{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Päivitetty 2022-09-11 13:41:41.389289\n"
]
}
],
"source": [
"from datetime import datetime\n",
"print(f'Päivitetty {datetime.now()}')"
]
},
{
"cell_type": "markdown",
"metadata": {
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"status": "completed"
},
"tags": []
},
"source": [
"# Auton hinnan ennustaminen\n",
"\n",
"Olen lainannut ideoita lähteestä https://www.kaggle.com/mohaiminul101/car-price-prediction"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
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"shell.execute_reply": "2020-12-21T20:34:26.342714Z"
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"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# train_test_split osaa jakaa datan opetusdataan ja testidataan\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Käytettävät mallit\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Funktio mallin sovittamiseen ja tarkasteluun\n",
"\n",
"Jos samaa koodia käytetään toistuvasti, niin siitä kannattaa tehdä funktio."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:31.172080Z",
"iopub.status.busy": "2020-12-21T20:34:31.171244Z",
"iopub.status.idle": "2020-12-21T20:34:31.186866Z",
"shell.execute_reply": "2020-12-21T20:34:31.187386Z"
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"end_time": "2020-12-21T20:34:31.187643",
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"start_time": "2020-12-21T20:34:31.121402",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def mallinna(malli):\n",
" \n",
" # Mallin sovitus opetusdataan\n",
" malli.fit(X_train, y_train)\n",
" \n",
" # Selityskerroin opetusdatalle\n",
" y_pred_train = malli.predict(X_train)\n",
" R2_train_malli = malli.score(X_train, y_train)\n",
" \n",
" # Selityskerroin testidatalle\n",
" y_pred_test = malli.predict(X_test)\n",
" R2_test_malli = malli.score(X_test, y_test)\n",
" \n",
" # Selityskertoimien tulostus\n",
" print(f'Opetusdatan selityskerroin {R2_train_malli:.3f}')\n",
" print(f'Testidatan selityskerroin {R2_test_malli:.3f}')\n",
" \n",
" # Opetusdatan virhetermit kaaviona\n",
" fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
" ax[0].set_title('Ennustevirheiden jakauma opetusdatassa')\n",
" sns.histplot((y_train-y_pred_train), kde=True, ax=ax[0])\n",
" ax[0].set_xlabel('y_train - y_pred_train')\n",
" \n",
" # toteutuneet ja ennustetut hajontakaaviona testidatalle\n",
" ax[1].set_title('Toteutuneet ja ennustetut testidatassa')\n",
" ax[1].scatter(x=y_test, y=y_pred_test)\n",
" ax[1].set_xlabel('toteutunut')\n",
" ax[1].set_ylabel('ennuste')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Datan tarkastelua"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-12-21T20:34:26.417195Z",
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"shell.execute_reply": "2020-12-21T20:34:26.431280Z"
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"papermill": {
"duration": 0.05608,
"end_time": "2020-12-21T20:34:26.432107",
