countvectorizer | Topic | Ecosyste.ms: Repos

Topic: "countvectorizer"

melodygr/grocery_recommendation

Grocery Recommendation on Instacart Data

Language: Jupyter Notebook - Size: 190 MB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 16 - Forks: 14

tamanna18/ML-NLP-DL

For learning Purposes

Language: Jupyter Notebook - Size: 6.25 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 16 - Forks: 0

garg-priya-creator/Netflix-Recommendation-System

A web-app which can be used to get recommendations for a series/movie, the app recommends a list of media according to list of entered choices of movies/series in your preferred language using Python and Flask for backend and HTML, CSS and JavaScript for frontend.

Language: Jupyter Notebook - Size: 12.3 MB - Last synced at: 10 months ago - Pushed at: almost 3 years ago - Stars: 8 - Forks: 8

vaitybharati/Assignment-11-Text-Mining-01-Elon-Musk

Assignment-11-Text-Mining-01-Elon-Musk, Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv), Text Preprocessing: remove both the leading and the trailing characters, removes empty strings, because they are considered in Python as False, Joining the list into one string/text, Remove Twitter username handles from a given twitter text. (Removes @usernames), Again Joining the list into one string/text, Remove Punctuation, Remove https or url within text, Converting into Text Tokens, Tokenization, Remove Stopwords, Normalize the data, Stemming (Optional), Lemmatization, Feature Extraction, Using BoW CountVectorizer, CountVectorizer with N-grams (Bigrams & Trigrams), TF-IDF Vectorizer, Generate Word Cloud, Named Entity Recognition (NER), Emotion Mining - Sentiment Analysis.

Language: Jupyter Notebook - Size: 1.34 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 3

binnazcabuk/Sentiment-Analysis-in-Turkish-Film

Graduation Project/Sentiment Analysis in Turkish Film Reviews

Language: Python - Size: 3.43 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 5 - Forks: 2

Bhavik-Ardeshna/Spam_SMS_Detection

Spam message detection using classifier

Language: Jupyter Notebook - Size: 226 KB - Last synced at: almost 2 years ago - Pushed at: about 4 years ago - Stars: 5 - Forks: 0

Kawaljeet2001/Movie-Recommendation-System

This is the Movie Recommendation System project using a Content-Based recommender system trained on more than 5000 movies for generating movie recommendations based on user search.

Language: Jupyter Notebook - Size: 31.5 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 4

Bakar31/Resume-Match-NLP

Hire the Perfect candidate. HackerEarth Competitions solution.

Language: Python - Size: 3.87 MB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 1

mandar196/Hate_Speech_Detection-NLP

Created Hate speech detection model using Count Vectorizer & XGBoost Classifier with an Accuracy upto 0.9471, which can be used to predict tweets which are hate or non-hate.

Language: Jupyter Notebook - Size: 27.3 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 3

charumakhijani/fake-and-real-news-detection

Language: Jupyter Notebook - Size: 1.29 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 0

abhi7585/Movie-Recommendation-System

Using content-based approach to construct a suggestion for films. Films based on user feedback are recommended. By the machine learning model, all connected and equivalent films are suggested for the consumer.

Language: Jupyter Notebook - Size: 5.95 MB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 3 - Forks: 0

pleonova/jd-classifier

What is the difference between a data scientist and a data analyst? An NLP approach.

Language: Python - Size: 4.37 MB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 3 - Forks: 1

avannaldas/EmailsClassification

Classification of emails received on a mass distribution group

Language: Jupyter Notebook - Size: 25.4 KB - Last synced at: over 1 year ago - Pushed at: almost 8 years ago - Stars: 3 - Forks: 6

Chandrakant817/Semantic-Analysis-of-Restaurant-Reviews

Semantic Analysis of Restaurant Reviews (NLP Use Case)

Language: C - Size: 20.4 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 2 - Forks: 0

rimmelasghar/Language-Detector-Model_Django

A Machine Learning Model that detects different language syntax.

Language: Python - Size: 5.44 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

LunaticPrakash/Movie-Recommender

This project suggests you the list of movies based on the movie title that you have entered. It uses Count Vectorizer (Text-Feature Extraction tool) to find the relation between similar movies.

