What Is Predictive Analytics? A Beginner-Friendly Guide

Learning Objectives

• Understand the definition and purpose of predictive analytics.
• Recognize the key steps in a predictive analytics workflow.
• Get hands-on experience building simple predictions in SQL and Python.
• Identify real-world applications and business value.
• Learn best practices, common pitfalls, and how Galaxy fits into the process.

1. Introduction: Why Predict The Future?

Companies sit on mountains of historical data—transactions, user events, sensor readings, support tickets, and more. Predictive analytics turns that data into foresight by training statistical or machine-learning models that estimate what is likely to happen next. Whether it’s predicting customer churn, estimating demand, or flagging fraudulent payments, the goal is actionable probability: a quantitative signal that drives proactive decisions.

2. Foundations of Predictive Analytics

2.1 Descriptive vs. Predictive vs. Prescriptive

• Descriptive analytics: What has happened? (e.g., monthly revenue report)
• Predictive analytics: What will happen? (e.g., next month’s revenue forecast)
• Prescriptive analytics: What should we do? (e.g., dynamic pricing recommendations)

Predictive analytics is the bridge between descriptive hindsight and prescriptive action.

2.2 Core Concepts

FeaturesInput variables—columns in your dataset—that the model uses to learn patterns.Target (Label)The outcome you want to predict (e.g., churn = 1).Training vs. Test DataTraining data teaches the model; test data evaluates its generalization.ModelA mathematical function mapping features to predicted targets.Evaluation MetricA quantitative score (AUC, RMSE, etc.) indicating predictive quality.

3. Typical Workflow

1. Problem Formulation – Define the business question and success metric.
2. Data Collection – Gather historical data (often via SQL queries).
3. Data Preparation – Clean, transform, and engineer features.
4. Modeling – Choose algorithm(s) and train on labeled data.
5. Evaluation & Validation – Measure performance on unseen data.
6. Deployment – Integrate model predictions into products or processes.
7. Monitoring & Iteration – Track drift, retrain, and refine.

4. Key Algorithms (Beginner-Friendly)

4.1 Logistic Regression

Binary classification; outputs probability between 0 and 1. Great for churn or fraud flags.

4.2 Decision Trees & Random Forests

Non-linear, handle categorical variables well, robust to outliers.

4.3 Time-Series Forecasting (ARIMA, Prophet)

Specialized models for temporal data like revenue or sensor readings.

4.4 Gradient Boosting (XGBoost, LightGBM)

Ensemble technique that often tops Kaggle leaderboards; good balance of accuracy and speed.

5. Hands-On Example 1: Customer Churn Prediction in Python

Estimated time: 15 minutes

# 1. Load libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

# 2. Load dataset (CSV or Galaxy-exported query)
churn = pd.read_csv('telecom_churn.csv')

# 3. Feature engineering
features = ['tenure', 'monthly_charges', 'contract_type', 'support_tickets']
X = pd.get_dummies(churn[features], drop_first=True)
y = churn['churn']

# 4. Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 5. Scale numeric features
scaler = StandardScaler()
X_train[X_train.columns] = scaler.fit_transform(X_train)
X_test[X_test.columns] = scaler.transform(X_test)

# 6. Train model
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

# 7. Evaluate
pred_proba = model.predict_proba(X_test)[:, 1]
print('AUC:', roc_auc_score(y_test, pred_proba))


Exercise: Add internet_service and payment_method as new features. Does the AUC improve?

6. Hands-On Example 2: Predicting Next-Week Orders with SQL + Galaxy

You can build simple predictive signals directly in SQL, especially for time-series forecasts.

6.1 Querying Rolling Averages

-- Predict next 7-day order count using 4-week rolling average
WITH orders AS (
SELECT order_date::date AS d, COUNT(*) AS orders
FROM ecommerce.orders
GROUP BY 1
),
roll_avg AS (
SELECT d,
AVG(orders) OVER (ORDER BY d ROWS BETWEEN 28 PRECEDING AND 1 PRECEDING) AS avg_4w
FROM orders
)
SELECT d,
avg_4w AS predicted_orders
FROM roll_avg
ORDER BY d DESC
LIMIT 14;


Paste this query into Galaxy, save it in a Forecasts Collection, and endorse it so teammates can pull predictions reliably. Galaxy’s AI copilot can suggest window-function syntax and auto-complete your table names from metadata.

6.2 Exporting to Notebooks

From Galaxy, click Run ▸ Export ▸ Notebook to open the result set in a Jupyter notebook for further visualization.

7. Real-World Applications

• Marketing – Predict click-through rate for ad targeting.
• Finance – Credit-risk scoring, stock price forecasting.
• Healthcare – Early diagnosis of diseases from patient records.
• Manufacturing – Predictive maintenance on equipment sensors.
• SaaS (with Galaxy) – Churn propensity scores feeding in-app retention workflows.

8. Best Practices

1. Start simple – Baseline with logistic regression or naive forecast before deep models.
2. Avoid data leakage – Ensure test data truly represents unseen future.
3. Track experiments – Use version control (Galaxy + Git) for queries and feature sets.
4. Automate retraining – Schedule regular jobs when data grows or schema changes.
5. Communicate uncertainty – Provide confidence intervals, not just point estimates.

9. Common Mistakes & Troubleshooting

MistakeWhy It HappensFixUsing future data in trainingTemporal joins leak informationLag features; split chronologicallyOverfittingToo complex modelCross-validate; regularizeIgnoring class imbalanceRare events like fraudUse balanced metrics; oversample minority class

10. How Galaxy Supercharges Predictive Analytics

• Context-Aware AI Copilot drafts feature-engineering SQL in seconds.
• Collections & Endorsements keep training datasets versioned and discoverable.
• Live APIs (roadmap) will let you serve real-time predictions directly from endorsed queries.
• Secure Collaboration—share queries with data scientists while keeping prod credentials locked down.

11. Practice Challenges

1. Download a public taxi-rides dataset, predict tip percentage using a random forest.
2. Write a Galaxy SQL query that builds a 30-day moving-average sales forecast table.
3. Compare ARIMA vs. Prophet on time-series energy usage data; which performs better?

Key Takeaways

• Predictive analytics uses historical data to estimate future outcomes.
• The workflow spans problem framing, data prep, modeling, deployment, and monitoring.
• Beginner-friendly tools like logistic regression, decision trees, and SQL rolling averages go a long way.
• Galaxy streamlines data prep, collaboration, and governance, making predictive projects faster and safer.

Next Steps

1. Install Galaxy (desktop or web) and connect your database.
2. Recreate the churn example on your own company data.
3. Explore advanced algorithms (XGBoost, neural nets) once you have reliable baselines.
4. Set up automated retraining and evaluation dashboards.