{"id":15176690,"url":"https://github.com/shubhamahobia/rnn-classification","last_synced_at":"2026-02-28T12:39:56.329Z","repository":{"id":254476721,"uuid":"846652581","full_name":"ShubhaMahobia/RNN-Classification","owner":"ShubhaMahobia","description":"This project involves building a sentiment analysis model using Recurrent Neural Networks (RNN) to classify movie reviews from the IMDb dataset as either positive or negative. 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The IMDb dataset consists of 50,000 highly polarized movie reviews, with 25,000 labeled as positive and 25,000 as negative, making it an ideal dataset for binary sentiment classification tasks.\n\n\n## Live Deployment \n\nThis project is hosted on - https://rnn-classification-4qyd35scpyeq4u9xjycxmd.streamlit.app/\n\n\n\n\n\n## Run Locally\n\n1. Clone this repo into your system.\n2. Create virtual environment using the command -\n\n```bash\n conda create -p myenv python==3.9.0\n```\n3. Now install all the packages which are listed in requirements.txt\n\n```bash\n pip install -r requirements.txt\n```\n\n4. Now run all the cell in the Experiments.ipynb And Prediction.ipynb as per your need.\n\n5. To run on streamlit - \n\n```bash\n    streamlit run main.py\n```\n## Tech Stack\n\n**Frontend Client:** Streamlit Services\n\n**Model Used:** RNN - Recurrent Neural Network\n\n**Dataset Used:** IMDB Dataset  \n\n## Feedback\nIf you have any feedback or just to say Hi!, please reach out to me at mahobiashubham4@gmail.com\n## Authors\n\n- [@ShubhaMahobia](https://github.com/ShubhaMahobia)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamahobia%2Frnn-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshubhamahobia%2Frnn-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamahobia%2Frnn-classification/lists"}