diff --git a/your-code/lab_boston_housing.ipynb b/your-code/lab_boston_housing.ipynb
index 3176602..0f9caf2 100644
--- a/your-code/lab_boston_housing.ipynb
+++ b/your-code/lab_boston_housing.ipynb
@@ -35,11 +35,603 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(404, 14)\n"
+     ]
+    },
+    {
+     "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>black</th>\n",
+       "      <th>lstat</th>\n",
+       "      <th>medv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.15876</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.413</td>\n",
+       "      <td>5.961</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>5.2873</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>305.0</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>376.94</td>\n",
+       "      <td>9.88</td>\n",
+       "      <td>21.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.10328</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>5.13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.453</td>\n",
+       "      <td>5.927</td>\n",
+       "      <td>47.2</td>\n",
+       "      <td>6.9320</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>284.0</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>9.22</td>\n",
+       "      <td>19.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.34940</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.90</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.544</td>\n",
+       "      <td>5.972</td>\n",
+       "      <td>76.7</td>\n",
+       "      <td>3.1025</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>396.24</td>\n",
+       "      <td>9.97</td>\n",
+       "      <td>20.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.73397</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.58</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>5.597</td>\n",
+       "      <td>94.9</td>\n",
+       "      <td>1.5257</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>351.85</td>\n",
+       "      <td>21.45</td>\n",
+       "      <td>15.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.04337</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>6.115</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>6.8147</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>243.0</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>393.97</td>\n",
+       "      <td>9.43</td>\n",
+       "      <td>20.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      crim    zn  indus  chas    nox     rm   age     dis  rad    tax  \\\n",
+       "0  0.15876   0.0  10.81   0.0  0.413  5.961  17.5  5.2873  4.0  305.0   \n",
+       "1  0.10328  25.0   5.13   0.0  0.453  5.927  47.2  6.9320  8.0  284.0   \n",
+       "2  0.34940   0.0   9.90   0.0  0.544  5.972  76.7  3.1025  4.0  304.0   \n",
+       "3  2.73397   0.0  19.58   0.0  0.871  5.597  94.9  1.5257  5.0  403.0   \n",
+       "4  0.04337  21.0   5.64   0.0  0.439  6.115  63.0  6.8147  4.0  243.0   \n",
+       "\n",
+       "   ptratio   black  lstat  medv  \n",
+       "0     19.2  376.94   9.88  21.7  \n",
+       "1     19.7  396.90   9.22  19.6  \n",
+       "2     18.4  396.24   9.97  20.3  \n",
+       "3     14.7  351.85  21.45  15.4  \n",
+       "4     16.8  393.97   9.43  20.5  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "boston = pd.read_csv(\"../data/boston_data.csv\")\n",
+    "print(boston.shape)\n",
+    "boston.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "#Attribute Information (in order):\n",
+    "#   - CRIM     per capita crime rate by town\n",
+    "#   - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
+    "#   - INDUS    proportion of non-retail business acres per town\n",
+    "#   - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
+    "#   - NOX      nitric oxides concentration (parts per 10 million)\n",
+    "#   - RM       average number of rooms per dwelling\n",
+    "#   - AGE      proportion of owner-occupied units built prior to 1940\n",
+    "#   - DIS      weighted distances to five Boston employment centres\n",
+    "#   - RAD      index of accessibility to radial highways\n",
+    "#   - TAX      full-value property-tax rate per $10,000\n",
+    "#   - PTRATIO  pupil-teacher ratio by town\n",
+    "#   - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
+    "#   - LSTAT    % lower status of the population\n",
+    "#   - MEDV     Median value of owner-occupied homes in $1000's"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "crim       float64\n",
+       "zn         float64\n",
