Solving Business Problems with Supervised Learning

Solving Business Problems with Supervised Learning

Enhanced Problem-Solving through Organisational Analysis

Customer Churn Prediction

Identify customers who are likely to cancel services based on behaviour patterns.

  • Usage patterns
  • Service history
  • Customer demographics
Classification Problem
Sales Forecasting

Predict future sales volumes based on historical data and external factors.

  • Seasonal trends
  • Marketing spend
  • Economic indicators
Regression Problem
Fraud Detection

Identify suspicious transactions that may indicate fraudulent activity.

  • Transaction patterns
  • Account activity
  • Location data
Anomaly Detection
Inventory Optimisation

Determine optimal stock levels to minimise costs whilst meeting demand.

  • Historical demand
  • Lead times
  • Seasonal factors
Predictive Modelling

Key Analytical Skills Developed:

🔍

Problem Identification

Accurately defining business challenges as data problems

🔄

Systems Thinking

Understanding interconnected factors in organisational contexts

📊

Data Interpretation

Extracting meaningful insights from complex datasets

🎯

Impact Assessment

Evaluating the business value of potential solutions

Informing Data-Driven Decision-Making

Regression
R

Predicting continuous values

  • Demand forecasting
  • Price optimisation
  • Resource allocation
Classification
C

Categorising into discrete classes

  • Customer segmentation
  • Risk assessment
  • Quality control
Boosting Algorithms
B

Enhancing model performance

  • XGBoost
  • AdaBoost
  • Gradient Boosting
Ensemble Techniques
E

Combining multiple models

  • Random Forests
  • Voting Classifiers
  • Stacking Ensembles

Decision-Making Impact

Before ML Implementation

  • Decisions based on intuition
  • Reactive problem solving
  • Limited insight into causation
  • One-size-fits-all approach

After ML Implementation

  • Data-backed strategic planning
  • Proactive issue prevention
  • Clear understanding of drivers
  • Personalised approaches

Developing Accurate Predictions with Advanced ML Models

1
Data Collection & Preparation
2
Feature Selection & Engineering
3
Model Selection & Training
4
Evaluation & Validation
5
Deployment & Monitoring

Cross-Industry Success Metrics

93%

Prediction Accuracy

86%

Model Precision

91%

Model Recall

89%

F1 Score

Business Scenarios and Model Applications

Retail

Inventory forecasting with seasonal demand patterns and promotion impacts

Impact: 24% reduction in stockouts, 18% reduction in overstock

Finance

Credit risk assessment with complex financial and behavioural indicators

Impact: 31% reduction in default rates, 15% increase in approval rates

Healthcare

Patient readmission prediction based on medical history and treatment data

Impact: 22% reduction in readmissions, £2.6M annual savings

Manufacturing

Predictive maintenance using sensor data and equipment performance metrics

Impact: 38% reduction in downtime, 27% increase in equipment lifespan

Leveraging Deep Learning for Complex Data Insights

In
In
In
In
H
H
H
H
H
H
H
H
Out
Out

Extracting Value from Diverse Data Types

T

Text Data

Sentiment analysis, document classification

I

Image Data

Visual inspection, object recognition

A

Audio Data

Speech recognition, audio classification

T

Time Series

Sequential prediction, anomaly detection

U

Unstructured

Complex, mixed-format data

Business Value from Deep Learning

Automated Document Analysis

  • Contract review automation
  • Invoice processing
  • Regulatory compliance

ROI: 73% cost reduction in document processing

Visual Quality Control

  • Defect detection in manufacturing
  • Product consistency verification
  • Safety inspection

ROI: 64% increase in defect detection accuracy