Assignment 02 Regression Boston Housing.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Predicting Housing Prices with Linear Regression\n",
    "\n",
    "Source: https://www.kaggle.com/c/boston-housing\n",
    "\n",
    "## Housing Values in Suburbs of Boston\n",
    "The ***medv*** variable is the target variable.  \n",
    "\n",
    "## Data description\n",
    "\n",
    "The Boston data frame has 506 rows and 14 columns.  \n",
    "\n",
    "This data frame contains the following columns:  \n",
    "\n",
    "**crim**  \n",
    "per capita crime rate by town.\n",
    "\n",
    "**zn**  \n",
    "proportion of residential land zoned for lots over 25,000 sq.ft.  \n",
    "\n",
    "**indus**  \n",
    "proportion of non-retail business acres per town.  \n",
    "\n",
    "**chas**  \n",
    "Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).  \n",
    "\n",
    "**nox**  \n",
    "nitrogen oxides concentration (parts per 10 million).  \n",
    "\n",
    "**rm**\n",
    "average number of rooms per dwelling.  \n",
    "\n",
    "**age**  \n",
    "proportion of owner-occupied units built prior to 1940.  \n",
    "\n",
    "**dis**  \n",
    "weighted mean of distances to five Boston employment centres.  \n",
    "\n",
    "**rad**  \n",
    "index of accessibility to radial highways.  \n",
    "\n",
    "**tax**\n",
    "full-value property-tax rate per \\$10,000.  \n",
    "\n",
    "**ptratio**  \n",
    "pupil-teacher ratio by town.   \n",
    "\n",
    "**black**  \n",
    "1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.  \n",
    "\n",
    "**lstat**  \n",
    "lower status of the population (percent).  \n",
    "\n",
    "**medv**  \n",
    "median value of owner-occupied homes in \\$1000s.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Build a linear regression model using Boston Housing dataset. You can download dataset directly from Kaggle or load from sklearn library.  \n",
    "\n",
    "Hint: Loading from sklearm library and save into a pandas dataframe \n",
    "\n",
    "from sklearn.datasets import load_boston  \n",
    "import pandas as pd  \n",
    "boston = load_boston()  \n",
    "df = pd.DataFrame(boston.data)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "from sklearn.datasets import load_boston\n",
    "import pandas as pd\n",
    "boston = load_boston()\n",
    "df = pd.DataFrame(boston.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
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       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</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",
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       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "         0     1     2    3      4      5     6       7    8      9    10  \\\n",
       "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
       "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
       "2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
       "3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
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       "\n",
       "       11    12  \n",
       "0  396.90  4.98  \n",
       "1  396.90  9.14  \n",
       "2  392.83  4.03  \n",
       "3  394.63  2.94  \n",
       "4  396.90  5.33  "
      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n"
     ]
    }
   ],
   "source": [
    "print(boston.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <th>TAX</th>\n",
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       "      <td>0.00632</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = boston.feature_names\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>House_Price</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
