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Electric-Vehicle-Cost-Performances/EV Dissertation Work.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "09bda262",
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},
"tags": []
},
"source": [
"# Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7c226e68",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"import plotly.express as px\n",
"import seaborn as sns\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"from contextlib import contextmanager\n",
"from time import time\n",
"from tqdm import tqdm\n",
"import lightgbm as lgbm\n",
"import category_encoders as ce\n",
"\n",
"from tensorflow.keras.utils import to_categorical\n",
"from sklearn.metrics import classification_report, log_loss, accuracy_score\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import KFold"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f1df97b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "raw",
"id": "038a3094",
"metadata": {
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"exception": false,
"start_time": "2021-12-23T02:58:54.886514",
"status": "completed"
},
"tags": []
},
"source": [
"# Read The Data\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "93828462",
"metadata": {
"execution": {
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"tags": []
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Subtitle</th>\n",
" <th>Acceleration</th>\n",
" <th>TopSpeed</th>\n",
" <th>Range</th>\n",
" <th>Efficiency</th>\n",
" <th>FastChargeSpeed</th>\n",
" <th>Drive</th>\n",
" <th>NumberofSeats</th>\n",
" <th>PriceinGermany</th>\n",
" <th>PriceinUK</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Opel Ampera-e</td>\n",
" <td>Battery Electric Vehicle | 58 kWh</td>\n",
" <td>7.3 sec</td>\n",
" <td>150 km/h</td>\n",
" <td>335 km</td>\n",
" <td>173 Wh/km</td>\n",
" <td>210 km/h</td>\n",
" <td>Front Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>€42,990</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Renault Kangoo Maxi ZE 33</td>\n",
" <td>Battery Electric Vehicle | 31 kWh</td>\n",
" <td>22.4 sec</td>\n",
" <td>130 km/h</td>\n",
" <td>160 km</td>\n",
" <td>194 Wh/km</td>\n",
" <td>-</td>\n",
" <td>Front Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>£31,680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Nissan Leaf</td>\n",
" <td>Battery Electric Vehicle | 36 kWh</td>\n",
" <td>7.9 sec</td>\n",
" <td>144 km/h</td>\n",
" <td>220 km</td>\n",
" <td>164 Wh/km</td>\n",
" <td>230 km/h</td>\n",
" <td>Front Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>€29,990</td>\n",
" <td>£25,995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Audi e-tron Sportback 55 quattro</td>\n",
" <td>Battery Electric Vehicle | 86.5 kWh</td>\n",
" <td>5.7 sec</td>\n",
" <td>200 km/h</td>\n",
" <td>375 km</td>\n",
" <td>231 Wh/km</td>\n",
" <td>600 km/h</td>\n",
" <td>All Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>£79,900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Porsche Taycan Turbo S</td>\n",
" <td>Battery Electric Vehicle | 83.7 kWh</td>\n",
" <td>2.8 sec</td>\n",
" <td>260 km/h</td>\n",
" <td>390 km</td>\n",
" <td>215 Wh/km</td>\n",
" <td>860 km/h</td>\n",
" <td>All Wheel Drive</td>\n",
" <td>4</td>\n",
" <td>€186,336</td>\n",
" <td>£138,830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>MG Marvel R</td>\n",
" <td>Battery Electric Vehicle | 65 kWh</td>\n",
" <td>7.9 sec</td>\n",
" <td>200 km/h</td>\n",
" <td>340 km</td>\n",
" <td>191 Wh/km</td>\n",
" <td>390 km/h</td>\n",
" <td>Rear Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>€40,000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>Tesla Model 3 Long Range Dual Motor</td>\n",
" <td>Battery Electric Vehicle | 76 kWh</td>\n",
" <td>4.4 sec</td>\n",
" <td>233 km/h</td>\n",
" <td>490 km</td>\n",
" <td>155 Wh/km</td>\n",
" <td>820 km/h</td>\n",
" <td>All Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>€53,560</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>MG MG5 EV Long Range</td>\n",
" <td>Battery Electric Vehicle | 57 kWh</td>\n",
" <td>7.7 sec</td>\n",
" <td>185 km/h</td>\n",
" <td>340 km</td>\n",
" <td>168 Wh/km</td>\n",
" <td>340 km/h</td>\n",
" <td>Front Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>£26,495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>Audi Q4 e-tron 45 quattro</td>\n",
" <td>Battery Electric Vehicle | 76.6 kWh</td>\n",
" <td>6.9 sec</td>\n",
" <td>180 km/h</td>\n",
" <td>385 km</td>\n",
" <td>199 Wh/km</td>\n",
" <td>470 km/h</td>\n",
" <td>All Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>€50,900</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>Audi Q4 Sportback e-tron 40</td>\n",
" <td>Battery Electric Vehicle | 76.6 kWh</td>\n",
" <td>8.5 sec</td>\n",
" <td>160 km/h</td>\n",
" <td>425 km</td>\n",
" <td>180 Wh/km</td>\n",
" <td>520 km/h</td>\n",
" <td>Rear Wheel Drive</td>\n",
" <td>5</td>\n",
" <td>€49,500</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>180 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Name \\\n",
"0 Opel Ampera-e \n",
"1 Renault Kangoo Maxi ZE 33 \n",
"2 Nissan Leaf \n",
"3 Audi e-tron Sportback 55 quattro \n",
"4 Porsche Taycan Turbo S \n",
".. ... \n",
"175 MG Marvel R \n",
"176 Tesla Model 3 Long Range Dual Motor \n",
"177 MG MG5 EV Long Range \n",
"178 Audi Q4 e-tron 45 quattro \n",
"179 Audi Q4 Sportback e-tron 40 \n",
"\n",
" Subtitle Acceleration TopSpeed Range \\\n",
"0 Battery Electric Vehicle | 58 kWh 7.3 sec 150 km/h 335 km \n",
"1 Battery Electric Vehicle | 31 kWh 22.4 sec 130 km/h 160 km \n",
"2 Battery Electric Vehicle | 36 kWh 7.9 sec 144 km/h 220 km \n",
"3 Battery Electric Vehicle | 86.5 kWh 5.7 sec 200 km/h 375 km \n",
