Lesson20-使用Random Forests Classifiers&Regressor两种方式建模.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二十课 使用Random Forests Classifiers&Regressor两种方式建模\n",
    "\n",
    "\n",
    "Python零基础学习建模：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "Python源文件及数据下载链接: https://pan.baidu.com/s/1Ihu484EfT1EKZpGeeDjs2A 提取码: bspr\n",
    "\n",
    "-Feature_Importances_\n",
    "\n",
    "-Feature Scaling Scikit-Learn's StandardScaler\n",
    "\n",
    "-调参随机森林决策树的大小n_estimators\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一 随机森林对于离散数据的处理 Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=sns.load_dataset('iris') #参照第19课和第11课\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = data.drop(['species'], axis=1)\n",
    "#y = data['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]  # Features\n",
    "y=data['species']  # Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             5.1          3.5           1.4          0.2\n",
       "1             4.9          3.0           1.4          0.2\n",
       "2             4.7          3.2           1.3          0.2\n",
       "3             4.6          3.1           1.5          0.2\n",
       "4             5.0          3.6           1.4          0.2\n",
       "..            ...          ...           ...          ...\n",
       "145           6.7          3.0           5.2          2.3\n",
       "146           6.3          2.5           5.0          1.9\n",
       "147           6.5          3.0           5.2          2.0\n",
       "148           6.2          3.4           5.4          2.3\n",
       "149           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         setosa\n",
       "1         setosa\n",
       "2         setosa\n",
       "3         setosa\n",
       "4         setosa\n",
       "         ...    \n",
       "145    virginica\n",
       "146    virginica\n",
       "147    virginica\n",
       "148    virginica\n",
       "149    virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "##from sklearn.tree import DecisionTreeClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['versicolor', 'setosa', 'virginica', 'versicolor', 'versicolor',\n",
       "       'setosa', 'versicolor', 'virginica', 'versicolor', 'versicolor',\n",
       "       'virginica', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor',\n",
       "       'virginica', 'versicolor', 'versicolor', 'virginica', 'setosa',\n",
       "       'virginica', 'setosa', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'versicolor', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
       "       'setosa', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
       "       'versicolor', 'setosa', 'setosa'], dtype=object)"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73     versicolor\n",
       "18         setosa\n",
       "118     virginica\n",
       "78     versicolor\n",
       "76     versicolor\n",
       "31         setosa\n",
       "64     versicolor\n",
       "141     virginica\n",
       "68     versicolor\n",
       "82     versicolor\n",
       "110     virginica\n",
       "12         setosa\n",
       "36         setosa\n",
       "9          setosa\n",
       "19         setosa\n",
       "56     versicolor\n",
       "104     virginica\n",
       "69     versicolor\n",
       "55     versicolor\n",
       "132     virginica\n",
       "29         setosa\n",
       "127     virginica\n",
       "26         setosa\n",
       "128     virginica\n",
       "131     virginica\n",
       "145     virginica\n",
       "108     virginica\n",
       "143     virginica\n",
       "45         setosa\n",
       "30         setosa\n",
       "22         setosa\n",
       "15         setosa\n",
       "65     versicolor\n",
       "11         setosa\n",
       "42         setosa\n",
       "146     virginica\n",
       "51     versicolor\n",
       "27         setosa\n",
       "4          setosa\n",
       "32         setosa\n",
       "142     virginica\n",
       "85     versicolor\n",
       "86     versicolor\n",
       "16         setosa\n",
       "10         setosa\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['virginica'], dtype=object)"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[1, 2, 4, 3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征变量权重分析 Feature_Importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list=list(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "petal_width     0.448146\n",
       "petal_length    0.411498\n",
       "sepal_length    0.109529\n",
       "sepal_width     0.030828\n",
       "dtype: float64"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp = pd.Series(clf.feature_importances_,index=feature_list).sort_values(ascending=False)\n",
    "feature_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['petal_width', 'petal_length', 'sepal_length', 'sepal_width'], dtype='object')"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a bar plot\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "# Add labels to your graph\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizing Important Features\")\n",