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       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
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       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</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  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  House_Price  \n",
       "0     15.3  396.90   4.98         24.0  \n",
       "1     17.8  396.90   9.14         21.6  \n",
       "2     17.8  392.83   4.03         34.7  \n",
       "3     18.7  394.63   2.94         33.4  \n",
       "4     18.7  396.90   5.33         36.2  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['House_Price'] = boston.target\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.455621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.603800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.055892</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.053929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.590879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.613808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.602339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.496996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.488676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.543993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6   \\\n",
       "0   1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "1  -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "2   0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "3  -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "4   0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "5  -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "6   0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "7  -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "8   0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "9   0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "10  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "11 -0.385064  0.175520 -0.356977  0.048788 -0.380051  0.128069 -0.273534   \n",
       "12  0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "\n",
       "          7         8         9         10        11        12  \n",
       "0  -0.379670  0.625505  0.582764  0.289946 -0.385064  0.455621  \n",
       "1   0.664408 -0.311948 -0.314563 -0.391679  0.175520 -0.412995  \n",
       "2  -0.708027  0.595129  0.720760  0.383248 -0.356977  0.603800  \n",
       "3  -0.099176 -0.007368 -0.035587 -0.121515  0.048788 -0.053929  \n",
       "4  -0.769230  0.611441  0.668023  0.188933 -0.380051  0.590879  \n",
       "5   0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.613808  \n",
       "6  -0.747881  0.456022  0.506456  0.261515 -0.273534  0.602339  \n",
       "7   1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.496996  \n",
       "8  -0.494588  1.000000  0.910228  0.464741 -0.444413  0.488676  \n",
       "9  -0.534432  0.910228  1.000000  0.460853 -0.441808  0.543993  \n",
       "10 -0.232471  0.464741  0.460853  1.000000 -0.177383  0.374044  \n",
       "11  0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.366087  \n",
