Proof beats claims
Your repo is your proof. A recruiter or hackathon reviewer should open it and see what data you used, how you split it, what model you trained, and what result you got.
Do not just upload a notebook called final.ipynb. Ship a small project that runs from clean steps.
What your repo needs
Keep it boring and clear. A simple scikit-learn model with honest evaluation is better than a messy deep learning repo that nobody can run.
Your repo must answer five questions: Where is the dataset from? What is the target? How did you split train and test? Which model did you choose? What metric did it score on test data?
Use this structure
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
README starter
Use this as a plain starting point. Replace the example text with your project details.
If you use AI to draft the README, check every command, file name, metric value, and dataset link before committing it.
# Student Placement Predictor
## Problem
Predict whether a student gets placed using tabular student data.
## Dataset
Source: <paste dataset link or official source here>
Target column: placed
Rows used: <number>
## Train/test split
Split: 80% train, 20% test
Random state: 42
Test data was not used for training.
## Model
scikit-learn LogisticRegression
Reason: simple baseline for a binary classification problem.
## Result
Metric: accuracy
Test accuracy: 0.82
## How to run
1. Clone this repo
2. Install dependencies:
pip install -r requirements.txt
3. Train and evaluate:
python train.pySmall train file
This is enough for a starter repo if your CSV is already cleaned. Keep names readable.
Do not copy this blindly. Change the file name, target column, model, and metric for your own project.
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
DATA_PATH = "data.csv"
TARGET = "placed"
df = pd.read_csv(DATA_PATH)
X = df.drop(columns=[TARGET])
y = df[TARGET]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)
preds = model.predict(X_test)
score = accuracy_score(y_test, preds)
print(f"Test accuracy: {score:.3f}")Do not hide data
A common mistake is saying “dataset from Kaggle” without a link, file name, or target column. That makes your work hard to trust.
If the dataset is too large to commit, do not upload a random sample without saying so. Put the source link and explain where to place the file before running.
Commit like a builder
Make small commits with useful messages: add dataset notes, add training script, add evaluation result. This shows how you worked.
Before submission, open your GitHub repo in an incognito window. Check that a stranger can read it without your local files.
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