auta-public/Untitled.ipynb

import numpy as np
import matplotlib
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

#with open('train/train.tsv') as file:
 #   for line in file.readlines()[:10]:
  #      print(line)

#with open('names') as file:
 #   for line in file.readlines():
  #          header.append(line.strip())

#train

with open('names') as file:
    header = file.read().rstrip('\n').split('\t')

train_path='train/train.tsv'



train = pd.read_csv(train_path, sep='\t', names=header)
#removing discrete value
train.drop('brand', inplace=True, axis=1)
train.drop('engineType', inplace=True, axis=1)

#output
y_train = pd.DataFrame(train['price'])


#removing output
train.drop('price', inplace=True, axis=1)
x_train = pd.DataFrame(train)

model = LinearRegression()
model.fit(x_train, y_train)

header=['price','year','brand','engineType','engineCapacity']

#dev

dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=header)

print(dev)

      price  year          brand engineType  engineCapacity
0     77000  2015           Ford     diesel            2000
1    186146  2006  Mercedes-Benz    benzyna            1498
2    192000  2007         Nissan     diesel            2500
3    220000  2003           Ford     diesel            1997
4    248000  2008     Volkswagen     diesel            1900
..      ...   ...            ...        ...             ...
995  146000  2004           Opel     diesel            1686
996   19323  2015        Renault    benzyna            1598
997   27561  2016         Toyota     diesel            1598
998  155000  2012        Hyundai    benzyna            1600
999   31438  2015           Land     diesel            3000

[1000 rows x 5 columns]

with open('dev-0/expected.tsv', 'r') as file:
    y_dev = np.array([float(x.rstrip('\n')) for x in file.readlines()])

dev.drop('brand', inplace=True, axis=1)
dev.drop('engineType', inplace=True, axis=1)

print(dev)

      price  year  engineCapacity
0     77000  2015            2000
1    186146  2006            1498
2    192000  2007            2500
3    220000  2003            1997
4    248000  2008            1900
..      ...   ...             ...
995  146000  2004            1686
996   19323  2015            1598
997   27561  2016            1598
998  155000  2012            1600
999   31438  2015            3000

[1000 rows x 3 columns]

x_dev = pd.DataFrame(dev)

predict = model.predict(x_dev)
print(predict)

