| # SVM Model with TF-IDF | |
| Step by step instruction: | |
| ## Installation | |
| <br>Before running the code, ensure you have all the required libraries installed: | |
| ```python | |
| pip install nltk beautifulsoup4 scikit-learn pandas | |
| ``` | |
| <br> Download necessary NTLK resources for preprocessing. | |
| ```python | |
| import nltk | |
| nltk.download('stopwords') | |
| nltk.download('wordnet') | |
| ``` | |
| # How to Use: | |
| 1. Pre-Trained Model and Vectorizer | |
| <br> The repository includes: | |
| - model.pkl : The pre-trained SVM model | |
| - tfidf.pkl: The saved TF-IDF vectorizer used to transform the text data. | |
| 2. Testing a new dataset | |
| <br> To test the model with the new dataset, follow these steps: | |
| - Step 1: Prepare the dataset: | |
| <br> 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 | |
| <br>Use the clean() function from data_cleaning.py to preprocess the text data: | |
| ```python | |
| 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 | |