Sales Analysis

On This Page

• C1.1  Project Overview
• C1.2  Business Problem
• C1.3  Dataset Description
• C1.4  Objectives
• C1.5  Step-by-Step Solution
• C1.6  Complete Python Code
• C1.7  Expected Output
• C1.8  Visualizations
• C1.9  Key Insights
• C1.10  Business Recommendations

Capstone Project 1: Sales Analysis

C1.1  Project Overview

This capstone project analyzes a full year of retail sales data to uncover trends, top-performing regions and products, and actionable recommendations for the sales team — a portfolio-ready project demonstrating the complete analytics workflow taught in this course.

C1.2  Business Problem

A retail company wants to understand which months, regions, and products are driving the most sales, so they can plan inventory, staffing, and marketing budget more effectively for next year.

C1.3  Dataset Description

Sample size: 12 months of daily transaction records (~3,000 rows).

C1.4  Objectives

• Identify the monthly sales trend across the full year
• Determine which region generates the highest revenue
• Find the top-performing products by sales share
• Provide clear, actionable business recommendations

C1.5  Step-by-Step Solution

• Step 1: Load the sales CSV file into a Pandas DataFrame
• Step 2: Clean the data — check for missing values and duplicates
• Step 3: Convert order_date to a proper datetime type and extract the month
• Step 4: Group and aggregate revenue by month, region, and product
• Step 5: Visualize each aggregation with an appropriate chart
• Step 6: Summarize findings into clear business insights

C1.6  Complete Python Code

import pandas as pd
import matplotlib.pyplot as plt

# 1. Load data
df = pd.read_csv("sales_data.csv")

# 2. Clean data
df = df.drop_duplicates()
df = df.dropna(subset=["revenue"])

# 3. Prepare dates
df["order_date"] = pd.to_datetime(df["order_date"])
df["month"] = df["order_date"].dt.month_name()

# 4. Aggregate
monthly_sales = df.groupby("month")["units_sold"].sum()
region_sales = df.groupby("region")["revenue"].sum().sort_values(ascending=False)
product_share = df.groupby("product")["revenue"].sum().sort_values(ascending=False)

print("Top Region:", region_sales.index[0])
print("Top Product:", product_share.index[0])

# 5. Visualize
monthly_sales.plot(kind="line", marker="o", title="Monthly Sales Trend")
plt.savefig("monthly_trend.png")

C1.7  Expected Output

C1.8  Visualizations

C1.9  Key Insights

• Sales show a strong upward trend from Jan to Dec, more than doubling by year-end — likely driven by seasonal demand and year-end promotions.
• The North region is the strongest performer (Rs.420L), notably ahead of East (Rs.280L) — a 50% gap worth investigating.
• Product A alone drives 32% of total revenue, making it the company's clear flagship product.
• The bottom 2 products (D and E) together contribute only 24% of revenue — worth a portfolio review.

C1.10  Business Recommendations

• Increase inventory and staffing ahead of Q4 (Oct-Dec) to match the clear seasonal demand spike.
• Study what's working in the North region and replicate those tactics (staffing, local promotions) in the East region.
• Protect and promote Product A with dedicated marketing spend, given its outsized revenue contribution.
• Consider bundling or discontinuing consistently low-performing products (D, E) to simplify inventory and free up working capital.