[[ 7.72392063e+04]
 [ 1.21746103e+04]
 [ 4.92626456e+04]
 [ 1.37190947e+04]
 [ 2.40946032e+04]
 [ 7.88318837e+04]
 [ 4.47318598e+04]
 [ 9.44511674e+03]
 [-8.77567502e+03]
 [ 1.34841352e+04]
 [ 2.51235429e+04]
 [ 8.25232472e+04]
 [ 5.73949851e+03]
 [ 1.47356316e+03]
 [ 3.58932978e+04]
 [ 2.41566138e+04]
 [ 6.28327771e+04]
 [ 9.44318776e+04]
 [ 3.63165742e+04]
 [ 6.93253866e+03]
 [-4.87879907e+01]
 [ 7.40420208e+03]
 [-8.74108566e+03]
 [ 5.85018256e+04]
 [-5.31833451e+03]
 [ 6.90090820e+04]
 [ 7.90958938e+04]
 [ 1.21888104e+04]
 [ 1.01686280e+04]
 [ 3.72544993e+04]
 [ 7.59714805e+04]
 [ 6.35250818e+04]
 [ 6.65801358e+03]
 [ 4.95984416e+04]
 [ 3.91985020e+04]
 [ 5.94405897e+04]
 [ 3.25405996e+04]
 [ 2.49435551e+04]
 [ 4.25956589e+04]
 [ 7.14460623e+04]
 [ 7.49405450e+04]
 [ 2.85287223e+04]
 [ 3.90573961e+04]
 [ 6.80943863e+04]
 [ 1.23555014e+04]
 [ 1.21925126e+05]
 [ 9.18307794e+04]
 [ 9.82420042e+03]
 [ 4.28924356e+04]
 [ 5.07739058e+04]
 [ 7.50514879e+04]
 [-7.25748788e+03]
 [ 9.55299727e+04]
 [ 3.95122188e+04]
 [ 1.19340537e+04]
 [-6.55254554e+03]
 [-4.10177479e+03]
 [ 2.49776566e+04]
 [ 7.10790038e+04]
 [ 2.06810033e+04]
 [ 6.23198797e+04]
 [-1.07284521e+04]
 [-6.46381409e+02]
 [ 7.30727534e+04]
 [ 8.30811199e+04]
 [ 2.86989359e+04]
 [-7.03756627e+03]
 [ 8.72640461e+04]
 [ 3.31901123e+04]
 [ 1.88453572e+03]
 [ 2.06604184e+04]
 [ 3.29929632e+04]
 [ 1.89676836e+04]
 [ 1.95236628e+04]
 [ 1.67547668e+05]
 [ 4.85499441e+04]
 [ 6.27455250e+04]
 [ 4.20967880e+04]
 [ 6.68641130e+04]
 [ 1.66520495e+04]
 [ 4.76051559e+04]
 [ 2.03516912e+04]
 [ 4.52221063e+04]
 [ 1.66323131e+04]
 [ 1.79557880e+04]
 [ 1.92879834e+04]
 [ 5.65858379e+04]
 [ 4.10082404e+04]
 [ 4.05624418e+04]
 [ 2.35217266e+04]
 [ 4.10092641e+04]
 [ 3.26970987e+04]
 [ 5.52463184e+04]
 [ 4.53240110e+04]
 [ 3.34969210e+03]
 [ 2.65136699e+03]
 [ 6.43185703e+04]
 [ 6.01867692e+04]
 [ 3.64334293e+04]
 [ 3.01009442e+04]
 [ 6.47733211e+03]
 [ 9.33718072e+03]
 [ 1.01767327e+04]
 [ 1.96917839e+04]
 [ 5.89513195e+03]
 [ 2.51338800e+04]
 [ 2.62510931e+04]
 [ 4.24081831e+04]
 [ 6.31779344e+04]
 [ 1.97391316e+04]
 [ 7.68646191e+04]
 [-1.53232910e+04]
 [ 5.42528855e+04]
 [ 5.99216155e+04]
 [ 8.74438207e+03]
 [ 4.07106246e+04]
 [ 1.17569351e+04]
 [ 3.42320268e+04]
 [ 5.58880945e+04]
 [ 2.67149647e+04]
 [ 2.44165413e+04]
 [ 3.06502434e+04]
 [-2.47116768e+04]
 [ 3.02109289e+04]
 [ 6.69114096e+04]
 [ 7.51099605e+04]
 [-2.01692811e+04]
 [ 4.42081612e+04]
 [ 2.20836791e+04]
 [ 4.64605808e+04]
 [ 1.23637492e+04]
 [-7.60995807e+03]
 [ 4.57435502e+04]
 [ 3.78355245e+04]
 [-2.88493424e+04]
 [ 6.29914716e+04]
 [ 1.00418697e+05]
 [ 5.67414418e+04]
 [ 3.16945267e+04]
 [ 6.27334737e+04]
 [ 1.47491206e+04]
 [ 5.12304959e+04]
 [ 6.98188482e+04]
 [ 1.79823749e+04]
 [ 6.62751512e+04]
 [ 5.18421259e+04]
 [ 9.08771880e+04]
 [ 1.48160390e+04]
 [ 1.08181408e+04]
 [ 5.76520947e+04]
 [ 3.22203519e+04]
 [ 1.80650050e+04]
 [ 4.64112525e+04]
 [-3.87907792e+04]
 [ 7.49071294e+04]
 [-1.39798993e+04]
 [ 7.57818485e+04]
 [ 1.77386762e+04]
 [ 5.87321021e+04]
 [ 6.03558654e+04]
 [ 3.63825302e+04]
 [ 6.71251159e+04]
 [ 6.72245534e+03]
 [-3.56414333e+03]
 [ 3.00644261e+03]
 [-6.20961682e+03]
 [ 4.15049154e+04]
