import pandas as pd
df = pd.read_csv(‘archivesname.csv’, delimiter=’;’, decimal=’,’)
previsores = df.iloc[:, 0:8].values
y1 = df.iloc[:, 8].values
y2 = df.iloc[:, 9].values
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_previsores = LabelEncoder()
previsores[:, :8] = labelencoder_previsores.fit_transform(previsores[:, :8])
previsores[:, 8] = labelencoder_previsores.fit_transform(previsores[:, 8])
previsores[:, 9] = labelencoder_previsores.fit_transform(previsores[:, 9])
onehotencoder = OneHotEncoder(categorical_features = [0,1,2,3,4,5,6,7])
previsores = onehotencoder.fit_transform(previsores).toarray()
labelencoder_y1 = LabelEncoder()
labelencoder_y2 = LabelEncoder()
y1 = labelencoder_y1.fit_transform(y1)
y2 = labelencoder_y2.fit_transform(y2)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
previsores = scaler.fit_transform(previsores)
from sklearn.model_selection import train_test_split
previsores_treinamento, previsores_teste, y1_treinamento, y1_teste = train_test_split(previsores, y1, test_size=0.15, random_state=0)
previsores_treinamento, previsores_teste, y2_treinamento, y2_teste = train_test_split(previsores, y2, test_size=0.15, random_state=0)
from sklearn.ensemble import RandomForestClassifier
classificador = RandomForestClassifier(n_estimators=40, criterion=‘entropy’, random_state=0)
classificador.fit(previsores_treinamento, y1_treinamento)
previsoes = classificador.predict(previsores_teste)
from sklearn.metrics import confusion_matrix, accuracy_score
precisao = accuracy_score(y1_teste, previsoes)
matriz = confusion_matrix(y1_teste, previsoes)
I’m brazillian so please ignore the translated words. Sorry @erohmensing it took so long to respond