📄 Dataset Description: Electrical Appliances Sales vs Temperature

This dataset contains 100 records of synthetic sales data for various electrical appliances sold across North Indian cities over a 2-year period (2023–2024). The data reflects seasonal demand trends by associating daily sales activity with temperature conditions, mimicking real-world seasonal buying behavior.

---

🗂️ Key Features:

• Date – The date of the transaction
• City – The city where the sale occurred (e.g., Delhi, Lucknow)
• Store – Store identifier (e.g., Store_1 to Store_10)
• Category – Appliance category (e.g., AC, Heater, Fan, Geyser)
• Product – Specific product name (e.g., CoolBreeze AC, AquaHot)
• Quantity_Sold – Number of units sold
• Unit_Price – Price per unit (in INR)
• Discount_% – Discount applied on the revenue
• Revenue – Total amount before discount
• Final_Price – Net sale amount after discount
• Temperature_C – Approximate outdoor temperature (°C) on the sale day

Get the dataset here : https://github.com/slidescope/electrical-and-electronics-sales-vs-temperature

---

This dataset is suitable for seasonal trend analysis, sales forecasting, temperature-sensitive product insights, and practicing advanced DAX / Power BI measures.

DAX Practice questions based on dataset, ideal for Power BI or Excel Power Pivot analysis.

---

✅ Dataset Columns Recap

• Date, City, Store, Category, Product
• Quantity_Sold, Unit_Price, Discount_%, Revenue, Final_Price, Temperature_C

---

🔍 Question 1: What is the total revenue generated in Summer months (May to July) vs Winter months (December to February)?

✅ DAX Answer:

SummerRevenue :=
CALCULATE(
    SUM('Sales'[Revenue]),
    MONTH('Sales'[Date]) IN {5, 6, 7}
)

WinterRevenue :=
CALCULATE(
    SUM('Sales'[Revenue]),
    MONTH('Sales'[Date]) IN {12, 1, 2}
)

💡 Explanation:

• Uses MONTH() to filter based on seasonal months.
• CALCULATE modifies the context to include only specific months.
• Can be visualized in a comparison bar chart.

---

🔍 Question 2: Which city has the highest average temperature on days when ACs were sold?

✅ DAX Answer:

AvgTemp_AC_Sales := 
CALCULATE(
    AVERAGE('Sales'[Temperature_C]),
    'Sales'[Category] = "AC"
)

To find the top city, create a measure and add a visual filtered by City and sort descending.

💡 Explanation:

• Filters the context to only “AC” category.
• Averages the temperature during those sales.
• City with the max of this measure gives the answer.

---

🔍 Question 3: Calculate the total discount value given per category.

✅ DAX Answer:

Total_Discount_Value :=
SUMX(
    'Sales',
    ('Sales'[Revenue] * 'Sales'[Discount_%]) / 100
)

💡 Explanation:

• SUMX evaluates row by row.
• Multiplies revenue by discount percent for each row, then sums it.
• Can be used in a matrix with Category as row.

---

🔍 Question 4: What is the month-over-month growth in Final Sales Price?

✅ DAX Answer:

MonthlyFinalSales := 
CALCULATE(
    SUM('Sales'[Final_Price]),
    DATESINPERIOD('Sales'[Date], MAX('Sales'[Date]), -1, MONTH)
)

MoM_Growth :=
DIVIDE(
    [Final Sales This Month] - [MonthlyFinalSales],
    [MonthlyFinalSales],
    0
)

You’ll need a Date table and a measure like:

Final Sales This Month = SUM('Sales'[Final_Price])

💡 Explanation:

• Measures the change in Final Sales month over month.
• Uses DATESINPERIOD() for time intelligence.
• DIVIDE() handles divide-by-zero errors.

---

🔍 Question 5: Find the best-selling product category (by Final Price) in high-temperature days (above 40°C).

✅ DAX Answer:

HighTemp_Sales := 
CALCULATE(
    SUM('Sales'[Final_Price]),
    'Sales'[Temperature_C] > 40
)

Add this to a visual grouped by Category and sort descending to find the top.

Time Intelligence Functions

MoM Sales = CALCULATE([Total Sales], DATEADD('Date'[Date], -1, MONTH))
MoM % = 
DIVIDE(
    [Total Sales] - [MoM Sales],
    [MoM Sales],
    0
)

💡 Explanation:

• Filters rows where Temperature_C > 40.
• Calculates total final sales on hot days (likely summer).
• Useful for seasonality-based product planning.

---