"exception": false,
"start_time": "2020-12-21T20:34:26.376027",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Car_Name | \n",
" Year | \n",
" Selling_Price | \n",
" Present_Price | \n",
" Kms_Driven | \n",
" Fuel_Type | \n",
" Seller_Type | \n",
" Transmission | \n",
" Owner | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" ritz | \n",
" 2014 | \n",
" 3.35 | \n",
" 5.59 | \n",
" 27000 | \n",
" Petrol | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" sx4 | \n",
" 2013 | \n",
" 4.75 | \n",
" 9.54 | \n",
" 43000 | \n",
" Diesel | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
"
\n",
" \n",
" 2 | \n",
" ciaz | \n",
" 2017 | \n",
" 7.25 | \n",
" 9.85 | \n",
" 6900 | \n",
" Petrol | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
"
\n",
" \n",
" 3 | \n",
" wagon r | \n",
" 2011 | \n",
" 2.85 | \n",
" 4.15 | \n",
" 5200 | \n",
" Petrol | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
"
\n",
" \n",
" 4 | \n",
" swift | \n",
" 2014 | \n",
" 4.60 | \n",
" 6.87 | \n",
" 42450 | \n",
" Diesel | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Car_Name Year Selling_Price Present_Price Kms_Driven Fuel_Type \\\n",
"0 ritz 2014 3.35 5.59 27000 Petrol \n",
"1 sx4 2013 4.75 9.54 43000 Diesel \n",
"2 ciaz 2017 7.25 9.85 6900 Petrol \n",
"3 wagon r 2011 2.85 4.15 5200 Petrol \n",
"4 swift 2014 4.60 6.87 42450 Diesel \n",
"\n",
" Seller_Type Transmission Owner \n",
"0 Dealer Manual 0 \n",
"1 Dealer Manual 0 \n",
"2 Dealer Manual 0 \n",
"3 Dealer Manual 0 \n",
"4 Dealer Manual 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('https://taanila.fi/car_data.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:26.688027Z",
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"iopub.status.idle": "2020-12-21T20:34:26.691991Z",
"shell.execute_reply": "2020-12-21T20:34:26.691185Z"
},
"papermill": {
"duration": 0.05177,
"end_time": "2020-12-21T20:34:26.692119",
"exception": false,
"start_time": "2020-12-21T20:34:26.640349",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 301 entries, 0 to 300\n",
"Data columns (total 9 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Car_Name 301 non-null object \n",
" 1 Year 301 non-null int64 \n",
" 2 Selling_Price 301 non-null float64\n",
" 3 Present_Price 301 non-null float64\n",
" 4 Kms_Driven 301 non-null int64 \n",
" 5 Fuel_Type 301 non-null object \n",
" 6 Seller_Type 301 non-null object \n",
" 7 Transmission 301 non-null object \n",
" 8 Owner 301 non-null int64 \n",
"dtypes: float64(2), int64(3), object(4)\n",
"memory usage: 21.3+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:26.774108Z",
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"shell.execute_reply": "2020-12-21T20:34:26.797562Z"
},
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"end_time": "2020-12-21T20:34:26.798311",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Year | \n",
" Selling_Price | \n",
" Present_Price | \n",
" Kms_Driven | \n",