Language: Jupyter Notebook - Size: 8.71 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 1

watcharap0n/m-business

linebot messengerbot @mango by fastapi

Language: Jupyter Notebook - Size: 6.53 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

buketgencaydin/Dynamic-Malware-Modelling

Malware classification using Extreme Gradient Boosting - XGBoost, CountVectorizer, TruncatedSVD

Language: Jupyter Notebook - Size: 5.38 MB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 2 - Forks: 0

SudhanshuBlaze/Email-SMS-spam-detection

Used NLTK library from text pre-processing, Data Visualisation and Analysis done with matplotlib, used sklearn CountVectorizer and Tfidf transformer for feature extraction from text, then used Linear SVC algorithm to train the ML model. Got 99% accuracy.

Language: Jupyter Notebook - Size: 405 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 1

samarth0174/SMS-SPAM-FILTERING

SMS SPAM FILTERING

Language: Jupyter Notebook - Size: 514 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0

JUGG097/Kaggle-NLP-Competition-Real-or-Not

This competition is hosted by Kaggle https://www.kaggle.com/c/nlp-getting-started/overview. I participated in the competition in order to try my hands on the field of Artificial Intelligence known as Natural Language Processing.

Language: Jupyter Notebook - Size: 1.11 MB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 2 - Forks: 1

cltai9145/Multilabel-Text-Classification

Predicting Tags for Stack Overflow

Language: HTML - Size: 221 KB - Last synced at: 11 months ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 1

Aman0807/NLP-in-Python-

Silicon Valley (TV Show on HBO) language analysis

Language: Jupyter Notebook - Size: 1.21 MB - Last synced at: 12 months ago - Pushed at: almost 6 years ago - Stars: 2 - Forks: 1

gabrielpreda/Support-Tickets-Classification Fork of imironica/Support-Tickets-Classification

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

Language: Python - Size: 5.62 MB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 2 - Forks: 5

mahnoorsheikh16/NLP-Approach-to-AI-Text-Classification Fork of andrew-jxhn/STT811_StatsProject

Language: Jupyter Notebook - Size: 40.7 MB - Last synced at: 10 days ago - Pushed at: 10 days ago - Stars: 1 - Forks: 0

hperer02/Automated-essay-scoring

This repository contains my solution for the Kaggle competition Automated Essay Scoring 2.0. The goal of this project is to develop an automated system capable of scoring essays based on their content and quality using machine learning techniques.

Language: Jupyter Notebook - Size: 24.4 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

5hraddha/sentiment-analysis

An innovative system for filtering and categorizing movie reviews

Language: Jupyter Notebook - Size: 23.8 MB - Last synced at: about 1 month ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0

adelelwan24/Arabic-Dialect-Classification

Many countries speak Arabic; however, each country has its own dialect, the aim of this project is to build a model that predicts the dialect given the text.

Language: Jupyter Notebook - Size: 46.4 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 1

anujahlawat/project2-email-sms-text-spam-classifier

Language: Jupyter Notebook - Size: 838 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

SANJAIKUMAR-28/Sentimental-Analysis

This repository explores the world of restaurant reviews, using Support Vector Machines (SVM) and CountVectorizer to predict the sentiment (positive or negative) expressed in each review. By analyzing textual data, we aim to provide valuable insights for restaurants and improve the overall customer experience.

Language: Jupyter Notebook - Size: 14.6 KB - Last synced at: 4 days ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

Prakriti0501/Fake-News-Prediction

Text classification using various techniques, including Naive Bayes and Passive Aggressive classifiers, along with different vectorization methods such as Count Vectorization

Language: Python - Size: 3.91 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

Kool-Cool/Kool-Cool-Movie-Recommendations-Flask

The provided code snippet performs movie recommendation based on movie metadata using the TMDB Movie Metadata dataset from Kaggle.

Language: Python - Size: 95.7 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

bathinamahesh/Movie_Recommendation_System

Movie Recommendation System powered by Python machine learning algorithms and the Streamlit framework. Get personalized movie suggestions based on your viewing history and preferences, all at the click of a button. Streamline your movie selection process today and enjoy a stress-free movie night every time.

Language: Jupyter Notebook - Size: 3.04 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

EgorovYuriy/Yandex.Practicum_Data_Science_Projects

Language: Jupyter Notebook - Size: 1.43 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

chey97/ProteinSeq_Classifier

Proteins have different family types, this modal determine a protein's family type based on sequence. Inspired by search engines such as BLAST which has this capability, but it want to try out and see if a machine learning approach can do a good job in classifying a protein's family based on the protein sequence.