    "plt.legend(feature_imp.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缩减特征变量X,对于成百上千特征变量的大数据集有非常重要的意义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into features and labels\n",
    "X=data[['petal_length', 'petal_width','sepal_length']]  # Removed feature \"sepal width\"\n",
    "y=data['species']                                       \n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=5) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "#Create a Gaussian Classifier\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "# prediction on test set\n",
    "y_pred=clf.predict(X_test)\n",
    "\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二 随机森林对于连续数据的处理 Random Forest for Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('D:/python/Project0-Python-MachineLearning/Lesson20-petrol_consumption.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "      <th>Petrol_Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3571</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.525</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4092</td>\n",
       "      <td>1250</td>\n",
       "      <td>0.572</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3865</td>\n",
       "      <td>1586</td>\n",
       "      <td>0.580</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.50</td>\n",
       "      <td>4870</td>\n",
       "      <td>2351</td>\n",
       "      <td>0.529</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4399</td>\n",
       "      <td>431</td>\n",
       "      <td>0.544</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.00</td>\n",
       "      <td>5342</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.571</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.00</td>\n",
       "      <td>5319</td>\n",
       "      <td>11868</td>\n",
       "      <td>0.451</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.00</td>\n",
       "      <td>5126</td>\n",
       "      <td>2138</td>\n",
       "      <td>0.553</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4447</td>\n",
       "      <td>8577</td>\n",
       "      <td>0.529</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4512</td>\n",
       "      <td>8507</td>\n",
       "      <td>0.552</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4391</td>\n",
       "      <td>5939</td>\n",
       "      <td>0.530</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.50</td>\n",
       "      <td>5126</td>\n",
       "      <td>14186</td>\n",
       "      <td>0.525</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4817</td>\n",
       "      <td>6930</td>\n",
       "      <td>0.574</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4207</td>\n",
       "      <td>6580</td>\n",
       "      <td>0.545</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4332</td>\n",
       "      <td>8159</td>\n",
       "      <td>0.608</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4318</td>\n",
       "      <td>10340</td>\n",
       "      <td>0.586</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4206</td>\n",
       "      <td>8508</td>\n",
       "      <td>0.572</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3718</td>\n",
       "      <td>4725</td>\n",
       "      <td>0.540</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4716</td>\n",
       "      <td>5915</td>\n",
       "      <td>0.724</td>\n",
       "      <td>865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.50</td>\n",
       "      <td>4341</td>\n",
       "      <td>6010</td>\n",
       "      <td>0.677</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4593</td>\n",
       "      <td>7834</td>\n",
       "      <td>0.663</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4983</td>\n",
       "      <td>602</td>\n",
       "      <td>0.602</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4897</td>\n",
       "      <td>2449</td>\n",
       "      <td>0.511</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4258</td>\n",
       "      <td>4686</td>\n",
       "      <td>0.517</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.50</td>\n",
       "      <td>4574</td>\n",
       "      <td>2619</td>\n",
       "      <td>0.551</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3721</td>\n",
       "      <td>4746</td>\n",
       "      <td>0.544</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8.00</td>\n",
       "      <td>3448</td>\n",
       "      <td>5399</td>\n",
       "      <td>0.548</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.50</td>\n",
       "      <td>3846</td>\n",
       "      <td>9061</td>\n",
       "      <td>0.579</td>\n",
       "      <td>631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4188</td>\n",
       "      <td>5975</td>\n",
       "      <td>0.563</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3601</td>\n",
       "      <td>4650</td>\n",
       "      <td>0.493</td>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3640</td>\n",
