Krishika Shivnani's Projects
K-Means clustering concepts are applied on "Airplane crashes in US since 1908" dataset in Python using pandas, NumPy and scikit-learn modules.
A key indicator of engagement for a mobile app is session length. This repository contains two SQL queries to track average session length on a weekly basis, as well as total number of sessions over the course of the week.
Generates normalized ratings based on yelp data set by removing biasness introduced by fake reviews. Uses neural networks including SVM and NLP techniques.
This is a revision code of the basic concepts of JSON formatting and HTTP requests in Python.
A stab at creating a good repository with all possible features embedded.
This is a repository where I would code data structures in Python. I have worked on Binary Tree and Binary Search Tree problems. Currently, I'm practicing Dynamic Programming Qs.
Polly enables the creation of robust polls that support the use of a large number of users. Each host can create their own virtual room where they can publish their questions and share them with a large number of people
Automation program that consumes YAML configuration file as input and deploys a Linux AWS EC2 instance with two volumes and two users.
Software Engineering Project 1 - Sentiment Analysis of Product Reviews, PDF Text to gain more insight about the user's perspective on the a topic, document, review etc..
Concise, consistent, and legible badges in SVG and raster format
this repository is our team's work for september
TeachersPet is a Discord Bot for class instructors to streamline their Discord servers
This is a classification task to find different terrains from time-series data. The idea is to train a neural network using given data to classify which terrain an unknown data represents. We will use the F1 score as the evaluation metric of this project.
Twitter data stream collection using Tweepy and OAuth.
This is a wholesale-store chain Database System. We have used MariaDB and JDBC together to construct this system. This is based on the Entity-Relationship model.