SVM Model with TF-IDF

Step by step instruction:

Installation

Before running the code, ensure you have all the required libraries installed:

pip install nltk beautifulsoup4 scikit-learn pandas

Download necessary NTLK resources for preprocessing.

import nltk
nltk.download('stopwords')
nltk.download('wordnet')

How to Use:

Pre-Trained Model and Vectorizer
The repository includes:

model.pkl : The pre-trained SVM model
tfidf.pkl: The saved TF-IDF vectorizer used to transform the text data.

Testing a new dataset
To test the model with the new dataset, follow these steps:

Step 1: Prepare the dataset:
Ensure the dataset is in CVS format and has three columns: title, outlet and labels. title column containing the text data to be classified.
Step 2: Preprocess the Data
Use the clean() function from data_cleaning.py to preprocess the text data:

from data_cleaning import clean
import pandas as pd

# Load your data
df = pd.read_csv('test_data_random_subset.csv')

# Clean the data
cleaned_df = clean(df)

Step 3: Load the pre-trained model and TF-IDF Vectorizer