Created
August 21, 2025 22:24
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| import numpy as np | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import r2_score | |
| df = pd.DataFrame() | |
| df['x'] = np.random.uniform(0, 2, size=100) | |
| df['y'] = np.random.uniform(0, 3, size=100) | |
| corr_matrix = df.corr() | |
| cov_matrix = df.cov() | |
| print("Correlation Matrix") | |
| print(corr_matrix, end='\n\n') | |
| print("Covariance Matrix") | |
| print(cov_matrix, end='\n\n') | |
| sns.scatterplot(data=df, x='x', y='y') | |
| sns.regplot(data=df, x='x', y='y') | |
| X = df[['x']] | |
| y = df['y'] | |
| linreg = LinearRegression() | |
| linreg.fit(X, y) | |
| print("slope:", linreg.coef_[0]) | |
| print("intercept:", linreg.intercept_) | |
| plt.savefig('plot.png') | |
| y_pred = linreg.predict(X) | |
| print("R2:", r2_score(X, y_pred)) |
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