+       "indus      float64\n",
+       "chas       float64\n",
+       "nox        float64\n",
+       "rm         float64\n",
+       "age        float64\n",
+       "dis        float64\n",
+       "rad        float64\n",
+       "tax        float64\n",
+       "ptratio    float64\n",
+       "black      float64\n",
+       "lstat      float64\n",
+       "medv       float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "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",
+       "black      0\n",
+       "lstat      0\n",
+       "medv       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston.isna().sum() #no NaN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>black</th>\n",
+       "      <th>lstat</th>\n",
+       "      <th>medv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>404.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.730912</td>\n",
+       "      <td>10.509901</td>\n",
+       "      <td>11.189901</td>\n",
+       "      <td>0.069307</td>\n",
+       "      <td>0.55671</td>\n",
+       "      <td>6.30145</td>\n",
+       "      <td>68.601733</td>\n",
+       "      <td>3.799666</td>\n",
+       "      <td>9.836634</td>\n",
+       "      <td>411.688119</td>\n",
+       "      <td>18.444554</td>\n",
+       "      <td>355.068243</td>\n",
+       "      <td>12.598936</td>\n",
+       "      <td>22.312376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.943922</td>\n",
+       "      <td>22.053733</td>\n",
+       "      <td>6.814909</td>\n",
+       "      <td>0.25429</td>\n",
+       "      <td>0.117321</td>\n",
+       "      <td>0.67583</td>\n",
+       "      <td>28.066143</td>\n",
+       "      <td>2.109916</td>\n",
+       "      <td>8.834741</td>\n",
+       "      <td>171.073553</td>\n",
+       "      <td>2.150295</td>\n",
+       "      <td>94.489572</td>\n",
+       "      <td>6.925173</td>\n",
+       "      <td>8.837019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.00632</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>3.561</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1.1691</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>1.73</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.082382</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.453</td>\n",
+       "      <td>5.90275</td>\n",
+       "      <td>45.8</td>\n",
+       "      <td>2.087875</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>281.0</td>\n",
+       "      <td>17.375</td>\n",
+       "      <td>374.71</td>\n",
+       "      <td>7.135</td>\n",
+       "      <td>17.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.253715</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.795</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.538</td>\n",
+       "      <td>6.2305</td>\n",
+       "      <td>76.6</td>\n",
+       "      <td>3.20745</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>391.065</td>\n",
+       "      <td>11.265</td>\n",
+       "      <td>21.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.053158</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.631</td>\n",
+       "      <td>6.62925</td>\n",
+       "      <td>94.15</td>\n",
+       "      <td>5.222125</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>666.0</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>396.0075</td>\n",
+       "      <td>16.91</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>88.9762</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>27.74</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>8.78</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>12.1265</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>711.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>396.9</td>\n",
+       "      <td>34.37</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IQR</th>\n",
+       "      <td>3.970775</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>12.91</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.178</td>\n",
+       "      <td>0.7265</td>\n",
+       "      <td>48.35</td>\n",
+       "      <td>3.13425</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>385.0</td>\n",
+       "      <td>2.825</td>\n",
+       "      <td>21.2975</td>\n",
+       "      <td>9.775</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>outliers_low</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>outliers_up</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  crim         zn      indus      chas       nox       rm  \\\n",
