Problem Statement
In our daily lives, we encounter news through various channels. However, distinguishing between authentic and fabricated news can be challenging. The objective is to develop a predictive model that determines the authenticity of a given news article.
Project Topics:
- Data Collection
- Data Preparation: This involves several steps such as tokenization, converting to lowercase, removing stopwords, and lemmatization/stemming.
- Vectorization: Converting text data into numerical vectors using techniques like Bag of Words (CountVectorizer) and TF-IDF.
- Model Creation: Initializing a model object and training/testing the model.
- Model Evaluation: Assessing the model using metrics like accuracy score, confusion matrix, and classification report.
- Model Deployment
- Predicting on Client Data
Technology Stack Used:
- Python
- Natural Language Processing (NLP)
- Machine Learning Algorithms