Topic: count-vectorizer Goto Github
Some thing interesting about count-vectorizer
Some thing interesting about count-vectorizer
count-vectorizer,This is a restaurant reviewer model which was bulit using the concept of NLP. It was built Jupyter notebook on python version 3.10.
User: abhijit460
count-vectorizer,Intent classification is the automatic categorization of text data based on customer goals. It is known to be a complex problem in NLP. Sequence Labelling aims to classify each token (word) in a class space C. This project addresses these two problem statements by covering the basic concepts of NLP to advanced ones. For instance, linguistics analysis of a certain corpus, static word embeddings, contextual word embeddings. machine learning, deep learning, transformers and BIO tagging. This project was developed as part of the major project in our NLP coursework for the Data Science Master's degree. All of the work starting from problem statement formulation to project proposal, data collection, preparation. analysis, modelling, feature engineering, presentation, research paper creation were done by two members in the group: myself and Abhisek Panigrahi (https://github.com/Abhisekgit1994).
User: abhijit57
count-vectorizer,A content-based books recommender system using cosine similarity on goodbooks-10k_books datasets
User: abhrojyoti2001
count-vectorizer,A spam email chacking system using the Complement-Naive-Bayes algorithm on SpamAssassin datasets
User: abhrojyoti2001
count-vectorizer,A content-based movie recommender system using cosine similarity on TMDB datasets
User: abhrojyoti2001
count-vectorizer,Twitter Sentiment Analysis Using InSet (Indonesia Sentiment Lexicon) and Random Forest Classifier
User: agushendra7
count-vectorizer,Twitter Sentiment Analysis Using Vader Lexicon and Random Forest Classifier
User: agushendra7
count-vectorizer,A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomialNB & GaussianNB to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.70% .
User: ambarish-224
count-vectorizer,A machine learning model that predicts tags for a given question and body.
User: ankit152
count-vectorizer,AI-powered classifier mobile app using NLP to spot fake job ads and protect users from online scams. Our system analyzes language patterns and leverages algorithms to create a safe and trustworthy job search experience.
User: annareddy1
count-vectorizer,The movie recommendation system is implemented using content based filtering
User: anshul21107
count-vectorizer,The scope of this project is to classify fake and true news. After performing an analysis on the dataset using two different vectorizers and two machine learning algorithms, the results are conveyed in the form of accuracy score and confusion matrices.
User: astonglen
count-vectorizer,A simple Sklearn based example to demonstrate the working of TF-IDF.
User: bhattbhavesh91
count-vectorizer,This is a NLP - Sentiment Analysis Project built using Bernoulli-Naive-Bayes Algorithm to Predict is the IMDB Movie Review is Positive or Negative.
User: datarohit
Home Page: https://www.kaggle.com/code/datarohitingole/movie-review-sentiment-analysis-naivebayes
count-vectorizer,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/
User: esharma3
count-vectorizer,To put a halt to the distribution of incorrect information from any online news outlet. Build an NLP Classifier that can identify news as Real or Fake.
User: gurujayanth48
Home Page: https://fakenews-analysis.herokuapp.com/
count-vectorizer,Text Mining project about Sentiment Analysis of Drugs Reviews.
User: helemanc
count-vectorizer,A Google Chrome Extension that estimates the Reliability, Polarity and Subjectivity of any news article on the web. It allows you to like/dislike any article and recommends you articles based on your choices.
User: jas-haria
Home Page: https://drive.google.com/file/d/1XVCdQv7KhQN1BbhRmKEd2rnzJtDBKGX0/view?usp=sharing
count-vectorizer,Application of Machine Learning Techniques for Text Classification and Topic Modelling on CrisisLexT26 dataset.
User: jeyadosstimothy
count-vectorizer,Built using Python, Streamlit, and NLTK, the Hate Speech Detection App employs a Decision Tree Classifier for identifying hate speech in text. It features real-time speech input, NLP preprocessing, and a user-friendly Streamlit interface, offering both visual and text-to-speech result presentation.
User: justmirr
count-vectorizer,Built a movie recommender system with Streamlit and deploy in Heroku Platform.
User: kamal2511
Home Page: https://movie-recommender-system-kamal.herokuapp.com/
count-vectorizer,Analyzing online Job Postings
User: kirtigupta10007
count-vectorizer,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 using LSTM and Word Embeddings to gain accuracy of 97.84%.
User: ksdkamesh99
count-vectorizer,Text Classification: Predicting product categories from their text descriptions.
User: kvarun07
count-vectorizer,Kaggle Competition - Natural Language Processing with Disaster Tweets
User: mahalavanyasriram
count-vectorizer,MediaEval challenge 2019 - to predict the memorability of the Videos
User: mrraghav
count-vectorizer,A machine learning system that takes a comment and classifies it as offensive or non-offensive (neutral). This system will be trained in a data set with comments in which the tags (insult or non-insult) are known. Classification algorithms used: Naive Bayes, SVM, Random Forest.
User: myrto-iglezou
count-vectorizer,We aim to predict whether a given review is positive or negative, through sentiment analysis
User: nikhileshgarnepudy
Home Page: https://medium.com/analytics-vidhya/your-first-simple-machine-learning-project-f1d427c61760
count-vectorizer,my exercises of course natural language processing datacamp
User: nourshosharah
count-vectorizer,Movie genre classification in NLP using multinomial navie bayes classification and linear support vector classification.
User: raghuls-github
count-vectorizer,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.
User: raj1603chdry
count-vectorizer,Movie Recommendation - provides user with the top choices of movie he/she wanted to watch based on their current choice
User: ritika-0111
count-vectorizer,Scrapped tweets using twitter API (for keyword βNetflixβ) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard
User: rochitasundar
count-vectorizer,Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.
User: roshansridhar
count-vectorizer,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.
User: samimakhan
count-vectorizer,Short Stories Recommendations.
User: sandeepurankar
count-vectorizer,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 400 visitors!
User: sannketnikam
Home Page: https://emotion-detection-in-text.streamlit.app
count-vectorizer,Classifying a tweet as positive, neutral, or negative sentiment using Natural Language Processing (CBOW approaches) and Traditional Machine Learning Algorithms.
User: sarthak-mohapatra
count-vectorizer,This project involves detecting fake news using a decision tree classifier in Jupyter Notebook. Fake news detection is an important task in the field of natural language processing and machine learning, as it helps identify and filter out misleading or false information.
User: shaadclt
count-vectorizer,Detecting the Fake News using Count Vectorizer and Tfidf Vectorizer
User: shaheer-khan-github
count-vectorizer,Perform sentimental analysis on the Elon-musk tweets and Extract reviews of any product from ecommerce website like amazon, Perform emotion mining.
User: shanuhalli
count-vectorizer,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.
User: shanuhalli
count-vectorizer,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
User: sharmaroshan
count-vectorizer,Fake news detection
User: shreyadhananjay
count-vectorizer,This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
User: shreyans29
Home Page: https://www.youtube.com/c/thesemicolon
count-vectorizer,Natural Language Processing Recipes
User: shubhamchouksey
count-vectorizer,:syringe: Vaccine Sentiment Classifier is a deep learning classifier trained on real world twitter data, that distinguishes 3 types of tweets: Neutral, Anti-vax & Pro-vax.
User: spchalk
count-vectorizer,In this repo I have develop a Sentiment Analysis of Restaurant Reviews project in machine learning using NLP. In this dataset, there are reviews from the customers which are either positive or negative. And now we are going to build a machine learning model using Count Vectorizer method. And finally, this model is going to predict whether the given review is either positive or negative.
User: vijaymahajan2312
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