       "12 -0.496996  0.488676  0.543993  0.374044 -0.366087  1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0s+6IraqTquqPqupPquqmqvrZKYcBAMBqlhZ43fuSPH+McXdVbU/ye1X13jHGByfaBgAAR7XuiB1jjCR3rzy7feXXmGIUAAA8koXuia2qbVV1Q5J9Sa4ZY3xomlkAALC6hSJ2jPHAGOOZSc5O8uyquuDhj6mq3VW1t6r2Hj54YJHDAQBAkom+O8EY4ytJPpBk11H+bM8YY3mMsby0Y+cUhwMA4DFuke9OcEZVnbry9OOSfE+Sj081DAAAVrPIdyd4cpK3VdW2HInhd4wxfmOaWQAAsLpFvjvBR5I8a8ItAACwJn5iFwAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0s7SZB9t28qE8/nmf38xDbohTXnvS3BMm8fL/9d65J0zmb1xx2dwTJnHLD71x7gmT+K6f/vG5J0ziFW/49bknTOYd558594RJnH/dpn7a2jCnb7977gmTuPwPLpp7wiTO/bUx94TJnP+Gj849YWH7f+3gmh7nSiwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANDOwhFbVduq6sNV9RtTDAIAgGOZ4krsq5LcPMHbAQCANVkoYqvq7CTfl+TN08wBAIBjW/RK7H9K8i+TPDjBFgAAWJN1R2xVvTjJvjHGdcd43O6q2ltVew/dec96DwcAAH9hkSuxz01ycVV9Nsnbkzy/qn714Q8aY+wZYyyPMZa3P/5xCxwOAACOWHfEjjFeN8Y4e4xxbpJLkvzOGONlky0DAIBV+D6xAAC0szTFGxljfCDJB6Z4WwAAcCyuxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOzXG2LSD7Tz9nHH+xT+zacfbKHe+6MDcEyZx1uUnzj1hMre+cPvcEyZx5ocenHvCJH7vv7xp7gmT+DuvuWzuCZPZd9HhuSdM4vQ/WJp7wiROPLB5n3s30vYDD8w9YRKfe+G2uSdM5m/9xz+be8LC/uD2K3LnfV+oYz3OlVgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKCdpUVeuao+m+SrSR5IcniMsTzFKAAAeCQLReyKvzfG+OIEbwcAANbE7QQAALSzaMSOJO+rquuqavcUgwAA4FgWvZ3guWOM26vqiUmuqaqPjzGufegDVuJ2d5KcuPMJCx4OAAAWvBI7xrh95fd9Sd6d5NlHecyeMcbyGGN56aSdixwOAACSLBCxVbWzqk7+2tNJ/n6SG6caBgAAq1nkdoInJXl3VX3t7fzPMcbVk6wCAIBHsO6IHWN8Osm3TbgFAADWxLfYAgCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2ljbzYA+e+kDuecmdm3nIDfHNr7lv7gmTuPAdH557wmQ+977vmnvCJF7xhl+fe8Ik/s5rLpt7wiQ+9PNvnHvCZHY9dXnuCZM4dPVT5p4wiZO3b43PIx+9dWu8P570m9vmnjCZb37n5+eesLDrXnZ4TY9zJRYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhnoYitqlOr6qqq+nhV3VxVz5lqGAAArGZpwdf/z0muHmP8o6o6McmOCTYBAMAjWnfEVtUpSS5K8k+SZIxxf5L7p5kFAACrW+R2gm9Ksj/Jr1TVh6vqzVW1c6JdAACwqkUidinJhUneOMZ4VpIDSV778AdV1e6q2ltVex+46+AChwMAgCMWidjbktw2xvjQyvNX5UjU/hVjjD1jjOUxxvK2U9wyCwDA4tYdsWOMzyf5XFWdt/KiFyT52CSrAADgESz63Ql+KskVK9+Z4NNJXrH4JAAAeGQLRewY44YkyxNtAQCANfETuwAAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7S5t6sP0n5PQ37dzMQ26I77zq+rknTOL3n3Pa3BMms2N3zT1hEu84/8y5J0xi3y8fnnvCJHY9dXnuCZO5+ta9c0+YxK6LXzb3hEncd+/W+Bg5/cKT5p4wif0X3zP3hMl88ifOm3vCwu790w+s6XGuxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2sO2Kr6ryquuEhv+6qqldPOQ4AAI5mab2vOMb4RJJnJklVbUvyZ0nePdEuAABY1VS3E7wgyf8bY/zpRG8PAABWNVXEXpLkyqP9QVXtrqq9VbX30P0HJjocAACPZQtHbFWdmOTiJP/7aH8+xtgzxlgeYyxvP3HnoocDAIBJrsS+KMn1Y4wvTPC2AADgmKaI2Euzyq0EAACwERaK2KrakeSFSd41zRwAADi2dX+LrSQZYxxMctpEWwAAYE38xC4AANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaGdpMw+286yDWf65vZt5yA1x9c9+99wTJvHMD9ww94TJfPb/zL1gGudft6kfkhvmy/9ta5zHoaufMveEyey6+GVzT5jE1e/51bknTOLWw3fPPWESr/zUpXNPmMQprz9t7gmTWfqFL809YWG1+4E1Pc6VWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoJ2FIraqfqaqbqqqG6vqyqo6aaphAACwmnVHbFWdleSnkyyPMS5Isi3JJVMNAwCA1Sx6O8FSksdV1VKSHUluX3wSAAA8snVH7Bjjz5L8QpJbk9yR5M4xxvse/riq2l1Ve6tq78Gv3Lf+pQAAsGKR2wmekOQlSZ6W5ClJdlbVyx7+uDHGnjHG8hhjecepX7f+pQAAsGKR2wm+J8lnxhj7xxiHkrwryXdOMwsAAFa3SMTemuQ7qmpHVVWSFyS5eZpZAACwukXuif1QkquSXJ/koytva89EuwAAYFVLi7zyGOP1SV4/0RYAAFgTP7ELAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANpZ2syDPZDKXYdP2sxDbogacy+YxgU7b5t7wmR+9/C3zz1hEqdvv3vuCZM48cDW+CA5eft9c0+YzH33Hp57wiRuPbw1PkaeuvT1c0+YxIOj5p4wiRNqa5xHkpx64j1zT1jYUj24pse5EgsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALSzUMRW1auq6saquqmqXj3VKAAAeCTrjtiquiDJjyV5dpJvS/Liqnr6VMMAAGA1i1yJPT/JB8cYB8cYh5P83yQvnWYWAACsbpGIvTHJRVV1WlXtSPK9Sc6ZZhYAAKxuab2vOMa4uap+Psk1Se5O8idJDj/8cVW1O8nuJPn6M3eu93AAAPAXFvqHXWOMy8cYF44xLkry5SSfOspj9owxlscYyyc94esWORwAACRZ4EpsklTVE8cY+6rqqUm+P8lzppkFAACrWyhik7yzqk5LcijJT44x/nyCTQAA8IgWitgxxt+daggAAKyVn9gFAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO0sbebB7rr3pPzOLedt5iE3xDl3PzD3hEnsueW75p4wme13z71gGpf/wUVzT5jENx7YGh8jH731KXNPmMzpF54094RJvPJTl849YRIPjpp7wiTe/4z3zD1hEt9+/mVzT5jMF/efOfeEhd1zePuaHudKLAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0M4xI7aq3lJV+6rqxoe87Buq6pqq+tTK70/Y2JkAAPCX1nIl9q1Jdj3sZa9N8v4xxtOTvH/leQAA2BTHjNgxxrVJvvywF78kydtWnn5bkn848S4AAFjVeu+JfdIY444kWfn9idNNAgCAR7bh/7CrqnZX1d6q2vvgVw9s9OEAAHgMWG/EfqGqnpwkK7/vW+2BY4w9Y4zlMcbyCSfvXOfhAADgL603Yt+T5OUrT788ya9PMwcAAI5tLd9i68okf5jkvKq6rapemeTfJXlhVX0qyQtXngcAgE2xdKwHjDEuXeWPXjDxFgAAWBM/sQsAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2lnazIOd+KXK2W/b1ENuiM+8dGu0/ze+6ZS5J0zm9ueOuSdM4txf2xrnceuubXNPmMSTfnNrnEeS7L/4nrknTOKU158294RJnFA194RJfPv5l809YRLX/ds3zj1hMs/70R+be8LCtu1fW2dtjRoDAOAxRcQCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEfEAgDQjogFAKAdEQsAQDsiFgCAdo4ZsVX1lqraV1U3PuRlP1hVN1XVg1W1vLETAQDgr1rLldi3Jtn1sJfdmOT7k1w79SAAADiWpWM9YIxxbVWd+7CX3ZwkVbUxqwAA4BG4JxYAgHY2PGKrandV7a2qvYfuP7DRhwMA4DFgwyN2jLFnjLE8xljefuLOjT4cAACPAW4nAACgnbV8i60rk/xhkvOq6raqemVVvbSqbkvynCS/WVW/vdFDAQDga9by3QkuXeWP3j3xFgAAWBO3EwAA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALQjYgEAaEe4/o1fAAADKklEQVTEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2ljb1aGcezgn/Yt+mHnIjfNPPnTH3hEl8y7//yNwTJvPFq/723BMmcf4bPjr3hEns+Edb42Pkm9/5+bknTOaTP3He3BMmsfQLX5p7wiROPfGeuSdM4ov7z5x7wiSe96M/NveEyXzgzb8894SFPfsffHFNj3MlFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7IhYAgHZELAAA7YhYAADaEbEAALRzzIitqrdU1b6quvEhL/sPVfXxqvpIVb27qk7d2JkAAPCX1nIl9q1Jdj3sZdckuWCM8a1JPpnkdRPvAgCAVR0zYscY1yb58sNe9r4xxuGVZz+Y5OwN2AYAAEc1xT2xP5LkvRO8HQAAWJOFIraq/nWSw0mueITH7K6qvVW199BXDi5yOAAASLJAxFbVy5O8OMkPjTHGao8bY+wZYyyPMZa3n7pjvYcDAIC/sLSeV6qqXUlek+S7xxgurwIAsKnW8i22rkzyh0nOq6rbquqVSf5bkpOTXFNVN1TVf9/gnQAA8BeOeSV2jHHpUV58+QZsAQCANfETuwAAaEfEAgDQjogFAKAdEQsAQDsiFgCAdkQsAADtiFgAANoRsQAAtCNiAQBoR8QCANCOiAUAoB0RCwBAOyIWAIB2RCwAAO2IWAAA2hGxAAC0I2IBAGhHxAIA0I6IBQCgHRELAEA7NcbYvINV7U/ypxt8mNOTfHGDj7EZnMfxZ6uci/M4vjiP48tWOY9k65yL8zi+bMZ5fOMY44xjPWhTI3YzVNXeMcby3DsW5TyOP1vlXJzH8cV5HF+2ynkkW+dcnMfx5Xg6D7cTAADQjogFAKCdrRixe+YeMBHncfzZKufiPI4vzuP4slXOI9k65+I8ji/HzXlsuXtiAQDY+rbilVgAALY4EQsAQDsiFgCAdkQsAADtiFgAANr5/zAWPZZkUqrUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "def plot_corr(df, size=12):\n",
    "    \"\"\"\n",
    "    Function plots a graphical correlation matrix for each pair of columns in the dataframe.\n",
    "\n",
    "    Input:\n",
    "        df: pandas DataFrame\n",
    "        size: vertical and horizontal size of the plot\n",
    "\n",
    "    Displays:\n",
    "        matrix of correlation between columns.\n",
    "    \"\"\"\n",
    "\n",
    "    corr = df.corr()    # data frame correlation function\n",
    "    fig, ax = plt.subplots(figsize=(size, size))#The subplots command in the background will call plt.figure(), and any keywords will be passed along\n",
    "    ax.matshow(corr)   # heatmap with matshow, color code the rectangles by correlation value\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns)  # draw x tick marks\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)  # draw y tick marks\n",
    "    \n",
    "plot_corr(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6713466169624294\n"
     ]
    }
   ],
   "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 = 0.33, random_state = 0)\n",
    "\n",
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X_train, y_train)\n",
    "\n",
    "#Check R-square on training data\n",
    "print(linear_model.score(X_train, y_train))\n",
    "\n",
    "# Predicting the Test set results\n",
    "y_predict = linear_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.48154216, 17.0806026 , 12.56125311, 22.44606612, 10.28381502,\n",
       "       12.64634228, 13.28254272, 11.80066557, 15.32321533, 15.7746666 ,\n",
       "       30.3583735 , 18.93969203, 16.54183466, 25.82643574,  7.33816124,\n",
       "        2.4819131 , 12.71720074,  3.07270982,  6.30440546, 10.62911162,\n",
       "        9.21693877, 16.8843142 , 13.87660141,  7.43656476, 10.42894222,\n",
