|
import sklearn |
|
from sklearn import datasets |
|
import numpy as np |
|
|
|
iris = datasets.load_iris() |
|
digits = datasets.load_digits() |
|
|
|
from sklearn.datasets import load_iris |
|
iris_data = load_iris() |
|
|
|
print(iris_data.data[0]) |
|
print(iris_data.target[0]) |
|
|
|
|
|
|
|
|
|
from sklearn.impute import SimpleImputer |
|
|
|
imputer = SimpleImputer(strategy='mean') |
|
imputed_data = imputer.fit_transform(iris_data.data) |
|
|
|
|
|
|
|
from sklearn.preprocessing import StandardScaler |
|
|
|
scaler = StandardScaler() |
|
scaled_data = scaler.fit_transform(iris_data.data) |
|
|
|
|
|
|
|
|
|
import matplotlib.pyplot as plt |
|
plt.scatter(iris_data.data[:, 0], iris_data.data[:, 1], c=iris_data.target) |
|
plt.xlabel('Sepal Length') |
|
plt.ylabel('Sepal Width') |
|
plt.show() |
|
|
|
|
|
|
|
|
|
from sklearn.linear_model import LogisticRegression |
|
|
|
model = LogisticRegression() |
|
model.fit(scaled_data, iris_data.target) |
|
|
|
|