" Owner | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 301.000000 | \n",
" 301.000000 | \n",
" 301.000000 | \n",
" 301.000000 | \n",
" 301.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 2013.627907 | \n",
" 4.661296 | \n",
" 7.628472 | \n",
" 36947.205980 | \n",
" 0.043189 | \n",
"
\n",
" \n",
" std | \n",
" 2.891554 | \n",
" 5.082812 | \n",
" 8.644115 | \n",
" 38886.883882 | \n",
" 0.247915 | \n",
"
\n",
" \n",
" min | \n",
" 2003.000000 | \n",
" 0.100000 | \n",
" 0.320000 | \n",
" 500.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 2012.000000 | \n",
" 0.900000 | \n",
" 1.200000 | \n",
" 15000.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 2014.000000 | \n",
" 3.600000 | \n",
" 6.400000 | \n",
" 32000.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 2016.000000 | \n",
" 6.000000 | \n",
" 9.900000 | \n",
" 48767.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" max | \n",
" 2018.000000 | \n",
" 35.000000 | \n",
" 92.600000 | \n",
" 500000.000000 | \n",
" 3.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Year Selling_Price Present_Price Kms_Driven Owner\n",
"count 301.000000 301.000000 301.000000 301.000000 301.000000\n",
"mean 2013.627907 4.661296 7.628472 36947.205980 0.043189\n",
"std 2.891554 5.082812 8.644115 38886.883882 0.247915\n",
"min 2003.000000 0.100000 0.320000 500.000000 0.000000\n",
"25% 2012.000000 0.900000 1.200000 15000.000000 0.000000\n",
"50% 2014.000000 3.600000 6.400000 32000.000000 0.000000\n",
"75% 2016.000000 6.000000 9.900000 48767.000000 0.000000\n",
"max 2018.000000 35.000000 92.600000 500000.000000 3.000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:27.017845Z",
"iopub.status.busy": "2020-12-21T20:34:27.017151Z",
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"exception": false,
"start_time": "2020-12-21T20:34:26.980462",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Muunnetaan 'Year' auton iäksi olettaen, että data vuodelta 2020\n",
"df['Age'] = 2020 - df['Year']\n",
"df = df.drop('Year', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:29.904605Z",
"iopub.status.busy": "2020-12-21T20:34:29.903927Z",
"iopub.status.idle": "2020-12-21T20:34:29.911595Z",
"shell.execute_reply": "2020-12-21T20:34:29.911035Z"
},
"papermill": {
"duration": 0.054384,
"end_time": "2020-12-21T20:34:29.911743",
"exception": false,
"start_time": "2020-12-21T20:34:29.857359",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Pudotetaan 'Car_Name' pois, koska sitä ei tarvita\n",
"df = df.drop('Car_Name', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:27.420890Z",
"iopub.status.busy": "2020-12-21T20:34:27.420146Z",
