Create logisticregression

logisticregression

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+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]) # Feature values for first sample
+print(iris_data.target[0]) # Target value for first sample
+
+
+# The imputer replaces missing values with the mean
+
+from sklearn.impute import SimpleImputer
+
+imputer = SimpleImputer(strategy='mean')  
+imputed_data = imputer.fit_transform(iris_data.data)
+
+# Feature Scaling
+
+from sklearn.preprocessing import StandardScaler
+
+scaler = StandardScaler()
+scaled_data = scaler.fit_transform(iris_data.data)
+
+
+# Visualizing the 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()
+
+
+# Training a Simple Model
+ 
+from sklearn.linear_model import LogisticRegression
+
+model = LogisticRegression()
+model.fit(scaled_data, iris_data.target)
+