Diwali Sales Analysis: A Comprehensive Python Guide for Retail Success
Figure 1: Sample Diwali Sales Analysis Dashboard (Data used for demonstration only).
1. Why Python Dominates Festival Sales Analysis
Python has become the gold standard for analyzing massive Diwali datasets due to its versatility and powerful library ecosystem.
Powerful Data Handling
Libraries like Pandas allow for efficient manipulation of millions of transaction records.
import pandas as pd
# Load large Diwali datasets
df = pd.read_csv('diwali_sales.csv')Advanced Visualization
Seaborn and Matplotlib enable the creation of heatmaps and correlation matrices to spot trends.
import seaborn as sns
sns.heatmap(df.corr(), annot=True)2. Preparing Diwali Sales Dataset
Clean data is the foundation of accurate analysis. Here are the essential steps for preparing your retail data:
Handling Missing Values
# Fill missing values with median
df['customer_age'] = df['customer_age'].fillna(df['customer_age'].median())
# Drop rows with missing critical data
df = df.dropna(subset=['product_category'])| Feature Name | Description | Analysis Importance |
|---|---|---|
| Purchase Timestamp | Exact date/time of transaction | Critical for Time-series & Peak Hour analysis |
| Product Hierarchy | Category > Subcategory > SKU | Essential for Inventory optimization |
| Customer Demographics | Age, Gender, Location | Used for Segmentation & Targeting |
3. Advanced EDA Techniques
Sales Trend Analysis
Resampling data to find daily or weekly trends during the festival month.
df['date'] = pd.to_datetime(df['date'])
daily_sales = df.resample('D', on='date')['amount'].sum()Demographic Binning
Categorizing customers into age groups to understand purchasing power.
age_groups = pd.cut(df['age'],
bins=[18,25,35,45,55,65],
labels=['18-25','26-35','36-45','46-55','56+'])4. Customer Segmentation Analysis
We use RFM Analysis (Recency, Frequency, Monetary) to classify customers into segments like "Big Spenders," "Loyalists," and "At-Risk".
# Calculating Recency
snapshot_date = df['date'].max() + timedelta(days=1)
df_rfm = df.groupby('customer_id').agg({
'date': lambda x: (snapshot_date - x.max()).days,
'order_id': 'count',
'amount': 'sum'
})5. Time-Series Forecasting
Using Facebook's Prophet library allows for accurate forecasting of daily sales, accounting for holiday effects like Diwali.
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
Figure 2: Heatmap showing sales concentration across states.
7. Machine Learning Applications
Price Optimization Model
Using Random Forest to determine the optimal discount strategy for maximum conversion.
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 Insights:
- Stock up on Smart Home Devices (predicted 32% growth).
- Increase inventory for Premium Ethnic Wear in Tier 1 cities.
Marketing Strategy
Top Insights:
- Prime Time: 8 PM - 11 PM sees 45% of total conversions.
- Top Channel: Mobile App drives 68% of sales; focus ads there.
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