Supermarket Sales Dashboard
1. Introduction
Objective: Build a dashboard to analyze supermarket sales data and derive
time-based sales insights.
Purpose: Provide actionable intelligence to improve inventory management,
optimize sales strategies, and enhance customer satisfaction.
2. Project Workflow
1. Problem Definition:
- Analyze supermarket sales trends
over time.
- Key questions:
- What are the peak sales periods?
- Which products perform best in
specific time frames?
- How do sales vary across branches
and payment methods?
2. Data Collection:
- Source: Publicly available datasets
from Kaggle or custom datasets from supermarket chains.
- Example: A dataset containing
attributes like `Invoice ID`, `Branch`, `Date`, `Time`, `Product Line`,
`Total`, and `Payment Method`.
3. Data Preprocessing:
- Clean and preprocess the dataset for
analysis.
- Convert time-based data into usable
formats for insights.
4. Analysis and Dashboard Development:
- Summarize trends and create an
interactive dashboard to visualize sales insights.
5. Insights and Recommendations:
- Provide actionable insights for
inventory and sales strategies.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn,
Plotly
- Dashboard Development: Streamlit or
Dash
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn plotly streamlit
```
Step 2: Load and Explore Dataset
Load the supermarket sales dataset:
```
import pandas as pd
df = pd.read_csv('supermarket_sales.csv')
```
Explore the dataset:
```
print(df.head())
print(df.info())
```
Step 3: Data Cleaning and Preprocessing
Clean and preprocess the data:
```
df.dropna(inplace=True)
df['Date'] = pd.to_datetime(df['Date'])
df['Time'] = pd.to_datetime(df['Time'], format='%H:%M').dt.time
df['Hour'] = pd.to_datetime(df['Time'].astype(str)).dt.hour
```
Aggregate sales by different timeframes:
```
df['Day'] = df['Date'].dt.day_name()
df['Month'] = df['Date'].dt.month_name()
```
Step 4: Analyze and Visualize Data
1. Sales Trends Over Time:
```
import matplotlib.pyplot as plt
sales_by_date = df.groupby('Date')['Total'].sum()
sales_by_date.plot(kind='line', title='Daily Sales Trend')
plt.show()
```
2. Hourly Sales Analysis:
```
import seaborn as sns
sns.barplot(data=df, x='Hour', y='Total', ci=None)
plt.title('Sales by Hour')
plt.show()
```
3. Product Line Sales Distribution:
```
sns.barplot(data=df, x='Product Line', y='Total', ci=None)
plt.title('Sales by Product Line')
plt.xticks(rotation=45)
plt.show()
```
Step 5: Develop Interactive Dashboard
Build an interactive dashboard using Streamlit:
```
import streamlit as st
import plotly.express as px
st.title('Supermarket Sales Dashboard')
# Daily Sales Trend
daily_trend = px.line(df.groupby('Date')['Total'].sum().reset_index(),
x='Date', y='Total', title='Daily Sales Trend')
st.plotly_chart(daily_trend)
# Hourly Sales
hourly_sales = px.bar(df.groupby('Hour')['Total'].sum().reset_index(),
x='Hour', y='Total', title='Hourly Sales')
st.plotly_chart(hourly_sales)
# Product Line Sales
product_sales = px.bar(df.groupby('Product Line')['Total'].sum().reset_index(),
x='Product Line', y='Total', title='Product Line Sales')
st.plotly_chart(product_sales)
```
Run the app:
```
streamlit run dashboard.py
```
Step 6: Generate Reports
Export summarized data and visualizations:
```
with pd.ExcelWriter('sales_analysis_report.xlsx') as writer:
df.groupby('Date')['Total'].sum().to_excel(writer, sheet_name='Daily
Sales')
df.groupby('Product
Line')['Total'].sum().to_excel(writer, sheet_name='Product Sales')
```
Save visualizations as images for reporting.
5. Expected Outcomes
1. Interactive dashboard with real-time insights into supermarket sales trends.
2. Identification of peak sales periods and high-performing product lines.
3. Recommendations for improving inventory and sales strategies.
6. Additional Suggestions
- Advanced Analysis:
- Incorporate customer segmentation to
tailor marketing strategies.
- Predictive Modeling:
- Use machine learning models to
predict sales trends based on historical data.
- Geospatial Insights:
- Add location-based sales data to
analyze regional performance.