Language: Python - Size: 5.22 MB - Last synced at: about 1 year ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

elifftosunn/Bert-Bank-Model

It is a Turkish BERT-based model that will analyze people's bank complaints and classify them according to one of eight categories.

Language: Jupyter Notebook - Size: 5.23 MB - Last synced at: about 2 months ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

CS-Ponkoj/Fake-News-Detection-NLP

20800 train and 5200 test news dataset used to classify the fake and real news using Count Vectorizer and TF-IDF. Seven ML algorithms are applied to find the best model for the dataset.

Language: Jupyter Notebook - Size: 45.9 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 1

EricPaul075/OCP6-Consumer-goods-automatic-classification-with-NLP-and-CNN

Unsupervised classification of products based on their text description (NLP) or image (computer vision)

Language: Jupyter Notebook - Size: 3.02 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

Nesreen1999/SMS-Spam-Classification

Detect Spam SMS using supervised Learning Models

Language: Jupyter Notebook - Size: 6.84 KB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

tonoy30/movie-recommender

A movie recommender system based on Content-Based Filtering using tmdb dataset

Language: Jupyter Notebook - Size: 18.6 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

sushantlokhande14/MovieRecommendationUsingML

This Machine learning powered Recommendation Engine suggests Movies for a user based on the user's past intrests by content based filtering. In this ML model the attributes of movies like genres , cast , director , description are taken into consideration while being converted into vector format. The cosine distance is found between the vectors to find the most similar movies based on the user's input . The dataset used is TMDB_5000 Movies dataset. The framework is made using streamlit.

Language: Python - Size: 46.3 MB - Last synced at: 5 months ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

Tanjib-Rafi/Spam-SMS-Detection

Language: Jupyter Notebook - Size: 102 KB - Last synced at: 3 months ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

VivanVatsa/DNA-sequencing-NLP-machinelearning-project

DNA sequencing using NLP (Natural Language Processing)

Language: Jupyter Notebook - Size: 1.99 MB - Last synced at: 6 months ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

vaitybharati/Assignment-11-Text-Mining-02-Amazon-Product-Reviews

NLP: Sentiment Analysis or Emotion Mining on Amazon Product Reviews - Part-1. Let’s learn the NLP techniques to perform Sentiment Analysis or Emotion Mining on extracted Product Reviews from Amazon. Part-1 covers Text preprocessing and Feature extraction, the next part covers Sentiment Analysis or Emotion Mining on text corpus. https://medium.com/@vaitybharati/nlp-sentiment-analysis-or-emotion-mining-on-amazon-product-reviews-part-1-428d43112027

Language: Jupyter Notebook - Size: 1.29 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 2

NajiAboo/TextClassification

Build custom vacab, Ham /Spam using tfidf , Movie review classification using TFIDF

Language: Jupyter Notebook - Size: 5.11 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 1

1620mansi/Movie-Recommender-System

A content based movie recommender system using cosine similarity on TMDB datasets.

Language: Jupyter Notebook - Size: 1.14 MB - Last synced at: 9 months ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

TangoJP/rust_vectorizer

Practice Rust by making Vectorizer

Language: Rust - Size: 58.6 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 2

Zauverer/Test-TSA-Models

Twitter Sentiment Analisys, comparing different models

Language: Jupyter Notebook - Size: 2.9 MB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

LenSin3/Conservatory-CovidTweetsSentiments

Develop Machine Learning models to predict sentiments on COVID-19 tweets.

Language: Jupyter Notebook - Size: 85.5 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0

vaitybharati/Reviews_Classification_Naive_Bayes

Data Cleaning, N-gram, WordCloud, Applying naive bayes for classification, Using TFIDF

Language: Jupyter Notebook - Size: 4.88 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

vaitybharati/Named_Entity_Recognition_Emotion_Mining

Named Entity Recognition , Emotion Mining in Python

Language: Jupyter Notebook - Size: 3.91 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

kalideir/Resume-NLP

To what extent a resume is matching a job add requirements, description? What are the most similar applications?

