#Data Enrichment for Machine Learning: Better Features, Better Models

📅 20.12.25 ⏱️ Read time: 8 min

A machine learning model can only learn from the features in its training data. If those features are sparse, generic, or missing the signals that actually predict the outcome, the model underperforms — no matter how sophisticated the algorithm.

Data enrichment for machine learning is the discipline of systematically adding more and better features to a training dataset before model training begins. It's one of the highest-leverage steps in any ML project.

The fundamental equation of supervised learning is: better features → better predictions.

More precisely:

• More relevant features give the model more signal to separate classes or predict values
• Derived features capture non-linear relationships that raw features don't express
• External features add information that your internal data simply doesn't contain
• Aggregated features summarize behavioral history at the right level of abstraction

Consider a churn prediction model trained on only two features: plan tier and signup date. Now add product usage frequency, support ticket count, days since last login, and company headcount from a firmographic API. The enriched model has dramatically more signal — and will perform accordingly.

The ceiling of any ML model is determined by the ceiling of its training data. Data enrichment raises that ceiling.

Creating new features from existing data through arithmetic, aggregation, or transformation. This is the first and most important enrichment step — and it's free.

Time-based features:

• Days since last event (last login, last purchase, last support contact)
• Rolling windows (purchases in last 30 days, logins in last 7 days)
• Time of day, day of week, month, quarter extracted from timestamps
• Recency, frequency, monetary (RFM) scores from transaction history

Ratio and interaction features:

• Revenue per user per month (revenue / months active)
• Feature adoption rate (features used / features available)
• Support ticket rate (tickets / months active)

Categorical encoding:

• One-hot encoding for low-cardinality categoricals
• Target encoding for high-cardinality categoricals
• Embedding lookup for text fields (word or sentence vectors)

Combining records from multiple internal systems at the record level:

• Joining CRM account data with product usage events
• Adding support ticket counts per customer to a customer-level training dataset
• Merging historical weather data with transaction records on date + location

This is internal enrichment that uses your own data, just from different systems. The join key — a customer ID, a date, a location code — is what makes it possible.

Calling third-party services to append data your systems don't contain:

• Company firmographics (size, industry, revenue, technology stack) from enrichment APIs
• Geolocation and geographic attributes from IP addresses or addresses
• Economic indicators and external signals matched to timestamps
• News sentiment scores relevant to your domain

Extracting structured features from unstructured text:

• Sentiment polarity from customer reviews or support tickets
• Topic categories from product descriptions or emails
• Named entity recognition (locations, people, products mentioned)
• Document embeddings for semantic similarity features

Python is the standard language for data enrichment in ML workflows. The core library is pandas, with supporting libraries for specific enrichment types.

Feature engineering with pandas:

import pandas as pd

df = pd.read_csv("customers.csv", parse_dates=["last_login", "signup_date"])

# Time-based features
df["days_since_login"] = (pd.Timestamp.now() - df["last_login"]).dt.days
df["tenure_days"] = (pd.Timestamp.now() - df["signup_date"]).dt.days

# Rolling aggregations (from a transactions table joined in)
df["purchases_last_30d"] = df.groupby("customer_id")["amount"] \
    .transform(lambda x: x.rolling("30D").sum())

# Interaction features
df["revenue_per_day"] = df["total_revenue"] / df["tenure_days"].clip(lower=1)

Joining external data:

# Load internal data
customers = pd.read_csv("customers.csv")

# Load external data (e.g., from an enrichment API export)
firmographics = pd.read_csv("firmographics.csv")  # company_domain, headcount, industry

# Join on shared key
enriched = customers.merge(firmographics, on="company_domain", how="left")

NLP enrichment with a pre-trained model:

from transformers import pipeline

sentiment = pipeline("sentiment-analysis")

df["ticket_sentiment"] = df["support_ticket_text"].apply(
    lambda text: sentiment(text[:512])[0]["label"]
)

Calling an enrichment API:

import requests

def enrich_company(domain):
    response = requests.get(
        f"https://api.enrichment-service.com/companies/{domain}",
        headers={"Authorization": "Bearer YOUR_API_KEY"}
    )
    return response.json() if response.ok else {}

df["company_data"] = df["email_domain"].apply(enrich_company)
df["headcount"] = df["company_data"].apply(lambda x: x.get("headcount"))
df["industry"] = df["company_data"].apply(lambda x: x.get("industry"))

Python data enrichment gives you maximum flexibility — but it also means writing and maintaining the enrichment code, managing API keys and rate limits, and integrating the enrichment step into a reproducible pipeline.

Churn prediction: Enrich customer records with usage frequency features, support interaction counts, and days since last active session. These behavioral signals are far more predictive than static account attributes.

Fraud detection: Enrich transaction records with velocity features (transactions per hour, per IP, per device), geographic distance from previous transactions, and time-of-day features. Derived behavioral patterns are the strongest fraud signals.

Demand forecasting: Enrich historical sales records with day-of-week, holiday indicators, local event data, and weather. External signals often explain the variance that internal data cannot.

NLP classification: Enrich text data with sentence embeddings, entity counts, and topic probabilities before classification. Raw text is rarely the best input to a classifier — structured NLP features often outperform end-to-end text models on small datasets.

Recommendation systems: Enrich user-item interaction data with content-based features (product category, price tier, description embeddings) to address cold-start problems for new users and new items.

Python data enrichment is powerful but requires engineering skill and maintenance overhead. For teams that need to move faster — or that don't have ML engineering resources — low-code AI pipeline platforms handle enrichment in the processing step without code.

In Aicuflow, enrichment is configured on the visual canvas: joining datasets, applying transformations, and computing derived features through the chat interface and node configuration. The platform handles the enrichment automatically each time the pipeline runs — no Python required.

This is the vibe data engineering approach: describe the enrichment you need, let the platform implement it, focus your energy on evaluating the resulting model.

→ See how Aicuflow handles data processing and enrichment → Learn how enriched training data becomes a deployed model → Read about vibe data engineering