[Case Study]
DataRobot AI Tool Case Study: Predicting Apple Stock Prices
By Krizma Nagi
LinkedIn Profile
Introduction
DataRobot is an automated machine learning platform designed to democratize data science by enabling users without deep technical expertise to build and deploy predictive models. This case study, initiated by an engineer at Think Technologies, demonstrates how DataRobot was used to analyze Apple (AAPL) stock data and predict closing prices, showcasing the platform's ability to quickly deliver sophisticated predictive analytics with minimal manual intervention.
Problem Statement
Financial analysts and investors typically spend significant time building predictive models for stock price movements. This process traditionally requires:
Extensive data science expertise
Manual feature engineering
Time-consuming algorithm selection and tuning
Validation and testing across multiple models
These requirements create barriers for many organizations, resulting in either underutilization of available data or dependence on expensive specialized talent. For this case study, we aimed to predict Apple's stock closing prices using historical data, a task that would traditionally require significant time and specialized knowledge.
Implementation
Data Overview
We used historical AAPL stock data containing the following features:
Date
Opening price
High price
Low price
Closing price (target variable)
Volume
Additional technical indicators (50-day moving average, 200-day moving average, RSI, MACD)
Setup Process
Data Preparation
Collected 5 years of historical AAPL stock data (2019-2024)
Performed initial data cleaning to handle missing values
Created a training dataset (80% of data) and a validation dataset (20%)
DataRobot Platform Configuration
Uploaded the prepared dataset to DataRobot
Specified "Closing Price" as the target variable
Selected time-series forecasting as the modeling approach
Set forecast window to 5 trading days
Model Training
DataRobot automatically
Parsed the time-series data and identified the date column
Created derived features (lags, rolling statistics)
Tested hundreds of algorithm combinations
Ranked models based on predictive accuracy
Key Parameters Used
Optimization metric: RMSE (Root Mean Square Error)
Feature engineering depth: Medium
Max training time: 2 hours
Backtesting: 5 folds
Results & Benefits
Performance Metrics
Key Findings
Efficiency Gains: DataRobot reduced model development time from approximately 4 hours using traditional methods to just under 15 minutes total (including data upload and automated model generation).
Performance Improvement: The best model (XGBoost) outperformed the manually built model by 21% in RMSE.
Feature Importance: DataRobot identified unexpected influential features:
Volume-weighted average price was the most important predictor
The previous day's trading range showed stronger correlation than expected
The 14-day RSI provided significant predictive power
Exploratory Analysis Insights:
Identified seasonal patterns in AAPL price movements around quarterly earnings
Discovered that AAPL price volatility increases significantly following product announcements
Detected strong correlation between market sentiment indicators and short-term price movements
Visualization Highlights
Prediction vs. Actual chart showing the model's 5-day forecasts against actual closing prices
Feature importance graph highlighting the top 10 predictors
Partial dependence plots showing how specific features influence the predictions
Time-series decomposition revealing trend, seasonal, and residual components
Conclusion & Next Steps
Summary
DataRobot successfully automated the process of building predictive models for AAPL stock prices, delivering superior performance compared to traditional methods while dramatically reducing the time and expertise required. The platform's ability to automatically test hundreds of algorithms and feature combinations revealed insights that might have been overlooked in a manual approach.
Business Value
Time Savings: 95% reduction in model development time
Resource Efficiency: Enabled analysis without requiring specialized data science talent
Improved Accuracy: 21% improvement in predictive accuracy over manual methods
Actionable Insights: Discovery of previously unknown patterns and relationships in the data
Next Steps
Expand the Model: Incorporate additional external data sources like market sentiment, macroeconomic indicators, and industry-specific news.
Deployment: Implement the model in a production environment to generate daily forecasts.
Portfolio Expansion: Apply the same approach to other stocks to create a comprehensive market analysis tool.
Monitoring: Set up automated model monitoring to detect drift and trigger retraining when necessary.
Potential Applications
Portfolio optimization for investment strategies
Risk assessment for trading operations
Automated trading signals based on predicted price movements
Scenario analysis for different market conditions
This case study, initiated by an engineer at Think Technologies, demonstrates how DataRobot enables organizations to leverage advanced predictive analytics without extensive data science resources, delivering both time savings and performance improvements.