 [ 3.80286210e+04]
 [ 2.62211308e+04]
 [ 8.18037844e+04]
 [ 6.12139209e+04]
 [ 7.10600141e+04]
 [ 2.01274133e+04]
 [ 2.25298349e+04]
 [ 7.50498152e+04]
 [ 3.69820172e+04]
 [ 7.68439140e+04]
 [ 9.55286656e+04]
 [ 8.21351233e+04]
 [ 7.49405450e+04]
 [ 5.32157390e+04]
 [ 3.35058778e+04]
 [ 1.66136440e+04]
 [-1.39818122e+04]
 [ 2.14885095e+04]
 [ 5.52127456e+03]
 [ 8.16206384e+04]
 [ 3.79281385e+04]
 [ 1.09335304e+04]
 [ 2.34982408e+04]
 [-1.17828688e+04]
 [ 3.98538772e+04]
 [ 4.42215829e+04]
 [ 6.27808750e+04]
 [ 1.50281434e+03]
 [ 5.47538677e+04]
 [ 4.09626275e+04]
 [ 5.79964638e+04]
 [ 2.15737773e+04]
 [ 3.73251402e+04]
 [ 1.00436068e+05]
 [ 9.55298275e+04]
 [-6.01901332e+03]
 [-1.81190118e+04]
 [ 1.89912585e+04]
 [ 2.18783873e+04]
 [ 3.23953698e+04]
 [ 6.48850564e+04]
 [ 6.76483307e+04]
 [ 3.28916102e+04]
 [ 2.56118154e+04]
 [ 6.94500220e+03]
 [ 9.16650596e+04]
 [ 4.22596892e+04]
 [ 3.45678228e+04]
 [ 7.74875097e+04]
 [ 5.02744211e+04]
 [ 6.18923966e+04]
 [ 1.20091628e+05]
 [ 5.33609688e+04]
 [-1.42105218e+04]
 [ 3.37136699e+04]
 [ 2.68020500e+04]
 [-9.57488945e+03]
 [ 3.75269757e+04]
 [ 2.69785796e+04]
 [-1.02671788e+03]
 [ 4.02983297e+04]
 [ 3.92837698e+04]
 [ 5.43089837e+04]
 [ 6.25751477e+03]
 [ 4.44839525e+04]
 [ 2.56231695e+04]
 [ 4.65805047e+04]
 [-7.53446900e+02]
 [ 4.59814301e+03]
 [ 7.17227007e+04]
 [ 4.98409271e+04]
 [ 1.39219345e+04]
 [ 6.12628755e+04]
 [ 3.67973692e+04]
 [ 2.48314325e+04]
 [ 2.19641304e+04]
 [ 4.85477660e+04]
 [ 3.74685962e+04]
 [ 1.46107897e+04]
 [ 1.32977276e+04]
 [ 4.30436593e+04]
 [ 3.09683777e+04]
 [ 1.23090152e+04]
 [ 5.48703011e+04]
 [ 2.40886093e+04]
 [ 4.96906762e+04]
 [ 7.45968093e+04]
 [ 1.25781616e+04]
 [ 8.62434536e+04]
 [ 3.23735853e+04]
 [ 8.25441151e+04]
 [ 3.34542991e+04]
 [ 2.17662054e+04]
 [ 6.79525419e+04]
 [ 7.67910195e+03]
 [ 2.05659498e+04]
 [ 1.45568094e+04]
 [ 7.32280041e+04]
 [ 2.74655993e+04]
 [-2.78234822e+03]
 [ 9.55293918e+04]
 [ 2.70399512e+03]
 [ 2.01194307e+04]
 [-1.26097057e+04]
 [ 2.41052835e+04]
 [ 1.88758039e+04]
 [ 1.70969595e+04]
 [ 6.87331454e+04]
 [ 1.54055323e+04]
 [ 1.81482242e+04]
 [ 1.49857523e+05]
 [ 1.91716243e+04]
 [ 2.44616990e+04]
 [ 1.20994614e+05]
 [ 6.22825860e+04]
 [ 1.32896850e+04]
 [ 1.48926177e+04]
 [ 7.49401093e+04]
 [ 2.98847085e+04]
 [ 7.83718016e+04]
 [ 7.63975954e+03]
 [ 3.39814987e+04]
 [ 5.64987000e+04]
 [ 2.73889631e+04]
 [ 7.29763962e+04]
 [ 3.84496848e+04]
 [-6.36290177e+02]
 [ 2.73829692e+04]
 [ 3.77668236e+04]
 [ 4.35553958e+04]
 [ 3.00975414e+04]
 [ 2.84308776e+04]
 [ 5.38059327e+04]
 [ 2.34657860e+04]
 [ 1.14752909e+04]
 [ 3.29090825e+03]
 [ 3.68497030e+04]
 [ 1.40531278e+04]
 [ 3.52155681e+04]
 [ 5.89700199e+03]
 [ 5.24862337e+04]
 [ 3.92933948e+04]
 [ 8.59376845e+03]
 [ 4.92554503e+03]
 [-2.32014656e+04]
 [ 2.44390308e+04]
 [-2.12286176e+04]
 [ 6.80774508e+04]
 [ 8.87696844e+04]
 [ 2.47949144e+03]
 [ 2.65942205e+04]
 [ 4.74497286e+04]
 [ 2.60220384e+04]
 [ 4.58110680e+04]
 [ 4.88588451e+04]
 [ 2.37969368e+04]
 [ 2.15490883e+04]
 [ 5.77794692e+04]
 [ 7.63565582e+04]
 [ 1.73454390e+04]
 [ 4.46006665e+04]
 [ 6.03239564e+04]
 [ 3.04161206e+04]
 [ 2.88105503e+04]
 [ 6.45221803e+04]
 [ 3.93983198e+03]
