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In addition to last week’s assignment please include the following:
- Research question
- Description of the datasets
- Description of the specific data preparation process conducted
- Description of analytical techniques
- Description of the parallelization technologies used or a potential need in using those technologies
- Results of the analysis including tables and charts following basics of data visualization.
- Conclusions of the results, limitation, and the process of the conducted data analysis.
1
Exploring the Impact of Weather Conditions on Online Retail Sales: A Preliminary
Analysis Using KMeans Clustering
Student’s Name
Institutional Affiliation
2
Introduction
This study examines two separate datasets: one recording past weather conditions and
the other documenting online shopping activity. Over several years, the weather dataset collects
temperature, humidity, and wind speed data. As opposed to this, the online retail dataset
provides a detailed look at product quantities, prices, and consumer transactions. The primary
motivation for combining these 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?” We hope this study will
show how merchants can best adapt their marketing approaches to varying weather conditions.
Identifying the Best Technology for Data Conversion, Cleaning, and Munging
Choosing the right technological stack is critical for effective data processing, analysis,
and visualization in data science. Python was selected as the primary programming language
for this study because of its rich library and active user base. Pandas was used for data
transformation, Matplotlib for plotting, and Scikit-learn for enacting ML procedures.
Converting the data was the first stage in our data preparation process. In the original
version of the weather dataset, the ‘Formatted Date’ column was of object type, rendering it
unusable for time-series analysis. Using Pandas’ to_datetime function, we changed this column
to a datetime64[ns] format. By making this adjustment, we could harmonize our time-based
data with the ‘InvoiceDate’ column in the retail dataset, making further manipulation and
analysis much more manageable.
The data cleaning process is a crucial part of any data analysis workflow. Several
columns in our datasets have NaN (Not a Number) placeholders for missing data. We used the
3
dropna function in Pandas to eliminate the rows containing blanks. This method may result in
a smaller dataset, but it would be much cleaner and more dependable for further research if it
did. The cleaned weather and retail datasets were combined in the data munging process. We
did an inner join using the shared ‘Date’ column, and the final dataset contained weather and
retail transaction information. Our future studies build upon this combined data set.
In summary, we could effectively convert, clean, and combine our datasets by using
Python’s robust modules. Our data preparation step is complete, and we have a single, clean,
and merged dataset ready for in-depth analysis.
Identifying the Research Question and Variables to Study
The aim of this study is motivated by the following research question: “How do climate
variables like temperature and humidity affect sales volume and unit cost in an e-commerce
setting?” The motivation for this inquiry is to learn more about the influence of the natural
environment on customer behavior and, by extension, retail sales. The reasoning for this
investigation is that the weather might affect people’s dispositions and levels of comfort, which
can impact their willingness to make a purchase. For instance, as temperatures drop, more
people are likely to buy sweaters, but more air conditioners are sold when temperatures rise.
We have selected foundational factors from both data sets that will allow us to answer
this research question. We will analyze ‘Temperature (C)’ and ‘Humidity’ from the weather
dataset since these are the most straightforward indications of how the weather may influence
consumer behavior. ‘Quantity’ and ‘Price’ are the selected variables from the online shopping
dataset. ‘Quantity’ will tell us how many products were sold, while ‘Price’ will tell us how much
those products cost. We hope that by analyzing the correlations between these factors, we may
get some understanding of the factors that influence the performance of online retailers in
connection to the weather.
4
Need for Distributed Computing
The datasets used in this analysis are currently of a reasonable size for conventional
computer resources. Therefore, distributed computing frameworks are not required at the
present project phase. However, the project’s potential for expansion must be taken into
account. Datasets might become too complex for a single computer to process if they include
too many variables or span too much time. More robust processing resources would also be
necessary if real-time analysis became mandatory (Kunkel, 2021). Distributed computing
solutions like Apache Spark may provide a workable answer. Due to its in-memory processing
capabilities, Spark can efficiently process huge datasets and complicated calculations.
Distributed computing is therefore not essential at the moment, but it should be thought about
for future scalability and complexity.
Conducting Preliminary Analysis
The well-known unsupervised machine learning technique KMeans Clustering was
used for our preliminary analysis of the merged dataset. KMeans was chosen because of how
well it can divide large datasets into smaller, less overlapping groups (Ikotun et al., 2022). To
put the KMeans technique into practice, we tapped into Python’s robust machine learning
toolkit, scikit-learn.
