This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
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Updated
Sep 16, 2020 - Jupyter Notebook
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization
NLP based Classification Model that predicts a person's personality type as one of the 16 Myers Briggs personality types. Extremely challenging project dealing with correlation between human psychology and casual writing styles and handling heavily imbalanced classes. Check the app here - https://mb-predictor-motetuzs5q-uc.a.run.app/
Fake News Detection System for detecting whether news is fake or not. The model is trained using "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Link for dataset: https://arxiv.org/abs/1705.00648.
Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.
Built a movie recommender system with Streamlit and deploy in Heroku Platform.
my exercises of course natural language processing datacamp
A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented usi…
Analyzing online Job Postings
Spam Classifier project for my end-of-semester project for Intro to AI class. We were a group of four people. I worked on all the Naive Bayes models.
Text Mining project about Sentiment Analysis of Drugs Reviews.
Bank Reviews-Complaints Analysis
This project employs emotion detection in textual data, specifically trained on Twitter data comprising tweets labeled with corresponding emotions. It seamlessly takes text inputs and provides the most fitting emotion assigned to it. This app has more than 370 visitors!
Kaggle Competition - Natural Language Processing with Disaster Tweets
Content-based Recommender System with Natural Language Processing using TF-IDF Vectorizer, Count Vectorizer and KNN.
Natural Language Processing Recipes
A simple Sklearn based example to demonstrate the working of TF-IDF.
Twitter US Airline Sentiment
Movie Recommendation - provides user with the top choices of movie he/she wanted to watch based on their current choice
The document classification solution should significantly reduce the manual human effort in the HRM. It should achieve a higher level of accuracy and automation with minimal human intervention.
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