Machine Learning - lesson 3 | HackIndia Skills

Machine Learning - lesson 3 | HackIndia Skills | HackIndia

Example

Create X and y

This example uses a small student dataset. It predicts pass_fail from study and attendance columns.

Keep the target out of X. Drop ID-like columns unless they contain real signal you can explain.

import pandas as pd

# Load data
df = pd.read_csv("data/students.csv")

# Quick check
print(df.head())
print(df.isna().sum())

# Simple cleanup
# Fill missing numeric values with the column median
df["hours_studied"] = df["hours_studied"].fillna(df["hours_studied"].median())
df["attendance_percent"] = df["attendance_percent"].fillna(df["attendance_percent"].median())

# Convert a text category into numbers
# yes -> 1, no -> 0
df["has_internet"] = df["has_internet"].map({"yes": 1, "no": 0})

# Choose label
y = df["pass_fail"]

# Choose features
X = df.drop(columns=["pass_fail", "student_id", "final_marks"])

print("Feature columns:", list(X.columns))
print("Label column: pass_fail")
print("X shape:", X.shape)
print("y shape:", y.shape)

Prepare features and labels

One split matters

Features and label

Prepare the table

Avoid label leakage

Create X and y

Use AI carefully

Write choices down

Do this now

Quick check