[딥러닝] 주짓수 훈련과 복음 활동이 지능적 성장과 심리적 안정에 미치는 영향 분석

import numpy as np

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split #pip install scikit-learn

from sklearn.linear_model import LinearRegression

import tkinter as tk

from tkinter import ttk #pip install ttkthemes

# 데이터 생성 (예시 데이터)

np.random.seed(0)

n_samples = 100

# 주짓수 훈련 시간, 복음 활동 시간, 심리적 안정 및 지능적 성장 점수 생성

jujitsu_hours = np.random.normal(5, 2, n_samples)

gospel_hours = np.random.normal(3, 1, n_samples)

psychological_stability = 50 + jujitsu_hours * 2 + gospel_hours * 3 + np.random.normal(0, 5, n_samples)

cognitive_growth = 40 + jujitsu_hours * 4 + gospel_hours * 2 + np.random.normal(0, 5, n_samples)

# 데이터 나누기

X = np.column_stack((jujitsu_hours, gospel_hours))

y_psychological = psychological_stability

y_cognitive = cognitive_growth

X_train, X_test, y_psychological_train, y_psychological_test = train_test_split(X, y_psychological, test_size=0.2, random_state=0)

_, _, y_cognitive_train, y_cognitive_test = train_test_split(X, y_cognitive, test_size=0.2, random_state=0)

# 모델 학습

model_psychological = LinearRegression().fit(X_train, y_psychological_train)

model_cognitive = LinearRegression().fit(X_train, y_cognitive_train)

# 예측

y_psychological_pred = model_psychological.predict(X_test)

y_cognitive_pred = model_cognitive.predict(X_test)

# 결과 시각화

def plot_results():

    fig, ax = plt.subplots(1, 2, figsize=(12, 5))

   

    # 심리적 안정성 그래프

    ax[0].scatter(y_psychological_test, y_psychological_pred, color='blue', label='Predicted vs Actual')

    ax[0].plot([y_psychological_test.min(), y_psychological_test.max()], [y_psychological_test.min(), y_psychological_test.max()], 'k--', lw=2)

    ax[0].set_xlabel('Actual Psychological Stability')

    ax[0].set_ylabel('Predicted Psychological Stability')

    ax[0].set_title('Psychological Stability')

    ax[0].legend()

    # 지능적 성장 그래프

    ax[1].scatter(y_cognitive_test, y_cognitive_pred, color='green', label='Predicted vs Actual')

    ax[1].plot([y_cognitive_test.min(), y_cognitive_test.max()], [y_cognitive_test.min(), y_cognitive_test.max()], 'k--', lw=2)

    ax[1].set_xlabel('Actual Cognitive Growth')

    ax[1].set_ylabel('Predicted Cognitive Growth')

    ax[1].set_title('Cognitive Growth')

    ax[1].legend()

   

    plt.tight_layout()

    plt.show()

plot_results()