This is a demonstration of plotly data visualisation in jupyter notebook with world population data throughout years.
It is a known fact that the world population is growing in an accelerating manner. The growing population in different are in the world may lead to certain severe consequences, such as food shortage, unaffordable housing prices, or medical services failure. Therefore, it is worthy to see which part of the world has a growing population and how the profile of the population is changing. In this project, some infographics are created with python using the graph plotting module ‘plotly’.
This project is dealing with a Kaggle dataset in the following link:
https://www.kaggle.com/iabhishekofficial/mobile-price-classification
It contains the differnt features of differnt models of phone and their respective price range. A simple demonstration of training a Random Forest Classifier to predict the price ranges of phones is included, and the the classifier achieves an accuracy of around 90%, which is a ~10% improvement compare to the baseline model (Random forest classifier with default parameters).
This is a simple report on the data of the 2019 Hong Kong Districts Council Election. This mini project involves the whole ETL process with python and SQL database (SQLite3). Some web scrapping is involved to obtain information from different sources and a simple analysis and report with articulating infographics are included.
The following shows the voters ratios with different political views in the 18 districts along with the age group distribution and voting rates.
ETL Process
Data sources are included in this notebook.