Tina-Mulu/Road-Accidents-Analysis-in-Kenya

The data used was scraped from twitter and it shows a trend on the Road accidents in Kenya # Road-Accidents-Analysis-in-Kenya The data used was scraped from twitter and it shows a trend on the Road accidents in Kenya # Problem Statement With increasing population and motorization, Kenya as well as other African countries are faced with tragic road traffic accidents. As we know at the moment ntsa has no proper data collection and documentation on accident data. At this time and age tech is taking over and it would be relevant to have certain data points collected and documented in good time. # Proposed Solution We will scrape data from twitter, based on tweets that have accident data. This will give us details like time, location and on some occasions the nature of the accident. By doing so we can therefore use sentiment analysis to primarily give us classes of tweets based on some factors (whether the tweets contain the words fatal, minor, grisly, or just neutral tweets about accidents) using this data we can now cluster the accidents in respective levels or clusters. # Objectives Analyse prevalent times and locations in which most accidents occur. This will help the police in the identification of problem locations and times for enforcement; Have appropriate clusters in which they could assist policy-makers at national, regional and local levels in setting crash reduction targets, developing road safety action plans, and monitoring performance. .On the sentiment analysis part: show what most people tweet about when it comes to accidents.