       "      <td>6905</td>\n",
       "      <td>0.518</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3333</td>\n",
       "      <td>6594</td>\n",
       "      <td>0.513</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8.00</td>\n",
       "      <td>3063</td>\n",
       "      <td>6524</td>\n",
       "      <td>0.578</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7.50</td>\n",
       "      <td>3357</td>\n",
       "      <td>4121</td>\n",
       "      <td>0.547</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8.00</td>\n",
       "      <td>3528</td>\n",
       "      <td>3495</td>\n",
       "      <td>0.487</td>\n",
       "      <td>487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6.58</td>\n",
       "      <td>3802</td>\n",
       "      <td>7834</td>\n",
       "      <td>0.629</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.00</td>\n",
       "      <td>4045</td>\n",
       "      <td>17782</td>\n",
       "      <td>0.566</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3897</td>\n",
       "      <td>6385</td>\n",
       "      <td>0.586</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8.50</td>\n",
       "      <td>3635</td>\n",
       "      <td>3274</td>\n",
       "      <td>0.663</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4345</td>\n",
       "      <td>3905</td>\n",
       "      <td>0.672</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4449</td>\n",
       "      <td>4639</td>\n",
       "      <td>0.626</td>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3656</td>\n",
       "      <td>3985</td>\n",
       "      <td>0.563</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4300</td>\n",
       "      <td>3635</td>\n",
       "      <td>0.603</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3745</td>\n",
       "      <td>2611</td>\n",
       "      <td>0.508</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6.00</td>\n",
       "      <td>5215</td>\n",
       "      <td>2302</td>\n",
       "      <td>0.672</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4476</td>\n",
       "      <td>3942</td>\n",
       "      <td>0.571</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4296</td>\n",
       "      <td>4083</td>\n",
       "      <td>0.623</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>7.00</td>\n",
       "      <td>5002</td>\n",
       "      <td>9794</td>\n",
       "      <td>0.593</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Petrol_tax  Average_income  Paved_Highways  Population_Driver_licence(%)  \\\n",
       "0         9.00            3571            1976                         0.525   \n",
       "1         9.00            4092            1250                         0.572   \n",
       "2         9.00            3865            1586                         0.580   \n",
       "3         7.50            4870            2351                         0.529   \n",
       "4         8.00            4399             431                         0.544   \n",
       "5        10.00            5342            1333                         0.571   \n",
       "6         8.00            5319           11868                         0.451   \n",
       "7         8.00            5126            2138                         0.553   \n",
       "8         8.00            4447            8577                         0.529   \n",
       "9         7.00            4512            8507                         0.552   \n",
       "10        8.00            4391            5939                         0.530   \n",
       "11        7.50            5126           14186                         0.525   \n",
       "12        7.00            4817            6930                         0.574   \n",
       "13        7.00            4207            6580                         0.545   \n",
       "14        7.00            4332            8159                         0.608   \n",
       "15        7.00            4318           10340                         0.586   \n",
       "16        7.00            4206            8508                         0.572   \n",
       "17        7.00            3718            4725                         0.540   \n",
       "18        7.00            4716            5915                         0.724   \n",
       "19        8.50            4341            6010                         0.677   \n",
       "20        7.00            4593            7834                         0.663   \n",
       "21        8.00            4983             602                         0.602   \n",
       "22        9.00            4897            2449                         0.511   \n",
       "23        9.00            4258            4686                         0.517   \n",
       "24        8.50            4574            2619                         0.551   \n",
       "25        9.00            3721            4746                         0.544   \n",
       "26        8.00            3448            5399                         0.548   \n",