"4 Battery Electric Vehicle | 83.7 kWh 2.8 sec 260 km/h 390 km \n",
".. ... ... ... ... \n",
"175 Battery Electric Vehicle | 65 kWh 7.9 sec 200 km/h 340 km \n",
"176 Battery Electric Vehicle | 76 kWh 4.4 sec 233 km/h 490 km \n",
"177 Battery Electric Vehicle | 57 kWh 7.7 sec 185 km/h 340 km \n",
"178 Battery Electric Vehicle | 76.6 kWh 6.9 sec 180 km/h 385 km \n",
"179 Battery Electric Vehicle | 76.6 kWh 8.5 sec 160 km/h 425 km \n",
"\n",
" Efficiency FastChargeSpeed Drive NumberofSeats \\\n",
"0 173 Wh/km 210 km/h Front Wheel Drive 5 \n",
"1 194 Wh/km - Front Wheel Drive 5 \n",
"2 164 Wh/km 230 km/h Front Wheel Drive 5 \n",
"3 231 Wh/km 600 km/h All Wheel Drive 5 \n",
"4 215 Wh/km 860 km/h All Wheel Drive 4 \n",
".. ... ... ... ... \n",
"175 191 Wh/km 390 km/h Rear Wheel Drive 5 \n",
"176 155 Wh/km 820 km/h All Wheel Drive 5 \n",
"177 168 Wh/km 340 km/h Front Wheel Drive 5 \n",
"178 199 Wh/km 470 km/h All Wheel Drive 5 \n",
"179 180 Wh/km 520 km/h Rear Wheel Drive 5 \n",
"\n",
" PriceinGermany PriceinUK \n",
"0 €42,990 NaN \n",
"1 NaN £31,680 \n",
"2 €29,990 £25,995 \n",
"3 NaN £79,900 \n",
"4 €186,336 £138,830 \n",
".. ... ... \n",
"175 €40,000 NaN \n",
"176 €53,560 NaN \n",
"177 NaN £26,495 \n",
"178 €50,900 NaN \n",
"179 €49,500 NaN \n",
"\n",
"[180 rows x 11 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe1 = pd.read_csv(\"Cheapestelectriccars-EVDatabase.csv\")\n",
"dataframe1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fe89ebaa",
"metadata": {
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},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Name', 'Subtitle', 'Acceleration', 'TopSpeed', 'Range', 'Efficiency', 'FastChargeSpeed', 'Drive', 'NumberofSeats', 'PriceinGermany', 'PriceinUK']\n"
]
}
],
"source": [
"print(dataframe1.columns.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d052c679",
"metadata": {
"execution": {
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},
"tags": []
},
"outputs": [],
"source": [
"data0=dataframe1.fillna('0')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "aec72926",
"metadata": {
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},
"tags": []
},
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"source": [
"PriceinUK=[]\n",
"for item in data0['PriceinUK']:\n",
" PriceinUK+=[int(item.replace('£','').replace(',',''))]\n",
"data0['PriceinUK']=PriceinUK"
]
},
{
"cell_type": "code",
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},
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"source": [
"PriceinGermany=[]\n",
"for item in data0['PriceinGermany']:\n",
" PriceinGermany+=[int(item.replace('€','').replace(',',''))]\n",
"data0['PriceinGermany']=PriceinGermany"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0de4f006",
"metadata": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"FastChargeSpeed=[]\n",
"for item in data0['FastChargeSpeed']:\n",
" FastChargeSpeed+=[int(item.replace(' km/h','').replace('-','0'))]\n",
"data0['FastChargeSpeed']=FastChargeSpeed"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "349e28d4",
"metadata": {
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},
"tags": []
},
"outputs": [],
"source": [
"Efficiency=[]\n",
"for item in data0['Efficiency']:\n",
" Efficiency+=[int(item.replace(' Wh/km',''))]\n",
"data0['Efficiency']=Efficiency"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "29acace6",
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},
"tags": []
},
"outputs": [],
"source": [
"Range=[]\n",
"for item in data0['Range']:\n",
" Range+=[int(item.replace(' km',''))]\n",
"data0['Range']=Range"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f14e35af",
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"tags": []
},
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"source": [
"TopSpeed=[]\n",
"for item in data0['TopSpeed']:\n",
" TopSpeed+=[int(item.replace(' km/h',''))]\n",
"data0['TopSpeed']=TopSpeed"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f82e143e",
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},
"tags": []
},
"outputs": [],
"source": [
"Acceleration=[]\n",
"for item in data0['Acceleration']:\n",
" Acceleration+=[float(item.replace(' sec',''))]\n",
"data0['Acceleration']=Acceleration"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "108a4c8d",
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"source": [
"Subtitle=[]\n",
"for item in data0['Subtitle']:\n",
" Subtitle+=[float(item.replace('Battery Electric Vehicle | ','').replace(' kWh','').replace(' ',''))]\n",
"data0['Subtitle']=Subtitle"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2e9a39e5",
"metadata": {
"execution": {
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},
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"source": [
"df=data0\n",
"from sklearn.preprocessing import LabelEncoder\n",
"for c in df.columns:\n",
" if df[c].dtype=='object': \n",
" df[c] = df[c].fillna('N')\n",
" lbl = LabelEncoder()\n",
" lbl.fit(list(df[c].values))\n",
" df[c] = lbl.transform(df[c].values)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "66fdb586",
"metadata": {
"execution": {
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},
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{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Subtitle</th>\n",
" <th>Acceleration</th>\n",
" <th>TopSpeed</th>\n",
" <th>Range</th>\n",
" <th>Efficiency</th>\n",
" <th>FastChargeSpeed</th>\n",
" <th>Drive</th>\n",
" <th>NumberofSeats</th>\n",
" <th>PriceinGermany</th>\n",
" <th>PriceinUK</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>127</td>\n",
" <td>31.0</td>\n",
" <td>22.4</td>\n",
