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Browse files- .gitattributes +1 -0
- README.me +23 -0
- data.csv +0 -0
- glove.6B.100d.txt +3 -0
- model.ipynb +0 -0
- naive_bayes_model.pkl +3 -0
- vectorizer_bong.pkl +3 -0
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README.me
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Naive Bayes Text Classification with Pre-trained Model
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This project demonstrates how to use a pre-trained Naive Bayes model and vectorizer for text classification. It includes data preprocessing, text vectorization, and evaluation of the model's accuracy on a given dataset.
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Prerequisites
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Make sure you have the following installed:
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Python 3.7 or later
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Required Python libraries:
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pandas
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nltk
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scikit-learn
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joblib
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To install the necessary libraries, run: pip install pandas scikit-learn nltk joblib
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The input data should be a CSV file (data.csv) located in the ./data directory. The file must include the following columns:
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title: The text data to classify.
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news: The target label, where fox will be encoded as 1 and all other values as 0.
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Place your dataset in a CSV file named data.csv under the ./data directory.
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Ensure it has the required columns (title and news).
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open the jupyternotebook and run the Prediction section in beginning, the model will predict and compare the result with true answer, and accuracy score is printed.
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data.csv
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glove.6B.100d.txt
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version https://git-lfs.github.com/spec/v1
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oid sha256:95dde4dfd627ab26608d33e76d1195ec059734bd29089ea52cadb08d07c64544
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size 347116733
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model.ipynb
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naive_bayes_model.pkl
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version https://git-lfs.github.com/spec/v1
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size 951671
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vectorizer_bong.pkl
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version https://git-lfs.github.com/spec/v1
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