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š° Fake News Detector ā NLP & ML Web Application (Python | Streamlit)
I developed a Fake News Detection system that identifies whether a news article is real or fake using Natural Language Processing (NLP) and machine learning classification techniques. The project analyzes textual patterns, word usage, and linguistic features to make accurate predictions.
š¹ Project Features
š§ NLP-based text preprocessing and feature extraction
š¤ Classification model built using scikit-learn
š° Detects and classifies news as Fake or Real
š Interactive web application developed with Streamlit
š¹ Technology Stack
Python ā Core implementation
scikit-learn ā Machine learning & classification algorithms
NLP techniques ā Text cleaning, tokenization, vectorization
Streamlit ā Web app development and deployment
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This project is an in-depth Strategic & Growth Analysis of retail business performance as a global market leader. The main focus is to evaluate how digital technology integration, e-commerce innovation, and supply chain optimization contribute to the company's financial health amid a competitive market dynamic.
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Nice breakdown of the retail growth drivers. The connection between e-commerce expansion and supply chain optimization is particularly interesting. Visualizing operational efficiency alongside revenue trends adds strong strategic context.
I have completed the E commerce sales forecasting project about which i posted yesterday.
Developed it using Python and some of its libraries like Scikit-Learn, Pandas, etc. Deployed it on the streamlit cloud through which anybody can use it.
In this you have to fill in some of the details about the product like product category, price, discount percentage, etc. After getting this details the model will then predict the sales according to the details.
The Accuracy of the model is approx 96% which makes it a very trust worthy model for your E commerce business.
machinelearningdataanalyticspredictiveanalyticsStreamlitData EngineeringData ScienceMachine Learning EngineeringPythonscikit-learn
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