Sales Performance Analysis.ipynb

Kernel: Python 3 (ipykernel)

Case Study: Sales Performance Analysis Objective To analyze and visualize the sales performance of a company over the past year using Matplotlib and Seaborn.

Dataset

• Month: The month of the year.

• Sales: Total sales in dollars.

• Profit: Total profit in dollars.

• Region: The region where the sales were made.

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

# Step 1: Load the data
# Read the sales data from the Excel file
df = pd.read_excel("sales_data.xlsx")

# Step 2: Data Overview
# Display the first few rows of the dataset to understand its structure
print("First 5 rows:")
print(df.head())

# Generate summary statistics for numerical columns
print("\nData Summary:")
print(df.describe())

# Check for missing values in the dataset
print("\nMissing Values:")
print(df.isnull().sum())

# Step 3: Data Cleaning (if required)
# Remove any missing values from the dataset
df.dropna(inplace=True)  # Drop missing values if any

# Step 4: Data Analysis
# Analyze sales trends by month
monthly_sales = df.groupby("Month")["Sales"].sum().sort_index()

# Visualizing Monthly Sales Trend
plt.figure(figsize=(10, 5))
plt.plot(monthly_sales.index, monthly_sales.values, marker='o', linestyle='-', color='b')
plt.xlabel("Month")
plt.ylabel("Total Sales")
plt.title("Monthly Sales Trend")
plt.grid()
plt.show()

# Step 5: Region-wise sales distribution
# Using boxplot to understand sales distribution across different regions
plt.figure(figsize=(8, 5))
sns.boxplot(x="Region", y="Sales", data=df)
plt.title("Sales Distribution by Region")
plt.show()

# Step 6: Sales vs Profit Analysis
# Scatter plot to visualize the relationship between sales and profit across regions
plt.figure(figsize=(6, 6))
sns.scatterplot(x=df['Sales'], y=df['Profit'], hue=df['Region'])
plt.xlabel("Sales")
plt.ylabel("Profit")
plt.title("Sales vs Profit Analysis")
plt.show()

# Step 7: Conclusion
# Summarizing key insights from the analysis
print("Key Takeaways:")
print("1. Identify seasonal trends in sales.")
print("2. Understand regional variations in sales.")
print("3. Analyze the relationship between sales and profit.")

First 5 rows:
  Month  Sales  Profit Region
0   Jan  23000    3000   East
1   Feb  15000    4800  North
2   Jun  27000    6500  North
3   Sep  30000    4000   West
4   Jul  15000    5200   East

Data Summary:
              Sales       Profit
count   1000.000000  1000.000000
mean   24131.000000  5468.100000
std     5471.908331  1429.165807
min    15000.000000  3000.000000
25%    21000.000000  4500.000000
50%    23000.000000  5200.000000
75%    27000.000000  6500.000000
max    35000.000000  8000.000000

Missing Values:
Month     0
Sales     0
Profit    0
Region    0
dtype: int64

Key Takeaways:
1. Identify seasonal trends in sales.
2. Understand regional variations in sales.
3. Analyze the relationship between sales and profit.