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" <td>220</td>\n",
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" <td>230</td>\n",
" <td>1</td>\n",
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" <td>29990</td>\n",
" <td>25995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>15</td>\n",
" <td>86.5</td>\n",
" <td>5.7</td>\n",
" <td>200</td>\n",
" <td>375</td>\n",
" <td>231</td>\n",
" <td>600</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>79900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>125</td>\n",
" <td>83.7</td>\n",
" <td>2.8</td>\n",
" <td>260</td>\n",
" <td>390</td>\n",
" <td>215</td>\n",
" <td>860</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>186336</td>\n",
" <td>138830</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <tr>\n",
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" <td>75</td>\n",
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" <td>5</td>\n",
" <td>40000</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>176</th>\n",
" <td>147</td>\n",
" <td>76.0</td>\n",
" <td>4.4</td>\n",
" <td>233</td>\n",
" <td>490</td>\n",
" <td>155</td>\n",
" <td>820</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>53560</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>73</td>\n",
" <td>57.0</td>\n",
" <td>7.7</td>\n",
" <td>185</td>\n",
" <td>340</td>\n",
" <td>168</td>\n",
" <td>340</td>\n",
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" <td>2</td>\n",
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"<p>180 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Name Subtitle Acceleration TopSpeed Range Efficiency \\\n",
"0 97 58.0 7.3 150 335 173 \n",
"1 127 31.0 22.4 130 160 194 \n",
"2 94 36.0 7.9 144 220 164 \n",
"3 15 86.5 5.7 200 375 231 \n",
"4 125 83.7 2.8 260 390 215 \n",
".. ... ... ... ... ... ... \n",
"175 75 65.0 7.9 200 340 191 \n",
"176 147 76.0 4.4 233 490 155 \n",
"177 73 57.0 7.7 185 340 168 \n",
"178 6 76.6 6.9 180 385 199 \n",
"179 2 76.6 8.5 160 425 180 \n",
"\n",
" FastChargeSpeed Drive NumberofSeats PriceinGermany PriceinUK \n",
"0 210 1 5 42990 0 \n",
"1 0 1 5 0 31680 \n",
"2 230 1 5 29990 25995 \n",
"3 600 0 5 0 79900 \n",
"4 860 0 4 186336 138830 \n",
".. ... ... ... ... ... \n",
"175 390 2 5 40000 0 \n",
"176 820 0 5 53560 0 \n",
"177 340 1 5 0 26495 \n",
"178 470 0 5 50900 0 \n",
"179 520 2 5 49500 0 \n",
"\n",
"[180 rows x 11 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1=df\n",
"data1"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "cc7dd903",
"metadata": {
"execution": {
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"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Name', 'Subtitle', 'Acceleration', 'TopSpeed', 'Range', 'Efficiency',\n",
" 'FastChargeSpeed', 'Drive', 'NumberofSeats', 'PriceinGermany',\n",
" 'PriceinUK'],\n",
" dtype='object')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1.columns"
]
},
{
"cell_type": "code",
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"id": "f20b3602",
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},
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{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x1080 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data1.iloc[:,:].hist(figsize=(15,15))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "64562f31",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:58:58.667119Z",
"iopub.status.busy": "2021-12-23T02:58:58.666309Z",
"iopub.status.idle": "2021-12-23T02:59:01.944195Z",
"shell.execute_reply": "2021-12-23T02:59:01.943509Z",
"shell.execute_reply.started": "2021-08-31T08:19:22.203202Z"
},
"papermill": {
"duration": 3.365001,
"end_time": "2021-12-23T02:59:01.944360",
"exception": false,
"start_time": "2021-12-23T02:58:58.579359",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 20 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"datap=data1[['Range','Efficiency','Subtitle','FastChargeSpeed']]\n",
"sns.pairplot(datap)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "88355bac",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-17T04:40:47.715829Z",
"iopub.status.busy": "2021-08-17T04:40:47.715546Z",
"iopub.status.idle": "2021-08-17T04:40:47.722127Z",
"shell.execute_reply": "2021-08-17T04:40:47.720984Z",
"shell.execute_reply.started": "2021-08-17T04:40:47.715798Z"
},
"papermill": {
"duration": 0.058325,
"end_time": "2021-12-23T02:59:02.062133",
"exception": false,
"start_time": "2021-12-23T02:59:02.003808",
"status": "completed"
},
"tags": []
},
"source": [
"# Power Ranking on Battery & Its Features"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "93aaa589",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:02.224482Z",
"iopub.status.busy": "2021-12-23T02:59:02.223734Z",
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"x": 0.05
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0,
1
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"title": {
"text": "Name"
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0,
1
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}
},
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" \n",
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" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.bar(data2d[0:40], x='Name', y='FastChargeSpeed(km/h)',title=\"FastChargeSpeed Ranknig of EVs\")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "4b52158c",
"metadata": {
"papermill": {
"duration": 0.06557,
"end_time": "2021-12-23T02:59:04.819265",
"exception": false,
"start_time": "2021-12-23T02:59:04.753695",
"status": "completed"
},
"tags": []
},
"source": [
"# Target setting"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "5e247522",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:04.959441Z",
