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LICENSE (1076, 2023-11-30)

# Document-Classification-Using-Machine-Learning-Models Leveraging a dataset containing 1000 documents categorized into eight diverse classes—Business, Entertainment, Food, Graphics, Historical, Politics, Space, Sport, and Technology—the aim is to develop robust classifiers capable of accurately assigning documents to their respective categories. Overview This repository contains code and resources for performing document classification using various machine learning models. The dataset used comprises 1000 documents categorized into eight classes: Business, Entertainment, Food, Graphics, Historical, Politics, Space, Sport, and Technology. Models Used The document classification task was implemented using the following machine learning models: Random Forest K-Nearest Neighbors (KNN) Classifier Gradient Boosting Naive Bayes Support Vector Machine (SVM) Dataset The dataset used for this classification task consists of 1000 documents categorized into different classes. Due to privacy reasons, the dataset cannot be provided directly in this repository. However, instructions on how to obtain a similar dataset and preprocess it for model training are included in the code. Code Structure data/: Placeholder for the dataset. Instructions on how to structure and use your own dataset are provided in the README. models/: Implementation of each machine learning model used for document classification. notebooks/: Jupyter notebooks demonstrating the training, evaluation, and testing of the models. utils/: Utility functions and preprocessing scripts used in the project. requirements.txt: List of Python dependencies required to run the code. Usage Clone this repository. Install the necessary dependencies using pip install -r requirements.txt. Follow the instructions in the README and notebooks to train and evaluate the models. Results The performance metrics, confusion matrices, and evaluation results for each model are documented in the notebooks. License This project is licensed under the MIT License.

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