 [ 3.52486354e+04]
 [-2.00595799e+03]
 [ 4.25393763e+04]
 [ 4.17506277e+04]
 [ 3.00921486e+04]
 [ 5.29598062e+04]
 [ 6.35070240e+04]
 [ 1.30497869e+05]
 [ 8.72599621e+04]
 [ 4.49456832e+04]
 [ 6.62783780e+04]
 [ 1.30458250e+04]
 [ 6.51087000e+04]
 [ 1.47700561e+04]
 [ 3.97172087e+03]
 [ 5.45698206e+04]
 [ 3.92757272e+04]
 [ 5.86862154e+04]
 [ 1.46647577e+04]
 [ 4.85958666e+04]
 [ 3.27656923e+04]
 [ 5.16166193e+04]
 [ 6.37901595e+04]
 [ 8.27669537e+03]
 [ 4.44470403e+03]
 [ 5.48899029e+04]
 [ 2.12692812e+04]
 [ 9.44542556e+04]
 [ 4.17926078e+04]
 [-9.12329402e+02]
 [ 4.87988831e+04]
 [ 5.82550345e+04]
 [ 7.83716564e+04]
 [ 1.79254278e+05]
 [ 6.80775960e+04]
 [ 5.46045563e+04]
 [ 8.51910622e+03]
 [ 3.08604417e+04]
 [ 3.12439259e+04]
 [-2.40882505e+04]
 [ 7.05766223e+04]
 [ 8.18032034e+04]
 [ 4.21722806e+04]
 [ 2.83109876e+04]
 [ 4.74276265e+04]
 [ 7.67639692e+04]
 [ 3.71113168e+04]
 [-1.32297547e+04]
 [ 3.85111404e+04]
 [ 3.67009333e+04]
 [ 5.11705339e+04]
 [-3.82518515e+03]
 [ 2.77726540e+04]
 [ 1.88429707e+04]
 [-1.49374038e+04]
 [ 2.64349542e+04]
 [-1.66357904e+03]
 [ 5.01625419e+04]
 [ 7.14267877e+04]
 [ 4.83545621e+04]
 [ 2.37668499e+04]
 [ 7.40267890e+03]
 [ 6.59193248e+04]
 [ 2.13228654e+04]
 [ 7.04900421e+04]
 [ 4.47360866e+04]
 [ 3.47583889e+04]
 [ 5.18087774e+04]
 [ 3.68748252e+04]
 [ 8.76196156e+04]
 [ 6.09000252e+04]
 [ 5.37751549e+04]
 [ 2.64138959e+04]
 [-6.04990349e+02]
 [ 1.94719666e+04]
 [ 8.00517596e+04]
 [ 8.14486150e+04]
 [ 4.52312476e+04]
 [ 6.54858499e+03]
 [ 1.93094735e+04]
 [ 2.38485465e+05]
 [ 9.89609389e+04]
 [ 1.20329797e+05]
 [ 1.12266687e+05]
 [ 1.06043174e+04]
 [ 4.44880574e+04]
 [ 4.67570344e+04]
 [ 4.31948831e+04]
 [ 6.58194314e+04]
 [ 6.76323430e+04]
 [ 7.83710754e+04]
 [ 5.87025823e+04]
 [ 4.86412816e+04]
 [ 5.13357942e+04]
 [-2.55895708e+04]
 [ 5.52489561e+04]
 [ 4.30457080e+04]
 [ 3.98529801e+04]
 [ 7.44798402e+04]
 [ 5.58178664e+04]
 [ 2.83682779e+04]
 [ 4.32556374e+03]
 [ 7.49401093e+04]
 [ 2.03092220e+04]
 [ 4.24847819e+04]
 [ 2.75741244e+04]
 [ 6.61066642e+04]
 [ 3.19111792e+04]
 [ 5.76034631e+04]
 [ 2.34045699e+04]
 [ 6.80770151e+04]
 [ 6.12145019e+04]
 [ 1.12895812e+05]
 [ 4.40665677e+04]
 [ 9.59321744e+04]
 [ 6.53237825e+04]
 [ 6.60966158e+04]
 [ 6.16700812e+04]
 [ 2.92109674e+04]
 [ 7.02154658e+03]
 [ 1.79197752e+04]
 [ 2.50673350e+04]
 [ 4.72636015e+04]
 [ 9.23610370e+03]
 [ 6.51091237e+04]
 [ 5.06992027e+04]
 [ 3.08554420e+04]
 [ 7.80239759e+04]
 [ 1.36781688e+04]
 [ 2.51278860e+04]
 [ 2.45923032e+04]
 [ 8.67040464e+03]
 [ 3.98119192e+04]
 [ 1.35470374e+04]
 [ 8.18032034e+04]
 [ 4.20587052e+04]
 [ 3.95035872e+04]
 [ 2.75801183e+04]
 [ 1.76679845e+04]
 [ 7.99267520e+04]
 [ 6.98405293e+04]
 [ 2.34331025e+04]
 [ 5.22103653e+04]
 [ 2.85215129e+04]
 [ 1.93674407e+04]
 [ 1.49962178e+04]
 [ 5.82847453e+04]
 [-2.38509784e+04]
 [ 4.52841169e+04]
 [ 2.31038551e+04]
 [ 2.01960669e+04]
 [ 1.78780640e+04]
 [ 2.49140626e+04]
 [ 5.22276943e+04]
 [ 4.72633660e+04]
 [ 3.27478400e+04]
 [ 2.58243217e+04]
 [ 4.42135403e+04]
 [ 4.79002889e+04]
 [ 7.83716564e+04]
 [ 2.40941980e+04]
 [ 1.90258207e+04]
 [ 3.69520505e+04]
 [ 6.36189659e+04]
 [ 4.14228743e+04]
 [ 7.66286754e+04]
 [ 2.93001912e+02]