+       "count            404.0      404.0      404.0     404.0     404.0    404.0   \n",
+       "mean          3.730912  10.509901  11.189901  0.069307   0.55671  6.30145   \n",
+       "std           8.943922  22.053733   6.814909   0.25429  0.117321  0.67583   \n",
+       "min            0.00632        0.0       0.46       0.0     0.392    3.561   \n",
+       "25%           0.082382        0.0       5.19       0.0     0.453  5.90275   \n",
+       "50%           0.253715        0.0      9.795       0.0     0.538   6.2305   \n",
+       "75%           4.053158       12.5       18.1       0.0     0.631  6.62925   \n",
+       "max            88.9762       95.0      27.74       1.0     0.871     8.78   \n",
+       "IQR           3.970775       12.5      12.91       0.0     0.178   0.7265   \n",
+       "outliers_low     False      False      False     False     False     True   \n",
+       "outliers_up       True       True      False      True     False     True   \n",
+       "\n",
+       "                    age       dis       rad         tax    ptratio  \\\n",
+       "count             404.0     404.0     404.0       404.0      404.0   \n",
+       "mean          68.601733  3.799666  9.836634  411.688119  18.444554   \n",
+       "std           28.066143  2.109916  8.834741  171.073553   2.150295   \n",
+       "min                 2.9    1.1691       1.0       187.0       12.6   \n",
+       "25%                45.8  2.087875       4.0       281.0     17.375   \n",
+       "50%                76.6   3.20745       5.0       330.0       19.0   \n",
+       "75%               94.15  5.222125      24.0       666.0       20.2   \n",
+       "max               100.0   12.1265      24.0       711.0       22.0   \n",
+       "IQR               48.35   3.13425      20.0       385.0      2.825   \n",
+       "outliers_low      False     False     False       False       True   \n",
+       "outliers_up       False      True     False       False      False   \n",
+       "\n",
+       "                   black      lstat       medv  \n",
+       "count              404.0      404.0      404.0  \n",
+       "mean          355.068243  12.598936  22.312376  \n",
+       "std            94.489572   6.925173   8.837019  \n",
+       "min                 0.32       1.73        5.0  \n",
+       "25%               374.71      7.135       17.1  \n",
+       "50%              391.065     11.265       21.4  \n",
+       "75%             396.0075      16.91       25.0  \n",
+       "max                396.9      34.37       50.0  \n",
+       "IQR              21.2975      9.775        7.9  \n",
+       "outliers_low        True      False       True  \n",
+       "outliers_up        False       True       True  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats = boston.describe().transpose()\n",
+    "stats['IQR'] = stats['75%'] - stats['25%']\n",
+    "stats['outliers_low'] = (stats['25%'] - 1.5*stats['IQR'])>stats['min']\n",
+    "stats['outliers_up'] = (stats['75%'] + 1.5*stats['IQR'])<stats['max']\n",
+    "stats.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "380    88.97620\n",
+       "285    73.53410\n",
+       "114    67.92080\n",
+       "400    51.13580\n",
+       "116    41.52920\n",
+       "         ...   \n",
+       "30      0.01439\n",
+       "314     0.01381\n",
+       "387     0.01360\n",
+       "107     0.01301\n",
+       "367     0.00632\n",
+       "Name: crim, Length: 404, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#looking at upper outliers in different columns, unreasonable? didn't find any I want to delete, maybe just solve by rescaling?\n",
+    "boston[\"crim\"].sort_values(ascending=False)\n",
+    "#drop every crim over 50?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boston.iloc[380:381][:]\n",
+    "#boston.drop(380, axis=0, inplace=True)\n",
+    "boston.drop([285,114,400], axis=0, inplace=True)"
    ]
   },
   {
@@ -51,11 +643,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your plots here"
+    "# Your plots here\n",
+    "plot_options, (chart_1, chart_2, chart_3, chart_4, chart_5) = plt.subplots(nrows=1, ncols=5, figsize=(12,3))\n",
+    "chart_1.plot(boston[\"crim\"], boston[\"medv\"], \"r+\")\n",
+    "poly1d_fn = np.poly1d(np.polyfit(boston[\"crim\"], boston[\"medv\"], 1)) \n",
+    "chart_1.plot(boston[\"crim\"], poly1d_fn(boston[\"crim\"]), '-k')\n",
+    "chart_1.set_title(\"crim\")\n",
+    "chart_2.plot(boston[\"indus\"], boston[\"medv\"], \"r+\")\n",
+    "poly1d_fn = np.poly1d(np.polyfit(boston[\"indus\"], boston[\"medv\"],1)) \n",
