{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ac670bbd",
   "metadata": {},
   "source": [
    "# Data analysis of new energy vehicles in China"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46b97e3",
   "metadata": {},
   "source": [
    "Author: Kaiyuan Chen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5251cc07",
   "metadata": {},
   "source": [
    "Course Project, UC Irvine, Math10, Fall 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7293300d",
   "metadata": {},
   "source": [
    "I would like to post my notebook on the course website.   Yes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dcd3d53",
   "metadata": {},
   "source": [
    "## 1, Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149783a1",
   "metadata": {},
   "source": [
    "In modern society, the development of new energy vehicles has formed a trend. This is not only the needs of the applicable people, but also the inevitable step of technological advancement. The popularity of electric vehicles is a good testimony to the development of new energy. It is worth mentioning that my family is engaged in new energy technology companies. Influenced by my family environment, I also plan to conduct data-based research on the popularity of electric vehicles in the world."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395762bb",
   "metadata": {},
   "source": [
    "I am from mainland China and I participated in an internship related to China vehicles Insurance Analysis, so in addition to macroscopic analysis of the world market, my analysis will also pay more attention to the data of the Chinese NEVs market."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a9718b",
   "metadata": {},
   "source": [
    "## 2, Background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d63a9c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.metrics import r2_score, accuracy_score, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b6a3221",
   "metadata": {},
   "outputs": [],
   "source": [
    "evs1=pd.read_csv('IEA-EV-dataEV salesCarsHistorical.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06b14144",
   "metadata": {},
   "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>region</th>\n",
       "      <th>category</th>\n",
       "      <th>parameter</th>\n",
       "      <th>mode</th>\n",
       "      <th>powertrain</th>\n",
       "      <th>year</th>\n",
       "      <th>unit</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV sales</td>\n",
       "      <td>Cars</td>\n",
       "      <td>BEV</td>\n",
       "      <td>2011</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV sales</td>\n",
       "      <td>Cars</td>\n",
       "      <td>BEV</td>\n",
       "      <td>2012</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV sales</td>\n",
       "      <td>Cars</td>\n",
       "      <td>PHEV</td>\n",
       "      <td>2012</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV sales</td>\n",
       "      <td>Cars</td>\n",
       "      <td>PHEV</td>\n",
       "      <td>2013</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV sales</td>\n",
       "      <td>Cars</td>\n",
       "      <td>BEV</td>\n",
       "      <td>2013</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region    category parameter  mode powertrain  year      unit  value\n",
       "0  Australia  Historical  EV sales  Cars        BEV  2011  Vehicles     49\n",
       "1  Australia  Historical  EV sales  Cars        BEV  2012  Vehicles    170\n",
       "2  Australia  Historical  EV sales  Cars       PHEV  2012  Vehicles     80\n",
       "3  Australia  Historical  EV sales  Cars       PHEV  2013  Vehicles    100\n",
       "4  Australia  Historical  EV sales  Cars        BEV  2013  Vehicles    190"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evs1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0edc8048",
   "metadata": {},
   "source": [
    "This table records EVs sales value data for all major economies in the world from 2011 to 2035, and contains a certain forecast component. We firstly focus on BEV powertrain EV. All the data in this part would be based on BEV powertrain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8dcdb69",
   "metadata": {},
   "outputs": [],
   "source": [
    "evs=evs1[evs1['powertrain']=='BEV']\n",
    "evsCN=evs[evs['region']=='China']\n",
    "evsIN=evs[evs['region']=='India']\n",
    "evsEU=evs[evs['region']=='Europe']\n",
    "evsBR=evs[evs['region']=='Brazil']\n",
    "evsJP=evs[evs['region']=='Japan']\n",
    "evsUS=evs[evs['region']=='USA']\n",
    "evs=evs[evs['year']=='2022']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb953c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x134bdfd90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(evsCN['year'],evsCN['value'],label='value trend China')\n",
    "plt.plot(evsIN['year'],evsIN['value'],label='value trend India')\n",
    "plt.plot(evsEU['year'],evsEU['value'],label='value trend Europe')\n",
    "plt.plot(evsBR['year'],evsBR['value'],label='value trend Brazil')\n",
    "plt.plot(evsJP['year'],evsJP['value'],label='value trend Japan')\n",
