Table of Contents
- 1. Why Python Dominates Festival Sales Analysis
- 2. Preparing Diwali Sales Dataset for Analysis
- 3. Advanced EDA Techniques with Pandas
- 4. Customer Segmentation Analysis
- 5. Time-Series Forecasting for Stock Optimization
- 6. Geographic Sales Pattern Visualization
- 7. Machine Learning for Sales Prediction
- 8. Actionable Business Insights from Analysis
1. Why Python Dominates Festival Sales Analysis
Python has become the gold standard for Diwali sales analysis due to:
Powerful Data Handling
import pandas as pd
# Load large Diwali datasets
df = pd.read_csv('diwali_sales_10m_records.csv', low_memory=False)Advanced Visualization
import seaborn as sns
sns.heatmap(df.corr(), annot=True)2. Preparing Diwali Sales Dataset
Essential data cleaning steps:
Handling Missing Values
# Fill missing values
df['customer_age'] = df['customer_age'].fillna(df['customer_age'].median())
df = df.dropna(subset=['product_category'])Key Dataset Features
| Feature | Description | Analysis Importance |
|---|---|---|
| Purchase Timestamp | Exact transaction time | Time-series analysis |
| Product Hierarchy | Category > Subcategory > SKU | Inventory optimization |
3. Advanced EDA Techniques
Sales Trend Analysis
df['purchase_date'] = pd.to_datetime(df['purchase_date'])
daily_sales = df.resample('D', on='purchase_date')['amount'].sum()Customer Demographics
age_groups = pd.cut(df['customer_age'],
bins=[18,25,35,45,55,65],
labels=['18-25','26-35','36-45','46-55','56-65'])4. Customer Segmentation Analysis
RFM (Recency, Frequency, Monetary) Analysis:
from datetime import datetime
snapshot_date = df['purchase_date'].max() + timedelta(days=1)
df_rfm = df.groupby('customer_id').agg({
'purchase_date': lambda x: (snapshot_date - x.max()).days,
'order_id': 'count',
'amount': 'sum'
})5. Time-Series Forecasting
Prophet Forecasting Model
from prophet import Prophet
model = Prophet(seasonality_mode='multiplicative')
model.fit(df_prophet)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)6. Geographic Analysis
7. Machine Learning Applications
Price Optimization Model
from sklearn.ensemble import RandomForestRegressor
X = df[['product_age', 'competitor_price', 'discount%']]
y = df['units_sold']
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)8. Actionable Business Insights
Inventory Planning
Top 5 Products for Next Diwali:
- Smart Home Devices (32% growth)
- Premium Ethnic Wear (28% growth)
Marketing Strategy
- Prime Time: 8-11 PM (45% conversions)
- Top Channels: Mobile App (68% sales)