Predicting the level of damage to buildings caused by the 2015 Gorkha earthquake in Nepal
Table of Contents
- Project Summary
- Motivation
- File Description
- Installation
- Result
- License and Acknowledgement
The Capstone project was carried out with the support of the competition made available by Driven Data. Following the 2015 Gorkha earthquake in Nepal a survey was carried out by Living Labs and the Central Bureau of Statistics, the survey is one of the largest post-disaster datasets ever collected, containing valuable information on earthquake impacts, household conditions, and socio-economic-demographic statistics.
- Business Case
Predicting Earthquake damage grade level has been a much needed and important research area, where the later instances of the destructive damage can be speculated. Following the effect of an earthquake, monitoring and detecting the damage caused on the buildings is still a difficult task, as there involves many buildings that are affected. As a result it is important to have infomations about the building prior to when the earth quake occurs so that we can be able to determine the building that will be affeacted
- Goal
Based on aspects of building location and construction, the goal is to analyze predict the level of damage to buildings caused by the 2015 Gorkha earthquake in Nepal
The project was carried out ot help investigators effectively manage, monitor and detect the damage caused on the buildings following the effect of an earthquake
- A Readme:md file
- Data folder: containing the training and test datset used in this project.
- Link to the dataset can be found here
- However the totality of the data is available through the 2015 Nepal Earthquake Open Data Portal
The following libraries will be installed using Pip with Python 3.7 to run the files
- Pandas
- Scikit Learn
- Numpy
- Seaborn
The main findings of the project can be found on this blogpost
License: MIT
Am usingng this medium to thank and appreciate the entire Udacity for the effort made in making this course available and also the DrivenData global community for hosting the competition with the business case which was the motivation for the capstone project.