+    "chart_2.plot(boston[\"indus\"], poly1d_fn(boston[\"indus\"]), '-k')\n",
+    "chart_2.set_title(\"indus\")\n",
+    "chart_3.plot(boston[\"rm\"], boston[\"medv\"], \"r+\")\n",
+    "poly1d_fn = np.poly1d(np.polyfit(boston[\"rm\"], boston[\"medv\"], 1)) \n",
+    "chart_3.plot(boston[\"rm\"], poly1d_fn(boston[\"rm\"]), '-k')\n",
+    "chart_3.set_title(\"rm\")\n",
+    "chart_4.plot(boston[\"ptratio\"], boston[\"medv\"], \"r+\")\n",
+    "poly1d_fn = np.poly1d(np.polyfit(boston[\"ptratio\"], boston[\"medv\"], 1)) \n",
+    "chart_4.plot(boston[\"ptratio\"], poly1d_fn(boston[\"ptratio\"]), '-k')\n",
+    "chart_4.set_title(\"ptratio\")\n",
+    "chart_5.plot(boston[\"lstat\"], boston[\"medv\"], \"r+\")\n",
+    "poly1d_fn = np.poly1d(np.polyfit(boston[\"lstat\"], boston[\"medv\"], 1)) \n",
+    "chart_5.plot(boston[\"lstat\"], poly1d_fn(boston[\"lstat\"]), '-k')\n",
+    "chart_5.set_title(\"lstat\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -67,11 +692,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "#Yes, house prices are higher with low crime rates, with less industry in the area, \n",
+    "#  with a smaller pupil-teacher-ratio, with a higher status of the population.\n",
+    "#The only upward trend is in chart 3, house prices are higher with more rooms in the house. \n",
+    "# All of these were to be expected. Linear trends seem to hold up for the lower to medium house prices.\n",
+    "# Trends are stronger for crime rate, room number and population status, industry does not seem to be such a good predictor.\n"
    ]
   },
   {
@@ -83,11 +713,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your response here\n",
+    "import seaborn as sn\n",
+    "corr=np.abs(boston[['zn', 'chas', 'nox', 'age', 'dis', 'rad', 'tax', 'black', 'medv']].corr())\n",
+    "\n",
+    "mask = np.zeros_like(corr, dtype=bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(8, 8))\n",
+    "cmap = sn.diverging_palette(220, 10, as_cmap=True)\n",
+    "sn.heatmap(corr, mask=mask,  vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# actually nearly every variable shows some correlation, so it is hard to pick. But there is also a lot of cross correlation.\n",
+    "# Tax and Rad are highly correlated, so rad could be taken out without loosing much"
    ]
   },
   {
@@ -100,11 +761,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    401.000000\n",
+       "mean      22.407481\n",
+       "std        8.793753\n",
+       "min        5.000000\n",
+       "25%       17.200000\n",
+       "50%       21.500000\n",
+       "75%       25.000000\n",
+       "max       50.000000\n",
+       "Name: medv, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "boston[\"medv\"].describe()"
    ]
   },
   {
@@ -126,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +816,8 @@
     "def performance_metric(y_true, y_predict):\n",
     "    \"\"\" Calculates and returns the performance score between \n",
     "        true and predicted values based on the metric chosen. \"\"\"\n",
-    "    # Your code here:"
+    "    # Your code here:\n",
+    "    return r2_score(y_true, y_predict)"
    ]
   },
   {
@@ -148,11 +830,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = boston.drop(columns=[\"medv\"], axis=1)\n",
+    "Y = boston[\"medv\"]\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y)"
    ]
   },
   {
@@ -175,11 +862,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 723 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(max_depth=10)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Five separate RFR here with the given max depths"
+    "%%time\n",
+    "# Five separate RFR here with the given max depths\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "forest2 = RandomForestRegressor(n_estimators=100, max_depth=2)\n",
+    "forest2.fit(X_train, y_train)\n",
+    "forest4 = RandomForestRegressor(n_estimators=100, max_depth=4)\n",
+    "forest4.fit(X_train, y_train)\n",
+    "forest6 = RandomForestRegressor(n_estimators=100, max_depth=6)\n",
+    "forest6.fit(X_train, y_train)\n",
+    "forest8 = RandomForestRegressor(n_estimators=100, max_depth=8)\n",
+    "forest8.fit(X_train, y_train)\n",
+    "forest10 = RandomForestRegressor(n_estimators=100, max_depth=10)\n",
+    "forest10.fit(X_train, y_train)"
    ]
   },
   {
@@ -191,13 +910,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Produce a plot with the score for the testing and training for the different max depths"
+    "# Produce a plot with the score for the testing and training for the different max depths\n",
