Assignment 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>b</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        b  lstat  medv  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"boston_housing.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   crim     506 non-null    float64\n",
      " 1   zn       506 non-null    float64\n",
      " 2   indus    506 non-null    float64\n",
      " 3   chas     506 non-null    int64  \n",
      " 4   nox      506 non-null    float64\n",
      " 5   rm       506 non-null    float64\n",
      " 6   age      506 non-null    float64\n",
      " 7   dis      506 non-null    float64\n",
      " 8   rad      506 non-null    int64  \n",
      " 9   tax      506 non-null    int64  \n",
      " 10  ptratio  506 non-null    float64\n",
      " 11  b        506 non-null    float64\n",
      " 12  lstat    506 non-null    float64\n",
      " 13  medv     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       0\n",
       "zn         0\n",
       "indus      0\n",
       "chas       0\n",
       "nox        0\n",
       "rm         0\n",
       "age        0\n",
       "dis        0\n",
       "rad        0\n",
       "tax        0\n",
       "ptratio    0\n",
       "b          0\n",
       "lstat      0\n",
       "medv       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Visualization (Part I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heat Map (Check Correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=data.corr(), annot=True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Preprocessing (Part I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['zn', 'chas', 'dis', 'ptratio', 'b', 'lstat', 'age'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "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>crim</th>\n",
       "      <th>indus</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim  indus    nox     rm  rad  tax  medv\n",
       "0  0.00632   2.31  0.538  6.575    1  296  24.0\n",
       "1  0.02731   7.07  0.469  6.421    2  242  21.6\n",
       "2  0.02729   7.07  0.469  7.185    2  242  34.7\n",
       "3  0.03237   2.18  0.458  6.998    3  222  33.4\n",
       "4  0.06905   2.18  0.458  7.147    3  222  36.2"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Data Visualization (Part II)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heat Map (Check Correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=data.corr(), annot=True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displot (Check Distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['indus'])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vnHrG+2OGz+7FSz6364e/n8d2zHo4Zli/hyV5c5KPDJ/fBR1zjq6q5vLG4g/5/gX4BuAo4CPAqffwumuAtwDnds+9nOsHPAH4m+5ZR1q3hwMfBzYPz4/vnns51++A1/8YcE333Mv8+f068LLh8Qbgs8BR3bMv922et4BnPez5+cBfAvumHG4ZrOXDumdZt58ELq+qWwCqajV9fvf1s3sG8IZJJlses6xfAQ9JEuAYFgO8f9oxxzfPAd4I3Lrk+Z5h2Vck2Qg8BXjVhHMtl0Ou3+Bxw3/z/i7JadOMdr/Nsm7fDKxP8o4k1yZ59mTT3X+zfnYkORo4i8WNhNVilvX7A+ARLB6I9THgBVV11zTjTWdF/R7wxA552DPwCuBXq+rOxX+IV5VZ1u9DwNdX1X8meTLw18CWsQdbBrOs2zrgO4EzgQcB703yvqr657GHWwazrN/dfgz4x6r67IjzLLdZ1u9JwHXAE4FvBK5O8u6q+sLIs01qnreAZznseSvwxiT/CpwL/FGScyaZ7v475PpV1Req6j+Hx28BHpDkuOlGPGyzfHZ7gKuq6otVdQfwLuBRE813f92XQ/LPZ3XtfoDZ1u8CFnchVVXdBHwS+NaJ5ptO907orhuLW0g3A6fw1R8EnHYvr38tq+uHcIdcP+Br+erBOI8Bbrn7+Uq+zbhujwB2Dq89GrgeeGT37Mu1fsPrHsbivtEHd888wuf3SuC3hscnAJ8Gjuuefblvc7sLou7hsOckPzt8fTXu9/2KGdfvXODnkuwH/hs4v4a/8SvZLOtWVbuTXAV8FLgLuKSqru+benb34e/mU4C3VdUXm0Y9LDOu328Dr03yMRZ3WfxqLf5PZk3xUGRJajLP+4AlqZUBlqQmBliSmhhgSWpigCWpiQGWpCYGWJKaGGDNlSQnJ9md5I+H88y+LcmDknx7kvcl+WiSv0qyPsnXJ7kxyXFJjkjy7iQ/1L0OWjsMsObRFuAPq+o04HPAU4HXsXi01bexePatl1TVp4CXsXg2vF8CPl5Vb+sZWWuRAdY8+mRVXTc8vpbFs209vKruvmrGDuB7AarqEuAhwM8CvzzxnFrjDLDm0ZeWPL6TxatnHNRwvt2ThqfHjDiT5pABluDzwL8n+Z7h+bOAu7eGXwa8HvhN4I8bZtMaNrdnQ5MOsA141bDFezNwQZLvA04HHl+LJ+V/apILqupPWyfVmuHZ0CSpibsgJKmJAZakJgZYkpoYYElqYoAlqYkBlqQmBliSmvwvd41WhRoxf6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['nox'])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['rm'])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['rad'])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['tax'])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['medv'])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Data Preprocessing (Part II)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Min-Max Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "mms = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-150-72bbad649eee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata_fix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'medv'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'medv'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
     ]
    }
   ],
   "source": [
    "data_fix['medv'] = mms.fit_transform(data[['medv']].to_numpy().reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-133-6f5283faa6cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata_fix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'crim'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'crim'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
     ]
    }
   ],
   "source": [
    "data_fix['crim'] = mms.fit_transform(data[['crim']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where did it goes wrong?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Data Visualization (Part III)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot (Check Correlation-Distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rad', ylabel='tax'>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data.plot.scatter(x='rad', y='tax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rm', ylabel='medv'>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data.plot.scatter(x='rm', y='medv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>rm</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.575</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.421</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.185</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.998</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.147</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rm  medv\n",
       "0  6.575  24.0\n",
       "1  6.421  21.6\n",
       "2  7.185  34.7\n",
       "3  6.998  33.4\n",
       "4  7.147  36.2"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = data.drop(['crim', 'indus', 'nox', 'rad', 'tax'], axis = 1)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(dataset, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>rm</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6.069</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>6.059</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.129</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>6.389</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>6.211</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rm  medv\n",
       "44   6.069  21.2\n",
       "338  6.059  20.6\n",
       "154  6.129  17.0\n",
       "84   6.389  23.9\n",
       "93   6.211  25.0"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>rm</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>8.266</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>7.088</td>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>6.861</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>8.780</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.786</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rm  medv\n",
       "224  8.266  44.8\n",
       "284  7.088  32.2\n",
       "290  6.861  28.5\n",
       "364  8.780  21.9\n",
       "46   5.786  20.0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependent and Indpendent Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label = train_data['medv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features = train_data.drop(['medv'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_label = test_data['medv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_features = test_data.drop(['medv'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model.fit(X=train_features, y=train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "medv_pred = lr_model.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 54.58924742000303\n"
     ]
    }
   ],
   "source": [
    "accuracy_score = lr_model.score(test_features, test_label)\n",
    "print('Accuracy: {}'.format(accuracy_score*100.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c5383f3b20>]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(test_data['rm'], test_data['medv'], color='black')\n",
    "plt.plot(test_data['rm'], medv_pred, color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coeffiecient 9.098557715192298\n",
      "Intercept -34.46759843949974\n"
     ]
    }
   ],
   "source": [
    "print('Coeffiecient', lr_model.coef_[0])\n",
    "print('Intercept', lr_model.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model: y = 9.098557715192298x - 34.46759843949974"
   ]
  }
 ],
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