 [ 6.41098944e+04]
 [ 2.61873769e+04]
 [ 3.42067209e+04]
 [ 2.49200566e+04]
 [ 6.06011246e+04]
 [ 5.92036537e+04]
 [ 6.64316504e+04]
 [ 7.91520970e+03]
 [ 8.93294867e+04]
 [ 3.22776596e+04]
 [-3.05700209e+04]
 [ 1.06293618e+05]
 [ 7.26140278e+04]
 [ 2.04653179e+04]
 [ 1.96907665e+05]
 [ 4.98539697e+04]
 [ 1.83720059e+04]
 [ 4.73004447e+04]
 [ 3.58295545e+04]
 [-2.85150061e+04]
 [ 2.35837372e+04]
 [ 5.02479170e+04]
 [ 1.05426261e+05]
 [ 1.60085306e+04]
 [ 2.57377687e+04]
 [ 9.40069725e+04]
 [-1.23465247e+04]
 [ 9.33117395e+04]
 [ 7.11769593e+04]
 [ 6.86786909e+04]
 [ 5.86844219e+04]
 [ 2.78019843e+04]
 [ 9.48765839e+04]
 [ 1.28080702e+04]
 [ 7.91108006e+04]
 [ 1.19733962e+04]
 [ 7.38588734e+04]
 [ 6.40128498e+03]
 [ 6.52553159e+04]
 [ 4.17775391e+04]
 [ 7.47860903e+04]
 [ 6.84652518e+04]
 [ 2.73123269e+04]
 [-1.65561112e+03]
 [ 1.29133685e+04]
 [ 5.98767472e+03]
 [ 4.61408243e+04]
 [ 1.96114873e+04]
 [ 6.70697999e+04]
 [ 7.49401093e+04]
 [ 5.22677240e+04]
 [ 5.66326605e+04]
 [ 1.29819621e+04]
 [ 2.81924020e+04]
 [ 1.64104017e+04]
 [ 2.93185506e+03]
 [ 1.85786207e+04]
 [-1.34830572e+04]
 [ 2.24563755e+03]
 [ 5.43229835e+04]
 [ 1.36788086e+05]
 [ 8.40736383e+04]
 [ 1.46914537e+03]
 [ 4.05282383e+04]
 [ 4.12403508e+04]
 [-2.75501503e+04]
 [ 5.54335283e+04]
 [ 2.17228781e+04]
 [-5.71333908e+03]
 [ 1.69133186e+03]
 [ 7.38187266e+04]
 [ 3.61783340e+04]
 [ 9.24524667e+04]
 [ 6.58295354e+04]
 [ 8.19438840e+04]
 [ 5.58232923e+04]
 [ 4.93964766e+04]
 [ 9.08771880e+04]
 [ 3.02788588e+04]
 [ 6.33019617e+04]
 [-5.03969853e+03]
 [ 5.56652904e+04]
 [-2.16618511e+04]
 [ 5.82403041e+04]
 [ 7.26419803e+04]
 [ 1.42516992e+04]
 [ 6.87006433e+03]
 [ 3.63505118e+04]
 [ 6.31680013e+04]
 [ 2.21299799e+04]
 [-6.59547416e+02]
 [ 2.46069288e+04]
 [ 7.47691128e+04]
 [ 1.44882281e+04]
 [ 4.59137287e+04]
 [ 6.32079327e+04]
 [ 6.78693880e+04]
 [ 1.28197836e+05]
 [ 5.14657587e+04]
 [ 2.82655674e+03]
 [ 2.73143756e+04]
 [ 7.02539343e+04]
 [ 4.41058855e+04]
 [ 6.30195447e+04]
 [ 1.41057313e+04]
 [ 3.47756149e+04]
 [ 1.26588798e+04]
 [-4.90514987e+04]
 [ 2.50685131e+04]
 [ 5.58952421e+04]
 [ 4.63293876e+04]
 [ 1.59109320e+04]
 [-2.06855765e+03]
 [ 5.64793881e+04]
 [ 4.23821320e+04]
 [ 4.14498311e+04]
 [ 2.65861779e+04]
 [ 2.20780603e+04]
 [ 5.41557113e+04]
 [ 4.25185577e+04]
 [ 2.01668948e+05]
 [ 1.43182815e+04]
 [-2.00103554e+04]
 [ 5.64148158e+04]
 [ 3.80370409e+04]
 [ 1.79511196e+04]
 [ 3.93804365e+04]
 [ 4.83253109e+04]
 [ 1.10400068e+04]
 [ 6.93919470e+04]
 [-2.85203089e+03]
 [ 1.32499937e+05]
 [ 9.00454527e+04]
 [-1.00602366e+03]
 [ 2.65320678e+04]
 [-2.50424704e+03]
 [ 3.02109289e+04]
 [-1.06622818e+03]
 [ 6.76334202e+04]
 [ 2.59685741e+04]
 [ 3.97799524e+04]
 [-6.07239227e+03]
 [ 2.06478415e+04]
 [ 2.32711985e+04]
 [ 3.40614911e+04]
 [ 2.14548517e+04]
 [ 4.92339835e+04]
 [ 3.28563650e+04]
 [ 5.80275285e+04]
 [ 5.51835135e+04]
 [ 5.69571871e+04]
 [ 2.18148793e+04]
 [ 2.68786862e+04]
 [ 4.25999274e+04]
 [ 6.18609331e+04]
 [ 2.39114906e+04]
 [ 5.54507916e+04]
 [ 8.99193491e+04]
 [ 8.74626162e+04]
 [ 4.00472126e+04]
 [ 5.09581277e+04]
 [ 1.25970242e+03]
 [ 4.50495006e+04]
 [ 2.49973465e+04]
 [ 3.35491655e+04]
 [ 3.96208450e+04]
 [ 1.67892367e+04]
