Grocery Recommendation on Instacart Data

For learning Purposes

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.

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.

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.

Graduation Project/Sentiment Analysis in Turkish Film Reviews

Spam message detection using classifier

Hire the Perfect candidate. HackerEarth Competitions solution.

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.

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

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.

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.

Classification of emails received on a mass distribution group

I have done some Natural Language Processing on the Twitter US Airline Sentiment Dataset, which contains data for over 14000 tweets. Then I have used several classifiers namely, Support Vector Machine, Multinomial Naive Bayes, Random Forest and Decision Trees to predict the sentiment of the tweet i.e. positive, negative or neutral.

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.

Semantic Analysis of Restaurant Reviews (NLP Use Case)

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

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.