       "27        7.50            3846            9061                         0.579   \n",
       "28        8.00            4188            5975                         0.563   \n",
       "29        9.00            3601            4650                         0.493   \n",
       "30        7.00            3640            6905                         0.518   \n",
       "31        7.00            3333            6594                         0.513   \n",
       "32        8.00            3063            6524                         0.578   \n",
       "33        7.50            3357            4121                         0.547   \n",
       "34        8.00            3528            3495                         0.487   \n",
       "35        6.58            3802            7834                         0.629   \n",
       "36        5.00            4045           17782                         0.566   \n",
       "37        7.00            3897            6385                         0.586   \n",
       "38        8.50            3635            3274                         0.663   \n",
       "39        7.00            4345            3905                         0.672   \n",
       "40        7.00            4449            4639                         0.626   \n",
       "41        7.00            3656            3985                         0.563   \n",
       "42        7.00            4300            3635                         0.603   \n",
       "43        7.00            3745            2611                         0.508   \n",
       "44        6.00            5215            2302                         0.672   \n",
       "45        9.00            4476            3942                         0.571   \n",
       "46        7.00            4296            4083                         0.623   \n",
       "47        7.00            5002            9794                         0.593   \n",
       "\n",
       "    Petrol_Consumption  \n",
       "0                  541  \n",
       "1                  524  \n",
       "2                  561  \n",
       "3                  414  \n",
       "4                  410  \n",
       "5                  457  \n",
       "6                  344  \n",
       "7                  467  \n",
       "8                  464  \n",
       "9                  498  \n",
       "10                 580  \n",
       "11                 471  \n",
       "12                 525  \n",
       "13                 508  \n",
       "14                 566  \n",
       "15                 635  \n",
       "16                 603  \n",
       "17                 714  \n",
       "18                 865  \n",
       "19                 640  \n",
       "20                 649  \n",
       "21                 540  \n",
       "22                 464  \n",
       "23                 547  \n",
       "24                 460  \n",
       "25                 566  \n",
       "26                 577  \n",
       "27                 631  \n",
       "28                 574  \n",
       "29                 534  \n",
       "30                 571  \n",
       "31                 554  \n",
       "32                 577  \n",
       "33                 628  \n",
       "34                 487  \n",
       "35                 644  \n",
       "36                 640  \n",
       "37                 704  \n",
       "38                 648  \n",
       "39                 968  \n",
       "40                 587  \n",
       "41                 699  \n",
       "42                 632  \n",
       "43                 591  \n",
       "44                 782  \n",
       "45                 510  \n",
       "46                 610  \n",
       "47                 524  "
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48 entries, 0 to 47\n",
      "Data columns (total 5 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Petrol_tax                    48 non-null     float64\n",
      " 1   Average_income                48 non-null     int64  \n",
      " 2   Paved_Highways                48 non-null     int64  \n",
      " 3   Population_Driver_licence(%)  48 non-null     float64\n",
      " 4   Petrol_Consumption            48 non-null     int64  \n",
      "dtypes: float64(2), int64(3)\n",
      "memory usage: 2.0 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, 0:4]\n",
    "y = dataset.iloc[:, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3571</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4092</td>\n",
       "      <td>1250</td>\n",
       "      <td>0.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3865</td>\n",
       "      <td>1586</td>\n",
       "      <td>0.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.50</td>\n",
       "      <td>4870</td>\n",
       "      <td>2351</td>\n",
       "      <td>0.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4399</td>\n",
       "      <td>431</td>\n",
       "      <td>0.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.00</td>\n",
