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merged 1 commit into from
Jun 12, 2024
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SayantikaLaskar
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Related Issue

Classified the movie according genre

Description

This project aims to develop a machine learning model to classify movies into genres based on various features such as plot summaries, cast, crew, and other metadata.

The workflow includes:

  1. Data Preprocessing: Load and clean the dataset, extract relevant features, and split the data into training and testing sets.
  2. Model Training: Train a machine learning model (e.g., Logistic Regression, Random Forest, Neural Network) on the training data.
  3. Model Evaluation: Evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.
  4. Usage Instructions: Provide guidelines for using the trained model to predict genres for new movies.

This project aims to automate movie genre classification, enhancing user experience by helping them find movies of interest more easily. It also serves as an educational resource for participants, fostering learning and growth in data science and machine learning.

Type of PR

  • Bug fix
  • [ ✘] Feature enhancement
  • Documentation update
  • Other (specify): _______________

Checklist:

  • [ ✘] I have performed a self-review of my code
  • [ ✘] I have read and followed the Contribution Guidelines.
  • [ ✘] I have tested the changes thoroughly before submitting this pull request.
  • I have provided relevant issue numbers, screenshots, and videos after making the changes.
  • I have commented my code, particularly in hard-to-understand areas.

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@sanjay-kv sanjay-kv left a comment

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Unlike the machine learning repo we are not accepting folder kinda structure commits here, you can add to the existing deployment and improve it. thanks

@sanjay-kv sanjay-kv added duplicate This issue or pull request already exists wontfix This will not be worked on gssoc and removed gssoc labels Jun 11, 2024
@SayantikaLaskar
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Okay I will make the changes accordingly. Thank you

@sanjay-kv sanjay-kv merged commit 415da22 into recodehive:main Jun 12, 2024
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2 participants