Data-400-Final-Project

Introduction

With grocery prices seeming to continue rising and variations of economic pressures, people are finding challenges living. We wanted to explore grocery pricing regionally and divisonally through the lens of rural, suburban, and urban separations.

Research Question : How do factors like location (regionally, division, state, urban, suburban, or rural), rent prices, unemployment, distance to distribution centers, median income, and population impact Target's pricing strategies?

Data Sources and Retrieval

Data was collected from Target, Rent, Census, Warehouse Data, and the github file located here: Zip Code Latitudes and Longitudes.

These websites were scraped. See Data Scraping section for more information.

Here are all the packages that we used to complete this project.

from selenium import webdriver
from selenium.webdriver.firefox.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time
import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
import seaborn as sns
import statsmodels.api as sm
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler

Variables:

For our variables:
-Unemployment rate
-Target data (bananas, diapers, t-shirts, AirPods, vegetable oil, gum, toilet paper, cotton swabs, eggs, shampoo, and milk)
-Target warehouse distance in relation to Target locations
-Median income
-Population
-Rent pricing

Various variables are from the Target data (y) compared to other target data and rest of variables (x).

Data Scraping

Please see the code in the file Target Data! updated 4-6-25.ipynb for the full code.

Data Cleaning

Certain zip codes were changed to strings as some selected zip codes had a 0 in front and were not registering as integers. The rent and price were changed to floats removing the $. Also some AirPods rows had name variations and therefore had to be unified. Since all of the data was in a separate file from web scraping, data was appended and then pivoted (columns turn into rows and rows into columns) so data is tidy with zip code as id. Then the distribution center distance was added.

Exploratory Data Analysis

Here is the first plot and analysis we did:

Figure 1:

AirPods and diapers were removed from this chart due to consistent pricing for each area and distortion of pricing ($179.99 and $44.99 respectively) which skewed data since everything else ranged from $1-14. Since there are confidence intervals on all of the milk and eggs, these are high price variance items within the divisions. While other things like toilet paper, gum, vegetable oil, shampoo, t-shirts, cotton swabs have either less or no confidence intervals for some divisions. Since Alaska does not have a distribution center and Hawaii, it needs to be shipped by plane or sailed. Another thing to note is the consistency between urban, suburban, and rural.

The rest of our analysis can be found under the Final Poster section on the github and the analysis section

Model

Inference multiple linear regression was chosen to find the relationship between one Target product and the rest of the variables.

Results

Perishable goods like milk and eggs show significant regional price variation probably influenced by transportation costs, supply chain logistics, and local retail infrastructure. Non-perishable and manufacturer-controlled items maintain stable pricing, such as AirPods and bananas, across regions, likely due to standardized national pricing.
Geographic area type influences pricing:
Urban areas benefit from lower prices due to proximity to distribution hubs and retail competition, while rural areas experience higher prices due to longer supply chains and fewer retailers. Suburban regions show mixed pricing patterns based on local conditions. Distance from distribution centers:
Longer distances drive up costs, especially for perishables.

Implications

Social:
Individual: knowledge and awareness. Lots of people feel financial pressure paying for their necessities.
Community: knowledge and advocation.
Law: gives policymakers insight to pricing differences between regions and area types.
Ethical:
Although there are supply chain issues to getting to rural areas, rural customers have lower median incomes but are paying more for necessities. Therefore, placing the burden on the consumers and creating financial stress. Legal:
Concerns of scraping legally for data privacy and in regulation with Target's policies.