       "      <td>5342</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.00</td>\n",
       "      <td>5319</td>\n",
       "      <td>11868</td>\n",
       "      <td>0.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.00</td>\n",
       "      <td>5126</td>\n",
       "      <td>2138</td>\n",
       "      <td>0.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4447</td>\n",
       "      <td>8577</td>\n",
       "      <td>0.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4512</td>\n",
       "      <td>8507</td>\n",
       "      <td>0.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4391</td>\n",
       "      <td>5939</td>\n",
       "      <td>0.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.50</td>\n",
       "      <td>5126</td>\n",
       "      <td>14186</td>\n",
       "      <td>0.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4817</td>\n",
       "      <td>6930</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4207</td>\n",
       "      <td>6580</td>\n",
       "      <td>0.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4332</td>\n",
       "      <td>8159</td>\n",
       "      <td>0.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4318</td>\n",
       "      <td>10340</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4206</td>\n",
       "      <td>8508</td>\n",
       "      <td>0.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3718</td>\n",
       "      <td>4725</td>\n",
       "      <td>0.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4716</td>\n",
       "      <td>5915</td>\n",
       "      <td>0.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.50</td>\n",
       "      <td>4341</td>\n",
       "      <td>6010</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4593</td>\n",
       "      <td>7834</td>\n",
       "      <td>0.663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4983</td>\n",
       "      <td>602</td>\n",
       "      <td>0.602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4897</td>\n",
       "      <td>2449</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4258</td>\n",
       "      <td>4686</td>\n",
       "      <td>0.517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.50</td>\n",
       "      <td>4574</td>\n",
       "      <td>2619</td>\n",
       "      <td>0.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3721</td>\n",
       "      <td>4746</td>\n",
       "      <td>0.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8.00</td>\n",
       "      <td>3448</td>\n",
       "      <td>5399</td>\n",
       "      <td>0.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.50</td>\n",
       "      <td>3846</td>\n",
       "      <td>9061</td>\n",
       "      <td>0.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8.00</td>\n",
       "      <td>4188</td>\n",
       "      <td>5975</td>\n",
       "      <td>0.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3601</td>\n",
       "      <td>4650</td>\n",
       "      <td>0.493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3640</td>\n",
       "      <td>6905</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3333</td>\n",
       "      <td>6594</td>\n",
       "      <td>0.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8.00</td>\n",
       "      <td>3063</td>\n",
       "      <td>6524</td>\n",
       "      <td>0.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7.50</td>\n",
       "      <td>3357</td>\n",
       "      <td>4121</td>\n",
       "      <td>0.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8.00</td>\n",
       "      <td>3528</td>\n",
       "      <td>3495</td>\n",
       "      <td>0.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6.58</td>\n",
       "      <td>3802</td>\n",
       "      <td>7834</td>\n",
       "      <td>0.629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.00</td>\n",
       "      <td>4045</td>\n",
       "      <td>17782</td>\n",
       "      <td>0.566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3897</td>\n",
       "      <td>6385</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8.50</td>\n",
       "      <td>3635</td>\n",
       "      <td>3274</td>\n",
       "      <td>0.663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4345</td>\n",
       "      <td>3905</td>\n",
       "      <td>0.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4449</td>\n",
       "      <td>4639</td>\n",
       "      <td>0.626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3656</td>\n",
       "      <td>3985</td>\n",
       "      <td>0.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4300</td>\n",
       "      <td>3635</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3745</td>\n",
       "      <td>2611</td>\n",
       "      <td>0.508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6.00</td>\n",
       "      <td>5215</td>\n",
       "      <td>2302</td>\n",
       "      <td>0.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>9.00</td>\n",
       "      <td>4476</td>\n",
