forked from kubapok/auta-public
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@ -1,49 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.metrics import mean_squared_error\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df =pd.read_csv('train/train.csv', sep=\"\\t\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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218
Auta.ipynb
218
Auta.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 67,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.metrics import mean_squared_error\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.preprocessing import PolynomialFeatures"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 115,
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"metadata": {},
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"outputs": [],
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"source": [
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"col_names = [\"Price\",\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 116,
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"metadata": {},
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"outputs": [],
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"source": [
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"df =pd.read_csv('train/train.tsv', sep=\"\\t\", names=col_names)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 187,
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"metadata": {},
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"outputs": [],
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"source": [
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"def prepareData(df):\n",
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" df[\"Age\"] = 2018 - df[\"Year\"]\n",
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" df[\"SqrtAge\"] = df.age**0.5\n",
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" df[\"SqrtMileage\"] = df.Mileage ** 0.5\n",
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" df[\"SqrtEngineCapacity\"] = df.EngineCapacity ** 0.5\n",
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" df = pd.concat([df, df['EngineType'].str.get_dummies()], axis = 1 )\n",
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" df = df.drop(['EngineType','Brand'], axis = 1)\n",
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" poly = PolynomialFeatures(2, interaction_only=True)\n",
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" df = poly.fit_transform(df)\n",
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" return df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 188,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_train = df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 190,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_train = df_train.Price\n",
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"x_train = df_train.drop('Price', axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 191,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = prepareData(x_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 192,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LinearRegression()"
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]
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},
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"execution_count": 192,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"linReg = LinearRegression()\n",
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"linReg.fit(x_train, y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 193,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_dev =pd.read_csv('dev-0/expected.tsv', sep=\"\\t\", names=[\"Price\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 194,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev =pd.read_csv('dev-0/in.tsv', sep=\"\\t\", names=[\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 195,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev = prepareData(x_dev)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 196,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.7535351650926749\n"
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]
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}
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],
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"source": [
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"score = linReg.score(x_dev, y_dev)\n",
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"print(score)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 197,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred = linReg.predict(x_dev)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 198,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = {'Price':y_pred}\n",
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"y_pred = pd.DataFrame(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 199,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"24989.603665517054"
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]
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},
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"execution_count": 199,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mean_squared_error(y_dev, y_pred, squared=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"24943.930732282024\n",
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"26863.621497665004 #BEZ AGE\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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62
Auta.py
62
Auta.py
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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from sklearn.preprocessing import PolynomialFeatures
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col_names = ["Price","Mileage","Year","Brand","EngineType","EngineCapacity"]
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def prepareData(df):
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df["Age"] = 2018 - df["Year"]
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df["SqrtAge"] = df.Age**0.5
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df = pd.concat([df, df['EngineType'].str.get_dummies()], axis = 1 )
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df = df.drop(['EngineType','Brand'], axis = 1)
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df["SqrtMileage"] = df.Mileage ** 0.5
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df["SqrtEngineCapacity"] = df.EngineCapacity ** 0.5
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poly = PolynomialFeatures(2, interaction_only=True)
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df = poly.fit_transform(df)
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return df
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def main():
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df =pd.read_csv('train/train.tsv', sep="\t", names=col_names)
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y_dev =pd.read_csv('dev-0/expected.tsv', sep="\t", names=["Price"])
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x_dev =pd.read_csv('dev-0/in.tsv', sep="\t", names=["Mileage","Year","Brand","EngineType","EngineCapacity"])
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x_test =pd.read_csv('test-A/in.tsv', sep="\t", names=["Mileage","Year","Brand","EngineType","EngineCapacity"])
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y_train = df.Price
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x_train = df.drop('Price', axis=1)
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x_train = prepareData(x_train)
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linReg = LinearRegression()
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linReg.fit(x_train, y_train)
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x_dev = prepareData(x_dev)
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x_test = prepareData(x_test)
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#Score modelu dla zbioru dev
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score = linReg.score(x_dev, y_dev)
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print(score)
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#Wartość RMSE dla zbioru dev
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y_pred = linReg.predict(x_dev)
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data = {'Price':y_pred}
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y_pred = pd.DataFrame(data)
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y_pred.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
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rmse = mean_squared_error(y_dev, y_pred, squared=False)
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print(rmse)
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#predict dla test-A
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y_pred_test = linReg.predict(x_test)
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data = {'Price':y_pred_test}
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y_pred_test = pd.DataFrame(data)
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y_pred_test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
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if __name__ == "__main__":
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main()
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1000
dev-0/out.tsv
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dev-0/out.tsv
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File diff suppressed because it is too large
Load Diff
1000
test-A/out.tsv
1000
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
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