29. What Is Machine Learning (Simple Explanation)
29.4 What You Need Before ML
Why. ML on a messy table is like cooking on a dirty stove. You already cleaned data in Chapters 9–12. That work is the real entrance ticket to ML.
What. Before ML you need: (1) a clean rectangular table, (2) a clear label if supervised, (3) Python + pandas skills from earlier chapters, (4) a honest question in one sentence.
How.
Pre-ML checklist
- One row = one case (grain). Example: one Blinkit order per row — not mixed with monthly totals.
- Columns have clear dtypes (numbers are numbers, dates are dates).
- Missing values are handled on purpose (fill, drop, or flag) — not ignored.
- For supervised: label column exists and is defined the same way for every row.
- You can say: “I want to predict ___ from ___.”
import pandas as pd
# Tiny clean table ready for later ML (fictional Maharashtra deliveries)
df = pd.DataFrame({
"order_id": ["BLK-001", "BLK-002", "BLK-003", "BLK-004"],
"city": ["Pune", "Solapur", "Pune", "Nashik"],
"items": [3, 5, 2, 6],
"distance_km": [2.1, 8.4, 1.2, 4.0],
"was_late": [0, 1, 0, 1],
})
assert df["order_id"].is_unique
assert df["was_late"].isin([0, 1]).all()
print(df.dtypes)
print("Ready idea: predict was_late from items + distance_km + city (later chapters).")
What you should see. Stable dtypes and a clear 0/1 label. No model fit yet — only readiness.
Ravindra Bagale's Tip
Khup students sklearn import kartat ani pehle cleaning skip kartat. Pehle Chapter 9 style clean table. Mag ML. Kram lakshat theva.