The ‘Temperature (C)’ and ‘Humidity’ variables from the meteorological dataset and the
‘Quantity’ and ‘Price’ variables from the e-commerce dataset were chosen for clustering. These
factors were selected because of their possible connection to the inquiry. The data was
standardized before the algorithm was run to ensure that each variable had an equal impact on
the distance measure.
5
The model’s inertia and silhouette score were supported using three groups. The inertia
metric calculates the average squared distance from each sample to the center of its nearest
cluster, and a smaller value implies a more accurate model. When comparing objects from
different clusters, the silhouette score indicates how well-defined each cluster is based on how
similar the objects are to those in their cluster. Using the KMeans method, we could divide the
data into three groups, each representing a unique set of circumstances regarding the weather
and the number of sales we made (Sinaga & Yang, 2020). Insights into the connection between
the weather and e-commerce sales may be gained from this preliminary analysis, which lays
the groundwork for additional in-depth studies.
Interpretation and Reporting of Preliminary Results
Three unique clusters were identified as a result of the preliminary analysis using KMeans
Clustering, each with its typical range of “Temperature (C),” “Humidity,” “Quantity,” and
“Price” variables. The summary table offers a quantitative overview of these groups:
A.
Cluster 0: Lower temperature (avg. 7.05°C) and higher humidity (avg. 84.8%), with
moderate quantity (avg. 12.28) and higher price (avg. £3.77).
B.
Cluster 1: Higher temperature (avg. 21.42°C) and lower humidity (avg. 51.4%), with
slightly higher quantity (avg. 13.86) and moderate price (avg. £3.39).
C.
Cluster 2: Moderate temperature (avg. 16.70°C) and humidity (avg. 71.6%), but
significantly higher quantity (avg. 7047.69) and lower price (avg. £0.13).
A scatter plot, using ‘Temperature (C)’ vs. ‘Quantity’ as the two independent variables, was
created to help illustrate these groups. Cluster 2 is distinguished from the other clusters because
of its much more significant average amount sold, which is graphically shown in the figure
6
below.
Interpreting these results in the context of our research question—how do weather
conditions like temperature and humidity affect the quantity and price of items sold online—
several insights emerge:
1. Cluster 0 suggests that lower temperatures and higher humidity levels correlate with
moderate sales quantities but at higher prices. This could imply that consumers may
purchase fewer but more expensive items, possibly winter-related products, during
colder, more humid conditions.
2. Cluster 1, characterized by higher temperatures and lower humidity, shows slightly
higher sales quantities at moderate prices. This could indicate that warm, dry conditions
might encourage more frequent but average-priced purchases, possibly summer-related
items.
7
3. Cluster 2 is the most intriguing, with moderate weather conditions but a significantly
higher quantity of items sold at much lower prices. This cluster could represent sales or
promotional events where the weather is not the primary sales driver but rather the
reduced pricing.
The preliminary analysis shows complex connections between weather and online
shopping metrics (Sinaga & Yang, 2020). The weather may affect the number and cost of goods
sold, but other variables, like sales campaigns, might override this effect. These findings give
preliminary solutions to our study topic and serve as a solid groundwork for future, more indepth examinations.
Conclusion
This study on the impact of weather on consumer spending effectively combined
meteorological and internet retail datasets. According to our preliminary KMeans Clustering
analysis, there are three groups, each with its weather circumstances and sales indicators. The
results indicate that weather does affect online sales in complex ways, both in terms of volume
and price. For instance, colder and more humid weather is associated with purchasing fewer
but more costly things, maybe seasonal. Understanding these trends may help internet
merchants optimize sales strategy in response to weather conditions; therefore, these findings
have important implications for the industry. Overall, the study is a solid foundation for further,
in-depth inquiry.
Future Work
This research lays the groundwork for future investigations into the impact of weather
on e-commerce. More advanced machine learning techniques might be used to get even deeper
insights, or the dataset could be expanded to include more factors, such as geographical
8
statistics. Real-time analysis, which provides insights that can be used to make quick changes
to a sales plan, is worth investigating.
9
References
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive
survey and performance evaluation. Electronics, 9(8), 1295.
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2022). K-means
clustering algorithms: A comprehensive review, variants analysis, and advances in the
era of big data. Information Sciences.
