import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.feature_extraction import DictVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

def ranfor_prediction(train, labels, test):
    RandomForest = RandomForestClassifier(n_estimators=10, max_depth = 7)
    RandomForest.fit(train, labels)
    return RandomForest.predict(test)


train  = pd.read_csv('train.csv', index_col=0)
test  = pd.read_csv('test.csv', index_col=0)

labels = train.Hazard

train.drop('T2_V10', axis=1, inplace=True)
train.drop('T2_V7', axis=1, inplace=True)
train.drop('T1_V13', axis=1, inplace=True)
train.drop('T1_V10', axis=1, inplace=True)

test.drop('T2_V10', axis=1, inplace=True)
test.drop('T2_V7', axis=1, inplace=True)
test.drop('T1_V13', axis=1, inplace=True)
test.drop('T1_V10', axis=1, inplace=True)

columns = train.columns
test_index = test.index

train_temp = np.array(train)
test_temp = np.array(test)

for i in range(train_temp.shape[1]):
    le = preprocessing.LabelEncoder()
    le.fit(list(train_temp[:,i]) + list(test_temp[:,i]))
    le.transform(train_temp[:,i])#при ошибке указывает на эту строку
    train_temp[:,i] = le.transform(train_temp[:,i])
    test_temp[:,i] = le.transform(test_temp[:,i])

train_temp = train_temp.astype(float)
test_temp = test_temp.astype(float)

prediction = ranfor_prediction(train_temp, labels, test_temp)

prediction = pd.DataFrame({"Id": test_index, "Hazard": prediction})
prediction = prediction.set_index('Id')
prediction.to_csv('result.csv')