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# SVM Model with TF-IDF
This repository provides a pre-trained Support Vector Machine (SVM) model for text classification using Term Frequency-Inverse Document Frequency (TF-IDF). The repository also includes utilities for data preprocessing and feature extraction:
## Start:
<br>Open your terminal.
<br> Clone the repo by using the following command:
```
git clone https://huggingface.co/CIS5190abcd/svm
```
<br> Go to the svm directory using following command:
```
cd svm
```
<br> Run ```ls``` to check the files inside svm folder. Make sure ```tfidf.py```, ```svm.py``` and ```data_cleaning.py``` are existing in this directory. If not, run the folloing commands:
```
git checkout origin/main -- tfidf.py
git checkout origin/main -- svm.py
git checkout origin/main -- data_cleaning.py
```
<br> Rerun ```ls```, double check all the required files are existing. Should look like this:
<br> keep inside the svm directory until ends.
## Installation
<br>Before running the code, ensure you have all the required libraries installed:
```python
pip install nltk beautifulsoup4 scikit-learn pandas datasets
```
<br> Download necessary NTLK resources for preprocessing.
```
python
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
```
<br> After downloading all the required packages,
```
exit()
```
## How to use:
Training a new dataset with existing SVM model, follow the steps below：
- Clean the Dataset
```python
from data_cleaning import clean
import pandas as pd
import nltk
nltk.download('stopwords')
```
<br> You can replace with any datasets you want by changing the file name inside ```pd.read_csv()```.
```
# Load your data
df = pd.read_csv("hf://datasets/CIS5190abcd/headlines_test/test_cleaned_headlines.csv")
# Clean the data
cleaned_df = clean(df)
```
- Extract TF-IDF Features
```python
from tfidf import tfidf
# Transform the cleaned dataset
X_new_tfidf = tfidf.transform(cleaned_df['title'])
```
- Make Predictions
```python
from svm import svm_model
# Make predictions
predictions = svm_model.predict(X_new_tfidf)
```
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