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49
.ipynb_checkpoints/Auta-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df =pd.read_csv('train/train.csv', sep=\"\\t\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

218
Auta.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.preprocessing import PolynomialFeatures"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
"col_names = [\"Price\",\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"df =pd.read_csv('train/train.tsv', sep=\"\\t\", names=col_names)"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [],
"source": [
"def prepareData(df):\n",
" df[\"Age\"] = 2018 - df[\"Year\"]\n",
" df[\"SqrtAge\"] = df.age**0.5\n",
" df[\"SqrtMileage\"] = df.Mileage ** 0.5\n",
" df[\"SqrtEngineCapacity\"] = df.EngineCapacity ** 0.5\n",
" df = pd.concat([df, df['EngineType'].str.get_dummies()], axis = 1 )\n",
" df = df.drop(['EngineType','Brand'], axis = 1)\n",
" poly = PolynomialFeatures(2, interaction_only=True)\n",
" df = poly.fit_transform(df)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [],
"source": [
"df_train = df"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": [
"y_train = df_train.Price\n",
"x_train = df_train.drop('Price', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [],
"source": [
"x_train = prepareData(x_train)"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 192,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linReg = LinearRegression()\n",
"linReg.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [],
"source": [
"y_dev =pd.read_csv('dev-0/expected.tsv', sep=\"\\t\", names=[\"Price\"])"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [],
"source": [
"x_dev =pd.read_csv('dev-0/in.tsv', sep=\"\\t\", names=[\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"])"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [],
"source": [
"x_dev = prepareData(x_dev)"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7535351650926749\n"
]
}
],
"source": [
"score = linReg.score(x_dev, y_dev)\n",
"print(score)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [],
"source": [
"y_pred = linReg.predict(x_dev)"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [],
"source": [
"data = {'Price':y_pred}\n",
"y_pred = pd.DataFrame(data)"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"24989.603665517054"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_squared_error(y_dev, y_pred, squared=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"24943.930732282024\n",
"26863.621497665004 #BEZ AGE\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

62
Auta.py
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import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import PolynomialFeatures
col_names = ["Price","Mileage","Year","Brand","EngineType","EngineCapacity"]
def prepareData(df):
df["Age"] = 2018 - df["Year"]
df["SqrtAge"] = df.Age**0.5
df = pd.concat([df, df['EngineType'].str.get_dummies()], axis = 1 )
df = df.drop(['EngineType','Brand'], axis = 1)
df["SqrtMileage"] = df.Mileage ** 0.5
df["SqrtEngineCapacity"] = df.EngineCapacity ** 0.5
poly = PolynomialFeatures(2, interaction_only=True)
df = poly.fit_transform(df)
return df
def main():
df =pd.read_csv('train/train.tsv', sep="\t", names=col_names)
y_dev =pd.read_csv('dev-0/expected.tsv', sep="\t", names=["Price"])
x_dev =pd.read_csv('dev-0/in.tsv', sep="\t", names=["Mileage","Year","Brand","EngineType","EngineCapacity"])
x_test =pd.read_csv('test-A/in.tsv', sep="\t", names=["Mileage","Year","Brand","EngineType","EngineCapacity"])
y_train = df.Price
x_train = df.drop('Price', axis=1)
x_train = prepareData(x_train)
linReg = LinearRegression()
linReg.fit(x_train, y_train)
x_dev = prepareData(x_dev)
x_test = prepareData(x_test)
#Score modelu dla zbioru dev
score = linReg.score(x_dev, y_dev)
print(score)
#Wartość RMSE dla zbioru dev
y_pred = linReg.predict(x_dev)
data = {'Price':y_pred}
y_pred = pd.DataFrame(data)
y_pred.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
rmse = mean_squared_error(y_dev, y_pred, squared=False)
print(rmse)
#predict dla test-A
y_pred_test = linReg.predict(x_test)
data = {'Price':y_pred_test}
y_pred_test = pd.DataFrame(data)
y_pred_test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
if __name__ == "__main__":
main()

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test-A/.ipynb_checkpoints/in-checkpoint.tsv
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