+    "train_scores = np.array([forest2.score(X_train, y_train),\n",
+    "                         forest4.score(X_train, y_train),\n",
+    "                         forest6.score(X_train, y_train),\n",
+    "                         forest8.score(X_train, y_train),\n",
+    "                         forest10.score(X_train, y_train)])\n",
+    "test_scores = np.array([forest2.score(X_test, y_test),\n",
+    "                        forest4.score(X_test, y_test),\n",
+    "                        forest6.score(X_test, y_test),\n",
+    "                        forest8.score(X_test, y_test),\n",
+    "                        forest10.score(X_test, y_test)])\n",
+    "fig, ax = plt.subplots(figsize=(6,6))\n",
+    "plt.plot(train_scores, test_scores, \"ro\")\n",
+    "plt.xlabel(\"train_scores\", size=12)\n",
+    "plt.ylabel(\"test_scores\", size=12)\n",
+    "plt.xlim(0.7,1)\n",
+    "plt.ylim(0.7,1)\n",
+    "for index in range(len(train_scores)):\n",
+    "    ax.text(train_scores[index], test_scores[index], [2,4,6,8,10][index], size=12)\n",
+    "plt.show()"
    ]
   },
   {
@@ -213,7 +962,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# The train scores are all higher than the test scores, so there is always overfitting.\n",
+    "# The overfitting gets higher with higher depth of the model, max_depth = 2 has the least overfitting."
    ]
   },
   {
@@ -226,11 +977,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model with max_depth 1, train score: 0.587321041768603\n",
+      "Model with max_depth 1, test score: 0.5788989695692828\n",
+      "Model with max_depth 10, train score: 0.9747533825294609\n",
+      "Model with max_depth 10, test score: 0.8223244299361981\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "forest1 = RandomForestRegressor(n_estimators=100, max_depth=1)\n",
+    "forest1.fit(X_train, y_train)\n",
+    "\n",
+    "print(f'Model with max_depth 1, train score: {forest1.score(X_train, y_train)}')\n",
+    "print(f'Model with max_depth 1, test score: {forest1.score(X_test, y_test)}')\n",
+    "print(f'Model with max_depth 10, train score: {forest10.score(X_train, y_train)}')\n",
+    "print(f'Model with max_depth 10, test score: {forest10.score(X_test, y_test)}')\n",
+    "\n",
+    "#Model with max_depth 1: low accuracy in both, a bit overfitting but not strongly. \n",
+    "# As the model was only allowed to use one split, this will have a high bias - one variable decides instead of a mix of variables.\n",
+    "# With the high bias probably comes a medium to high variance (because leaning to overfitting and again, only 1 variable makes the model.)\n",
+    "# Model 1 -> high bias.\n",
+    "\n",
+    "#Model with max_depth 10: high accuracy in train, 15% lower in test, overfitting a lot. \n",
+    "# As the model has up to 10 splits, the bias is probably small. But with the high variance (because of overfitting) \n",
+    "# predictions can often not be accurate.\n",
+    "# Model 10 -> high variance."
    ]
   },
   {
@@ -243,11 +1022,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 3.41 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
+       "             param_grid={'max_depth': [3, 4, 5, 6, 7, 8, 9]})"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your response here"
+    "%%time \n",
+    "\n",
+    "# Your response here\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "max_depth = [3,4,5,6,7,8,9]\n",
+    "grid = {\"max_depth\":max_depth}\n",
+    "forest = RandomForestRegressor()\n",
+    "\n",
+    "grid_search = GridSearchCV(estimator = forest, param_grid = grid, cv = 5) # cv usually 5 or 10. 5*7 = 35 runs in total\n",
+    "grid_search.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'max_depth': 8}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# and the winner is...\n",
+    "grid_search.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model with max_depth 8, train score: 0.9705503643566239\n",
+      "Model with max_depth 8, test score: 0.8409265856078486\n"
+     ]
+    }
+   ],
+   "source": [
+    "#ran the grid search multiple times, always a result between 7-9. So I'll choose 8.\n",
+    "print(f'Model with max_depth 8, train score: {forest8.score(X_train, y_train)}')\n",
+    "print(f'Model with max_depth 8, test score: {forest8.score(X_test, y_test)}')\n",
+    "\n",
+    "# there would be many other parameters to optimise. This max-depth of 8 leads to a lot of overfitting, even though the bias is\n",
+    "# probably small."
