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| Publish by | RUPESH SAHU |
| Project Name | Fake news detection by ML |
| Upload Date | February 10, 2023 |
| Platform | Python |
| Programming Language | html, css, javascript |
| Database | excel file |
| Front end | |
| Back end | |
| Project Type | web Application |
| View | 2778 |
The basic approach to fake news detection with ML involves collecting a large dataset of news articles, labeling them as true or false, and then training a machine learning model on this labeled data. The model can then be used to predict whether a new article is real or fake based on its features, such as word usage, writing style, and source credibility.
This project is a text-based code-guessing game built efficiently using only basic lists and tuples. Core Features & Functionality Hint System: Reveals a customizable portion of the secret code to assist the player. Smart Evaluation: Compares guesses against the secret code to track exact position matches and correct digits in the wrong po ... [ Download Source Code ]
A console-based game where the player must manage resources such as water, camel fatigue, and distance traveled, while evading pursuers in a harsh desert environment. It also has feture of finding OASIS(good luck) or SANDSTROM(bad luck) ... [ Download Source Code ]
Doctor Finder System is a web application developed using Django and SQLite that allows users to search doctors based on specialization, location, and availability. The application provides doctor profiles, appointment information, and hospital details through an easy-to-use interface. It follows Django's MVT architecture and includes responsive pa ... [ Download Source Code ]
Query any SQLite database using plain English with no SQL knowledge required. The agent automatically discovers the schema, translates plain English questions into SQL queries, executes them and returns human readable answers. Includes auto repair for broken queries. Features include automatic schema discovery, natural language to SQL conversion ... [ Download Source Code ]
A document summarization pipeline capable of handling texts of any length using map-reduce strategy. Long documents are split into chunks, each chunk is summarized independently then all summaries are combined into a final output. Works on books, reports and transcripts. Features include three summary modes Brief, Detailed and Bullet Points, map ... [ Download Source Code ]