A Data Driven Perspective of Austin, TX. Exploring numerous datasets in Python regarding different aspects of Austin to look at various trends in Austin and practice Statistical modeling and ML.
As a student at UT Austin, in this project, I wanted to explore everything I've ever wondered about Austin, Texas through the lens of data in Python, and I also practiced obtaining data through a variety of sources. I was able to look at ethnic data, University data, Crime data, Industry/Occupation data, Weather data, Biking Data, Tution data, and lastly, House Price Data.
Overall This analysis explores various aspects of Austin, TX through STL decomposition, Time Series analysis, clustering of high crime zones, mapping incidents using Folium, using an ARIMA model to create an accurate weather forecast, merging biking and weather data in Austin to explore various trends/patterns, and lastly utilizing various machine learning models and ensembling techniques to predict the sales price of houses in Austin.
Github doesn't properly load my Jupyter notebook due to some of the libraries I worked with, so to view my entire analysis, please use the following link: https://nbviewer.org/github/AdishSundar/Exploring-AustinTX-Project/blob/main/Exploring%20Austin%2C%20Texas%20-%20A%20Data%20Driven%20Perspective.ipynb