forked from kubapok/auta-public
25 lines
1017 B
Python
25 lines
1017 B
Python
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import numpy as np
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from sklearn import preprocessing
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LinearRegression
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from sklearn import linear_model
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import pandas as pd
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train=pd.read_csv('train/train.tsv',sep='\t',names=['price','mileage','year','brand','engineType','engineCapacity'])
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df = pd.DataFrame(train,columns=['price','mileage','year','brand','engineType','engineCapacity'])
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Y=df[['price']]
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X=df[['year','mileage','engineCapacity']]
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reg = linear_model.LinearRegression()
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reg.fit(X, Y)
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inn=pd.read_csv('dev-0/in.tsv',sep='\t',names=['mileage','year','brand','engineType','engineCapacity'])
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df = pd.DataFrame(inn,columns=['mileage','year','brand','engineType','engineCapacity'])
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r=df[['year','mileage','engineCapacity']]
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y_pred=reg.predict(r)
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y_pred=np.concatenate(y_pred)
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t=np.array2string(y_pred, precision=5, separator='\n',suppress_small=True)
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t=t.lstrip('[').rstrip(']')
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f = open("dev-0/out.tsv", "a")
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f.write(t)
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