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"shell.execute_reply.started": "2021-08-31T08:19:27.155341Z"
},
"papermill": {
"duration": 0.080517,
"end_time": "2021-12-23T02:59:04.968256",
"exception": false,
"start_time": "2021-12-23T02:59:04.887739",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 13, 18, 27, 28, 29, 34, 45, 48, 63, 64, 78, 79, 89, 93, 98, 102, 104, 105, 106, 107, 108, 110, 111, 112, 115, 116, 117, 118, 119, 128, 140, 141, 143, 161, 162, 163, 164, 165, 166, 175, 176, 178, 179]\n"
]
}
],
"source": [
"target=['PriceinUK','PriceinGermany']\n",
"\n",
"drop=[]\n",
"for i,item in enumerate(data1['PriceinUK']):\n",
" if item ==0:\n",
" drop+=[i]\n",
"print(drop)\n",
"data1=data1.drop(index=data1.index[drop]).reset_index(drop=True)\n",
"\n",
"dataY=data1[target[0]]\n",
"dataX=data1.drop(target,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "7ed8a963",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:05.107100Z",
"iopub.status.busy": "2021-12-23T02:59:05.106342Z",
"iopub.status.idle": "2021-12-23T02:59:05.118100Z",
"shell.execute_reply": "2021-12-23T02:59:05.118633Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.167518Z"
},
"papermill": {
"duration": 0.083109,
"end_time": "2021-12-23T02:59:05.118854",
"exception": false,
"start_time": "2021-12-23T02:59:05.035745",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 31680\n",
"1 25995\n",
"2 79900\n",
"3 138830\n",
"4 30255\n",
"Name: PriceinUK, dtype: int64\n",
"\n",
" 0 1 2 3 4\n",
"Name 127.0 94.0 15.0 125.0 96.0\n",
"Subtitle 31.0 36.0 86.5 83.7 36.0\n",
"Acceleration 22.4 7.9 5.7 2.8 14.0\n",
"TopSpeed 130.0 144.0 200.0 260.0 123.0\n",
"Range 160.0 220.0 375.0 390.0 165.0\n",
"Efficiency 194.0 164.0 231.0 215.0 218.0\n",
"FastChargeSpeed 0.0 230.0 600.0 860.0 170.0\n",
"Drive 1.0 1.0 0.0 0.0 1.0\n",
"NumberofSeats 5.0 5.0 5.0 4.0 7.0\n"
]
}
],
"source": [
"print(dataY[0:5].T)\n",
"print()\n",
"print(dataX[0:5].T)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "02c2545e",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:05.258407Z",
"iopub.status.busy": "2021-12-23T02:59:05.257763Z",
"iopub.status.idle": "2021-12-23T02:59:05.263713Z",
"shell.execute_reply": "2021-12-23T02:59:05.264324Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.191741Z"
},
"papermill": {
"duration": 0.076766,
"end_time": "2021-12-23T02:59:05.264514",
"exception": false,
"start_time": "2021-12-23T02:59:05.187748",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"136\n"
]
}
],
"source": [
"n=len(dataX)\n",
"print(n)\n",
"N=[]\n",
"for i in range(n):\n",
" N+=[i]\n",
"random.seed(2021)\n",
"random.shuffle(N)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "bdc7e6a0",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:05.407367Z",
"iopub.status.busy": "2021-12-23T02:59:05.406537Z",
"iopub.status.idle": "2021-12-23T02:59:05.414317Z",
"shell.execute_reply": "2021-12-23T02:59:05.414874Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.211185Z"
},
"papermill": {
"duration": 0.082259,
"end_time": "2021-12-23T02:59:05.415099",
"exception": false,
"start_time": "2021-12-23T02:59:05.332840",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"trainX=dataX.loc[N[0:(n//4)*3]]\n",
"trainY=dataY.loc[N[0:(n//4)*3]]\n",
"testX=dataX.loc[N[(n//4)*3:]]\n",
"testY=dataY.loc[N[(n//4)*3:]]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "29008c53",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:05.552579Z",
"iopub.status.busy": "2021-12-23T02:59:05.551988Z",
"iopub.status.idle": "2021-12-23T02:59:05.558269Z",
"shell.execute_reply": "2021-12-23T02:59:05.557739Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.22503Z"
},
"papermill": {
"duration": 0.075957,
"end_time": "2021-12-23T02:59:05.558406",
"exception": false,
"start_time": "2021-12-23T02:59:05.482449",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Name', 'Subtitle', 'Acceleration', 'TopSpeed', 'Range', 'Efficiency', 'FastChargeSpeed', 'Drive', 'NumberofSeats']\n"
]
}
],
"source": [
"df_columns = list(dataX.columns)\n",
"print(df_columns)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "5c7d11c8",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:05.696391Z",
"iopub.status.busy": "2021-12-23T02:59:05.695787Z",
"iopub.status.idle": "2021-12-23T02:59:05.700609Z",
"shell.execute_reply": "2021-12-23T02:59:05.701189Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.239824Z"
},
"papermill": {
"duration": 0.075002,
"end_time": "2021-12-23T02:59:05.701379",
"exception": false,
"start_time": "2021-12-23T02:59:05.626377",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def create_numeric_feature(input_df):\n",
" use_columns = df_columns \n",
" return input_df[use_columns].copy()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "9ed5e794",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:05.840690Z",
"iopub.status.busy": "2021-12-23T02:59:05.839737Z",
"iopub.status.idle": "2021-12-23T02:59:05.851248Z",
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"shell.execute_reply.started": "2021-08-31T08:19:27.251287Z"
},
"papermill": {
"duration": 0.084146,
"end_time": "2021-12-23T02:59:05.852389",
"exception": false,
"start_time": "2021-12-23T02:59:05.768243",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from contextlib import contextmanager\n",
"from time import time\n",
"\n",
"class Timer:\n",
" def __init__(self, logger=None, format_str='{:.3f}[s]', prefix=None, suffix=None, sep=' '):\n",
"\n",
" if prefix: format_str = str(prefix) + sep + format_str\n",
" if suffix: format_str = format_str + sep + str(suffix)\n",
" self.format_str = format_str\n",
" self.logger = logger\n",