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},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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lwz6H7Iiq2gLQvB/etM8D7u7rt7lpe4QkZyRZm2Tt+Ph4q8VKkiQNw6ic1J8p2mqqjlW1sqrGqmpszpw5LZclSZLUvmEHsnuTzAVo3rc27ZuBBX395gP3DLk2SZKkTgw7kF0JLG+mlwNX9LUvS3JgkqOARcCNQ65NkiSpE/u3teIknwJOBA5Lshl4B/BeYHWS04G7gFcCVNWGJKuB24DtwJlVtaOt2iRJkkZJa4Gsqk7byUcn7aT/CmBFW/VI0qOR5GLgN4GtVXVs03Yo8GlgIXAn8Kqq+nHz2bnA6cAO4M1VdVUHZUuaoUblpH5JGjWX4L0UJQ2JgUySpuC9FCUNk4FMkgbnvRQltcJAJkl7z3spStorBjJJGpz3UpTUCgOZJA3OeylKakVrt72QpJnMeylKGiYDmSRNwXspShomD1lKkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLH9u9io0nuBB4AdgDbq2osyaHAp4GFwJ3Aq6rqx13UJ0mSNExd7iF7YVUtrqqxZv4cYE1VLQLWNPOSJEmz3igdslwKrGqmVwGndFeKJEnS8HQVyAr46yQ3JTmjaTuiqrYANO+HT7VgkjOSrE2ydnx8fEjlSpIktaeTc8iA46vqniSHA1cn+c6gC1bVSmAlwNjYWLVVoCTtjOfBSppunewhq6p7mvetwOeBJcC9SeYCNO9bu6hNkgbkebCSps3QA1mSX0nyuIlp4DeAW4ErgeVNt+XAFcOuTZL2gufBStpjXRyyPAL4fJKJ7f9FVX05yd8Bq5OcDtwFvLKD2iRpEBPnwRbwP5tTKX7hPNjmlIxHaM6bPQPgSU960rDqlTTihh7Iqur7wLOmaP8RcNKw65GkPeB5sJKm1Sjd9kKSZgTPg5U03QxkkvQoeB6spDZ0ddsLaejueuczui5Bkzzp7eu7LmFPeB6spGlnIJOkR8HzYCW1wUOWkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUsf27LkCSpGG5653P6LqEkfGkt6/vugT1cQ+ZJElSxwxkkiRJHRu5QJbk5CS3J9mU5Jyu65GkQTl+SdpTIxXIkuwH/Dnw74GjgdOSHN1tVZK0e45fkvbGSAUyYAmwqaq+X1U/By4DlnZckyQNwvFL0h4btass5wF3981vBv5NR7VI0qPh+KV9zvEXHd91CSPl63/w9T1edtQCWaZoq1/okJwBnNHMPpjk9tarGl2HAT/suoi9lfOXd13CTDQrfva8Y6p/8rv15OkuY5rsdvyCGTuGjcTv2ywbK7r/nu7Zv79R1f33E8ibd/s93en4NWqBbDOwoG9+PnBPf4eqWgmsHGZRoyrJ2qoa67oODZ8/+5G02/ELZuYY5u/b9PN7Or1mw/dz1M4h+ztgUZKjkvwSsAy4suOaJGkQjl+S9thI7SGrqu1J3gRcBewHXFxVGzouS5J2y/FL0t4YqUAGUFVfBL7YdR0zxIw67KFp5c9+BM3i8cvft+nn93R6zfjvZ6oecc6pJEmShmjUziGTJEna5xjIOpCkknyyb37/JONJvjDEGl6X5EPD2t6+Jsmpzc/5Vwfoe3aSx7Zcz8Ikv9M3P5bkz9rcpmY+HwU1vZJcnGRrklu7rmW2SHJQkhuTfDvJhiR/3HVNe8pA1o2fAscm+eVm/teBH3RYj6bfacD19K60252zgVYDGbAQ+OdAVlVrq+rNLW9TM5iPgmrFJcDJXRcxy2wDXlRVzwIWAycneV63Je0ZA1l3vgT8h2b6NOBTEx8kWZLkG0m+1bw/rWl/XZLLk3w5yfeSvL9vmQf7pl+R5JJm+mVJbmjW9ZUkRwzji9uXJTkYOB44nSaQJTmxfw9okg81P883A0cC1yS5pvnstCTrk9ya5H19yzyY5H1Jbmp+lkuSXJvk+0l+q+mzMMnXktzcvJ7fLP5e4AVJ1iV5S389SQ5O8vFmm7ck+e0hfJs0+nwU1DSrquuA+7quYzapnon//w5oXjPy5HgDWXcuA5YlOQh4JnBD32ffAU6oquOAtwPv6ftsMfBq4BnAq5P034hyKtcDz2vWdRnwX6anfO3CKcCXq+q7wH1Jnr2zjlX1Z/RuHvrCqnphkiOB9wEvovezfm6SU5ruvwJcW1XPAR4A3k1v7+qpwDubPluBX6+qZ9P7PZk4LHkO8LWqWlxVF04q44+An1TVM6rqmcDf7PFXrtlkqkdBzeuoFmmnkuyXZB298e/qqrphN4uMpJG77cW+oqpuSbKQ3t6xyZfJPx5YlWQRvaR/QN9na6rqJwBJbqP3GIa72bn5wKeTzAV+Cbhjer4C7cJpwH9vpi9r5v9qwGWfSy90jQMkuRQ4AfhL4OfAl5t+64FtVfVPSdbTOyQJvd+VDyVZDOwAnjrANl9M36HVqvrxgLVqdhvoUVBS16pqB7A4yROAzyc5tqpm3Hl6BrJuXQmcD5wIPLGv/V3ANVV1ahParu37bFvf9A4e/hn2D5QH9U1fBFxQVVcmORE4b+/L1s4keSK9vVvHJil6Nwgtej/r/j3SB02xOEz9n+CEf6qH71PzEM3vQlU9lGTi9+AtwL3As5rt/WyQsvE/Wj3SQI+CkkZFVf1jkmvpnac34wKZhyy7dTHwzqpaP6n98Tx8kv/rBlzXvUmenuQx9A5hTbWuWfVk3hH1CuATVfXkqlpYVQt4eK/k0UkOTPJ44KS+ZR4AHtdM3wD8WpLDmpOqTwO++ii2/3hgS1U9BLyGXiCcvI3J/hp408RMkkMexfY0e/koKI28JHOaPWM0F8q9mN5pPzOOgaxDVbW5qj44xUfvB/4kydd5+D/U3TkH+AK983+29LWfB3wmydeAH+5FuRrMacDnJ7V9jt4VjquBW4BLgW/1fb4S+FKSa6pqC3AucA3wbeDmqrriUWz/w8DyJN+kd7jyp037LcD25tLwt0xa5t3AIc1FBN8GXvgotqdZqqq20wvqVwEbgdU+CmrvJPkU8LfA05JsTnJ61zXNAnPpXRR1C70/Iq6uqqHdQmo6ead+SZKkjrmHTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjLtkSRPbJ6LuC7JPyT5Qd/8L3VU0zf2YJl3JnlxG/VImt2SzE9yRfNs4b9P8sGuxj/NfN72QnstyXnAg1V1fl/b/s19jCRp1kkSejdy/khVfby5kfNK4L6q+sOWt+34Ogu5h0zTJsklSS5Icg3wviRLknwjybea96c1/V6X5PIkX27+snx/075fs45bk6yfuIFpkmuTXJjkuiQbkzy3Wf57Sd7dt/0Hm/e5Td91zbpesIt1X5LkFc30SU2t65NcnOTApv3OJH+c5Obms18d6jdW0ih6EfCzqvo4/PPzFN8CvCHJ3yR5JkAzpry9mX5Xkt9LcmIzrn02yXeSXNoEPJI8J8lXk9yU5Kr0nkM8MQ6+J8lXgbO6+ILVLp9lqen2VODFVbUjyb8ATqiq7c1hwfcAv930WwwcR+95jLcnuQg4HJhXVccCTDwOo/HzqjohyVnAFcBzgPuAv09yYVX9qK/v7wBXVdWK5q/Wxzbb29m6SXIQcAlwUlV9N8kngP/Iww8J/2FVPTvJfwL+M/B7e/E9kjTzHQPc1N9QVfcnuYve84dfkOROYDtwfNPl3wH/m97d5Y9r1nEP8HXg+CQ30Hv+8NKqGk/yamAF8IZm+SdU1a+1+UWpO+4h03T7TPOXIvSeq/iZJLcCF9IbfCasqaqfVNXPgNuAJwPfB/5VkouSnAzc39d/4hl664ENVbWlqrY1y/Q/ABl6j894fXMo9RlV9cBu1g3wNOCOqvpuM78KOKHv88ub95uAhYN8IyTNagGmOucn9J4/ewK9APZXwMFJHgssrKrbm343No/PewhYR29ceRpwLHB1knXAf6P3UPcJn57+L0OjwkCm6fbTvul3Adc0e6VeBhzU99m2vukdwP5V9WPgWfT+ujwT+F9T9H9o0rIPMWlPb1VdR28w/AHwySSv3c26oTeI7srENndM3p6kfdIGYKy/oTkqsIDes2rHgBcA1zXzv88v7lF7xBhIbxzaUFWLm9czquo3+vr1j6+aZQxkatPj6YUigNftrnOSw4DHVNXngD8Cnr0nG03yZGBrVX0U+Bjw7AHW/R1gYZJ/3cy/ht5fuZI0lTXAY5O8FnrnwAIfAC6pqvuBu4FXAd8EvkbvVIev7WadtwNzkvzbZp0HJDlmN8toljCQqU3vB/4kydeB/QboPw+4ttlVfwlw7h5u90RgXZJv0Ttn7YO7W3dz6PT19A6xrqe35+1/7OH2Jc1y1btFwanAK5N8D/gu8DPgbU2XrwH3VtX/a6bns5tAVlU/B15B76Kob9M7lPn8Vr4AjRxveyFJktQx95BJkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR37/5pV/qugdLeuAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Kategoristen muuttujien jakaumat\n",
"\n",
"cat_cols = ['Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']\n",
"\n",
"for i in range(0, 3, 2):\n",
" fig = plt.figure(figsize=(10, 4))\n",
" plt.subplot(1, 2, 1)\n",
" sns.countplot(x=cat_cols[i], data=df)\n",
" plt.subplot(1, 2, 2)\n",
" sns.countplot(x=cat_cols[i + 1], data=df)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:28.017958Z",
"iopub.status.busy": "2020-12-21T20:34:28.013334Z",
"iopub.status.idle": "2020-12-21T20:34:28.441624Z",
"shell.execute_reply": "2020-12-21T20:34:28.440937Z"
},
"papermill": {
"duration": 0.491483,
"end_time": "2020-12-21T20:34:28.441768",
"exception": false,
"start_time": "2020-12-21T20:34:27.950285",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Määrällisten muuttujien jakaumat\n",
"\n",
"num_cols = ['Selling_Price', 'Present_Price', 'Kms_Driven', 'Age']\n",
"\n",
"for i in range(0, 3, 2):\n",
" fig = plt.figure(figsize=(13, 3))\n",
" plt.subplot(1, 2, 1)\n",
" sns.boxplot(x=num_cols[i], data=df)\n",
" plt.subplot(1, 2, 2)\n",
" sns.boxplot(x=num_cols[i + 1], data=df)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:28.922361Z",
"iopub.status.busy": "2020-12-21T20:34:28.921287Z",
"iopub.status.idle": "2020-12-21T20:34:29.291196Z",
"shell.execute_reply": "2020-12-21T20:34:29.291674Z"
},
"papermill": {
"duration": 0.420676,
"end_time": "2020-12-21T20:34:29.291858",
"exception": false,
"start_time": "2020-12-21T20:34:28.871182",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Korrelaatiot\n",
"sns.heatmap(df.corr(), annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:30.014145Z",
