Personal Study Predictor
A least-squares regression model fitted live on your own study-hours-vs-score data - the same math behind demand forecasting, pointed at your next exam instead.
Log a handful of past study sessions and their scores, and the model tells you the trend line, how well it fits, and how many hours you'd need for a target grade.
01 /
The Problem.
// "how much should I actually study for this?"
Students plan study time by gut feel - "I'll do 3 hours" - with no reference to how their own past effort has actually translated into scores. The relationship is rarely made explicit, so the same over- or under-preparation mistakes repeat every exam cycle.
A simple regression model, fit on a student's own history rather than a generic study guide, turns "how much should I study" into an answerable question.
02 /
Methodology.
// least squares, fit live on your data
Least-Squares Fit
slope = Σ(x−x̄)(y−ȳ) / Σ(x−x̄)². The
line that minimizes total squared error between predicted and actual scores.
R² Goodness of Fit
R² = 1 − SS_res / SS_tot. How much of the score variation the study-hours
trend actually explains - low R² means other factors dominate.
Forward Prediction
Given a planned number of study hours, the fitted line predicts an expected score.
Reverse Solving
hours = (target − intercept) / slope. Given a target score, back-solves for the
study time the trend line implies.
03 /
What’s in the build.
// shipped features
Log past sessions
Add study-hours/score pairs from real past exams - the model refits instantly on every new point.
Live scatter + trend line
An inline SVG plot of every data point against the fitted regression line - no charting library required.
Forward prediction
Enter planned study hours, get a predicted score from your own trend.
Reverse target-solving
Enter a target score, get the study hours your own history implies you'd need.
Fit-quality warning
Flags when R² is too low to trust the prediction - teaching that not every trend line is reliable.
Sample dataset
Preloaded study-hours/score history so you can see the model work before logging your own.
04 /
Stack & Outcome.
// what it took, what it shipped
Stack
Outcome
A student sees, in real numbers, whether "study more" actually predicts a better score for them specifically - and gets a concrete hours target instead of a vague intention.
Live Tool