    ]
   },
   {
@@ -269,14 +1121,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# Prices changed a lot and society and neighborhoods too: with more working from home, access to highways might not be so \n",
+    "# important as a good internet connection nowadays. Gentrification is a process of previously lower class quarters being \n",
+    "# redeveloped, showing higher prices and a change in the population and subsequently, house prices. So no, I would not use \n",
+    "# the same model on modern day data without checking closely if the same correlations still hold up.\n",
+    "\n",
+    "# There is other important factors changing prices, like noise pollution from a near highway or distance to green spaces.\n",
+    "# But as one will never have all the factors, this is quite a varied set of factors to describe prices.\n",
+    "\n",
+    "# My model was not very robust, more parameter tuning might help. To use it on other towns in the US in the 80s, I think \n",
+    "# it would be robust enough then.\n",
+    "\n",
+    "# In rural cities other parameters might be more important, like distance to main industrial area. Prices in rural areas are \n",
+    "# normally lower (if not a vacation town), so the predictions might be very wrong."
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -290,7 +1155,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,
diff --git a/your-code/lab_overfitting.ipynb b/your-code/lab_overfitting.ipynb
index 3776411..6788c5b 100644
--- a/your-code/lab_overfitting.ipynb
+++ b/your-code/lab_overfitting.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### As our classification algorithm, we are going to use a type of SVM with a radial basis function. This basically works by mapping each point into a higher dimensional space that can be split by the SVM (gross oversimplificaiton). That looks something like this:\n",
+    "### As our classification algorithm, we are going to use a type of SVM with a radial basis function. This basically works by mapping each point into a higher dimensional space that can be split by the SVM (gross oversimplification). That looks something like this:\n",
     "![RBFSVM.png](../images/RBFSVM.png)"
    ]
   },
@@ -48,18 +48,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### We can change thecomplexity of the decision boundaries applied by the SVM by changignt the size of the radial basis function, through the parameter 'gamma'.\n",
+    "### We can change the complexity of the decision boundaries applied by the SVM by changing the size of the radial basis function, through the parameter 'gamma'.\n",
     "\n",
     "Instantiate a list of three SVM classifiers with three different gamma parameters, (.001, 1, and 20)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "svm_classifier1 = SVC(gamma=0.001)\n",
+    "svm_classifier2 = SVC(gamma=1)\n",
+    "svm_classifier3 = SVC(gamma=20)\n",
+    "\n",
+    "classifiers = [svm_classifier1, svm_classifier2, svm_classifier3]"
    ]
   },
   {
@@ -71,9 +78,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
@@ -134,7 +152,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# The dots in blue and red are the sample data, red in a circle around blue.\n",
+    "# The Support Vector Machine algorithms were fitted to this sample data. Their predictions are taken for every value \n",
+    "# in a close mesh. Those predicted values are then shown only as contours, which makes the colorgradient background.\n",
+    "\n",
+    "# With gamma=0.001, the prediction is very bad, it looks nearly linear, which of course does not match circles of data.\n",
+    "# With gamma=1, the prediction is quite good, it catches the circle-form, even though some points will be miss-classified due \n",
+    "# to the scatter.\n",
+    "# With gamma=20, the prediction is overfitted. It manages to catch any datapoint in this training-data, but would perform\n",
+    "# poorly with new, unknown data.\n",
+    "# So I would choose gamma=1 from these 3 as being optimal."
