Machine Learning - lesson 5 | HackIndia Skills

Machine Learning - lesson 5 | HackIndia Skills | HackIndia

Example

README model note

This is enough for many small HackIndia projects. It says the task, the models, and the reason.

Model choice

Task: Classification. The label is whether a student is likely to pass.

Baseline: LogisticRegression, because it is a simple classification model and easy to compare.

Second model: RandomForestClassifier, because the dataset is tabular and may have non-linear patterns.

Final model used: RandomForestClassifier, because it gave better test accuracy on the same train/test split.

Example

Try two models

Use this only after your features, labels, and train/test split are ready. The important part is that both models use the same split.

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

models = {
    "logistic_regression": LogisticRegression(max_iter=1000),
    "random_forest": RandomForestClassifier(random_state=42)
}

for name, model in models.items():
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    score = accuracy_score(y_test, predictions)
    print(name, score)

Choose common model families

One idea

Model families

Pick by task

Common mistake

Good default path

README model note

Try two models

Do this now

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