    "plt.plot(evsUS['year'],evsUS['value'],label='value trend USA')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8453d516",
   "metadata": {},
   "source": [
    "The sales value of electric cars has increased significantly around 2020, especially in China. The increase in the value of electric cars in China has surpassed most other mainstream economies in horizontal comparison. Based on this, we can try to explore what changes have taken place in China's EV market in recent times, and why this industry can achieve exponential development in China."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b297c123",
   "metadata": {},
   "source": [
    "## 3, Looking for reasons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c24278c3",
   "metadata": {},
   "source": [
    "Based on the report \"China-green-future-ev-ch-jan2021\" from international council on clean transportation, We can summarize the reasons why China is committed to new energy vehicles as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e7e9e75",
   "metadata": {},
   "source": [
    "1: The government hopes to mitigate the huge carbon emissions caused by rapid industrialization in China"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7581e30",
   "metadata": {},
   "source": [
    "2: The government hopes to strengthen China's international brand competitiveness in the automotive field"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06e12a5",
   "metadata": {},
   "source": [
    "### Carbon emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "539f0236",
   "metadata": {},
   "outputs": [],
   "source": [
    "carbon=pd.read_csv('Carbon_(CO2)_Emissions_by_Country.csv')\n",
    "carbon=carbon[carbon['Country']=='China']\n",
    "carbon['Date'] = pd.to_datetime(carbon['Date'])\n",
    "carbon['Date']=carbon['Date'].dt.year\n",
    "##I only collected data before 2020, because this is a node time for the development of new energy. We can also predict future carbon emissions to understand the necessity of new energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4615c297",
   "metadata": {},
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Kilotons of Co2</th>\n",
       "      <th>Metric Tons Per Capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1990</td>\n",
       "      <td>2173360.0</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1991</td>\n",
       "      <td>2302180.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>2418180.0</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1993</td>\n",
       "      <td>2645410.0</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1994</td>\n",
       "      <td>2767670.0</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country Region  Date  Kilotons of Co2  Metric Tons Per Capita\n",
       "1079   China   Asia  1990        2173360.0                    1.91\n",
       "1078   China   Asia  1991        2302180.0                    2.00\n",
       "1077   China   Asia  1992        2418180.0                    2.08\n",
       "1076   China   Asia  1993        2645410.0                    2.24\n",
       "1075   China   Asia  1994        2767670.0                    2.32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carbon=carbon.iloc[::-1]\n",
    "carbon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54e04233",
   "metadata": {},
   "outputs": [],
   "source": [
    "carbon1= carbon.copy()\n",
    "p = 3\n",
    "for lag in range(1, p+1):\n",
    "    carbon1[f'Value_t-{lag}'] = carbon1['Kilotons of Co2'].shift(lag)\n",
    "carbon1=carbon1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adf480bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=np.array(carbon)\n",
    "l=[d[0] for d in d]\n",
    "c1=d[:int(0.5*len(l))]\n",
    "train_carbon=pd.DataFrame({'Time':c1[:,2],'Value':c1[:,3]})\n",
    "c2=d[int(0.5*len(l)):]\n",
    "test_carbon=pd.DataFrame({'Time':c2[:,2],'Value':c2[:,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9acbe403",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg=LinearRegression()\n",
    "\n",
    "x=np.array(carbon1['Date']).reshape(-1,1)\n",
    "y=np.array(carbon1['Kilotons of Co2']).reshape(-1,1)\n",
    "reg.fit(x,y)\n",
    "y1=np.array(carbon1['Date'])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(train_carbon['Time'], train_carbon['Value'], label=f'Training')\n",
    "plt.plot(test_carbon['Time'], test_carbon['Value'], label=f'Testing')\n",
    "for l in range(1,p+1):\n",
    "    plt.plot(carbon1['Date'],carbon1[f'Value_t-{lag}'])\n",
    "plt.title(f'Actual vs Predicted Values for target')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a21405",
   "metadata": {},
   "source": [
    "Judging from this prediction result, China's carbon emissions have been in a state of rapid growth for a long time. The growth trend can also be roughly seen from the predicted purple line. There is no doubt that the development of new energy is urgent."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4043d3d9",
   "metadata": {},
   "source": [
    "### Brand competition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff014096",
   "metadata": {},
   "source": [
