Kentucky Poverty Rate Model

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.

Libraries:

This project is written in Python and uses the following Python libraries installed:

• Pandas
• matplotlib
• Seaborn
• SciPy Stats
• Sklearn

You will also need to have software installed to run and execute a Jupyter Notebook.

Steps for installation:

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

Code Kentucky Data Analysis 1 Project Requirements :

• 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.

Further work to be done:

Write a program where a user can input individual county data that feeds to the model for predictive analysis of choosen county.