"iopub.status.busy": "2020-12-21T20:34:30.013022Z",
"iopub.status.idle": "2020-12-21T20:34:30.017768Z",
"shell.execute_reply": "2020-12-21T20:34:30.017102Z"
},
"papermill": {
"duration": 0.061072,
"end_time": "2020-12-21T20:34:30.017883",
"exception": false,
"start_time": "2020-12-21T20:34:29.956811",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Selling_Price | \n",
" Present_Price | \n",
" Kms_Driven | \n",
" Fuel_Type | \n",
" Seller_Type | \n",
" Transmission | \n",
" Owner | \n",
" Age | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 3.35 | \n",
" 5.59 | \n",
" 27000 | \n",
" Petrol | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
" 6 | \n",
"
\n",
" \n",
" 1 | \n",
" 4.75 | \n",
" 9.54 | \n",
" 43000 | \n",
" Diesel | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
" 7 | \n",
"
\n",
" \n",
" 2 | \n",
" 7.25 | \n",
" 9.85 | \n",
" 6900 | \n",
" Petrol | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
" 3 | \n",
"
\n",
" \n",
" 3 | \n",
" 2.85 | \n",
" 4.15 | \n",
" 5200 | \n",
" Petrol | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
" 9 | \n",
"
\n",
" \n",
" 4 | \n",
" 4.60 | \n",
" 6.87 | \n",
" 42450 | \n",
" Diesel | \n",
" Dealer | \n",
" Manual | \n",
" 0 | \n",
" 6 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Selling_Price Present_Price Kms_Driven Fuel_Type Seller_Type \\\n",
"0 3.35 5.59 27000 Petrol Dealer \n",
"1 4.75 9.54 43000 Diesel Dealer \n",
"2 7.25 9.85 6900 Petrol Dealer \n",
"3 2.85 4.15 5200 Petrol Dealer \n",
"4 4.60 6.87 42450 Diesel Dealer \n",
"\n",
" Transmission Owner Age \n",
"0 Manual 0 6 \n",
"1 Manual 0 7 \n",
"2 Manual 0 3 \n",
"3 Manual 0 9 \n",
"4 Manual 0 6 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:30.115190Z",
"iopub.status.busy": "2020-12-21T20:34:30.113105Z",
"iopub.status.idle": "2020-12-21T20:34:30.122575Z",
"shell.execute_reply": "2020-12-21T20:34:30.122000Z"
},
"papermill": {
"duration": 0.060741,
"end_time": "2020-12-21T20:34:30.122703",
"exception": false,
"start_time": "2020-12-21T20:34:30.061962",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Kategoriset muuttujat dummy-muuttujiksi\n",
"df = pd.get_dummies(data=df, drop_first=True) "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:30.216268Z",
"iopub.status.busy": "2020-12-21T20:34:30.215233Z",
"iopub.status.idle": "2020-12-21T20:34:30.228417Z",
"shell.execute_reply": "2020-12-21T20:34:30.228951Z"
},
"papermill": {
"duration": 0.061437,
"end_time": "2020-12-21T20:34:30.229101",
"exception": false,
"start_time": "2020-12-21T20:34:30.167664",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Selling_Price | \n",
" Present_Price | \n",
" Kms_Driven | \n",
" Owner | \n",
" Age | \n",
" Fuel_Type_Diesel | \n",
" Fuel_Type_Petrol | \n",
" Seller_Type_Individual | \n",
" Transmission_Manual | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 3.35 | \n",
" 5.59 | \n",
" 27000 | \n",
" 0 | \n",
" 6 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
