import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.arima.model import ARIMA

# Load the CSV file with a specified encoding
df = pd.read_csv('Superstore.csv', encoding='ISO-8859-1')

# Convert Order Date to datetime
df["Order Date"] = pd.to_datetime(df["Order Date"])

### 1. Profitability by Category ###
category_profit = df.groupby("Category")["Profit"].sum().sort_values(ascending=False)

plt.figure(figsize=(8, 5))
# Assign the x variable to hue and set legend=False
sns.barplot(x=category_profit.index, y=category_profit.values, hue=category_profit.index, palette=sns.color_palette("viridis", len(category_profit)), legend=False)
plt.title("Total Profit by Product Category")
plt.xlabel("Category")
plt.ylabel("Total Profit ($)")
plt.grid(axis="y", linestyle="--", alpha=0.7)
plt.show()

# 🔥 Actionable Insights:
print("✅ Actionable Insights:")
print("- Focus on expanding 'Office Supplies' and 'Technology' categories as they are the most profitable.")
print("- Investigate why 'Furniture' is underperforming and optimize costs or pricing.")

✅ Actionable Insights:
- Focus on expanding 'Office Supplies' and 'Technology' categories as they are the most profitable.
- Investigate why 'Furniture' is underperforming and optimize costs or pricing.

### 2. Regional Profitability ###
region_profit = df.groupby("Region")["Profit"].sum().sort_values(ascending=False)

plt.figure(figsize=(8, 5))
sns.barplot(x=region_profit.index, y=region_profit.values, palette="coolwarm")
plt.title("Total Profit by Region")
plt.xlabel("Region")
plt.ylabel("Total Profit ($)")
plt.grid(axis="y", linestyle="--", alpha=0.7)
plt.show()

# 🔥 Actionable Insights:
print("\n✅ Actionable Insights:")
print("- The 'West' region is the most profitable—continue growth strategies here.")
print("- The 'South' region struggles in profitability—analyze cost structures and improve marketing strategies.")

✅ Actionable Insights:
- The 'West' region is the most profitable—continue growth strategies here.
- The 'South' region struggles in profitability—analyze cost structures and improve marketing strategies.

### 3. Impact of Discount on Profit ###
plt.figure(figsize=(8, 5))
sns.scatterplot(x=df["Discount"], y=df["Profit"], alpha=0.5)
plt.title("Discount vs Profit")
plt.xlabel("Discount (%)")
plt.ylabel("Profit ($)")
plt.grid(True)
plt.show()

# 🔥 Actionable Insights:
print("\n✅ Actionable Insights:")
print("- Avoid excessive discounting, especially above 20%, as it leads to losses.")
print("- Implement a structured discounting strategy to ensure profitability.")

✅ Actionable Insights:
- Avoid excessive discounting, especially above 20%, as it leads to losses.
- Implement a structured discounting strategy to ensure profitability.

### 4. Top 10 Cities by Sales ###
city_sales = df.groupby("City")["Sales"].sum().sort_values(ascending=False).head(10)

plt.figure(figsize=(10, 5))
sns.barplot(x=city_sales.index, y=city_sales.values, palette="magma")
plt.title("Top 10 Cities by Sales")
plt.xlabel("City")
plt.ylabel("Total Sales ($)")
plt.xticks(rotation=45)
plt.grid(axis="y", linestyle="--", alpha=0.7)
plt.show()

# 🔥 Actionable Insights:
print("\n✅ Actionable Insights:")
print("- Focus on improving logistics and supply chain in top-selling cities (New York, Los Angeles, Seattle).")
print("- Identify fast-moving products in these cities and increase inventory levels.")

✅ Actionable Insights:
- Focus on improving logistics and supply chain in top-selling cities (New York, Los Angeles, Seattle).
- Identify fast-moving products in these cities and increase inventory levels.

### 5. Sales Trend Over Time ###
time_series = df.groupby(df["Order Date"].dt.to_period("M"))["Sales"].sum()

plt.figure(figsize=(12, 5))
time_series.plot(kind="line", marker="o", color="blue")
plt.title("Sales Trend Over Time")
plt.xlabel("Year-Month")
plt.ylabel("Total Sales ($)")
plt.grid(True)
plt.xticks(rotation=45)
plt.show()

# 🔥 Actionable Insights:
print("\n✅ Actionable Insights:")
print("- Sales show a seasonal pattern—identify peak months for better inventory planning.")
print("- Maintain higher stock and marketing efforts during high-sales periods.")