    "Now let’s analyze it from the perspective of car brand share. If the government has this consideration in increasing investment in the new energy industry, then we can try to compare the share of each car brand and combine it with the previous research on carbon dioxide. topic, conduct comprehensive analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b285d67f",
   "metadata": {},
   "source": [
    "The data table used next contains statistics on the number of car brands, classification of fuel types and carbon dioxide emissions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edba49b9",
   "metadata": {},
   "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>Car</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Weight</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>1000</td>\n",
       "      <td>790</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>1200</td>\n",
       "      <td>1160</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Skoda</td>\n",
       "      <td>1000</td>\n",
       "      <td>929</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>900</td>\n",
       "      <td>865</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mini</td>\n",
       "      <td>1500</td>\n",
       "      <td>1140</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Car  Volume  Weight  CO2\n",
       "0      Toyota    1000     790   99\n",
       "1  Mitsubishi    1200    1160   95\n",
       "2       Skoda    1000     929   95\n",
       "3        Fiat     900     865   90\n",
       "4        Mini    1500    1140  105"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co=pd.read_csv('DATA.csv')\n",
    "co= co.loc[:, ~co.columns.str.contains('^Unnamed')]\n",
    "co=co.drop(['Model'],axis=1)\n",
    "co.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f33560e6",
   "metadata": {},
   "source": [
    "Car: Brand name  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3615b3",
   "metadata": {},
   "source": [
    "Volume: Volume of fuel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3acacb55",
   "metadata": {},
   "source": [
    "Weight: Weight of gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "882f7e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "numerical = co.select_dtypes(include=['int64', 'float64']).columns\n",
    "co[numerical] = scaler.fit_transform(co[numerical])\n",
    "\n",
    "for column in co.select_dtypes(include=['object']).columns:\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    sns.countplot(data=co, x=column)\n",
    "    plt.title(column)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f26ae09",
   "metadata": {},
   "source": [
    "This is the statistical number of each car brand in this data list. It is obvious that Chinese brands do not account for the majority. It is conceivable that it makes sense for the government to want to catch up in the new energy industry."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea76f98",
   "metadata": {},
   "source": [
    "Although this is just a random sample of data, and the original sample of this data does not include the Chinese market, given that our purpose is international brand influence, using a sample that does not include mainland China market data may be more valuable for exploration and analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5c90bf",
   "metadata": {},
   "source": [
    "### 4, Rrgression analyzing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b016911f",
   "metadata": {},
   "source": [
    "The core of new energy vehicles is to improve the energy structure and avoid excessive use of fuel. Fortunately, the previous data table contains records of fuel volume and exhaust weight. We can analyze these data to find the correlation between energy and carbon dioxide emissions, so that we can understand the importance of the Chinese government on new energy vehicles. It is closely related to environmental protection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d53980df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Co2 emission')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co=pd.read_csv('DATA.csv')\n",
    "co= co.loc[:, ~co.columns.str.contains('^Unnamed')]\n",
    "co=co.drop(['Model'],axis=1)\n",
    "x=np.array(co['Volume']).reshape(-1,1)\n",
    "y=np.array(co['Weight']).reshape(-1,1)\n",
    "z=np.array(co['CO2']).reshape(-1,1)\n",
    "\n",
    "fig, ax = plt.subplots(1,3,figsize=(15,5))\n",
    "l= linear_model.LinearRegression()\n",
    "l.fit(x,y)\n",
    "l.fit(x,z)\n",
    "l.fit(y,z)\n",
    "\n",
    "ax[0].scatter(x,y)\n",
    "ax[0].set_xlabel('volume of fuel')\n",
    "ax[0].set_ylabel('weight of gas')\n",
    "\n",
    "ax[1].scatter(x,z)\n",
    "ax[1].set_xlabel('volume of fuel')\n",
    "ax[1].set_ylabel('Co2 emission')\n",
    "\n",
    "ax[2].scatter(y,z)\n",
    "ax[2].set_xlabel('weight of gas')\n",
    "ax[2].set_ylabel('Co2 emission')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a4a74ec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-24.589972039990542"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoreR=l.score(x,y)\n",
