Chapter 14: Machine Learning Data

Machine Learning Data” — the most important thing in all of ML. Without good data, even the smartest algorithm is like a brilliant student with no books — it can’t learn anything useful!

I’ll explain this in very simple, detailed steps like your favorite teacher: stories, real Hyderabad examples, analogies, types of data, how it’s organized, and why “garbage in = garbage out” is the golden rule in 2026.

Step 1: What Exactly is “Machine Learning Data”?

Machine Learning Data = the raw material (examples, observations, measurements) that we feed to an ML model so it can learn patterns and make predictions or decisions.

Think of it like this:

• A child learns to identify mango varieties by looking at thousands of real mangoes (data) + someone telling “this is Alphonso”, “this is Kesar”.
• Similarly, an ML model learns by seeing tons of examples (data) — sometimes with correct answers (labels), sometimes without.

In 2026, almost every “AI” app you use (Ola route prediction, Swiggy recommendations, UPI fraud block, Google Photos tagging) runs on huge amounts of data collected from users, sensors, cameras, etc.

Key truth: The quality, quantity, and cleanliness of data decide 80–90% of your model’s success. Fancy algorithms can’t fix bad data.

Step 2: Basic Building Blocks of ML Data

Every ML dataset usually has these parts:

1. Samples / Examples / Instances Each row or individual record. Example: One flat sale in Hyderabad = one sample.
2. Features (Inputs / Independent Variables / Attributes) The measurable characteristics we know about each sample (what the model uses to learn). Example: For flat price prediction → size_sqft, bedrooms, bathrooms, location (Gachibowli/Kukatpally), age_years, floor_number.
3. Label / Target / Output / Dependent Variable What we want the model to predict (the correct answer — only in supervised learning). Example: price_lakh (₹85 lakh), or “spam” / “not spam” for emails.
4. No Label → unsupervised learning (find patterns without answers).

Step 3: Types of Data in Machine Learning (Very Important!)

Data comes in different “flavors” — the type decides which ML techniques work best.

• Structured → easiest for classical ML (trees, regression).
• Unstructured → needs deep learning (CNN for images, transformers for text) — powers most “wow” AI in 2026.
• Unstructured is 80–90% of all data in companies today!

Step 4: How We Split ML Data (The Golden Rule)

We never train and test on the same data — that’s cheating!

Standard split (common ratios in 2026):

• Training Set (60–80%) → Model learns patterns here (adjusts weights).
• Validation Set (10–20%) → Tune hyperparameters (learning rate, layers) during training.
• Test Set (10–20%) → Final honest check after everything — measures real-world performance.

Example: You have 10,000 Hyderabad flat records.

• Train: 7,000 → model learns “bigger size + Gachibowli = higher price”
• Validation: 1,500 → try different models → pick best
• Test: 1,500 → unseen flats → “How accurate on new Banjara Hills flats?”

In code (Python sklearn):

Step 5: Real-Life Hyderabad Examples of ML Data

1. Ola / Uber Ride Price & Route Prediction
   • Data: Millions of past rides (structured: pickup lat/long, time, distance, traffic level, fare; unstructured: driver notes, passenger feedback text).
   • Features: time_of_day, distance_km, rain=yes/no, surge_multiplier.
   • Label: actual_fare_₹ or best_route.
   • Split: Train on last 6 months → test on today’s rides.
2. Swiggy / Zomato Food Recommendations
   • Data: User orders (structured: dish, price, rating; unstructured: reviews “too spicy”, photos of food).
   • Features: past orders, location, time, cuisine preference.
   • Label: did user like it? (rating >4).
3. UPI Fraud Detection (PhonePe, Google Pay)
   • Data: Billions of transactions (structured: amount, time, merchant, device ID; unstructured: unusual remarks).
   • Features: amount, location change, time since last txn.
   • Label: fraud=1 / normal=0 (from human reviewers or confirmed cases).
4. Google Photos / Instagram Auto-Tagging
   • Data: Billions of user-uploaded photos (unstructured).
   • Features: pixels, colors, shapes (learned by CNN).
   • Label: “beach”, “food”, “selfie” (human-labeled subset).

Step 6: Properties of Good ML Data (2026 Reality Check)

• Volume → More data usually better (but quality > quantity).
• Variety → Mix structured + unstructured for powerful models.
• Velocity → Fresh data (streaming for real-time fraud).
• Veracity → Clean, accurate, unbiased (no fake labels).
• Value → Relevant to problem (don’t use weather data for spam detection).

Problems with bad data:

• Biased → model discriminates (e.g., loan approval favors certain areas).
• Noisy → wrong labels → model learns wrong patterns.
• Missing → gaps → model guesses badly.

Final Teacher Summary (Repeat to Anyone!)

Machine Learning Data = the fuel of ML. It’s examples (structured tables or unstructured images/text) with features (what we know) and labels (what to predict). We split it → train → validate → test → deploy.

In Hyderabad 2026: Your every Ola ride, Swiggy order, UPI payment, Instagram scroll adds to someone’s ML data — making apps smarter!

Got it? 🔥

Questions?

• Want to see sample CSV data for flat prices?
• How to clean messy unstructured data?
• Difference between features vs labels with code?

Just say — next class ready! 🚀