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README.md
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# SVM Model with TF-IDF
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Step by step instruction:
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1. install required packages:
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<br>Before running the code, install
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```python
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import nltk
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nltk.download('stopwords')
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from sklearn.svm import SVC
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```
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2.
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```python
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# SVM Model with TF-IDF
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Step by step instruction:
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1. install required packages:
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<br>Before running the code, install required packages.
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```python
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pip install nltk beautifulsoup4 scikit-learn pandas
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```
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<br> Download necessary packages.
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```python
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import nltk
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nltk.download('stopwords')
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from sklearn.svm import SVC
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```
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2. File Description
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- config.json: Configuration file for model and dataset parameters.
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- ml.py: Python script containing the machine learning pipeline.
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- model.pkl: Trained SVM model saved as a pickle file.
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- tfidf.pkl: TF-IDF vectorizer saved as a pickle file.
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- README.md: Documentation for the repository.
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3. Data Cleaning
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<br> The clean() function performs data preprocessing to prepare the input data for training. This includes:
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- Removing HTML tags using BeautifulSoup.
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- Removing non-alphanumeric characters and extra spaces.
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- Converting text to lowercase.
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- Removing stopwords using NLTK.
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- Lemmatizing words using WordNetLemmatizer.
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```python
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