Language: Jupyter Notebook - Size: 13.3 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 1

geekquad/Text-Learning

Basic usage of NLTK. Implementation of concepts like Stemmer, TfIdf, and text.CountVectors

Language: Jupyter Notebook - Size: 6.84 KB - Last synced at: 3 months ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 0

mb11797/captionate Fork of IOSD/captionate

Captionate - Image Captioning Toolkit

Language: Python - Size: 19.5 KB - Last synced at: about 1 year ago - Pushed at: almost 7 years ago - Stars: 1 - Forks: 0

Amol873/steam-judger

Steam 游戏库终极审判，AI 法官在此！

Language: Vue - Size: 208 KB - Last synced at: 2 days ago - Pushed at: 2 days ago - Stars: 0 - Forks: 0

maheshvarade/Enron-Spam-Detection-using-NLP-ML

This project leverages the Enron email dataset to build a spam detection model using classical machine learning techniques. The model processes and classifies emails based on their subject lines and message bodies, with a final accuracy of 90–91% using Logistic Regression and Multinomial Naive Bayes classifiers.

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

dhruvv1402/Spam-Detection-Python-

This project is a Spam Detection System built using Python. It classifies SMS messages as spam or ham (not spam) using machine learning techniques.

Language: Python - Size: 209 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

CheriCoder/MusicRecommendationSystem

Mini project for content-based music recommendation based on user-specific classification problem.

Language: Jupyter Notebook - Size: 4.04 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

RahulRmCoder/CraveCrafters-Food-App

CraveCrafters is an AI-powered food ordering web application that seamlessly integrates a chatbot to assist users with menu browsing, order placement, and customer service. The system features a Node.js backend, a FastAPI-based chatbot, and an interactive frontend built with HTML, CSS, and JavaScript. It utilizes MongoDB as its database.

Language: HTML - Size: 91.9 MB - Last synced at: about 2 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

PMDOUGLAS23/Customer-Reviews-Analysis

Data Science Project : Supply Chain - Satisfaction des clients. Prédire la satisfaction client à partir des commentaires. De ces commentaires, identifier les catégories de sujets problématiques A partir des commentaires clients, être capable d’automatiser une réponse Détection du sentiment client : positif, neutre ou négatif

Language: Jupyter Notebook - Size: 54 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Nikulsuthar2/Chatbot-Flask-PHP-MySQL

A simple chatbot in PHP, MySQL, Flask API and Python CountVectorizer

Language: PHP - Size: 322 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

munavarhs/AnalysisOfJakeAndHyde

Analysis of Jake and Hyde(Contrasting communities)

Language: Jupyter Notebook - Size: 10.7 KB - Last synced at: 3 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

SudarshanC00/Movie-Recommendation-System

This project is a content-based movie recommendation system built using Python and Streamlit. The system suggests movies similar to a user-selected movie by analyzing plot descriptions and using machine learning techniques like cosine similarity.

Language: Jupyter Notebook - Size: 1.04 MB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

Parag000/Content-Based-Movie-Recommender

This project builds a content-based movie recommendation system using the TMDB dataset. By combining metadata features like cast, genres, and directors into a "metadata soup," it calculates movie similarity with vectorizers (Count) and cosine similarity. Ideal for learning content-based filtering and text vectorization techniques.

Language: Jupyter Notebook - Size: 88.9 KB - Last synced at: about 2 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

petroritse1/topic_modeeling

This project aims to explore the underlying topics within Reddit discussions using advanced natural language processing techniques. By applying topic modeling algorithms like Latent Dirichlet Allocation (LDA), we can identify the dominant themes and patterns in large-scale Reddit datasets.

Language: Python - Size: 4.57 MB - Last synced at: 6 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

RedInfinityPro/RedditSuggestor

Rating: (6/10) The project uses Python libraries and APIs to analyze Reddit data, predict user input, suggest new titles based on cosine similarity, calculate combined scores, and output the best suggestion.

Language: Python - Size: 41 KB - Last synced at: 1 day ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

sanjanahombal/Sentiment-Analysis-using-Neural-Networks

This project explores sentiment analysis using neural networks

Language: Jupyter Notebook - Size: 7.03 MB - Last synced at: about 2 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

abdulrabbanisyed/Content-Based-Recommended-System

Recommendation System

Language: Jupyter Notebook - Size: 26.4 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

Meenu00615/Movie-Recommendation-System

Movies Recommendation using Machine Learning and Applied Python popular Streamlit library to simplifies the process of web application for data science and machine learning projects

Language: Jupyter Notebook - Size: 3.21 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

ESMAaN/Amazon_Product_Reviews

Amazon Product Reviews: Sentiment Analysis with NLP

Language: Jupyter Notebook - Size: 952 KB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

umarshahzadumar91/Movie_recommendation-system

movie recommendation systems aim to predict and suggest movies that align with individual user preferences.

