43 lines
1.0 KiB
Python
43 lines
1.0 KiB
Python
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_iris
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from sklearn.tree import DecisionTreeClassifier, plot_tree
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import matplotlib.pyplot as plt
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dtype_dict = {
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'season': str,
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'anomalies': str,
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'weather': str,
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'temp': int,
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'water': int,
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'nutri': int,
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'pests': str,
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'weeds': str,
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'type': str,
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'ripeness': int,
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'target_column': str
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}
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# Wczytaj dane z pliku CSV
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data = pd.read_csv('resources/dataset.csv', header=0, dtype=dtype_dict)
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#print(data)
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X = data.drop('target_column', axis=1)
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y = data['target_column']
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X = pd.get_dummies(X)
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print(X)
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print(y)
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# Podział danych na zbiór treningowy i testowy
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Dopasowanie modelu drzewa decyzyjnego
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model = DecisionTreeClassifier()
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model.fit(X_train, y_train)
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fig = plt.figure(figsize=(25,20))
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_ = plot_tree(model, feature_names=X.columns, class_names=model.classes_, filled=True)
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plt.savefig('drzewo_decyzyjne.png')
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plt.show()
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