100 records
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06e13b8f19
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23
dane.csv
23
dane.csv
@ -83,3 +83,26 @@ wilgotnosc_gleby,temperatura_gleby,opady_deszczu,wiek_rosliny,proc_ekspo_na_swia
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33, 20, 4, 4, 50, 12, 3, Pomidor, tak
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52, 23, 1, 5, 65, 16, 4, Salata, tak
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47, 24, 7, 6, 80, 10, 1, Cebula, tak
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38, 25, 2, 3, 65, 10, 1, Marchew, tak
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59, 21, 1, 2, 55, 15, 2, Ziemniaki, nie
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33, 23, 3, 1, 70, 9, 3, Pomidor, nie
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47, 22, 7, 4, 75, 11, 2, Marchew, tak
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58, 20, 4, 3, 60, 16, 1, Ziemniaki, nie
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32, 24, 6, 2, 70, 8, 3, Pomidor, nie
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53, 23, 3, 5, 80, 15, 4, Salata, tak
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45, 21, 8, 6, 75, 12, 1, Cebula, tak
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55, 26, 11, 4, 70, 17, 2, Papryka, nie
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36, 24, 5, 3, 60, 9, 3, Buraki, nie
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67, 19, 10, 4, 85, 13, 4, Brukselka, nie
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43, 22, 1, 3, 65, 14, 1, Rzepak, tak
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51, 24, 9, 5, 75, 10, 2, Szpinak, tak
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31, 27, 2, 2, 70, 15, 3, Marchew, tak
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62, 18, 12, 3, 60, 8, 1, Ziemniaki, nie
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38, 21, 6, 4, 50, 11, 2, Pomidor, tak
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49, 24, 4, 5, 65, 16, 4, Salata, tak
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42, 25, 7, 6, 80, 9, 3, Cebula, tak
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57, 23, 4, 3, 75, 12, 1, Papryka, nie
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35, 28, 9, 1, 70, 16, 2, Buraki, nie
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66, 20, 11, 4, 85, 10, 3, Brukselka, tak
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46, 22, 0, 3, 65, 13, 1, Rzepak, tak
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50, 25, 10, 5, 75, 9, 2, Szpinak, tak
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@ -2,7 +2,7 @@ from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier, plot_tree
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import matplotlib.pyplot as plt
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import pandas as pd
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from sklearn import tree
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data = pd.read_csv("dane.csv")
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print(data)
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