.
* Load the dataset into a pandas DataFrame.
* Preprocess the data by selecting relevant columns and cleaning the overview text.
* Split the data into training and testing sets.
* Choose a machine learning algorithm for text classification, such as Naive Bayes, Support Vector Machines (SVM), or   Random Forests.
* Train the model on the preprocessed data.
* Save the Model in the model folder

Getting Started

These instructions will guide how to set up and run the movie genre classification service.

1. Cloning a repository: git clone https://github.com/noopurdhawan/movie_genre_classification.git
2. Go to the folder using cd <folder_name>

• Option 1 to run the Model as a Service:

1. Run sh install.sh on the terminal which would set the localhost on port 8000. Please make sure that port 8000 is not in use.

• Option 2: To Re-train the Model

1. Download the Dataset Download the dataset from Kaggle using the following link: The Movies Dataset. Extract the file movies_metadata.csv from the downloaded zip file and save it in the data folder.

2. Set Up the Environment Install the required Python packages by running the following command. pip3 install -r ./requirements.txt

3. Download the Spacy-dependent library for pre-processing data using python -m spacy download en_core_web_sm

   • Load the dataset into a pandas DataFrame.
   • Preprocess the data by selecting relevant columns and cleaning the overview text.
   • Split the data into training and testing sets.
   • Choose a machine learning algorithm for text classification, such as Naive Bayes, Support Vector Machines (SVM), or Random Forests.
   • Train the model on the preprocessed data.
   • Save the Model in the model folder

4. Preprocess the Data and Train the Model using python model.py

5. Run the Service Locally on the terminal Start the Flask application by running the following command python predict.py: The service should now be running on http://localhost:8000.

6. Test the API using the below curl Use a tool like a curl to send a POST request to the API endpoint:

curl -d '{"overview": "A movie about penguins in Antarctica building a spaceship to go to Mars."}' -H "Content-Type: application/json" -X POST http://localhost:8000

The response should contain the predicted genre in JSON format.

7. To run unit tests run python -m unittest test.py on the terminal.