 [ 1.37534475e+04]
 [ 8.73896091e+03]
 [ 8.83452644e+04]
 [ 4.97945570e+04]
 [ 3.22646629e+04]
 [ 8.06827686e+03]
 [ 8.69380011e+04]
 [ 2.53637886e+04]
 [ 3.46235163e+04]
 [ 3.07286594e+04]
 [ 8.04655616e+04]
 [ 3.06299440e+04]
 [ 4.48446734e+04]
 [-3.89468345e+04]
 [ 3.91445340e+04]
 [ 1.67633417e+04]
 [ 1.16092773e+05]
 [ 7.61620467e+04]
 [ 5.03561131e+04]
 [ 6.35579938e+04]
 [ 3.06012819e+04]
 [ 3.61394556e+04]
 [-9.83433043e+03]
 [ 1.13452332e+04]
 [ 1.11683571e+04]
 [ 7.50257233e+03]
 [ 5.29760206e+04]
 [ 4.80661512e+04]
 [-9.30847054e+02]
 [-1.47641009e+04]
 [ 9.41345552e+04]
 [ 1.69338050e+03]
 [ 7.01320646e+04]
 [-4.13359245e+03]
 [ 3.31387820e+04]
 [ 9.54335401e+04]
 [ 4.02156996e+04]
 [ 2.15797713e+04]
 [ 5.04443850e+04]
 [ 5.72135796e+04]
 [ 9.55293918e+04]
 [ 3.33463530e+04]
 [-1.58640419e+04]
 [ 1.38862815e+04]
 [-2.89066667e+04]
 [ 2.19971557e+04]
 [ 5.19793131e+04]
 [ 7.12684490e+03]
 [ 3.58889140e+04]
 [-1.23545673e+04]
 [ 5.99529153e+04]
 [ 1.29480082e+05]
 [ 4.59496018e+04]
 [ 5.85892414e+04]
 [-2.87792583e+03]
 [ 4.14995105e+04]
 [ 5.77815025e+04]
 [-3.27996125e+03]
 [ 1.82457376e+03]
 [ 5.56717526e+04]
 [ 1.42649973e+04]
 [ 2.25823433e+04]
 [-3.08457716e+03]
 [ 2.68503273e+04]
 [ 8.00012708e+04]
 [ 1.03130472e+05]
 [ 1.91955435e+04]
 [ 1.71504693e+03]
 [ 8.18033487e+04]
 [-1.21180992e+03]
 [ 2.79698823e+04]
 [ 2.41738771e+04]
 [ 1.30961048e+03]
 [ 2.65035105e+04]
 [ 2.28322824e+04]
 [ 9.09400887e+04]
 [ 6.26556757e+04]
 [-3.62015998e+03]
 [ 4.63953435e+04]
 [ 5.12807099e+04]
 [ 4.29831083e+04]
 [ 5.46680369e+04]
 [ 1.64534407e+04]
 [ 5.77744683e+04]
 [-3.07626357e+04]
 [ 4.46185189e+04]
 [ 7.60557519e+04]
 [ 7.77895805e+03]
 [ 6.80770151e+04]
 [ 4.19211634e+04]
 [-1.27882453e+04]
 [-4.27656585e+04]
 [ 2.99285119e+04]
 [ 1.25009364e+04]
 [ 1.27659808e+04]
 [ 2.35303583e+04]
 [ 1.08538303e+05]
 [ 2.61444946e+04]
 [ 4.61070901e+04]
 [ 2.90919492e+04]
 [ 3.26301940e+04]
 [-3.59837362e+04]
 [ 4.08836664e+04]
 [-2.50733677e+04]
 [ 3.62939061e+04]
 [ 3.36585334e+04]
 [ 3.56449823e+04]
 [ 5.42363688e+04]
 [ 8.61907433e+03]
 [-2.55060788e+04]
 [ 1.77605089e+04]
 [ 3.94269510e+04]
 [ 1.73474877e+04]
 [ 2.28182458e+04]
 [ 3.73464248e+04]
 [-6.07778184e+04]
 [ 6.06230754e+04]
 [ 4.95028941e+04]
 [ 4.30465868e+03]
 [ 7.13864363e+04]
 [ 5.93920266e+04]
 [ 2.62271247e+04]
 [ 1.32790047e+04]
 [ 5.79284219e+04]
 [ 7.41990485e+03]
 [-9.84098811e+03]
 [ 3.46916920e+04]
 [ 1.72638308e+05]
 [-1.65317359e+04]
 [ 1.97624262e+04]
 [ 7.96505300e+04]
 [ 6.12139209e+04]
 [ 1.77458833e+04]
 [-1.16180069e+04]
 [ 6.05027437e+03]
 [ 6.19094962e+04]
 [ 1.63271997e+04]
 [ 2.35217266e+04]
 [ 3.33025067e+03]
 [ 2.94707434e+04]
 [ 3.51406993e+04]
 [ 5.84048545e+04]
 [ 7.34499762e+04]
 [-6.43144547e+03]
 [ 6.03239564e+04]
 [ 7.48168215e+04]
 [ 6.51708775e+03]
 [ 3.86342570e+04]
 [ 2.80017539e+04]
 [ 1.27833534e+04]
 [ 6.84501695e+03]
 [ 7.97847490e+04]
 [ 5.77815025e+04]
 [ 3.57901748e+04]
 [ 2.97666078e+04]
 [ 2.67605154e+03]
 [ 3.66836700e+04]
 [ 2.33084923e+04]
 [ 6.76334202e+04]
 [ 4.61015276e+04]
 [-3.99751815e+03]
 [ 5.77794692e+04]
 [ 9.18723367e+04]
 [ 2.09273075e+04]
 [ 4.77050493e+04]
 [ 5.12018338e+04]
