import zipfile import os

Define the file path

zip_file_path = "/mnt/data/1717583705_sales_datasets (5).zip" extract_folder = "/mnt/data/extracted_sales_data"

Extract the ZIP file

with zipfile.ZipFile(zip_file_path, 'r') as zip_ref: zip_ref.extractall(extract_folder)

List the extracted files

extracted_files = os.listdir(extract_folder) extracted_files import pandas as pd

Define the CSV file path

csv_file_path = os.path.join(extract_folder, "AusApparalSales4thQrt2020.csv")

Load the CSV file into a DataFrame

df = pd.read_csv(csv_file_path)

Display basic information and the first few rows

df.info(), df.head()

The dataset contains 7,560 sales records with the following columns:

Date: The transaction date. Time: The time of the transaction (e.g., Morning, Afternoon, Evening). State: The Australian state where the sale occurred. Group: Customer group (Kids, Men, Women, Seniors). Unit: Number of units sold. Sales: Total revenue from the sale.

Now, I'll analyze the total sales by state to identify the highest-revenue states and suggest strategies for lower-performing states.

Aggregate total sales by state

state_sales = df.groupby("State")["Sales"].sum().reset_index()

Sort by sales in descending order

state_sales = state_sales.sort_values(by="Sales", ascending=False)

Display the top states by revenue

state_sales

Here are the total sales by state for the fourth quarter of 2020:

VIC (Victoria) - $105,565,000 (Highest revenue) NSW (New South Wales) - $74,970,000 SA (South Australia) - $58,857,500 QLD (Queensland) - $33,417,500 TAS (Tasmania) - $22,760,000 NT (Northern Territory) - $22,580,000 WA (Western Australia) - $22,152,500 (Lowest revenue) Key Insights: Victoria (VIC) and New South Wales (NSW) are the top-performing states, contributing the highest revenue. Western Australia (WA), Northern Territory (NT), and Tasmania (TAS) are among the lowest-performing states. Now, I'll analyze sales by customer group in these lower-revenue states to identify potential areas for improvement. ​

Filter data for lower-performing states (WA, NT, TAS)

low_revenue_states = ["WA", "NT", "TAS"] low_state_sales = df[df["State"].isin(low_revenue_states)]

Aggregate sales by state and customer group

low_state_group_sales = low_state_sales.groupby(["State", "Group"])["Sales"].sum().reset_index()

Sort for better readability

low_state_group_sales = low_state_group_sales.sort_values(by=["State", "Sales"], ascending=[True, False])

Display the results

low_state_group_sales

Filter data for lower-performing states (WA, NT, TAS)

low_revenue_states = ["WA", "NT", "TAS"] low_state_sales = df[df["State"].isin(low_revenue_states)]

Aggregate sales by state and customer group

low_state_group_sales = low_state_sales.groupby(["State", "Group"])["Sales"].sum().reset_index()

Sort for better readability

low_state_group_sales = low_state_group_sales.sort_values(by=["State", "Sales"], ascending=[True, False])

Display the results

low_state_group_sales

#It looks like there might be an issue with filtering the lower-revenue states. Let me verify the state names in the dataset to ensure accuracy before proceeding with further analysis It looks like there might be an issue with filtering the lower-revenue states. Let me verify the state names in the dataset to ensure accuracy before proceeding with further analysis It looks like there might be an issue with filtering the lower-revenue states. Let me verify the state names in the dataset to ensure accuracy before proceeding with further analysis

Sales Breakdown by Customer Group in Lower-Revenue States:

Northern Territory (NT) Men: $5,762,500 (Highest) Kids: $5,700,000 Women: $5,652,500 Seniors: $5,465,000 (Lowest) Tasmania (TAS) Kids: $5,775,000 (Highest) Men: $5,757,500 Seniors: $5,650,000 Women: $5,577,500 (Lowest) Western Australia (WA) Men: $5,752,500 (Highest) Kids: $5,625,000 Seniors: $5,512,500 Women: $5,262,500 (Lowest) Insights & Recommendations Target Women’s Segment for Growth:

In WA, TAS, and NT, the Women’s category generates the lowest sales. Strategy: Run promotional campaigns, introduce new product lines, and optimize inventory for this category. Expand Kids’ Apparel Marketing in NT & WA:

Kids' sales in NT & WA are slightly lower than other groups. Strategy: Partner with schools, offer back-to-school discounts, and create bundled offers. Boost Senior Segment Sales in NT & WA:

Seniors' sales are the lowest in NT & WA. Strategy: Introduce senior citizen discounts and tailor marketing efforts toward comfort-based apparel.

import matplotlib.pyplot as plt import seaborn as sns

Set style

sns.set_style("whitegrid")

Create a bar plot for sales by customer group in lower-revenue states

plt.figure(figsize=(12, 6)) sns.barplot(data=low_state_group_sales, x="State", y="Sales", hue="Group", palette="coolwarm")

Labels and title

plt.title("Sales by Customer Group in Low-Revenue States", fontsize=14) plt.xlabel("State", fontsize=12) plt.ylabel("Total Sales ($)", fontsize=12) plt.legend(title="Customer Group")

Show plot

plt.show()

Re-import necessary libraries

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

Define the new file path

csv_file_path = "/mnt/data/AusApparalSales4thQrt2020.csv"

Load the CSV file into a DataFrame

df = pd.read_csv(csv_file_path)

Strip spaces from state names (if any)

df["State"] = df["State"].str.strip()

Define lower-revenue states

low_revenue_states = ["WA", "NT", "TAS"]

Filter data for lower-revenue states

low_state_sales = df[df["State"].isin(low_revenue_states)]

Aggregate sales by state and customer group

low_state_group_sales = low_state_sales.groupby(["State", "Group"])["Sales"].sum().reset_index()

Sort for better readability

low_state_group_sales = low_state_group_sales.sort_values(by=["State", "Sales"], ascending=[True, False])

Set style

sns.set_style("whitegrid")

Create a bar plot for sales by customer group in lower-revenue states

plt.figure(figsize=(12, 6)) sns.barplot(data=low_state_group_sales, x="State", y="Sales", hue="Group", palette="coolwarm")

Labels and title

plt.title("Sales by Customer Group in Low-Revenue States", fontsize=14) plt.xlabel("State", fontsize=12) plt.ylabel("Total Sales ($)", fontsize=12) plt.legend(title="Customer Group")

Show plot

plt.show()

Re-import necessary libraries

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

Define the new file path

csv_file_path = "/mnt/data/AusApparalSales4thQrt2020.csv"

Load the CSV file into a DataFrame

df = pd.read_csv(csv_file_path)

Strip spaces from state names (if any)

df["State"] = df["State"].str.strip()

Define lower-revenue states

low_revenue_states = ["WA", "NT", "TAS"]

Filter data for lower-revenue states

low_state_sales = df[df["State"].isin(low_revenue_states)]

Aggregate sales by state and customer group

low_state_group_sales = low_state_sales.groupby(["State", "Group"])["Sales"].sum().reset_index()

Sort for better readability

low_state_group_sales = low_state_group_sales.sort_values(by=["State", "Sales"], ascending=[True, False])

Set style

sns.set_style("whitegrid")

Create a bar plot for sales by customer group in lower-revenue states

plt.figure(figsize=(12, 6)) sns.barplot(data=low_state_group_sales, x="State", y="Sales", hue="Group", palette="coolwarm")

Labels and title

plt.title("Sales by Customer Group in Low-Revenue States", fontsize=14) plt.xlabel("State", fontsize=12) plt.ylabel("Total Sales ($)", fontsize=12) plt.legend(title="Customer Group")

Show plot

plt.show()

Key Takeaways from the Visualization: Men’s and Kids' segments generate the highest sales in NT and TAS. Women’s segment has the lowest sales across all three states. WA has the lowest overall sales, with Women’s and Seniors' categories lagging behind. Strategic Recommendations: ✅ Boost Women’s Apparel Sales: Run targeted marketing campaigns, introduce loyalty discounts, and expand fashion choices. ✅ Senior Citizen Promotions: Offer special discounts, promote comfort-focused apparel, and explore partnership deals with community centers. ✅ Expand Kids’ Apparel in WA: Promote schoolwear bundles, seasonal discounts, and influencer-driven campaigns.