JeffreyYAJ/African-news-classifier

categorize news articles relevant to the Central African region # African News Classifier ## Project Overview This project implements an **end-to-end Machine Learning pipeline** to categorize news articles relevant to the Central African region (in this case Cameroon) into distinct classes: *Politics, Economy, Sports, ...*. This project shows a complete **Data Engineering** workflow, from scraping real-time data using google `gnews` to training a discriminative model using TF-IDF vectorization. ## The Pipeline ### 1. Data Collection Data was obtained using the Google News API, targeting specific queries to build a localized dataset. - **Source:** Real-time news aggregation. - **Context:** Focus on Cameroonian keywords (e.g., *"Lions Indomptables"*, *"Économie Cameroun", ...*). ### 2. Preprocessing To reduce noise and dimensionality in the dataset, the raw text undergoes cleaning using `NLTK`: - **Normalization:** Text lowercasing and punctuation removal. - **Stop-words Removal:** Filtering out common French words to focus on semantic content. - **Tokenization:** Splitting text into individual semantic units. ### 3. Mathematical Modeling (TF-IDF) Text data is converted into numerical vectors using **Term Frequency-Inverse Document Frequency**, `TF-IDF`. This statistical measure evaluates how relevant a word is to a document in a collection and classification is performed. $$w_{i,j} = tf_{i,j} \times \log\left(\frac{N}{df_i}\right)$$ *Where $N$ is the total number of articles and $df_i$ is the number of articles containing term $i$.* ## Results & Performance The model was evaluated on a test set and produced the following. ### Confusion Matrix The matrix shows that the model effectively distinguishes 'Sports' from 'Politics', though some overlap exists between 'Economy' and 'Politics' due to factors like shared vocabulary.* ## How to Run 1. **Clone the repository:** ```bash git clone github.com cd African-news-classifier ``` 2. **Install dependencies:** ```bash …