 [ 7.28669250e+04]
 [ 4.01106329e+03]
 [ 4.25453703e+04]
 [ 5.93779900e+04]
 [ 1.58331791e+04]
 [-9.63513262e+03]
 [ 4.66142288e+04]
 [ 1.98223881e+04]
 [ 3.76360987e+04]
 [ 6.56289229e+04]
 [ 3.79609394e+04]
 [ 4.51897108e+03]
 [ 3.73240020e+04]
 [ 3.30489038e+04]
 [ 6.55462351e+04]
 [ 2.09842265e+04]
 [ 2.27813359e+04]
 [ 3.02900389e+04]
 [ 3.39256920e+04]
 [ 2.81837057e+04]
 [ 5.46933428e+04]
 [ 6.28676111e+04]
 [ 5.88397695e+04]
 [ 1.11565745e+04]
 [ 4.93879095e+04]
 [ 2.13959748e+04]
 [-2.54840529e+03]
 [ 2.04745386e+04]
 [ 1.34036150e+04]
 [-1.09562003e+04]
 [ 4.16027602e+04]
 [ 2.22832521e+04]
 [-1.50826336e+04]
 [ 2.20645758e+04]
 [ 2.07850287e+04]
 [ 2.93395501e+04]
 [ 1.49221568e+04]
 [ 6.99675986e+04]
 [ 4.87076214e+04]
 [ 3.57962061e+04]
 [ 3.55077951e+04]
 [ 7.08091948e+03]
 [ 9.55299727e+04]
 [ 4.47551170e+04]
 [ 1.28153172e+05]
 [ 6.11953351e+04]
 [ 7.55876171e+04]
 [ 3.34427164e+04]
 [ 2.62837305e+04]
 [ 5.99649032e+04]
 [ 1.80190796e+04]
 [ 4.01530999e+04]
 [ 8.00956463e+04]
 [ 4.53701032e+04]
 [ 5.05890257e+04]
 [ 9.69598772e+04]
 [ 1.06663280e+04]
 [ 3.06675785e+03]
 [ 2.55036135e+04]
 [ 5.62841964e+02]
 [ 2.59426591e+04]
 [ 3.71113168e+04]
 [-5.31103841e+04]
 [ 3.83280753e+04]
 [-1.67995706e+04]
 [ 5.90616355e+04]
 [ 8.18032034e+04]
 [ 4.92945345e+04]
 [ 2.68678377e+04]
 [ 7.83716564e+04]
 [-2.10308322e+03]
 [ 2.59623695e+04]
 [ 3.88953555e+04]
 [-1.52824204e+04]
 [ 1.56137759e+04]
 [ 4.57207020e+04]
 [ 9.49859035e+04]
 [ 6.01354389e+04]
 [ 1.15377416e+05]
 [ 9.26166504e+04]
 [ 6.01807752e+04]
 [ 2.00325322e+04]
 [-1.54956548e+04]
 [ 2.66551677e+04]
 [ 1.57952963e+04]
 [ 2.55009758e+04]
 [ 2.97045972e+04]
 [ 8.85042495e+04]
 [ 7.83716564e+04]
 [ 3.92959969e+02]
 [ 2.99391341e+04]
 [ 7.11929338e+04]
 [ 7.79254238e+04]
 [ 9.55299727e+04]
 [ 3.54251650e+04]
 [ 1.46659560e+04]
 [ 1.46707517e+04]
 [ 5.91641666e+04]
 [ 4.16628186e+04]
 [ 2.96565004e+04]
 [ 5.59482104e+04]
 [ 1.29428394e+04]
 [ 3.90105735e+04]
 [ 6.54708788e+04]
 [ 8.06836105e+04]
 [ 6.10708704e+04]
 [ 5.43846992e+04]
 [-6.98106482e+03]
 [-1.25945989e+04]
 [ 2.75201564e+04]
 [ 8.61682697e+04]
 [ 3.96568595e+04]
 [ 7.22317435e+04]
 [ 6.42532034e+04]
 [-1.33436918e+04]
 [ 7.42884259e+03]
 [ 8.86030759e+04]
 [ 2.87273311e+04]
 [ 3.63851679e+04]
 [ 1.17521020e+04]
 [-5.96818037e+03]
 [ 3.24832753e+04]
 [ 6.34601148e+04]
 [ 6.89318567e+04]
 [ 2.11220070e+04]
 [ 2.04199816e+04]
 [ 1.98807680e+04]
 [ 3.52155616e+03]
 [ 6.10402847e+04]
 [ 4.02624678e+04]
 [ 8.23222491e+04]
 [ 6.70045270e+04]
 [ 2.14444622e+04]
 [ 2.12126755e+04]
 [ 7.21347927e+04]
 [ 7.49057938e+04]
 [ 5.06850048e+03]
 [ 5.46107127e+04]
 [ 7.41207870e+04]
 [ 4.69191904e+04]
 [ 3.96488170e+04]
 [ 4.80348938e+04]
 [ 3.63791739e+04]
 [ 8.98588017e+01]
 [ 7.49405450e+04]
 [ 2.50679241e+04]
 [ 1.06129491e+04]
 [ 4.48075447e+04]
 [ 7.79221970e+04]
 [ 7.57540804e+04]
 [ 2.69957734e+03]
 [ 1.12705044e+04]
 [ 1.40757960e+04]
 [ 6.72862389e+04]
 [ 7.59470449e+04]
 [ 6.85960608e+04]
 [ 3.92444274e+04]
 [ 3.36973605e+04]
 [ 5.97828943e+03]
 [ 4.53820003e+04]
 [ 4.52929960e+04]
 [-2.87656795e+04]
 [ 1.73480968e+04]
 [ 7.18208059e+04]
 [ 7.41785116e+04]
 [ 4.15227678e+04]
 [ 1.18171637e+05]]

