Pythonで成績予測AIを作成｜教師あり学習で実践解説

Schools and cram schools often worry about how well a student will perform. If we could predict their grades in advance, we could provide more accurate instruction and support.

In this article, we will use Python and supervised learning toHow to build an AI model to predict student gradesWe will explain everything from the necessary libraries to setting up the environment, preprocessing data, learning models, and executing predictions.Expressions and specific codes that even elementary school students can understandWe will introduce it here.

We will proceed carefully so that even those who are new to AI will feel like, "I can do this."

What is the prediction of student performance?

conclusion

What is student grade prediction?Technology to predict future performance from past learning datais.

reason

A student's grades are influenced by the degree of completion of assignments, class attitude, past test results, etc. By learning from this data, it becomes possible to predict future scores.

Examples

• Predict final exam scores based on quiz scores and assignment submission status

• Predicting end-of-year overall grades from report card fluctuation patterns

• Use academic progress as a reference for choosing schools to apply to

reassertion

The prediction of the results isContributing to the realization of individually optimized instruction in the educational fieldI will.

Preparing the Python environment and installing libraries

conclusion

Create a Python virtual environment, install the necessary libraries, and you're ready to go.

procedure

1. Creating a virtual environment (Windows/Mac)

1	`python -m venv predict-env`

2. Activate the virtual environment

• Windows

1	`predict-env\Scripts\activate`

• Mac/Linux

1	`source` `predict-env/bin/activate`

3. Install required libraries

1	`pip` `install` `pandas numpy matplotlib scikit-learn`

point

• pandas: Useful for handling table data

• scikit-learn: A library for building machine learning models.

reassertion

Once the environment is ready, you can immediately implement AI.

Loading and preprocessing data

conclusion

Supervised learning requires data that pairs "features (input)" with "answers (output)."

Example of data used (student information)

hours_study	attendance	test_score
3	90	70
5	100	88
1	60	45

Code example

import pandas as pd data = pd.DataFrame({ 'hours_study': [3, 5, 1, 2, 4], 'attendance': [90, 100, 60, 70, 95], 'test_score': [70, 88, 45, 50, 85] }) X = data[['hours_study', 'attendance']] # Features y = data['test_score'] # Correct labels

reassertion

In supervised learning, a "set of inputs and outputs" is essential.

Building a supervised learning model

conclusion

In supervised learning, a predictive model is trained based on data.

Algorithm used: Linear regression

from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = LinearRegression() model.fit(X_train, y_train)

point

• train_test_splitSplit the data into 8:2

• fit() functionLearn on

reassertion

Linear regression isA powerful technique when the predicted values are numericalis.

Let's actually predict the results

conclusion

By feeding data into a trained model, we can predict performance.

new_student = [[4, 92]] # Study time 4 hours, attendance rate 92% predicted_score = model.predict(new_student) print(f"Predicted score: {predicted_score[0]:.1f}")

Example results

1	`Predicted score: 82.4`

reassertion

With just a few lines, future scores become predictable.

Accuracy evaluation and future use

conclusion

Let's check the numbers to see how accurately the AI made predictions.

Evaluation method: Mean Squared Error (MSE)

from sklearn.metrics import mean_squared_error y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print(f"MSE: {mse:.2f}")

What does the rating mean?

• The smaller the MSE, the closer the prediction is to reality.

• Generally, a value below 10 is considered to be fairly accurate.

reassertion

Visualizing accuracy is key to increasing trust in AI.

FAQ

Q1. What should I do when there is little training data?

A. If the amount of data is small, accuracy will decrease, so increase the amount of training data or use data augmentation (duplication or synthesis).

Q2. Can you predict things other than test scores?

A. Of course, that is possible. We can handle anything that can be expressed in numbers, such as the number of days attended, the number of submissions, and overall grades.

Q3. Can I use Random Forest or XGBoost?

A. Yes, if you want to achieve higher accuracy, these algorithms are also effective.