7

 import numpy as np, pandas as pd, seaborn as sns, matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import PolynomialFeatures

from sklearn.metrics import mean_squared_error, r2_score

from sklearn.datasets import fetch_openml

boston = fetch_openml(name='boston', version=1, as_frame=True)

X, y = boston.data.values, boston.target.values

Xt, Xs, yt, ys = train_test_split(X, y, test_size=0.2)

m = LinearRegression().fit(Xt, yt)

yp = m.predict(Xs)

print("Linear R²=%.2f" % r2_score(ys, yp))

plt.scatter(Xs[:,5], ys, c='b'); plt.scatter(Xs[:,5], yp, c='r'); plt.title("Boston"); plt.show()

df = sns.load_dataset('mpg').dropna()

df['horsepower'] = pd.to_numeric(df['horsepower'], errors='coerce')

df = df.dropna()

X, y = df[['horsepower', 'weight']], df['mpg']

Xt, Xs, yt, ys = train_test_split(X, y, test_size=0.2)

p = PolynomialFeatures(3).fit(Xt)

Xt_, Xs_ = p.transform(Xt), p.transform(Xs)

m = LinearRegression().fit(Xt_, yt)

yp = m.predict(Xs_)

print("Poly R²=%.2f" % r2_score(ys, yp))

plt.scatter(Xs['horsepower'], ys, c='b'); plt.scatter(Xs['horsepower'], yp, c='r'); plt.title("MPG"); plt.show()


Comments

Post a Comment

Popular posts from this blog

9

import scipy.io, numpy as np, matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.metrics import accuracy_score d = scipy.io.loadmat('olivettifaces.mat') X, y = d['faces'].T, np.repeat(np.arange(40), 10) Xt, Xs, yt, ys = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42) m = GaussianNB().fit(Xt, yt) yp = m.predict(Xs) print("Acc: %.2f%%" % (accuracy_score(ys, yp) * 100)) i = np.random.choice(len(Xs), 20, replace=False) imgs = Xs[i].reshape(-1, 64, 64).transpose(0, 2, 1) plt.figure(figsize=(12,6)) for j in range(20):     plt.subplot(4, 5, j+1)     plt.imshow(imgs[j], cmap='gray')     plt.title(f"T:{ys[i[j]]}, P:{yp[i[j]]}", fontsize=8)     plt.axis('off') plt.tight_layout(); plt.show() #we need to upload olivettiface dataset
Read more

10

from sklearn.datasets import load_breast_cancer from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.metrics import confusion_matrix, classification_report import matplotlib.pyplot as plt X, y = load_breast_cancer(return_X_y=True) X = StandardScaler().fit_transform(X) y_pred = KMeans(n_clusters=2, random_state=42, n_init=10).fit_predict(X) print(confusion_matrix(y, y_pred)) print(classification_report(y, y_pred)) X_pca = PCA(2).fit_transform(X) plt.scatter(*X_pca.T, c=y_pred, cmap='coolwarm', alpha=0.7) plt.title("K-Means Clustering (PCA)") plt.show()
Read more