 predict.tofile('dev-0/out.tsv', sep='\n') 

error = np.sqrt(mean_squared_error(y_dev, predict))
print(error)

34136.77274287094

#test

pd.DataFrame(predict).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)

test=pd.read_csv('test-A/in.tsv', sep='\t', names=header)
print(test)

      price  year       brand engineType  engineCapacity
0    203000  2010     Renault     diesel            1500
1     39000  2008     Citroen    benzyna            1000
2    190000  2005     Peugeot     diesel            1600
3    230000  2001  Volkswagen    benzyna            1598
4    189000  2000         BMW    benzyna            1600
..      ...   ...         ...        ...             ...
995  465000  2005     Renault     diesel            2500
996   89074  2014         BMW     diesel            2000
997   21711  2014      Toyota    benzyna            1329
998  144000  2014     Renault     diesel            1500
999  113606  2000      Jaguar    benzyna            4000

[1000 rows x 5 columns]

test.drop('brand', inplace=True, axis=1)
test.drop('engineType', inplace=True, axis=1)
y_expected = pd.DataFrame(test['price'])

y_expected.to_csv('test-A/expected.tsv', sep='\t', encoding='utf-8')

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-185-49d8d19457cd> in <module>
----> 1 test.drop('brand', inplace=True, axis=1)
      2 test.drop('engineType', inplace=True, axis=1)
      3 y_expected = pd.DataFrame(test['price'])
      4 
      5 y_expected.to_csv('test-A/expected.tsv', sep='\t', encoding='utf-8')

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\pandas\core\frame.py in drop(self, labels, axis, index, columns, level, inplace, errors)
   4303                 weight  1.0     0.8
   4304         """
-> 4305         return super().drop(
   4306             labels=labels,
   4307             axis=axis,

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\pandas\core\generic.py in drop(self, labels, axis, index, columns, level, inplace, errors)
   4148         for axis, labels in axes.items():
   4149             if labels is not None:
-> 4150                 obj = obj._drop_axis(labels, axis, level=level, errors=errors)
   4151 
   4152         if inplace:

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\pandas\core\generic.py in _drop_axis(self, labels, axis, level, errors)
   4183                 new_axis = axis.drop(labels, level=level, errors=errors)
   4184             else:
-> 4185                 new_axis = axis.drop(labels, errors=errors)
   4186             result = self.reindex(**{axis_name: new_axis})
   4187 

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\pandas\core\indexes\base.py in drop(self, labels, errors)
   5589         if mask.any():
   5590             if errors != "ignore":
-> 5591                 raise KeyError(f"{labels[mask]} not found in axis")
   5592             indexer = indexer[~mask]
   5593         return self.delete(indexer)

KeyError: "['brand'] not found in axis"

print(test)

     year  engineCapacity
0    2010            1500
1    2008            1000
2    2005            1600
3    2001            1598
4    2000            1600
..    ...             ...
995  2005            2500
996  2014            2000
997  2014            1329
998  2014            1500
999  2000            4000

[1000 rows x 2 columns]

x_test = pd.DataFrame(test)

predict = model.predict(x_test)
pd.DataFrame(predict).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-187-2e8bc4bccb95> in <module>
      1 x_test = pd.DataFrame(test)
      2 
----> 3 predict = model.predict(x_test)
      4 pd.DataFrame(predict).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\sklearn\linear_model\_base.py in predict(self, X)
    236             Returns predicted values.
    237         """
--> 238         return self._decision_function(X)
    239 
    240     _preprocess_data = staticmethod(_preprocess_data)

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\sklearn\linear_model\_base.py in _decision_function(self, X)
    219 
    220         X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
--> 221         return safe_sparse_dot(X, self.coef_.T,
    222                                dense_output=True) + self.intercept_
    223 

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

c:\users\mkoci\appdata\local\programs\python\python39\lib\site-packages\sklearn\utils\extmath.py in safe_sparse_dot(a, b, dense_output)
    150             ret = np.dot(a, b)
    151     else:
--> 152         ret = a @ b
    153 
    154     if (sparse.issparse(a) and sparse.issparse(b)

ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 3 is different from 2)

 predict.tofile('test-A/out.tsv', sep='\n')