" self.start = None\n",
" self.end = None\n",
"\n",
" @property\n",
" def duration(self):\n",
" if self.end is None:\n",
" return 0\n",
" return self.end - self.start\n",
"\n",
" def __enter__(self):\n",
" self.start = time()\n",
"\n",
" def __exit__(self, exc_type, exc_val, exc_tb):\n",
" self.end = time()\n",
" out_str = self.format_str.format(self.duration)\n",
" if self.logger:\n",
" self.logger.info(out_str)\n",
" else:\n",
" print(out_str)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "b8e6d8e8",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:05.991432Z",
"iopub.status.busy": "2021-12-23T02:59:05.990505Z",
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"shell.execute_reply": "2021-12-23T02:59:05.997428Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.263153Z"
},
"papermill": {
"duration": 0.076023,
"end_time": "2021-12-23T02:59:05.997614",
"exception": false,
"start_time": "2021-12-23T02:59:05.921591",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"\n",
"def to_feature(input_df):\n",
"\n",
" processors = [\n",
" create_numeric_feature,\n",
" ]\n",
" \n",
" out_df = pd.DataFrame()\n",
" \n",
" for func in tqdm(processors, total=len(processors)):\n",
" with Timer(prefix='create' + func.__name__ + ' '):\n",
" _df = func(input_df)\n",
"\n",
" assert len(_df) == len(input_df), func.__name__\n",
" out_df = pd.concat([out_df, _df], axis=1)\n",
" \n",
" return out_df"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "3108d765",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:06.136361Z",
"iopub.status.busy": "2021-12-23T02:59:06.135436Z",
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"shell.execute_reply": "2021-12-23T02:59:06.158698Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.276645Z"
},
"papermill": {
"duration": 0.093683,
"end_time": "2021-12-23T02:59:06.158904",
"exception": false,
"start_time": "2021-12-23T02:59:06.065221",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 500.33it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"createcreate_numeric_feature 0.001[s]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1000.07it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"createcreate_numeric_feature 0.000[s]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"train_feat_df = to_feature(trainX)\n",
"test_feat_df = to_feature(testX)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8384e074",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "f052608e",
"metadata": {
"papermill": {
"duration": 0.067935,
"end_time": "2021-12-23T02:59:06.294931",
"exception": false,
"start_time": "2021-12-23T02:59:06.226996",
"status": "completed"
},
"tags": []
},
"source": [
"# Learning Model"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "32d96c2e",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:06.450550Z",
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"shell.execute_reply": "2021-12-23T02:59:06.455451Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.303062Z"
},
"papermill": {
"duration": 0.092362,
"end_time": "2021-12-23T02:59:06.455654",
"exception": false,
"start_time": "2021-12-23T02:59:06.363292",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import lightgbm as lgbm\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"def fit_lgbm(X, y, cv, \n",
" params: dict=None, \n",
" verbose: int=50):\n",
"\n",
" if params is None:\n",
" params = {}\n",
"\n",
" models = []\n",
" oof_pred = np.zeros_like(y, dtype=np.float)\n",
"\n",
" for i, (idx_train, idx_valid) in enumerate(cv): \n",
" x_train, y_train = X[idx_train], y[idx_train]\n",
" x_valid, y_valid = X[idx_valid], y[idx_valid]\n",
"\n",
" clf = lgbm.LGBMRegressor(**params)\n",
" \n",
" with Timer(prefix='fit fold={} '.format(i)):\n",
" clf.fit(x_train, y_train, \n",
" eval_set=[(x_valid, y_valid)], \n",
" early_stopping_rounds=100,\n",
" verbose=verbose)\n",
"\n",
" pred_i = clf.predict(x_valid)\n",
" oof_pred[idx_valid] = pred_i\n",
" models.append(clf)\n",
" print(f'Fold {i} RMSLE: {mean_squared_error(y_valid, pred_i) ** .5:.4f}')\n",
" print()\n",
"\n",
" score = mean_squared_error(y, oof_pred) ** .5\n",
" print('-' * 50)\n",
" print('FINISHED | Whole RMSLE: {:.4f}'.format(score))\n",
" return oof_pred, models"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "428861f5",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:06.608937Z",
"iopub.status.busy": "2021-12-23T02:59:06.607971Z",
"iopub.status.idle": "2021-12-23T02:59:06.615112Z",
"shell.execute_reply": "2021-12-23T02:59:06.614395Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.316541Z"
},
"papermill": {
"duration": 0.079736,
"end_time": "2021-12-23T02:59:06.615279",
"exception": false,
"start_time": "2021-12-23T02:59:06.535543",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"params = {\n",
" 'objective': 'rmse', \n",
" 'learning_rate': .1,\n",
" 'reg_lambda': 1.,\n",
" 'reg_alpha': .1,\n",
" 'max_depth': 5, \n",
" 'n_estimators': 10000, \n",
" 'colsample_bytree': .5, \n",
" 'min_child_samples': 10,\n",
" 'subsample_freq': 3,\n",
" 'subsample': .9,\n",
" 'importance_type': 'gain', \n",
" 'random_state': 71,\n",
" 'num_leaves': 62\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "dd9641b1",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:06.760413Z",
"iopub.status.busy": "2021-12-23T02:59:06.759623Z",
"iopub.status.idle": "2021-12-23T02:59:06.763891Z",
"shell.execute_reply": "2021-12-23T02:59:06.764598Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.331776Z"
},
"papermill": {
"duration": 0.080557,
"end_time": "2021-12-23T02:59:06.764871",
"exception": false,
"start_time": "2021-12-23T02:59:06.684314",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(102,)\n",