    ]
   },
   {
@@ -146,11 +174,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.93"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "svm_classifier4 = SVC(gamma=0.7)\n",
+    "svm_classifier4.fit(X, y)\n",
+    "svm_classifier4.score(X, y)\n",
+    "#Just testing the accuracy score on the training data itself, it is 0.93, a quite good value."
    ]
   },
   {
@@ -162,11 +205,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gamma 20: Accuracy score train data: 0.96\n",
+      "Gamma 20: Accuracy score test data: 0.84\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
+    "svm_classifier3.fit(X_train, y_train)\n",
+    "print(\"Gamma 20: Accuracy score train data:\", svm_classifier3.score(X_train, y_train))\n",
+    "print(\"Gamma 20: Accuracy score test data:\", svm_classifier3.score(X_test, y_test))\n",
+    "#Yes, the model with gamma 20 is very overfitted. \n",
+    "#It matches the train data in 96%, but performs much worse on the test data wit 84%."
    ]
   },
   {
@@ -178,11 +238,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gamma 0.001: Accuracy score train data: 0.52\n",
+      "Gamma 0.001: Accuracy score test data: 0.44\n",
+      "Gamma 0.001: Result is, overfitted and poor quality, no better than chance.\n",
+      "\n",
+      "Gamma 1: Accuracy score train data: 0.92\n",
+      "Gamma 1: Accuracy score test data: 0.84\n",
+      "Gamma 1: Result is, overfitted but much better accuracy with 93% on train and 88% on test.\n",
+      "\n",
+      "Gamma 0.7: Accuracy score train data: 0.92\n",
+      "Gamma 0.7: Accuracy score test data: 0.84\n",
+      "Gamma 0.7: Result is exactly as with gamma=1, overfitted but much better accuracy with 93% on train and 88% on test.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "svm_classifier1.fit(X_train, y_train)\n",
+    "print(\"Gamma 0.001: Accuracy score train data:\", svm_classifier1.score(X_train, y_train))\n",
+    "print(\"Gamma 0.001: Accuracy score test data:\", svm_classifier1.score(X_test, y_test))\n",
+    "print(\"Gamma 0.001: Result is, overfitted and poor quality, no better than chance.\\n\")\n",
+    "\n",
+    "svm_classifier2.fit(X_train, y_train)\n",
+    "print(\"Gamma 1: Accuracy score train data:\", svm_classifier2.score(X_train, y_train))\n",
+    "print(\"Gamma 1: Accuracy score test data:\", svm_classifier2.score(X_test, y_test))\n",
+    "print(\"Gamma 1: Result is, overfitted but much better accuracy with 92% on train and 84% on test.\\n\")\n",
+    "\n",
+    "svm_classifier4.fit(X_train, y_train)\n",
+    "print(\"Gamma 0.7: Accuracy score train data:\", svm_classifier4.score(X_train, y_train))\n",
+    "print(\"Gamma 0.7: Accuracy score test data:\", svm_classifier4.score(X_test, y_test))\n",
+    "print(\"Gamma 0.7: Result is exactly as with gamma=1, overfitted but much better accuracy with 92% on train and 84% on test.\\n\")\n"
    ]
   },
   {
@@ -194,17 +287,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gamma 1 CV: Accuracy score train data: 0.9225\n",
+      "Gamma 1 CV: Accuracy score test data: 0.8899999999999999\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# Yes, the model is overfitting, with every gamma tested. \n",
+    "\n",
+    "#To get a better result, one could use cross-validation (run the split into train/test multiple times and take the mean)\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "\n",
+    "#We no longer use our train test split. The cross validation does that part\n",
+    "results = cross_validate(svm_classifier2, X, y, cv = 5, return_train_score=True)\n",
+    "\n",
+    "print(\"Gamma 1 CV: Accuracy score train data:\",results[\"train_score\"].mean())\n",
+    "print(\"Gamma 1 CV: Accuracy score test data:\",results[\"test_score\"].mean())\n",
+    "\n",
+    "# -> The Training score did not go up, but the test score did. Less overfitting"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -218,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,