This is a predictive model that works with population, governmental transfers, and employment data from the State of Kentucky to see if there are any tendencies for a county to be above or below the average poverty rate taken from 2003-2019.
This project is written in Python and uses the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook.
From your terminal at your desired location within your computer's file structure run:
git clone https://github.com/jsphotos205/kentucky_poverty_rate.git
Create a new Anaconda enviroment:
conda create -n "WHATEVERNAMEYOUWANTofenv"
Then activate newly created Anaconda enviroment:
conda activate "WHATEVERNAMEYOUWANTofenv"
From the terminal while located in the folder of kentucky_poverty_rate run:
pip install -r requirements.txt
The user might run into issues installing sklearn, this can be resolved by using the following command to install it:
python 3 -m pip install scikit-learn
- Standard Python data structures
- Read in data from local .csv file
- Line 3
- Clean data
- Line 5
- Python functions
- Pandas calculations
- Line 7
- Seaborn Plots
- Line 9
- Markdown and README
Throughout kentucky_pov_ml.ipynb look to the markdown notes for further information on the code presented.
Write a program where a user can input individual county data that feeds to the model for predictive analysis of choosen county.