ECE 143 final project by group2

Zhuomin Zhang
Sangeetha Viswanathan-Sakthivel
Jainish Chauhan
Orish Jindal
George Liu
Boyu Chen

The real world application of this solution is that both companies as well as individuals can use this to choose a location to build their company or live. For example, individuals can find a city based upon weather, salary, etc depending on their personal preferences. Using the general population’s preferences as well as the most popular cities, companies can strategically locate their locations to align with customers which can include financial status, weather, education, etc.

The data set is collected from Kaggle containing 265 cities and 21 columns.

root
│
├── data
│   └──uaScoreDataFrame.csv  
│
├── src
│   ├── main.py
│   ├── assign_weight.py
│   ├── data_cleaning.py
│   ├── set_category.py
│   ├── visualization.py
│   └── requirements.txt
│
├── plots
│   └── ...
│
├── final_notebook.ipynb
│
├── slides.pdf
│
└── README.md

Please download/import all third-party modules before executing the code，then do the following：

python src/main.py

It will run the whole project including read in .csv data, preprocess and clean data, analyze and assign weight, plot all figures and save to plots file.

pandas
numpy
matplotlib.pyplot
sklearn
seaborn
scipy
xgboost
folium
IPython.display