Kunkel, J. (2021). Data Models & Data Processing Strategies.
Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE
access, 8, 80716-80727.
1
Exploring the Impact of Weather Conditions on Online Retail Sales: A Preliminary
Analysis Using KMeans Clustering
Student’s Name
Institutional Affiliation
2
Introduction
This study examines two separate datasets: one recording past weather conditions and
the other documenting online shopping activity. Over several years, the weather dataset collects
temperature, humidity, and wind speed data. As opposed to this, the online retail dataset
provides a detailed look at product quantities, prices, and consumer transactions. The primary
motivation for combining these 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?” We hope this study will
show how merchants can best adapt their marketing approaches to varying weather conditions.
Identifying the Best Technology for Data Conversion, Cleaning, and Munging
Choosing the right technological stack is critical for effective data processing, analysis,
and visualization in data science. Python was selected as the primary programming language
for this study because of its rich library and active user base. Pandas was used for data
transformation, Matplotlib for plotting, and Scikit-learn for enacting ML procedures.
Converting the data was the first stage in our data preparation process. In the original
version of the weather dataset, the ‘Formatted Date’ column was of object type, rendering it
unusable for time-series analysis. Using Pandas’ to_datetime function, we changed this column
to a datetime64[ns] format. By making this adjustment, we could harmonize our time-based
data with the ‘InvoiceDate’ column in the retail dataset, making further manipulation and
analysis much more manageable.
The data cleaning process is a crucial part of any data analysis workflow. Several
columns in our datasets have NaN (Not a Number) placeholders for missing data. We used the
3
dropna function in Pandas to eliminate the rows containing blanks. This method may result in
a smaller dataset, but it would be much cleaner and more dependable for further research if it
did. The cleaned weather and retail datasets were combined in the data munging process. We
did an inner join using the shared ‘Date’ column, and the final dataset contained weather and
retail transaction information. Our future studies build upon this combined data set.
In summary, we could effectively convert, clean, and combine our datasets by using
Python’s robust modules. Our data preparation step is complete, and we have a single, clean,
and merged dataset ready for in-depth analysis.
Identifying the Research Question and Variables to Study
The aim of this study is motivated by the following research question: “How do climate
variables like temperature and humidity affect sales volume and unit cost in an e-commerce
setting?” The motivation for this inquiry is to learn more about the influence of the natural
environment on customer behavior and, by extension, retail sales. The reasoning for this
investigation is that the weather might affect people’s dispositions and levels of comfort, which
can impact their willingness to make a purchase. For instance, as temperatures drop, more
people are likely to buy sweaters, but more air conditioners are sold when temperatures rise.
We have selected foundational factors from both data sets that will allow us to answer
this research question. We will analyze ‘Temperature (C)’ and ‘Humidity’ from the weather
dataset since these are the most straightforward indications of how the weather may influence
consumer behavior. ‘Quantity’ and ‘Price’ are the selected variables from the online shopping
dataset. ‘Quantity’ will tell us how many products were sold, while ‘Price’ will tell us how much
those products cost. We hope that by analyzing the correlations between these factors, we may
get some understanding of the factors that influence the performance of online retailers in
connection to the weather.
4
Need for Distributed Computing
The datasets used in this analysis are currently of a reasonable size for conventional
computer resources. Therefore, distributed computing frameworks are not required at the
present project phase. However, the project’s potential for expansion must be taken into
account. Datasets might become too complex for a single computer to process if they include
too many variables or span too much time. More robust processing resources would also be
necessary if real-time analysis became mandatory (Kunkel, 2021). Distributed computing
solutions like Apache Spark may provide a workable answer. Due to its in-memory processing
capabilities, Spark can efficiently process huge datasets and complicated calculations.
Distributed computing is therefore not essential at the moment, but it should be thought about
for future scalability and complexity.
Conducting Preliminary Analysis
The well-known unsupervised machine learning technique KMeans Clustering was
used for our preliminary analysis of the merged dataset. KMeans was chosen because of how
well it can divide large datasets into smaller, less overlapping groups (Ikotun et al., 2022). To
put the KMeans technique into practice, we tapped into Python’s robust machine learning
toolkit, scikit-learn.