### FINAL SUMMARY ###
print("\n📌 FINAL SUMMARY & BUSINESS RECOMMENDATIONS:")
print("1️⃣ Office Supplies & Technology are highly profitable; Furniture needs improvement.")
print("2️⃣ West region is the best performer, while the South region needs strategic intervention.")
print("3️⃣ Discounts above 20% often lead to losses—implement a structured discounting plan.")
print("4️⃣ New York, Los Angeles, and Seattle drive the highest sales—strengthen logistics there.")
print("5️⃣ Sales have seasonal trends—optimize inventory for peak sales periods.")

✅ Actionable Insights:
- Sales show a seasonal pattern—identify peak months for better inventory planning.
- Maintain higher stock and marketing efforts during high-sales periods.

📌 FINAL SUMMARY & BUSINESS RECOMMENDATIONS:
1️⃣ Office Supplies & Technology are highly profitable; Furniture needs improvement.
2️⃣ West region is the best performer, while the South region needs strategic intervention.
3️⃣ Discounts above 20% often lead to losses—implement a structured discounting plan.
4️⃣ New York, Los Angeles, and Seattle drive the highest sales—strengthen logistics there.
5️⃣ Sales have seasonal trends—optimize inventory for peak sales periods.

# Convert 'Order Date' to datetime and set as index
df["Order Date"] = pd.to_datetime(df["Order Date"])
df.set_index("Order Date", inplace=True)

# Aggregate monthly sales
monthly_sales = df["Sales"].resample("M").sum()

# Plot historical sales trend
plt.figure(figsize=(12, 5))
plt.plot(monthly_sales, marker="o", linestyle="-", color="blue", label="Actual Sales")
plt.title("Historical Sales Trend")
plt.xlabel("Date")
plt.ylabel("Total Sales ($)")
plt.grid(True)
plt.legend()
plt.show()

# Train ARIMA model (p, d, q) parameters chosen heuristically
model = ARIMA(monthly_sales, order=(2, 1, 2))  # ARIMA(p=2, d=1, q=2)
model_fit = model.fit()

# Forecast sales for next 12 months
forecast_steps = 12
forecast = model_fit.forecast(steps=forecast_steps)

# Create date range for forecasted data
forecast_index = pd.date_range(start=monthly_sales.index[-1], periods=forecast_steps + 1, freq="M")[1:]

# Plot actual vs. forecasted sales
plt.figure(figsize=(12, 5))
plt.plot(monthly_sales, marker="o", linestyle="-", color="blue", label="Actual Sales")
plt.plot(forecast_index, forecast, marker="o", linestyle="dashed", color="red", label="Forecasted Sales")
plt.title("Sales Forecast for the Next 12 Months")
plt.xlabel("Date")
plt.ylabel("Total Sales ($)")
plt.grid(True)
plt.legend()
plt.show()

# 🔥 Actionable Insights:
print("\n✅ Actionable Insights:")
print("- Sales show seasonal trends—adjust inventory to meet demand during peak months.")
print("- Forecast indicates an upward trend—ensure supply chain readiness for increased demand.")
print("- Use predictive insights to optimize marketing and discount strategies for maximum profitability.")

# 📌 Business Recommendation:
print("\n📌 Business Recommendation:")
print("1️⃣ Plan inventory in advance based on forecasted peak sales months.")
print("2️⃣ Adjust marketing efforts according to expected demand fluctuations.")
print("3️⃣ Monitor and refine forecasting models periodically for better accuracy.")

✅ Actionable Insights:
- Sales show seasonal trends—adjust inventory to meet demand during peak months.
- Forecast indicates an upward trend—ensure supply chain readiness for increased demand.
- Use predictive insights to optimize marketing and discount strategies for maximum profitability.

📌 Business Recommendation:
1️⃣ Plan inventory in advance based on forecasted peak sales months.
2️⃣ Adjust marketing efforts according to expected demand fluctuations.
3️⃣ Monitor and refine forecasting models periodically for better accuracy.