Predictive Analytics
Embed predictive models into your product to forecast trends, detect anomalies, and surface actionable insights that help your users make better decisions.
from sklearn.pipeline import Pipeline
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler
pipeline = Pipeline([
("scaler", StandardScaler()),
("features", FeatureEngineer()),
("model", GradientBoostingClassifier(
n_estimators=200,
learning_rate=0.1,
max_depth=5
))
])
pipeline.fit(X_train, y_train)
predictions = pipeline.predict_proba(X_test)
Production ML Pipelines
We build end-to-end machine learning pipelines that handle feature engineering, model training, validation, and deployment. Scikit-learn pipelines ensure reproducibility and make retraining automated.
- Automated feature engineering
- Cross-validation and hyperparameter tuning
- Model versioning and experiment tracking
- Automated retraining on data drift
from prophet import Prophet
import pandas as pd
# Time series forecasting
model = Prophet(
seasonality_mode="multiplicative",
changepoint_prior_scale=0.05
)
model.add_regressor("marketing_spend")
model.add_regressor("seasonality_index")
model.fit(training_data)
future = model.make_future_dataframe(
periods=90, freq="D"
)
forecast = model.predict(future)
Accurate Demand Forecasting
Using Prophet and custom time series models, we build forecasting systems that account for seasonality, trends, holidays, and external regressors. Get 90-day forecasts with confidence intervals for smarter planning.
- Seasonal decomposition and trend analysis
- External regressor support
- Confidence intervals for risk assessment
- Automatic changepoint detection
Predictive Analytics Use Cases
Ready to Predict the Future of Your Business?
Turn your data into foresight. Our ML engineers build predictive models that help you anticipate customer behavior, optimize operations, and make data-driven decisions with confidence.