    "scoreR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eae071b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.2801890210508702"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.score(x,z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a169095d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30486966019513084"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.score(y,z)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839ce63d",
   "metadata": {},
   "source": [
    "Judging from these three tables and the calculated R-squared value, the amount of oil and gas energy used by automobiles has a strong correlation with carbon dioxide emissions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5bf5419d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(co.drop(['Car'],axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1578e8ee",
   "metadata": {},
   "source": [
    "The classification of new energy is roughly BEV and PHEV. These classifications were invented to reduce the proportion of oil and gas. Combined with the calculated data, we can already determine that the improvement of new energy is closely related to environmental governance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56494958",
   "metadata": {},
   "source": [
    "#### SVM use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e8caf63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test=train_test_split(co,test_size=0.2)\n",
    "\n",
    "train_X=np.array(train['Volume']).reshape(-1,1)\n",
    "test_X=np.array(test['Volume']).reshape(-1,1)\n",
    "\n",
    "train_Y=np.array(train['Weight']).reshape(-1,1)\n",
    "test_Y=np.array(test['Weight']).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "50599487",
   "metadata": {},
   "outputs": [],
   "source": [
    "s=StandardScaler()\n",
    "train_X=s.fit_transform(train_X)\n",
    "test_X=s.fit_transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "99f456d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/a20180815799/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:1184: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1365, 1112, 1112, 1365, 1365, 1365, 1365, 1112])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC()\n",
    "model.fit(train_X,train_Y)\n",
    "\n",
    "prediction=model.predict(test_X)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5d1aa6e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(test_Y,prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea3333a",
   "metadata": {},
   "source": [
    "I want to try the machine learning of support vector machines, but here it is more about practicing how to use its code. Support vector machines can essentially play a role in nonlinear and high-dimensional models. The data I used this time Very intuitive and common, strictly speaking this method is not needed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f3423a",
   "metadata": {},
   "source": [
    "Just like the accuracy I finally calculated, it is completely meaningless 0. According to this data test, using SVM is indeed not necessary. It's more about trying practical applications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4432f2f5",
   "metadata": {},
   "source": [
    "### 5, Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79227e0c",
   "metadata": {},
   "source": [
    "New energy is a key part of the future automobile industry entering a new era. Although this analysis focuses on analyzing the reasons for the development of new energy vehicles in China, this example can be seen as a microcosm of the entire world's treatment of new energy vehicles.The greatest value of new energy is to promote environmental protection and long-term substitutability of energy supply. People no longer need to excessively use fuels such as fuel to cause excessive exhaust emissions. We can use electricity or hybrid power to increase the efficiency of cars and Ensure natural circulation. China is now moving towards this goal. I hope that this can serve as a reference for other people in the world to contribute value to this new energy industry together, just like China and like my family."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46da9d3b",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae263b4",
   "metadata": {},
   "source": [
    "Kaggle: 'IEA-EV-dataEV salesCarsHistorical.csv', 'Carbon_(CO2)_Emissions_by_Country.csv', 'DATA.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91128303",
   "metadata": {},
   "source": [
    "Relavant article: https://theicct.org/wp-content/uploads/2021/06/China-green-future-ev-jan2021.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f17320",
   "metadata": {},
   "source": [
    "SVM: https://blog.csdn.net/Claire_chen_jia/article/details/110916001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77834088",
   "metadata": {},
   "source": [
    "Referred Project: https://rayzhangzirui.github.io/math10sp24/final_project_demo/zhaomickey.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6dd752e",
   "metadata": {},
   "source": [
    "https://rayzhangzirui.github.io/math10sp24/final_project_demo/zhangzhiwei.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c86b9e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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