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Regression

Student Test Scores

Uses a student's first test score, past failures, and mother's education level to predict their second test score.

Try It Yourself

Enter values below, then hit Predict to see what the model says.

Raw Dataset (original data)
schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardiantraveltimestudytimefailuresschoolsupfamsuppaidactivitiesnurseryhigherinternetromanticfamrelfreetimegooutDalcWalchealthabsencesG1G2G3
Cleaned Dataset (the version we feed to the model)
G1failuresMeduG3
The Code (how we built this model) 
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

df = pd.read_csv('student_scores_original.csv')

df = df[['G1', 'failures', 'Medu', 'G3']]

df = df.dropna()

df = pd.get_dummies(df, drop_first=True)

X = df.drop('G3', axis=1)
y = df['G3']

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

scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print(r2_score(y_test, y_pred))
Under the Hood (the equation the model learned)
Score₂ = 0.15 + 0.95 × Score₁ − 0.25 × Failures + 0.08 × Mom's Education

Score 1 is by far the biggest driver — nearly 1:1. Each past failure costs about a quarter of a point, and mom's education adds a small boost.

Try the Equation Yourself

Predicted Result —