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Make AI model detected Fake NEWS
Generate an AI market research report
This a project which predicts the stock price of Tesla for a given time period & based upon the previous 10 years of historical data. Here, numerical and sentimental analysis is performed with the help of natural language toolkit (NLKT), Textblob, sklearn etc. By observing the previous trends of the market stock price and sentiments of the news about the market, this model predicted the future variation of the stock market price of TELSA. Basically, four models were trained with the same dataset like: Random Forest, AdaBoost, LGBM & xgboost out of these model LGBM model predicted with the least mean squared error value.
This repositories contains all the materials and the supports used to perform a Sentiment Analysis Classification on Twitter's tweets. This project was part of a competion of the Data Science Lab course - Politecnico di Torino.
Using Machine Learning user can enter tv show or movie description to predict that description’s rating & OMDB genre
A sentimental analysis of data from Twitter regarding customer sentiment for 6 US airlines: American, Delta, Southwest Airlines, United, US Airways, and Virgin America. Then use Tensor Flow to predict the chance of a tweet to be positive, negative, or neutral.
Series of experiment sessions using NLP, Google Cloud Vision and AWS
Scripts used to solve exercises in Data Science course with Python.
Performance a Pipelines, grid search and text mining. Let's start with several basic exercises.
DS Practice
In this repository I explained the basics of Natural Language Toolkit.
NLP Sentiment Analysis Document Scoring Method