The ‘Temperature (C)’ and ‘Humidity’ variables from the meteorological dataset and the
‘Quantity’ and ‘Price’ variables from the e-commerce dataset were chosen for clustering. These
factors were selected because of their possible connection to the inquiry. The data was
standardized before the algorithm was run to ensure that each variable had an equal impact on
the distance measure.
5
The model’s inertia and silhouette score were supported using three groups. The inertia
metric calculates the average squared distance from each sample to the center of its nearest
cluster, and a smaller value implies a more accurate model. When comparing objects from
different clusters, the silhouette score indicates how well-defined each cluster is based on how
similar the objects are to those in their cluster. Using the KMeans method, we could divide the
data into three groups, each representing a unique set of circumstances regarding the weather
and the number of sales we made (Sinaga & Yang, 2020). Insights into the connection between
the weather and e-commerce sales may be gained from this preliminary analysis, which lays
the groundwork for additional in-depth studies.
Interpretation and Reporting of Preliminary Results
Three unique clusters were identified as a result of the preliminary analysis using KMeans
Clustering, each with its typical range of “Temperature (C),” “Humidity,” “Quantity,” and
“Price” variables. The summary table offers a quantitative overview of these groups:
A.
Cluster 0: Lower temperature (avg. 7.05°C) and higher humidity (avg. 84.8%), with
moderate quantity (avg. 12.28) and higher price (avg. £3.77).
B.
Cluster 1: Higher temperature (avg. 21.42°C) and lower humidity (avg. 51.4%), with
slightly higher quantity (avg. 13.86) and moderate price (avg. £3.39).
C.
Cluster 2: Moderate temperature (avg. 16.70°C) and humidity (avg. 71.6%), but
significantly higher quantity (avg. 7047.69) and lower price (avg. £0.13).
A scatter plot, using ‘Temperature (C)’ vs. ‘Quantity’ as the two independent variables, was
created to help illustrate these groups. Cluster 2 is distinguished from the other clusters because
of its much more significant average amount sold, which is graphically shown in the figure
6
below.
Interpreting these results in the context of our research question—how do weather
conditions like temperature and humidity affect the quantity and price of items sold online—
several insights emerge:
1. Cluster 0 suggests that lower temperatures and higher humidity levels correlate with
moderate sales quantities but at higher prices. This could imply that consumers may
purchase fewer but more expensive items, possibly winter-related products, during
colder, more humid conditions.
2. Cluster 1, characterized by higher temperatures and lower humidity, shows slightly
higher sales quantities at moderate prices. This could indicate that warm, dry conditions
might encourage more frequent but average-priced purchases, possibly summer-related
items.
7
3. Cluster 2 is the most intriguing, with moderate weather conditions but a significantly
higher quantity of items sold at much lower prices. This cluster could represent sales or
promotional events where the weather is not the primary sales driver but rather the
reduced pricing.
The preliminary analysis shows complex connections between weather and online
shopping metrics (Sinaga & Yang, 2020). The weather may affect the number and cost of goods
sold, but other variables, like sales campaigns, might override this effect. These findings give
preliminary solutions to our study topic and serve as a solid groundwork for future, more indepth examinations.
Conclusion
This study on the impact of weather on consumer spending effectively combined
meteorological and internet retail datasets. According to our preliminary KMeans Clustering
analysis, there are three groups, each with its weather circumstances and sales indicators. The
results indicate that weather does affect online sales in complex ways, both in terms of volume
and price. For instance, colder and more humid weather is associated with purchasing fewer
but more costly things, maybe seasonal. Understanding these trends may help internet
merchants optimize sales strategy in response to weather conditions; therefore, these findings
have important implications for the industry. Overall, the study is a solid foundation for further,
in-depth inquiry.
Future Work
This research lays the groundwork for future investigations into the impact of weather
on e-commerce. More advanced machine learning techniques might be used to get even deeper
insights, or the dataset could be expanded to include more factors, such as geographical
8
statistics. Real-time analysis, which provides insights that can be used to make quick changes
to a sales plan, is worth investigating.
9
References
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive
survey and performance evaluation. Electronics, 9(8), 1295.
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2022). K-means
clustering algorithms: A comprehensive review, variants analysis, and advances in the
era of big data. Information Sciences.
Kunkel, J. (2021). Data Models & Data Processing Strategies.
Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE
access, 8, 80716-80727.