"<class 'pandas.core.series.Series'>\n"
]
}
],
"source": [
"y = trainY\n",
"print(y.shape)\n",
"print(type(y))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "ca1fbbcc",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:06.918865Z",
"iopub.status.busy": "2021-12-23T02:59:06.917983Z",
"iopub.status.idle": "2021-12-23T02:59:06.922807Z",
"shell.execute_reply": "2021-12-23T02:59:06.922212Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.348566Z"
},
"papermill": {
"duration": 0.086937,
"end_time": "2021-12-23T02:59:06.922975",
"exception": false,
"start_time": "2021-12-23T02:59:06.836038",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PriceinUK</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>83580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>19795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>52500</td>\n",
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" </tr>\n",
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" <td>30345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>49980</td>\n",
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" </tr>\n",
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"</table>\n",
"<p>102 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" PriceinUK\n",
"38 83580\n",
"34 19795\n",
"71 52500\n",
"65 30895\n",
"15 30550\n",
".. ...\n",
"85 30345\n",
"43 49980\n",
"67 42250\n",
"35 30160\n",
"135 26495\n",
"\n",
"[102 rows x 1 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ydf=pd.DataFrame(y)\n",
"ydf"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "72f379b7",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:07.069356Z",
"iopub.status.busy": "2021-12-23T02:59:07.068639Z",
"iopub.status.idle": "2021-12-23T02:59:07.666565Z",
"shell.execute_reply": "2021-12-23T02:59:07.665888Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.367762Z"
},
"papermill": {
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"end_time": "2021-12-23T02:59:07.666774",
"exception": false,
"start_time": "2021-12-23T02:59:06.994976",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fit fold=0 0.078[s]\n",
"Fold 0 RMSLE: 9144.8385\n",
"\n",
"fit fold=1 0.030[s]\n",
"Fold 1 RMSLE: 7419.1824\n",
"\n",
"fit fold=2 0.022[s]\n",
"Fold 2 RMSLE: 11288.6116\n",
"\n",
"fit fold=3 0.032[s]\n",
"Fold 3 RMSLE: 11463.1361\n",
"\n",
"[500]\tvalid_0's rmse: 10601.8\n",
"fit fold=4 0.101[s]\n",
"Fold 4 RMSLE: 10513.1221\n",
"\n",
"--------------------------------------------------\n",
"FINISHED | Whole RMSLE: 10048.5872\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.model_selection import KFold\n",
"\n",
"for i in range(1):\n",
" fold = KFold(n_splits=5, shuffle=True, random_state=71)\n",
" ydfi=ydf.iloc[:,i]\n",
" y=np.array(ydfi)\n",
" cv = list(fold.split(train_feat_df, y))\n",
" oof, models = fit_lgbm(train_feat_df.values, y, cv, params=params, verbose=500)\n",
" \n",
" fig,ax = plt.subplots(figsize=(6,6))\n",
" ax.set_title(target[i],fontsize=20)\n",
" ax.set_ylabel('Predicted Train '+target[i],fontsize=12)\n",
" ax.set_xlabel('Actual Train '+target[i],fontsize=12)\n",
" ax.scatter(y,oof)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bff66807",
"metadata": {
"papermill": {
"duration": 0.071672,
"end_time": "2021-12-23T02:59:07.810579",
"exception": false,
"start_time": "2021-12-23T02:59:07.738907",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "482ca514",
"metadata": {
"papermill": {
"duration": 0.072453,
"end_time": "2021-12-23T02:59:07.955545",
"exception": false,
"start_time": "2021-12-23T02:59:07.883092",
"status": "completed"
},
"tags": []
},
"source": [
"# Cost Performance is defined as ...\n",
"### (Predicted price - Actual price) / (Actual price)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "306f6952",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:08.121495Z",
"iopub.status.busy": "2021-12-23T02:59:08.120754Z",
"iopub.status.idle": "2021-12-23T02:59:08.123568Z",
"shell.execute_reply": "2021-12-23T02:59:08.124187Z",
"shell.execute_reply.started": "2021-08-31T08:19:27.995093Z"
},
"papermill": {
"duration": 0.093953,
"end_time": "2021-12-23T02:59:08.124381",
"exception": false,
"start_time": "2021-12-23T02:59:08.030428",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PriceinUK</th>\n",
" <th>oof</th>\n",
" <th>rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>30375</td>\n",
" <td>49395.576361</td>\n",
" <td>0.626192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>19200</td>\n",
" <td>30754.768305</td>\n",
" <td>0.601811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>30255</td>\n",
" <td>45234.823352</td>\n",
" <td>0.495119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>39000</td>\n",
" <td>54619.649225</td>\n",
" <td>0.400504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>45900</td>\n",
" <td>63339.151174</td>\n",
" <td>0.379938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>91905</td>\n",
" <td>72018.074940</td>\n",
" <td>-0.216386</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>69100</td>\n",
" <td>53934.775938</td>\n",
" <td>-0.219468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>65720</td>\n",
" <td>51100.834360</td>\n",
" <td>-0.222446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>110980</td>\n",
" <td>85992.580028</td>\n",
" <td>-0.225152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>42900</td>\n",
" <td>31265.587167</td>\n",
" <td>-0.271198</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>102 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" PriceinUK oof rate\n",
"133 30375 49395.576361 0.626192\n",
"32 19200 30754.768305 0.601811\n",
"4 30255 45234.823352 0.495119\n",