       "      <td>3942</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>7.00</td>\n",
       "      <td>4296</td>\n",
       "      <td>4083</td>\n",
       "      <td>0.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>7.00</td>\n",
       "      <td>5002</td>\n",
       "      <td>9794</td>\n",
       "      <td>0.593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Petrol_tax  Average_income  Paved_Highways  Population_Driver_licence(%)\n",
       "0         9.00            3571            1976                         0.525\n",
       "1         9.00            4092            1250                         0.572\n",
       "2         9.00            3865            1586                         0.580\n",
       "3         7.50            4870            2351                         0.529\n",
       "4         8.00            4399             431                         0.544\n",
       "5        10.00            5342            1333                         0.571\n",
       "6         8.00            5319           11868                         0.451\n",
       "7         8.00            5126            2138                         0.553\n",
       "8         8.00            4447            8577                         0.529\n",
       "9         7.00            4512            8507                         0.552\n",
       "10        8.00            4391            5939                         0.530\n",
       "11        7.50            5126           14186                         0.525\n",
       "12        7.00            4817            6930                         0.574\n",
       "13        7.00            4207            6580                         0.545\n",
       "14        7.00            4332            8159                         0.608\n",
       "15        7.00            4318           10340                         0.586\n",
       "16        7.00            4206            8508                         0.572\n",
       "17        7.00            3718            4725                         0.540\n",
       "18        7.00            4716            5915                         0.724\n",
       "19        8.50            4341            6010                         0.677\n",
       "20        7.00            4593            7834                         0.663\n",
       "21        8.00            4983             602                         0.602\n",
       "22        9.00            4897            2449                         0.511\n",
       "23        9.00            4258            4686                         0.517\n",
       "24        8.50            4574            2619                         0.551\n",
       "25        9.00            3721            4746                         0.544\n",
       "26        8.00            3448            5399                         0.548\n",
       "27        7.50            3846            9061                         0.579\n",
       "28        8.00            4188            5975                         0.563\n",
       "29        9.00            3601            4650                         0.493\n",
       "30        7.00            3640            6905                         0.518\n",
       "31        7.00            3333            6594                         0.513\n",
       "32        8.00            3063            6524                         0.578\n",
       "33        7.50            3357            4121                         0.547\n",
       "34        8.00            3528            3495                         0.487\n",
       "35        6.58            3802            7834                         0.629\n",
       "36        5.00            4045           17782                         0.566\n",
       "37        7.00            3897            6385                         0.586\n",
       "38        8.50            3635            3274                         0.663\n",
       "39        7.00            4345            3905                         0.672\n",
       "40        7.00            4449            4639                         0.626\n",
       "41        7.00            3656            3985                         0.563\n",
       "42        7.00            4300            3635                         0.603\n",
       "43        7.00            3745            2611                         0.508\n",
       "44        6.00            5215            2302                         0.672\n",
       "45        9.00            4476            3942                         0.571\n",
       "46        7.00            4296            4083                         0.623\n",
       "47        7.00            5002            9794                         0.593"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     541\n",
       "1     524\n",
       "2     561\n",
       "3     414\n",
       "4     410\n",
       "5     457\n",
       "6     344\n",
       "7     467\n",
       "8     464\n",
       "9     498\n",
       "10    580\n",
       "11    471\n",
       "12    525\n",
       "13    508\n",
