One idea
Choose the simplest model that can answer your problem well enough. Your first ML repo is judged more on clear choices and honest testing than on a fancy algorithm.
Model families
A model family is a type of method, not one exact model. In scikit-learn, examples are LinearRegression, LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, and KNeighborsClassifier.
Your choice starts with the label. If the label is a number, it is usually regression. If the label is a category like pass/fail, spam/not spam, or high/low, it is classification.
Pick by task
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
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Common mistake
Do not start with the most complex model because it sounds better. If your train score is high and test score is poor, the model may be memorising patterns instead of learning useful ones.
Also do not compare models using different train/test splits. Keep the same split so the comparison is fair.
Good default path
For an internship-ready beginner repo, train one baseline model first. Then train one slightly stronger model only if you can explain why.
Write the reason in your README. A reviewer should know why you picked it without reading your mind.
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.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)Do this now
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
0 of 5 done