"48 39000 54619.649225 0.400504\n",
"120 45900 63339.151174 0.379938\n",
".. ... ... ...\n",
"108 91905 72018.074940 -0.216386\n",
"47 69100 53934.775938 -0.219468\n",
"83 65720 51100.834360 -0.222446\n",
"96 110980 85992.580028 -0.225152\n",
"119 42900 31265.587167 -0.271198\n",
"\n",
"[102 rows x 3 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ydf['oof']=oof\n",
"ydf['rate']=(ydf['oof']-ydf['PriceinUK'])/ydf['PriceinUK']\n",
"ydf.sort_values('rate',ascending=False)"
]
},
{
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"tags": []
},
"source": [
"## the Lowest on cost performance"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "48817927",
"metadata": {
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},
"outputs": [
{
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" <td>Polestar 2 Long Range Single Motor</td>\n",
" <td>Battery Electric Vehicle | 75 kWh</td>\n",
" <td>7.4 sec</td>\n",
" <td>160 km/h</td>\n",
" <td>425 km</td>\n",
" <td>176 Wh/km</td>\n",
" <td>550 km/h</td>\n",
" <td>Front Wheel Drive</td>\n",
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"</table>\n",
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"text/plain": [
" Name \\\n",
"153 Polestar 2 Long Range Single Motor \n",
"\n",
" Subtitle Acceleration TopSpeed Range \\\n",
"153 Battery Electric Vehicle | 75 kWh 7.4 sec 160 km/h 425 km \n",
"\n",
" Efficiency FastChargeSpeed Drive NumberofSeats \\\n",
"153 176 Wh/km 550 km/h Front Wheel Drive 5 \n",
"\n",
" PriceinGermany PriceinUK \n",
"153 €49,500 £42,900 "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe1[dataframe1['PriceinUK']=='£42,900']"
]
},
{
"cell_type": "markdown",
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"tags": []
},
"source": [
"# **Polestar 2 Long Range Single Motor**"
]
},
{
"cell_type": "markdown",
"id": "d4b3d606",
"metadata": {
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"status": "completed"
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"tags": []
},
"source": [
"![https://i1.wp.com/electrek.co/wp-content/uploads/sites/3/2020/08/Polestar2_US_Launch-0007-2000.jpg?w=2500&quality=82&strip=all&ssl=1](https://i1.wp.com/electrek.co/wp-content/uploads/sites/3/2020/08/Polestar2_US_Launch-0007-2000.jpg?w=2500&quality=82&strip=all&ssl=1)"
]
},
{
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"execution_count": null,
"id": "364d070e",
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"status": "completed"
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"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "c838e8e9",
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},
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},
"source": [
"## the Highest on cost performance"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "3184b595",
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" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Name Subtitle Acceleration TopSpeed Range Efficiency \\\n",
"133 108 45.0 11.7 135 200 225 \n",
"\n",
" FastChargeSpeed Drive NumberofSeats PriceinGermany PriceinUK \n",
"133 270 1 7 37590 30375 "
]
},
"execution_count": 43,
"metadata": {},
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"source": [
"data1.loc[133:133]"
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{
"cell_type": "code",
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"id": "c3116d40",
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"outputs": [
{
"data": {
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" <th>Drive</th>\n",
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" <tr>\n",
" <th>173</th>\n",
" <td>Peugeot e-Rifter Standard 50 kWh</td>\n",
" <td>Battery Electric Vehicle | 45 kWh</td>\n",
" <td>11.7 sec</td>\n",
" <td>135 km/h</td>\n",
" <td>200 km</td>\n",
" <td>225 Wh/km</td>\n",
" <td>270 km/h</td>\n",
" <td>Front Wheel Drive</td>\n",
" <td>7</td>\n",
" <td>€37,590</td>\n",
" <td>£30,375</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name \\\n",
"173 Peugeot e-Rifter Standard 50 kWh \n",
"\n",
" Subtitle Acceleration TopSpeed Range \\\n",
"173 Battery Electric Vehicle | 45 kWh 11.7 sec 135 km/h 200 km \n",
"\n",
" Efficiency FastChargeSpeed Drive NumberofSeats \\\n",
"173 225 Wh/km 270 km/h Front Wheel Drive 7 \n",
"\n",
" PriceinGermany PriceinUK \n",
"173 €37,590 £30,375 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe1[dataframe1['PriceinUK']=='£30,375']"
]
},
{
"cell_type": "markdown",
"id": "7fe89904",
"metadata": {
"papermill": {
"duration": 0.073241,
"end_time": "2021-12-23T02:59:09.690476",
"exception": false,
"start_time": "2021-12-23T02:59:09.617235",
"status": "completed"
},
"tags": []
},
"source": [
"# **Peugeot e-Rifter Standard 50 kWh**"
]
},
{
"cell_type": "markdown",
"id": "c5e6b735",
"metadata": {
"papermill": {
"duration": 0.073462,
"end_time": "2021-12-23T02:59:09.838020",
"exception": false,
"start_time": "2021-12-23T02:59:09.764558",
"status": "completed"
},
"tags": []
},
"source": [
"![https://ev-database.org/img/auto/Peugeot_e-Rifter/Peugeot_e-Rifter-02.jpg](https://ev-database.org/img/auto/Peugeot_e-Rifter/Peugeot_e-Rifter-02.jpg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f33a9464",
"metadata": {
"papermill": {
"duration": 0.074304,
"end_time": "2021-12-23T02:59:09.987828",
"exception": false,
"start_time": "2021-12-23T02:59:09.913524",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "ec9919f4",
"metadata": {
"papermill": {
"duration": 0.07374,
"end_time": "2021-12-23T02:59:10.138065",
"exception": false,
"start_time": "2021-12-23T02:59:10.064325",
"status": "completed"
},
"tags": []
},
"source": [
