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import pandas as pd
import numpy as np 
import matplotlib as plt


# 1. Load the CSV dataset and display the first 10 rows
data = pd.read_csv(r"C:\Users\USER\OneDrive\Desktop\project\sales_data.csv")
print("First 10 rows of the dataset:")
print(data.head(10))


# 2. Check for missing values and handle them appropriately
print("\nMissing values per column before cleaning:")
print(data.isnull().sum())


# Drop rows with missing values
data.dropna(inplace=True)


# Drop duplicate rows
data.drop_duplicates(inplace=True)


print("\nMissing values after cleaning:")
print(data.isnull().sum())


# 3. Calculate total amount spent by each customer
customer_total_spent = data.groupby('Customer_ID')['Total_Amount'].sum().reset_index()
print("\nTotal amount spent by each customer:")
print(customer_total_spent.head())


# 4. Filter transactions where payment method is 'Credit Card'
credit_card_transactions = data[data['Payment_Method'] == 'Credit Card']
print("\nCredit Card transactions:")
print(credit_card_transactions.head())


# 5. Create a new column for customer age groups
bins = [0, 17, 25, 35, 45, 60, 100]
labels = ['<18', '18-25', '26-35', '36-45', '46-60', '60+']
data['Age_Group'] = pd.cut(data['Age'], bins=bins, labels=labels, right=True)


print("\nAge groups added:")
print(data[['Age', 'Age_Group']].head())


# 6. Calculate total purchases per product category
total_purchases_per_product_category = data.groupby("Product_Category")['Total_Purchases'].sum()
print("\nTotal purchases per product category:")
print(total_purchases_per_product_category)


# 7. Analyze gender-wise distribution of customers
data['Gender'] = data['Gender'].astype(str).str.strip().str.capitalize()
gender_distribution = data['Gender'].value_counts()
print("\nGender-wise distribution of customers:")
print(gender_distribution)


# 8. Calculate correlation between Total_Purchases and amount spent
correlation = data['Total_Purchases'].corr(data['Total_Amount'])
print("\nCorrelation between Total_Purchases and Total_Amount Spent:", round(correlation, 3))


# 9. Identify top 10 customers based on amount spent
customer_spending = data.groupby('Customer_ID')['Total_Amount'].sum()
top_10_customers = customer_spending.sort_values(ascending=False).head(10)
print("\nTop 10 customers by amount spent:")
print(top_10_customers)


# 10. Group data by shipping method and calculate average Total_Purchases
avg_total_purchases = data.groupby('Shipping_Method')['Total_Purchases'].mean()
print("\nAverage Total_Purchases by shipping method:")
print(avg_total_purchases)


# 11. Find the most popular product brand by sales volume
popular_brand = data.groupby('Product_Brand')['Total_Purchases'].max()
print("\nMost popular product brand by sales volume:")
print(popular_brand)


# 12. Create a pivot table showing purchases by customer segment and product type
pivot_table = pd.pivot_table(
    data,
    values='Total_Amount',
    index='Customer_Segment',
    columns='Product_Type',
    aggfunc='sum',
    fill_value=0
)
print("\nPivot table: Total_Amount by Customer Segment and Product Type:")
print(pivot_table)


# 14. Detect outliers in amount spent using IQR
Q1 = data['Total_Amount'].quantile(0.25)
Q3 = data['Total_Amount'].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR


outliers = data[(data['Total_Amount'] < lower_bound) | (data['Total_Amount'] > upper_bound)]
print("\nDetected outliers in Total_Amount:")
print(outliers)


# 15. Generate summary statistics for all numerical columns
print("\nSummary statistics:")
print(data.describe())


# 16. Export cleaned and transformed dataset to a new CSV file
data.to_csv('cleaned_dataset.csv', index=False)
print("\nCleaned dataset saved as 'cleaned_dataset.csv'")