Datamine Tutorial -

pip install pandas numpy matplotlib scikit-learn We’ll use a built‑in dataset: Iris flowers.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) datamine tutorial

I notice you’ve asked for a "datamine tutorial" and to — I think you might have meant "make a piece" (as in create a tutorial piece ) or possibly "make peace" (unlikely here). pip install pandas numpy matplotlib scikit-learn We’ll use

model = DecisionTreeClassifier() model.fit(X_train, y_train) y_test = train_test_split(X

print(df.groupby('target').mean()) : Species 0 has smaller petals than species 2. Step 5: Predictive Mining (Classification) from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score X = df.drop('target', axis=1) y = df['target']

import pandas as pd from sklearn.datasets import load_iris iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) df['target'] = iris.target