Data Analytics & Engineering,
Rebuilt for Messaging

WITH quarterly_revenue AS (
    SELECT 
        DATE_TRUNC('quarter', order_date) AS quarter,
        SUM(revenue) AS total_revenue
    FROM sales_data
    WHERE order_date >= DATEADD('month', -24, CURRENT_DATE())
        AND revenue IS NOT NULL
    GROUP BY 1
)
SELECT 
    quarter,
    total_revenue,
    LAG(total_revenue) OVER (ORDER BY quarter) AS prev_quarter,
    ROUND(100.0 * (total_revenue - LAG(total_revenue) OVER (ORDER BY quarter)) 
        / NULLIF(LAG(total_revenue) OVER (ORDER BY quarter), 0), 2) AS growth_rate
FROM quarterly_revenue
ORDER BY quarter DESC
LIMIT 8;

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from datetime import datetime, timedelta
df['quarter_num'] = pd.to_datetime(df['quarter']).dt.quarter
df['year'] = pd.to_datetime(df['quarter']).dt.year
df['time_index'] = (df['year'] - 2023) * 4 + df['quarter_num']
X = df[['time_index']].values
y = df['total_revenue'].values
model = LinearRegression()
model.fit(X, y)
next_quarter_index = df['time_index'].max() + 1
forecast = model.predict([[next_quarter_index]])
print(f"Q1 2025 Revenue Forecast: ${forecast[0]/1e6:.1f}M")
print(f"Confidence: {model.score(X, y)*100:.1f}%")