Text Prediction App is explained.

This report aims to investigate the Swiftkey Dataset. Dataset has four language set. We will examine the English data. The English data has three files named en_US.blogs.txt, en_US.news.txt, en_US.twitter.txt. Sentences from blogs, news and tweets were gathered in the data. We will explore the files, calculate the basic statistical measurements and hopefully make you have basic ideas about the data.

This dashboard data was gathered from FAO.

This visualization is about temperatures that measured in Istanbul by NASA. Data can be provided in : https://power.larc.nasa.gov/data-access-viewer/ . Data has temperature measurements for everyday of the date but we transformed data to average temperatures for each month and make a visualization with it.

This is a simple leaflet study. Selected cities will pops up local time.

Project aims to predict human movements by wearable accelerometer records. In our data set users wore some devices and performed to do 5 different movements, and these accelerometers recorded changes in belt, arm, forearm and dumbell. The main idea is to investigate if we can determine which movement has been performed by these records.

This document is about exploring and extracting some insights from the Storm Data which is an official publication of National Oceanic and Atmospheric Administration. Across the USA starting from 1950 to 2011 meteorological events are observed with its results such as injuries, fatalities and damages on crops and on properties. We are going to investigate if event types are correlated with damage types. At first section We will download the data and clean some of it, transform the data, look at columns and group events to have ideas which events are more dangerous for human life and have strong impact on economy. We will compare top events that effects most. We will plot our data to understand better and see relations between events and damages. And we will examine by years and also by seasons of events. This data can be investigated by different points of view. But for more detailed analysis data has to be cleaned very carefully. Especially between 1950-1970 some stats are missing or deployed with personal measurements.