       "14    566\n",
       "15    635\n",
       "16    603\n",
       "17    714\n",
       "18    865\n",
       "19    640\n",
       "20    649\n",
       "21    540\n",
       "22    464\n",
       "23    547\n",
       "24    460\n",
       "25    566\n",
       "26    577\n",
       "27    631\n",
       "28    574\n",
       "29    534\n",
       "30    571\n",
       "31    554\n",
       "32    577\n",
       "33    628\n",
       "34    487\n",
       "35    644\n",
       "36    640\n",
       "37    704\n",
       "38    648\n",
       "39    968\n",
       "40    587\n",
       "41    699\n",
       "42    632\n",
       "43    591\n",
       "44    782\n",
       "45    510\n",
       "46    610\n",
       "47    524\n",
       "Name: Petrol_Consumption, dtype: int64"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling Scikit-Learn's StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.32525691e-01,  1.50577399e+00,  2.25854636e+00,\n",
       "        -9.15791057e-01],\n",
       "       [-6.28192647e-01, -1.83367022e+00,  2.12333843e-01,\n",
       "        -1.12852703e+00],\n",
       "       [-1.32525691e-01, -1.78897047e+00, -4.54194551e-01,\n",
       "        -5.25775100e-01],\n",
       "       [-1.32525691e-01, -8.78212954e-01,  8.77245103e-01,\n",
       "         4.15208375e-02],\n",
       "       [-6.28192647e-01,  1.27482526e+00,  1.07480487e+00,\n",
       "         2.89712810e-01],\n",
       "       [ 1.35447518e+00, -8.42825648e-01, -1.13743332e+00,\n",
       "         5.92488356e-02],\n",
       "       [-6.28192647e-01, -4.00925436e-02, -4.64436395e-01,\n",
       "         8.21552752e-01],\n",
       "       [-6.28192647e-01,  7.42153173e-01,  2.93282707e-02,\n",
       "         2.61208055e+00],\n",
       "       [-6.28192647e-01,  8.82232011e-04,  1.22196399e+00,\n",
       "         1.65616824e-01],\n",
       "       [ 3.63141265e-01, -2.41241442e-01,  4.54996025e-02,\n",
       "        -2.42127131e-01],\n",
       "       [ 1.35447518e+00,  1.07926383e+00, -9.04835665e-01,\n",
       "        -1.16398303e+00],\n",
       "       [-6.28192647e-01, -2.07716626e-01,  7.28199328e-01,\n",
       "        -8.25751488e-02],\n",
       "       [-6.28192647e-01, -1.23208602e+00, -4.90849570e-01,\n",
       "        -2.42127131e-01],\n",
       "       [-6.28192647e-01,  5.13066928e-01,  5.46541367e-01,\n",
       "         1.53067267e+00],\n",
       "       [-6.28192647e-01, -3.26425844e-02, -5.85182339e-01,\n",
       "         4.66992791e-01],\n",
       "       [ 3.63141265e-01,  2.41143416e-01,  7.46796360e-01,\n",
       "        -8.44879065e-01],\n",
       "       [-6.28192647e-01, -2.05854136e-01,  2.08560532e-01,\n",
       "        -5.61231096e-01],\n",
       "       [ 1.35447518e+00, -1.11102418e+00, -2.85743178e-01,\n",
       "        -5.78959094e-01],\n",
       "       [ 2.34580909e+00,  1.90807179e+00, -1.20562244e+00,\n",
       "        -1.00303147e-01],\n",
       "       [-6.28192647e-01, -1.11661165e+00, -2.91403144e-01,\n",
       "        -6.49871086e-01],\n",
       "       [-1.04455289e+00, -9.60162506e-01,  5.46541367e-01,\n",
       "         9.27920740e-01],\n",
       "       [-6.28192647e-01,  2.69570892e-02,  6.34136081e-01,\n",
       "         5.55632781e-01],\n",
       "       [ 8.58808221e-01, -1.27119830e+00, -6.82479852e-01,\n",
       "         1.53067267e+00],\n",
       "       [ 1.35447518e+00, -4.20040463e-01, -1.22799278e+00,\n",
       "        -8.25751488e-02],\n",
       "       [-6.28192647e-01,  9.30264643e-01,  3.02893301e-01,\n",
       "        -4.71191527e-02],\n",
       "       [-6.28192647e-01, -1.06632442e+00, -8.61173069e-01,\n",
       "        -1.21716702e+00],\n",
       "       [ 8.58808221e-01,  4.77679621e-01, -8.59016891e-01,\n",
       "        -4.54863108e-01],\n",
       "       [ 3.63141265e-01,  1.86523452e+00,  1.63379391e+00,\n",
       "        -2.22766291e+00],\n",
       "       [ 1.35447518e+00, -1.10867156e-01, -3.01914510e-01,\n",
       "        -1.05761504e+00],\n",
       "       [-2.61086047e+00, -5.07577484e-01,  3.22774818e+00,\n",
       "        -1.88943137e-01],\n",
       "       [ 3.63141265e-01,  1.23943795e+00, -1.40264316e+00,\n",
       "         4.49264793e-01],\n",
       "       [ 8.58808221e-01,  4.37194975e-02,  5.49328794e-02,\n",
       "         1.77886465e+00],\n",
       "       [-6.28192647e-01,  3.62205254e-01,  7.27929806e-01,\n",
       "        -4.37135110e-01],\n",
       "       [-6.28192647e-01,  5.11694567e-02, -5.12411346e-01,\n",
       "         1.69022466e+00],\n",
       "       [ 1.35447518e+00,  2.95155621e-01, -5.02439024e-01,\n",
       "        -1.00303147e-01],\n",
       "       [-1.32525691e-01,  1.02897660e+00, -9.31248840e-01,\n",
       "        -8.44879065e-01],\n",
       "       [ 1.35447518e+00, -1.39039765e+00, -1.03231966e+00,\n",
       "        -9.15791057e-01],\n",
       "       [-1.61952656e+00,  1.67153558e+00, -9.44455428e-01,\n",
       "         1.69022466e+00]])"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.35447518, -1.33452296, -0.31161731, -1.48308699],\n",