       "       30.24708487, 15.04018572, 19.3161048 ,  3.30650598, 14.43399333,\n",
       "       16.83016334, 15.81080819, 12.45500834, 11.3410162 , 11.35240079,\n",
       "       16.95735273, 20.04372284, 17.05268666, 17.78601774, 17.1522892 ,\n",
       "        7.32563355,  9.93178454,  7.6603952 , 19.91581869,  7.95252475,\n",
       "       11.1847217 , 12.25842947, 15.81562566, 21.55549107, 10.68220156,\n",
       "       17.10799806, 10.81191253, 12.36571139, -2.91099951, 19.5094518 ,\n",
       "        9.96745142, 10.0148702 , 10.9584659 , 28.17063337, 14.2143809 ,\n",
       "        8.23986667, 11.36907477,  3.43698692,  5.73176981, 15.45818075,\n",
       "        6.09680283, 14.75916442, 12.65793583, 16.72264284,  9.07515712,\n",
       "       12.09125186,  9.34261203,  6.810206  ,  5.26768472, 11.41888873,\n",
       "       23.13404275,  2.6226568 ,  9.84443031,  9.86803718, 13.92533277,\n",
       "        7.19715508, 15.1085791 , 20.51073433,  3.20386479,  1.66679224,\n",
       "        7.62746182, 11.13660917, 18.7797332 , 14.83108066, 15.74758525,\n",
       "       11.78137447, 17.81775539,  8.83693039,  6.33196294, 14.42905175,\n",
       "        8.94788962, 27.21828031,  7.94841841, 17.19210269, 13.52035852,\n",
       "        7.94704378, 13.31598812,  9.05479787,  8.34945274,  6.91163321,\n",
       "       11.80541619, 24.80165009, 21.76409644, 13.68426844,  5.69700794,\n",
       "       11.96832618, 19.6739281 , 13.680341  , 13.9955124 , 16.49605378,\n",
       "       10.94396567, 21.02018071, 15.51305462, 22.14937465, -0.66590673,\n",
       "        5.27385336, 19.8265365 , 13.22169086, 12.49347396, 13.59264333,\n",
       "       17.79686427,  6.64045393, 14.25735411, 11.02219858,  4.77932496,\n",
       "       15.67375643, 17.48854235, 17.07218165, 17.06697256, 16.01685922,\n",
       "        3.38136634, 11.3213094 , 18.87811208,  4.6998901 , 20.73742327,\n",
       "       17.75205999, 10.9064976 ,  6.74198786,  5.62889124,  5.85735722,\n",
       "       15.89859004,  2.97946038,  5.4765367 , 17.73915263, 21.90930532,\n",
       "       11.20293432,  9.03457745, 21.69646945,  9.50948587, 15.62053504,\n",
       "        6.45360201, 17.19008545, 14.49535798, 11.04150005, 17.77535759,\n",
       "       21.91872374, 14.12393841,  4.66713929,  6.88775502,  6.13460938,\n",
       "        9.92634388, 10.70652676])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price vs. No. of Rooms')"
      ]
     },
     "execution_count": 34,
     "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( df.RM, df.House_Price, s=5 )\n",
    "plt.xlabel( \"Avg. No. of Rooms\" )\n",
    "plt.ylabel( \"House Price in $10K\" )\n",
    "plt.title( \"Price vs. No. of Rooms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual Price vs. Predicted Price')"
      ]
     },
     "execution_count": 33,
     "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( y_test, linear_model.predict(X_test), s=5 )\n",
    "plt.xlabel( \"Actual Price\" )\n",
    "plt.ylabel( \"Predicted Price\" )\n",
    "plt.title( \"Actual Price vs. Predicted Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals')"
      ]
     },
     "execution_count": 35,
     "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( linear_model.predict(X_train), linear_model.predict(X_train) -y_train,c ='b', s=30, alpha=0.4 )\n",
    "plt.scatter( linear_model.predict(X_test), linear_model.predict(X_test) -y_test,c ='g', s=30 )\n",
    "plt.hlines( y=0, xmin=-5, xmax=55)\n",
    "plt.title( \"Residuals\" )\n",
    "plt.ylabel( \"Residuals\" )"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}