import pandas as pddf = pd.read_excel('data.xlsx')df.groupby('region').sum()model.fit(X_train, y_train)import numpy as npplt.plot(forecast)df.dropna(inplace=True)from sklearn import metricsprint(df.describe())np.mean(returns) * 252
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AI Forecasting & Predictive Analytics

Our predictive analytics service uses historical data to estimate demand, revenue and customer churn. Explore forecasting models and feature-attribution examples; forecasts are estimates, not guaranteed outcomes.

Examples

5-year price forecast

Projecting ETF prices and returns to plan long-term investing.

Churn prediction

Spotting customers likely to cancel so the team can win them back.

Ad results forecast

Estimating next month's leads and sales from today's ad spend.

How we do it in Python

from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(X_train, y_train)
forecast = model.predict(X_next_months)

Excel sheets

AI Forecasting Model

Prediction engine showing which factors drive the forecast.

Executive SummaryPredictive_EngineFeature_Attribution
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Advanced Patterns Model v2

Updated version of the advanced patterns model.

Executive KPI DashboardCUPED Variance CalculatorFeature Store & Churn Model
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ETF USD Analysis

Historical prices, yearly performance, forecast and risk.

Historical Prices5-Year ForecastRisk MetricsTechnical Analysis
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Advanced Patterns Model

Churn model, variance testing and advanced Excel patterns.

Executive KPI DashboardCUPED Variance CalculatorFeature Store & Churn Model
Download Excel