CIS 607 Unit 6

Submit a Word Document that would include the following components:

1. Title of the project
2. A paragraph-long problem statement.
3. A link to a Retail and weather dataset(s) you will be using to study the problem.
4. List of at least three hypothesis or research questions.
5. For each hypothesis list a suggested method to test it and the reason of choosing the method. You should use at least two different methods out of the following list: Regression analysis, Factor analysis, Propensity Score Matching, Classification, Cluster Analysis, Decision trees.

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Problem Statement
This study examines two separate datasets: one recording past weather conditions, one
explores e-commerce and documenting online shopping activity. Over several years, the weather
dataset collects temperature, humidity, and wind speed data. As opposed to this, the online retail
data provides a detailed look at product quantities, prices, and consumer transactions. The
primary motivation for combining these three data sets is to examine the impact of climate on
consumer spending. Combining these datasets, we want to thoroughly investigate using various
data mining methods, such as clustering (Kunkel, 2021). This research aims to address the
question, “What effect do weather conditions have on sales of online stores?” This study will
show how merchants can best adapt their marketing approaches to varying weather conditions.
Hypothesis 1: Impact of Weather on Retail Sales
Hypothesis: Adverse weather conditions negatively impact retail sales.
Methods:
1. Regression Analysis:
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Reason: Regression analysis can help establish a quantitative relationship
between weather variables (independent variables) and retail sales (dependent
variable). By examining the coefficients and weather levels, we can determine the
strength and direction of the impact. For instance, regression could model the
relationship between temperature, precipitation, and sales, providing insights into
how each factor influences sales.
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2. Cluster Analysis:
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Reason: Cluster analysis can be used to identify patterns in the dataset, grouping
similar weather patterns and their corresponding retail sales. This method may
reveal specific weather clusters that consistently lead to higher or lower sales. For
example, it might identify clusters where cold temperatures and snow coincide
with increased sales for winter clothing retailers.
Hypothesis 2: Seasonal Variation in Retail Sales
Hypothesis: Retail sales exhibit seasonal patterns influenced by weather conditions.
Methods:
1. Decision Trees:
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Reason: Decision trees can help identify the most significant predictors of
seasonal variation in retail sales. By considering weather variables as potential
decision points, the algorithm can create branches that highlight the key
conditions leading to increased or decreased sales during different seasons. This
method provides a visual representation of the decision-making process, making it
easy to interpret.
2. Factor Analysis:
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Reason: Factor analysis can be employed to identify latent factors that contribute
to seasonal variation. It helps uncover underlying patterns and relationships
among observed variables, such as specific weather conditions and sales. By
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reducing the dimensionality of the dataset, factor analysis can reveal the
fundamental factors influencing seasonal changes in retail sales.
Hypothesis 3: Effect of Weather on Customer Behavior
Hypothesis: Weather conditions impact customer behavior, influencing purchasing decisions.
Methods:
1. Propensity Score Matching:
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Reason: Propensity Score Matching can be used to create matched samples of
customers who experienced different weather conditions but are otherwise similar
in terms of demographics and other relevant factors. This method helps control
for confounding variables and isolates the effect of weather on customer behavior,
such as purchase frequency or average transaction value.
2. Classification:
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Reason: Classification algorithms, such as logistic regression or support vector
machines, can predict whether a customer is likely to make a purchase based on
weather conditions. By training the model on historical data, it can learn to
classify customers into groups (e.g., high vs. low spending) based on weatherrelated features. This allows for the identification of specific weather conditions
that are more likely to lead to positive customer behavior.
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References
Kunkel, J. (2021). Data Models & Data Processing Strategies.