Machine Learning - lesson 6 | HackIndia Skills

Machine Learning - lesson 6 | HackIndia Skills | HackIndia

Example

Read this script

This is a small, normal scikit-learn script. Read it top to bottom. The comments show the purpose of each block.

Your repo does not need fancy code. It needs code that someone else can run and understand.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report

# 1. Load dataset
# Example: CSV has columns: hours_studied, attendance, past_score, passed
df = pd.read_csv("data/student_pass.csv")

# 2. Separate features and label
X = df[["hours_studied", "attendance", "past_score"]]
y = df["passed"]

# 3. Split before training
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# 4. Build preprocessing + model together
model = Pipeline([
    ("scaler", StandardScaler()),
    ("classifier", LogisticRegression(max_iter=1000))
])

# 5. Learn only from training data
model.fit(X_train, y_train)

# 6. Test on unseen data
y_pred = model.predict(X_test)

# 7. Print evaluation
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

Common mistake

Watch for leakage

A common bug is preparing data before the split in a way that learns from the full dataset. For example, fitting a scaler on all rows before train_test_split can leak test information.

A Pipeline helps because the scaler is fitted only on the training data when you call model.fit(X_train, y_train). This keeps your test result more honest.

# Risky pattern
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)  # uses all rows, including future test rows
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2)

# Better pattern
model = Pipeline([
    ("scaler", StandardScaler()),
    ("classifier", LogisticRegression(max_iter=1000))
])
model.fit(X_train, y_train)

Read scikit-learn code

One mental model

Words you will see

Read this script

Trace any repo

Use AI as reviewer

Watch for leakage

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