"# Visualize Importance"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "81040e86",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:10.291151Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def visualize_importance(models, feat_train_df):\n",
"\n",
" feature_importance_df = pd.DataFrame()\n",
" for i, model in enumerate(models):\n",
" _df = pd.DataFrame()\n",
" _df['feature_importance'] = model.feature_importances_\n",
" _df['column'] = feat_train_df.columns\n",
" _df['fold'] = i + 1\n",
" feature_importance_df = pd.concat([feature_importance_df, _df], \n",
" axis=0, ignore_index=True)\n",
"\n",
" order = feature_importance_df.groupby('column')\\\n",
" .sum()[['feature_importance']]\\\n",
" .sort_values('feature_importance', ascending=False).index[:50]\n",
"\n",
" fig, ax = plt.subplots(figsize=(8, max(6, len(order) * .25)))\n",
" sns.boxenplot(data=feature_importance_df, \n",
" x='feature_importance', \n",
" y='column', \n",
" order=order, \n",
" ax=ax, \n",
" palette='viridis', \n",
" orient='h')\n",
" \n",
" ax.tick_params(axis='x', rotation=0)\n",
" #ax.set_title('Importance')\n",
" ax.grid()\n",
" fig.tight_layout()\n",
" \n",
" return fig,ax\n",
"\n",
"#fig, ax = visualize_importance(models, train_feat_df)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "a98d770a",
"metadata": {
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"start_time": "2021-12-23T02:59:10.377333",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fit fold=0 0.063[s]\n",
"Fold 0 RMSLE: 9144.8385\n",
"\n",
"fit fold=1 0.033[s]\n",
"Fold 1 RMSLE: 7419.1824\n",
"\n",
"fit fold=2 0.020[s]\n",
"Fold 2 RMSLE: 11288.6116\n",
"\n",
"fit fold=3 0.018[s]\n",
"Fold 3 RMSLE: 11463.1361\n",
"\n",
"[500]\tvalid_0's rmse: 10601.8\n",
"fit fold=4 0.091[s]\n",
"Fold 4 RMSLE: 10513.1221\n",
"\n",
"--------------------------------------------------\n",
"FINISHED | Whole RMSLE: 10048.5872\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for i in range(1):\n",
" fold = KFold(n_splits=5, shuffle=True, random_state=71)\n",
" ydfi=ydf.iloc[:,i]\n",
" y=np.array(ydfi)\n",
" cv = list(fold.split(train_feat_df, y))\n",
" oof, models = fit_lgbm(train_feat_df.values, y, cv, params=params, verbose=500)\n",
" fig, ax = visualize_importance(models, train_feat_df)\n",
" ax.set_title(target[i]+' Imortance',fontsize=20)\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "8499e0b8",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:11.416966Z",
"iopub.status.busy": "2021-12-23T02:59:11.416287Z",
"iopub.status.idle": "2021-12-23T02:59:11.431623Z",
"shell.execute_reply": "2021-12-23T02:59:11.432190Z",
"shell.execute_reply.started": "2021-08-31T08:19:28.942022Z"
},
"papermill": {
"duration": 0.095804,
"end_time": "2021-12-23T02:59:11.432420",
"exception": false,
"start_time": "2021-12-23T02:59:11.336616",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([40970.73679126, 54799.16280472, 94378.66315614, 40702.81877357,\n",
" 25137.40870638])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred0 = np.array([model.predict(test_feat_df.values) for model in models])\n",
"PRED = pred0[4]\n",
"PRED[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "86efe054",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:11.594812Z",
"iopub.status.busy": "2021-12-23T02:59:11.594113Z",
"iopub.status.idle": "2021-12-23T02:59:11.597629Z",
"shell.execute_reply": "2021-12-23T02:59:11.598305Z",
"shell.execute_reply.started": "2021-08-31T08:19:28.963953Z"
},
"papermill": {
"duration": 0.086794,
"end_time": "2021-12-23T02:59:11.598528",
"exception": false,
"start_time": "2021-12-23T02:59:11.511734",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([40800, 59990, 90000, 25995, 26795], dtype=int64)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ANS=np.array(testY)\n",
"ANS[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "b2a828a7",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:11.754048Z",
"iopub.status.busy": "2021-12-23T02:59:11.753360Z",
"iopub.status.idle": "2021-12-23T02:59:12.172263Z",
"shell.execute_reply": "2021-12-23T02:59:12.171694Z",
"shell.execute_reply.started": "2021-08-31T08:19:28.979128Z"
},
"papermill": {
"duration": 0.497674,
"end_time": "2021-12-23T02:59:12.172404",
"exception": false,
"start_time": "2021-12-23T02:59:11.674730",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,8))\n",
"sns.histplot(PRED, label='Predicted Test '+target[0], ax=ax, color='black',bins=30)\n",
"sns.histplot(oof, label='Predicted Train '+target[0], ax=ax, color='C1',bins=30)\n",
"ax.legend()\n",
"ax.grid()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "d6988ec5",
"metadata": {
"execution": {
"iopub.execute_input": "2021-12-23T02:59:12.355460Z",
"iopub.status.busy": "2021-12-23T02:59:12.354798Z",
"iopub.status.idle": "2021-12-23T02:59:12.544786Z",
"shell.execute_reply": "2021-12-23T02:59:12.544210Z",
"shell.execute_reply.started": "2021-08-31T08:19:29.398929Z"
},
"papermill": {
"duration": 0.293399,
"end_time": "2021-12-23T02:59:12.544946",
"exception": false,
"start_time": "2021-12-23T02:59:12.251547",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x1a25e5fff10>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig,ax = plt.subplots(figsize=(6,6))\n",
"ax.set_title(target[0],fontsize=20)\n",
"ax.set_xlabel('Actual Test '+target[0],fontsize=12)\n",
"ax.set_ylabel('Predicted Test '+target[0],fontsize=12)\n",
"ax.scatter(ANS,PRED)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40b4ec9f",
"metadata": {
"papermill": {
"duration": 0.078539,
"end_time": "2021-12-23T02:59:12.701474",
"exception": false,
"start_time": "2021-12-23T02:59:12.622935",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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