Arduino Microcontroller Circuit Course in Tay Ho, Hanoi

1. End-to-End Retail EDA

This project applies Pandas, Matplotlib, and Seaborn to a full-year retail dataset: load and inspect, engineer date features, aggregate, chart trends, and turn findings into business recommendations.

2. Dataset Profile

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

df = pd.read_csv("retail_sales.csv")
print(df.head(10))
print("Dataset Dimensions:", df.shape)  # (1825, 6)
print(df.dtypes)
print(df.isnull().sum())  # all zeros

Columns: Date, Category, Sales, Quantity, Profit, Region. Date range: 1 Jan–31 Dec 2023. No missing values.

3. Date Features

df["Date"] = pd.to_datetime(df["Date"])
df["Month"] = df["Date"].dt.month
df["Quarter"] = df["Date"].dt.quarter
df["Day Name"] = df["Date"].dt.day_name()
df["Month Name"] = df["Date"].dt.month_name()

4. Summaries

print(df.describe())
category_summary = df.groupby("Category").agg({
    "Sales": "sum", "Profit": "sum", "Quantity": "sum"
}).sort_values(by="Sales", ascending=False)
region_summary = df.groupby("Region").agg({
    "Sales": "sum", "Profit": "sum", "Quantity": "sum"
})

5. Visual Analysis

• Line — monthly sales & profit
• Bar — sales by product category
• Pie — sales by region (West/South high, East low)
• Line — average sales by weekday
• Heatmap — correlation of Sales, Quantity, Profit

monthly_data = df.groupby("Month").agg({"Sales": "sum", "Profit": "sum"})
plt.plot(monthly_data.index, monthly_data["Sales"], marker="o", label="Sales")
plt.plot(monthly_data.index, monthly_data["Profit"], marker="s", label="Profit")
plt.legend(); plt.show()

corr = df[["Sales", "Quantity", "Profit"]].corr()
sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f")
plt.show()

6. Four-Chart Dashboard

fig, axes = plt.subplots(2, 2, figsize=(14, 10))
# axes[0,0]: monthly trends | axes[0,1]: category bars
# axes[1,0]: region pie     | axes[1,1]: weekday line
plt.tight_layout()
plt.show()

7. Insights & Recommendations

• Top category by sales: Electronics
• Best region: West
• Highest sales month: July
• Best weekday: Friday (good for promotions)
• Annual profit margin: 24.71%; highest margin category Sports Equipment, lowest Electronics despite volume

Actions: push Electronics volume while lifting its margin; investigate East region; stock up for July peaks; promote on Fridays.