Apurva Yadav's repositories
cyberbullying-tweet-recognition-app
This app predicts the nature of the tweet into 6 Categories. i.e. Age, Ethnicity, Gender, Religion, Other Cyberbullying, Not Cyberbullying.
auto-mpg-prediction
A ML Web App which predicts Miles Per Gallon(MPG) efficiency of a vehicle when provided 7 inputs like Weight, Displacement etc.
Image-Generation-GAN
This app generates random Fashion images using Generative Adversarial Networks (GANs). I have used Fashion MNIST dataset for this task.
apurvayadav
Config files for my GitHub profile.
Bellabeat-Case-Study
Bellabeat case study is a case study of FitBit tracker data from Kaggle. This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. This Case study was a part of Google Data Analytics Professional Certificate.
Book-Record-Application
Book Record is Web Application where readers can keep track of their books. Data of books can be easily entered by ISBN. Other info such as book name, description, writer will be automatically fetched from google books API. Flask framework is used for the backend.
Comment-Hate-Prediction-App
This App predicts if the provided comment has the following properties: toxic, severe toxic, obscene, threat, insult, identity hate. Harmful comments could be flagged by using this app.
Ecommerce-text-classification
In this project, I have used Ecommerce data from kaggle to classify product categories. I have used NLP for the task.
flask-csv-file-viewer
A flask app with Login and Sign Up Functionality where user can upload and view a CSV file.
flask-planetary-api
A REST API created with the help of python flask. It has login, signup and forget password functionalities. Also, we can add data of planet, update, and delete it.
Python-Sentiment-Score-Generator
A python program that scrapes text from the links in the input file and generates sentiment scores using the NLTK library and saves the output in the output file.