Language: Jupyter Notebook - Size: 1.8 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

hugohiraoka/Airline_Tweets_Sentiment_Analysis

A Natural Language Processing model to perform Sentiment Analysis of US Airline Customers

Language: HTML - Size: 22.9 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

vn33/Intensity-Analysis-EmotionClassification

Predict emotions (happiness, anger, sadness) from WhatsApp chat data using machine learning and deep learning models. Includes text normalization, vectorization (TF-IDF, BoW, Word2Vec, GloVe), and model evaluation.

Language: Jupyter Notebook - Size: 3.57 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

tgchacko/Movie-Recommender

Movie Recommender

Language: Jupyter Notebook - Size: 27.3 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

tgchacko/Sentiment-Analysis

Sentiment Analysis of Reviews using Python

Language: Jupyter Notebook - Size: 3.3 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

OmPreetham/nlp-sentiment-analysis-document-scoring-method

NLP Sentiment Analysis Document Scoring Method

Language: Python - Size: 2.76 MB - Last synced at: 2 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

Gopalkholade/Language-Detection

Language-Detection

Language: Jupyter Notebook - Size: 1.56 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

Tech-Guyy/Text-processing-topic-labelling

The author implemented logistic regression and support vector machine for topic labelling and applied two feature extractions, Bag-of-Words (CountVectorizer) and TF-IDF (TfidfVectorizer), after which the results for both methods were analyzed.

Language: Jupyter Notebook - Size: 197 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

HayatiYrtgl/flm_reviews_analysis

Sentiment analysis using ML classifiers for text data.

Language: Jupyter Notebook - Size: 520 KB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

MuskanRaisinghani23/Movie-Recommendation-System

The project aims to create movie recommendation system with algorithms, including content-based, popularity-based, and collaborative filtering methods. Data of over 4800 movies is used.

Language: Jupyter Notebook - Size: 9.63 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

muspriandi/TextMining-SentimentAnalysis

Tugas Akhir - Mus Priandi

Language: JavaScript - Size: 266 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

JasmeetSingh7314/Recommendation-System

Recommendation System for games done using python and written in jupyter notebook.

Language: Jupyter Notebook - Size: 373 KB - Last synced at: about 2 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

Prateshmishra/Anime_Recommendation_system

It is Streamlit app which is designed to recommend similar anime based on user given anime.

Language: Jupyter Notebook - Size: 673 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

Blitz464/Call-Transcript-classification

This was a hackathon project that I worked on for BestBuy around classifying the call transcripts using ML & NLP techniques

Language: Jupyter Notebook - Size: 10.7 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

karl0706/Essay-quality-prediction

The goal is to create a model predicting the grade of an essay

Language: Jupyter Notebook - Size: 8.49 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

tharangachaminda/content_based_recommender_system

This Python project shows how to build a content based recommendation system. Data is related to movies.

Language: Jupyter Notebook - Size: 636 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

Akashkg03/Spam-Email-Classification

This notebook involves to build a spam email classifier using Naive bayes and feature extraction technique using countvectorizer

Language: Jupyter Notebook - Size: 237 KB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

ayseirmak/EBUG-Entity_Based_User_Geolocation_on_Twitter

In this study, we propose a geolocation method based on named entities that are present in user tweets. We try to predict the US states where Twitter users reside in. We present a base method, compare it with a common approach in literature, and also examine some design decisions about our method.

Language: Jupyter Notebook - Size: 2.33 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

SheikhEbadaBinAshraf/SMS-Spam-Classifier-App-Using-LogisticRegression

This SMS Spam Classifier App is a powerful and efficient tool designed to identify and filter out unwanted spam messages from your text messages. Using advanced machine learning algorithms

Language: Jupyter Notebook - Size: 295 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Dhruvil03/Spam_SMS_Detection_Web_App

Developed a web application using Streamlit library in python which can detect whether the sms is spam or not.

Language: Python - Size: 316 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

elmezianech/ClassifyReviews_NLP

Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.

Language: Jupyter Notebook - Size: 7.81 KB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0