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
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) datamine tutorial
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
pip install pandas numpy matplotlib scikit-learn We’ll use a built‑in dataset: Iris flowers. y_test = train_test_split(X
I’ll assume you want me to on datamine (data mining) basics — possibly in the context of game datamining or general data mining with Python .
