ML Problems In the world of machine learning. Data is the new gold but it only becomes valuable when you know how to refine it. Analytics helps us turn raw data into understanding, foresight, and action. Machine learning problems can be broken down into three main categories: descriptive, predictive, and prescriptive analytics. Think of it as a journey: first, you understand the past, then you predict the future, and finally, you shaped the future. Descriptive analytics is the foundation of data analysis, focusing on summarizing past data to understand what has already occurred. It’s like looking in the rearview mirror to see where you›ve been. This type of analysis doesn’t make predictions or suggestions; it simply presents the facts in an understandable way, often through reports, dashboards, and visualizations Predictive analytics takes things a step further by using historical data, statistical algorithms, and machine learning to forecast future outcomes. It’s like using your rearview mirror and a weather forecast to anticipate road conditions ahead. While not always 100% accurate, it pro - vides a powerful tool for proactive decision-making. Analogy: Imagine you’re a shop owner reviewing your sales records from the last quarter. You notice that ice cream sales spiked in July. That’s descriptive analytics – a clear snapshot of the past. Real-Life Example: So cial media platforms use descriptive analytics to provide metrics like likes, shares, and follower growth. These are straightforward counts of events that have already happened. Similarly, a hospital might use descriptive analytics to track the number of patients admitted to the emergency room each day, providing real-time data on patient volume and demographics. Analogy: As the shop owner, you now use past sales data to predict that ice cream sales will likely spike again next July. You might even forecast how much ice cream you’ll sell based on weather predictions. The Data Analytics Journey: From Past Insight to Future Action ML Problems Descriptive Analytics: What Has Happened? Predictive Analytics: What Could Happen? Real-Life Example: E-commerce: Online retailers like Amazon analyze your past purchases and browsing history to recommend products you might like in the future. Finance: Banks us e predictive models to detect fraudulent credit card transactions in real- time by identifying unusual spending patterns. Healthcare: M edical professionals can use predictive analytics to identify patients at high risk for certain diseases based on their health records and lifestyle data, allowing for preventative care. 01 01 02 02 03 03 Prescriptive analytics is the most advanced stage, going beyond prediction to recommend specific actions to achieve a desired outcome. It’s like having a GPS that not only predicts your arrival time but also suggests the best route to take to avoid traffic. This type of analysis uses a combination of predictive models, business rules, and optimization algorithms to guide decision-making. Analogy: The shop owner, armed with the prediction of a July sales spike, now receives a recommendation to order a specific amount of extra ice cream and to run a “buy one, get one free” promotion on the last week of the month to maximize profits and minimize waste. Real-Life Example: Logistics: UPS uses prescriptive analytics to optimize delivery routes for its drivers, considering factors like traffic, weather, and package data to save fuel and time. Hospitality: Hilton Worldwide adjusts its hotel room prices in real-time based on historical occupancy rates, customer preferences, and competitor pricing to maximize revenue. Retail: Prescriptive analytics can help retailers optimize inventory by forecasting demand and suggesting when to restock certain products to prevent shortages or overstocking. Prescriptive Analytics: What Should We Do? Aspect Descriptive Analytics Predictive Analytics Prescriptive Analytics Key Question Answered What has happened? What could happen? What should we do? Purpose Summarize past data for understanding Forecast future outcomes based on patterns Recommend best actions to achieve goals Focus Past events and trends Future probabilities and scenarios Optimal decisions and strategies Typical Applications Sales reports & dashboards - Social media metrics (likes, shares, followers) - Hospital admissions tracking Fraud detection in bank- ing - Product recommen- dations in e-commerce - Disease risk prediction in healthcare Route optimization in logistics (UPS) - Dynam - ic pricing in hospitality (Hilton) - Inventory optimi - zation in retail Data Usage Historical data only Historical data + statistical models + ML Predictive models + business rules + optimization algorithms Output Reports, charts, visualizations Forecasts, probabilities, risk scores Actionable recommendations, decision options Typical Tools Excel, SQL, Tableau, Power BI Python (scikit-learn), R, SAS, Azure ML, IBM SPSS Optimization engines, IBM Decision Optimization, Google OR-Tools, advanced AI/ML platforms The Power of Synergy While each type of analytics is useful on its own, their true power is unlocked when they are used together. Descriptive analytics provides the foundational understanding, predictive analytics offers a glimpse into the future, and prescriptive analytics provides the actionable steps to shape that future. By leveraging all three, businesses and organizations can move from simply reacting to past events to proactively creating their desired outcomes.