Grocery Recommendation on Instacart Data
Unlock Your Next Favorite Film! Our NLP-powered Movie Recommendation Web App delivers tailored suggestions based on cast, genres, and production companies. Explore a seamless Streamlit interface, also, you can see the description of selected movie. and all movies list.
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.
For learning Purposes
A Naive Bayes spam/ham classifier based on Bayes' Theorem. A bunch of email subject is first used to train the classifier and then a previously unseen email subject is fed to predict whether it is Spam or Ham.
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
Hire the Perfect candidate. HackerEarth Competitions solution.
Graduation Project/Sentiment Analysis in Turkish Film Reviews
Spam message detection using classifier
A Content-Based Recommendation System built using Python that suggests books to users based on their description. The system utilizes a Bag of Words (BoW) model and cosine similarity to find books similar to the one selected by the user. The project also includes a Streamlit-based web application to provide an interactive interface for recommendati
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.
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 …
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.
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.
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.
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.
This project aims to develop a machine learning algorithm that can accurately detect and filter out spam comments on YouTube videos.
What is the difference between a data scientist and a data analyst? An NLP approach.
Predicting Tags for Stack Overflow
To associate your repository with the countvectorizer topic, visit your repo's landing page and select "manage topics."