Text Sentiment Classification model

Code for my Medium article: "How you can quickly deploy your ML models with FastAPI"

This is a recommender system that lets you enter a paranormal romance book and get back a Spotify playlist of hair metal songs as a soundtrack for the book

A One-of-its kind Platform Offering E-books as a Rental Service integrated with their Digital Devices completely Redesigning the Reading Experience

Predict search relevance given a product name and its text attributes

Python natural language pre-processing scripts

NLP project in wich I analyse NLP model. And I start working on ML predictions interpretation.

I analyzed Spider-Man Movie reviews from IMDb. I employed basic NLP techniques like TF-IDF, Sentiment Analysis and Topic Modelling and I shared the results with solid visualizations. All done with R.

A Term Frequency and inverse distance Frenquency (TF-idF) algorithm in Java language using concurrent techniques

A Movie recommender system that reads overviews of movies and generates TF-IDF matrix and finds cosine similarity of each movie with other movies and displays the similar movies

IR System - TFIDF Implementation to search relevant covid19 clinical trails

Determining the class of cancer-causing mutations using text and genetic data

Prediction using KNN and it's hyperparameter tuning.

PROJECTS from Data Science and Analytics, MSc Program 2016-2017 | Hira Fatima

Predicting the success of Kickstarter Projects. 💲

Walkthrough a toy example of Latent Semantic Analysis

Use of inverted index to find similar documents in a data frame

Sentiment Analysis on IMDB movie reviews dataset on kaggle using TFIDF technique

Predicting customer sentiments from feedbacks for amazon. While exploring NLP and its fundamentals, I have executed many data preprocessing techniques. In this repository, I have implemented a bag of words using CountVectorizer class from sklearn. I have trained this vector using the LogisticRegression algorithm which gives approx 93% accuracy. …