" 1 | \n",
" 4.75 | \n",
" 9.54 | \n",
" 43000 | \n",
" 0 | \n",
" 7 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
" 2 | \n",
" 7.25 | \n",
" 9.85 | \n",
" 6900 | \n",
" 0 | \n",
" 3 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
" 3 | \n",
" 2.85 | \n",
" 4.15 | \n",
" 5200 | \n",
" 0 | \n",
" 9 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
" 4 | \n",
" 4.60 | \n",
" 6.87 | \n",
" 42450 | \n",
" 0 | \n",
" 6 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Selling_Price Present_Price Kms_Driven Owner Age Fuel_Type_Diesel \\\n",
"0 3.35 5.59 27000 0 6 0 \n",
"1 4.75 9.54 43000 0 7 1 \n",
"2 7.25 9.85 6900 0 3 0 \n",
"3 2.85 4.15 5200 0 9 0 \n",
"4 4.60 6.87 42450 0 6 1 \n",
"\n",
" Fuel_Type_Petrol Seller_Type_Individual Transmission_Manual \n",
"0 1 0 1 \n",
"1 0 0 1 \n",
"2 1 0 1 \n",
"3 1 0 1 \n",
"4 0 0 1 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.04556,
"end_time": "2020-12-21T20:34:30.319499",
"exception": false,
"start_time": "2020-12-21T20:34:30.273939",
"status": "completed"
},
"tags": []
},
"source": [
"## Mallien sovittaminen"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:30.416587Z",
"iopub.status.busy": "2020-12-21T20:34:30.415568Z",
"iopub.status.idle": "2020-12-21T20:34:30.421772Z",
"shell.execute_reply": "2020-12-21T20:34:30.422305Z"
},
"papermill": {
"duration": 0.05552,
"end_time": "2020-12-21T20:34:30.422442",
"exception": false,
"start_time": "2020-12-21T20:34:30.366922",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Selittävät muuttujat\n",
"X = df.drop('Selling_Price', axis=1)\n",
"\n",
"# Selitettävä/ennustettava muuttuja\n",
"y = df['Selling_Price']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:30.773046Z",
"iopub.status.busy": "2020-12-21T20:34:30.771967Z",
"iopub.status.idle": "2020-12-21T20:34:30.785738Z",
"shell.execute_reply": "2020-12-21T20:34:30.786720Z"
},
"papermill": {
"duration": 0.07607,
"end_time": "2020-12-21T20:34:30.786978",
"exception": false,
"start_time": "2020-12-21T20:34:30.710908",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Jako opetus- ja testidataan\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.045748,
"end_time": "2020-12-21T20:34:31.280392",
"exception": false,
"start_time": "2020-12-21T20:34:31.234644",
"status": "completed"
},
"tags": []
},
"source": [
"### Lineaarinen regressio"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-21T20:34:31.378144Z",
"iopub.status.busy": "2020-12-21T20:34:31.377469Z",
"iopub.status.idle": "2020-12-21T20:34:31.862044Z",
"shell.execute_reply": "2020-12-21T20:34:31.861308Z"
},
"papermill": {
"duration": 0.535588,
"end_time": "2020-12-21T20:34:31.862166",
"exception": false,
"start_time": "2020-12-21T20:34:31.326578",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Opetusdatan selityskerroin 0.888\n",
"Testidatan selityskerroin 0.842\n"
]
},
{
"data": {
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\n",
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