       "       [ 0.36314126,  0.15174391, -1.44873146, -0.57895909],\n",
       "       [ 0.36314126, -1.6194839 , -0.10974518, -0.5080471 ],\n",
       "       [-0.62819265, -1.26188585,  0.29615525, -1.03988704],\n",
       "       [ 0.36314126, -2.33654247,  0.19346729,  0.02379284],\n",
       "       [-0.62819265, -0.78322597,  0.1560037 ,  0.16561682],\n",
       "       [ 0.36314126, -1.47048471, -0.62291545, -1.58945498],\n",
       "       [-0.62819265,  0.2448684 , -0.31458205,  0.87473675],\n",
       "       [ 0.36314126,  1.50577399, -0.98865707, -0.41940711],\n",
       "       [ 0.36314126,  0.13684399,  0.0357968 , -0.82715107]])"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "regressor = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 47.972999999999985\n",
      "Mean Squared Error: 3618.161269999999\n",
      "Root Mean Squared Error: 60.151153521773786\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调参随机森林决策树的大小n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "n_estimators=20\n",
      "Mean Absolute Error: 51.76500000000001\n",
      "Mean Squared Error: 4216.166749999999\n",
      "Root Mean Squared Error: 64.93201637097064\n",
      "-------------------\n",
      "n_estimators=30\n",
      "Mean Absolute Error: 50.60333333333332\n",
      "Mean Squared Error: 3933.2429999999977\n",
      "Root Mean Squared Error: 62.715572228912954\n",
      "-------------------\n",
      "n_estimators=50\n",
      "Mean Absolute Error: 49.304000000000016\n",
      "Mean Squared Error: 3758.844960000001\n",
      "Root Mean Squared Error: 61.30941983088733\n",
      "-------------------\n",
      "n_estimators=80\n",
      "Mean Absolute Error: 49.67625\n",
      "Mean Squared Error: 3710.175765625001\n",
      "Root Mean Squared Error: 60.91121215035046\n",
      "-------------------\n",
      "n_estimators=100\n",
      "Mean Absolute Error: 47.972999999999985\n",
      "Mean Squared Error: 3618.161269999999\n",
      "Root Mean Squared Error: 60.151153521773786\n",
      "-------------------\n",
      "n_estimators=200\n",
      "Mean Absolute Error: 48.33899999999999\n",
      "Mean Squared Error: 3494.2330150000003\n",
      "Root Mean Squared Error: 59.112037818028234\n",
      "-------------------\n",
      "n_estimators=300\n",
      "Mean Absolute Error: 48.17666666666667\n",
      "Mean Squared Error: 3472.820688888889\n",
      "Root Mean Squared Error: 58.93064303814179\n",
      "-------------------\n",
      "n_estimators=400\n",
      "Mean Absolute Error: 48.17900000000001\n",
      "Mean Squared Error: 3484.2885800000013\n",
      "Root Mean Squared Error: 59.027862742945395\n",
      "-------------------\n",
      "n_estimators=500\n",
      "Mean Absolute Error: 47.70020000000001\n",
      "Mean Squared Error: 3464.177198800001\n",
      "Root Mean Squared Error: 58.85726122408348\n",
      "-------------------\n",
      "n_estimators=600\n",
      "Mean Absolute Error: 48.336\n",
      "Mean Squared Error: 3503.157473333335\n",
      "Root Mean Squared Error: 59.18747733544094\n",
      "-------------------\n",
      "n_estimators=700\n",
      "Mean Absolute Error: 48.776285714285706\n",
      "Mean Squared Error: 3554.63735755102\n",
      "Root Mean Squared Error: 59.62077957852463\n",
      "-------------------\n",
      "n_estimators=800\n",
      "Mean Absolute Error: 48.898625\n",
      "Mean Squared Error: 3563.269683593751\n",
      "Root Mean Squared Error: 59.69312928297319\n"
     ]
    }
   ],
   "source": [
    "rmse=nestimators=[]\n",
    "for n in [20,30,50,80,100,200,300,400,500,600,700,800]:\n",
    "    regressor = RandomForestRegressor(n_estimators=n, random_state=0)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_pred = regressor.predict(X_test)\n",
    "    print('-------------------')\n",
    "    print('n_estimators={}'.format(n))\n",
    "    print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "    print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "    print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "    rmse=np.append(rmse,np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "    nestimators=np.append(nestimators,n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64.93201637, 62.71557223, 61.30941983, 60.91121215, 60.15115352,\n",
       "       59.11203782, 58.93064304, 59.02786274, 58.85726122, 59.18747734,\n",
       "       59.62077958, 59.69312928])"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20.,  30.,  50.,  80., 100., 200., 300., 400., 500., 600., 700.,\n",
       "       800.])"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nestimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a bar plot\n",
    "sns.set_style('whitegrid')\n",
    "plt.plot(nestimators,rmse,'ro',linestyle='dashed',linewidth=1,markersize=10)\n",
    "# Add labels to your graph\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizing Important Features/RMSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}