Predicting Food Preparation Time (SkipTheDishes) - Doc2Vec

The objective is to predict food preparation time from ordered food items and quantity. This is a data challenge arranged by SkipTheDishes, Canada’s leading and largest food delivery company. The data includes upto 10 ordered food items along with the quantity. There are 20 features and 80,000 samples in the data. The goal is to predict food preparation time. Doc2Vec has been applied to vectorize food item names and feature engineering.

March 27, 2017

Fish Telemetry Data Analysis - R

A small subset of the Lake Winnipeg telemetry data is collected. The purpose is to analyze this data to find out interesting insights about the fish inhabitants. A few highlights are as follows: the distribution of different species, the certain area where specific species can be found, and the time period of their perambulation. The data has the following attributes: Date - A timestamp for when a receiver (station) detects a transmitter detection Name - A unique station name for each receiver in the study ReceiverLong - The longitude of the receiver ReceiverLat - The latitude of the receiver FishID - A unique identifier for each species tagged in the study Species - Species of the tagged fish This Project’s GitHub Repository

March 27, 2017

COVID 19 USA Dashboard - Tableau

An interactive visualization dashboard including different states’ (of USA) confirmed cases and the number of deaths. The visualization presents different calculations such as top N states (i.e., N can be changed from the dashboard) with total confirmed cases and deaths, new cases and deaths; case fatality rates (CFR) on time series data collected from https://github.com/nytimes/covid-19-data. The interactive dashboard is created using Tableau Desktop. Different pie charts, maps, line charts, and stack bars are used in this visualization.