Machine Learning - lesson 4 | HackIndia Skills

Machine Learning - lesson 4 | HackIndia Skills | HackIndia

Example

scikit-learn split

Use train_test_split for most starter projects. Keep random_state fixed so someone else can reproduce your result from the repo.

For classification, stratify keeps class balance similar in train and test. This helps when labels are uneven, like pass/fail, fraud/not fraud, or churn/stay.

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression

X = df[["study_hours", "attendance", "past_score"]]
y = df["passed"]

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42,
    stratify=y
)

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

predictions = model.predict(X_test)
score = accuracy_score(y_test, predictions)

print("Test accuracy:", round(score, 3))

Example

Safer preprocessing

If you scale or fill values, fit the preprocessing on training data only. The simplest way is a scikit-learn Pipeline.

The pipeline learns the scaler from X_train, then applies the same scaler to X_test during prediction.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

pipeline = Pipeline([
    ("scaler", StandardScaler()),
    ("model", LogisticRegression(max_iter=1000))
])

pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

print("Test accuracy:", round(accuracy_score(y_test, predictions), 3))

Split data and test honestly

One honest rule

Why split data

Basic split flow

scikit-learn split

Avoid data leakage

Safer preprocessing

What to report

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