From ff1a78a25b82b256d869a5f4b758ca85a9850df5 Mon Sep 17 00:00:00 2001
From: karthik-aru <kartik.amgm@gmail.com>
Date: Sat, 27 Feb 2021 10:31:36 +0100
Subject: [PATCH] Solved

---
 your-code/lab_boston_housing.ipynb | 676 +++++++++++++++++++++++++++--
 your-code/lab_overfitting.ipynb    | 124 +++++-
 2 files changed, 755 insertions(+), 45 deletions(-)

diff --git a/your-code/lab_boston_housing.ipynb b/your-code/lab_boston_housing.ipynb
index 4a25b49..413529b 100644
--- a/your-code/lab_boston_housing.ipynb
+++ b/your-code/lab_boston_housing.ipynb
@@ -35,11 +35,289 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "boston = pd.read_csv(\"../data/boston_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>9.32909</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.713</td>\n",
+       "      <td>6.185</td>\n",
+       "      <td>98.7</td>\n",
+       "      <td>2.2616</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>666.0</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>18.13</td>\n",
+       "      <td>14.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>51.13580</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.597</td>\n",
+       "      <td>5.757</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>1.4130</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>666.0</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>10.11</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>0.01501</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>1.21</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.401</td>\n",
+       "      <td>7.923</td>\n",
+       "      <td>24.8</td>\n",
+       "      <td>5.8850</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>395.52</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>0.02055</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.410</td>\n",
+       "      <td>6.383</td>\n",
+       "      <td>35.7</td>\n",
+       "      <td>9.1876</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>313.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>5.77</td>\n",
+       "      <td>24.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>0.08244</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4.93</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.428</td>\n",
+       "      <td>6.481</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>6.1899</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>379.41</td>\n",
+       "      <td>6.36</td>\n",
+       "      <td>23.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>404 rows × 14 columns</p>\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",
+       "399   9.32909   0.0  18.10   0.0  0.713  6.185   98.7  2.2616  24.0  666.0   \n",
+       "400  51.13580   0.0  18.10   0.0  0.597  5.757  100.0  1.4130  24.0  666.0   \n",
+       "401   0.01501  90.0   1.21   1.0  0.401  7.923   24.8  5.8850   1.0  198.0   \n",
+       "402   0.02055  85.0   0.74   0.0  0.410  6.383   35.7  9.1876   2.0  313.0   \n",
+       "403   0.08244  30.0   4.93   0.0  0.428  6.481   18.5  6.1899   6.0  300.0   \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  \n",
+       "..       ...     ...    ...   ...  \n",
+       "399     20.2  396.90  18.13  14.1  \n",
+       "400     20.2    2.60  10.11  15.0  \n",
+       "401     13.6  395.52   3.16  50.0  \n",
+       "402     17.3  396.90   5.77  24.7  \n",
+       "403     16.6  379.41   6.36  23.7  \n",
+       "\n",
+       "[404 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston"
    ]
   },
   {
@@ -52,11 +330,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your plots here"
+    "# Your plots here\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23fe0e90>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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up7/tsPf0+8mGEEI3L1TlrVO1o0//pdLt+ba7J752kWrv2+6SFIPMIYSO0skt+KoisYYQQkXu6twWfFWR9EMIoaJo6depRn39YaXbx/c4dhghhNBB7GjphxDCoNG1IJJ+W315VL4a+DvfOT9brNtPXD9brOnHPdb3QRWddu7W2WLlrH8P8L2p/54t1vJrj8kW659fylebX8sunS3W3GtfyBbrrOeGZ4s17dhts8UCOOiofPX5rzyw+Rhe/OutLd5JP4QQWikGckMIYRAZCEm/HRdnhRDCYsmuvvVF0lhJD0qaLenoGo9/U9J9kmZKmixpvRzvIZJ+CCFU5C5V3hZF0hDgB8AewAjgQEkjehw2HRhteyRwBXBGjvcQST+EECrqWqjKWx+2A2bbftj268DPgL3LB9i+yfbL6e4dwNo53kMk/RBCqKjLqryVi0OmbXwp1FpAeWrenLSvN4cC1+Z4D/06kCvpKmAdYChwru0Jkg4FjgKeAB4CXrN9uKRVgR8B66anf932bf15fiGEUI96Ls6yPQGY0MvDtQLVHAmQ9GlgNLBT5RdfhP5u6X8ulU0eDRwhaS3gWOD9FCWUNy0dey5wju1tgf2AC2oFLH96Tnz0yf49+xBCKMnVp0/Rsl+ndH9tiobwW0jaDTgG2Mv2azneQ39P2TxC0j7p9jrAZyjKJz8DIOlyYOP0+G7AiGJBLQBWkLS87XnlgOVPz2f23mkAXCoRQlhcZLw4awqwkaQNgMeBTwIHlQ+QtBXwY2Cs7adyvXC/JX1JYygS+fa2X5Z0M/Ag8N5enrJEOvaV/jqnEEJoRq55+rYXSDoc+C0wBLjQ9r2STgSm2p4EnAkMAy5PjeG/2W56RcH+bOmvCDybEv6mFF065wM7SVqZYqWs/SgWTAG4Hjic4o0iaZTtGf14fiGEUJeFXfl6xG1fA1zTY99xpdu7ZXuxkv7s078OWFLSTOAkiilHjwOnAHcCNwD3Ac+n448ARqcLEe4DvtSP5xZCCHXLeXFWu/RbSz8NOuzRc7+kqWkWz5LAlRQtfGzPBT7RX+cTQgjN6orSyg05Po1ID6VI+Fe14RxCCKFuUU+/AbaPzBXrizNWyhWKO07vbXy5fqsddlm2WKcN/0C2WDt9/NxssQBmrZev7HDOcsjz5tycLdYOIz+XLdYCL8wWa+wy6/R9UEXHbfh4tli7nPqPbLEAfrFeZ10/2sndNlVFlc3QkJwJP4TFRc6B3HaJpB9CCBVFn34IIQwiA6B3J5J+CCFUFS39EEIYRGL2ThMkHQ+8CKwA3Gr7hnadSwghVNHV7hPIoO0t/fJlxyGE0MkWDoCWfkvnH0k6Jq0JeQOwSdp3kaT90+3TSmtCntXKcwshhL50ocpbp2pZS1/SNhTlQ7dKrzsNuKv0+HBgH2BT25ZU88qrtPrMeICth4/k3cPW7+czDyGEgjs4mVfVypb+DsCVtl+2/QIwqcfjLwCvAhdI2hd4uWcAKOrp2x5te3Qk/BBCK3XVsXWqVl9e1us0V9sLKBYL/gXwMYoqnSGE0DGMKm+dqpVJ/1ZgH0nLSloe+Gj5QUnDgBVTjemvA6NaeG4hhNCnBXVsnaplSd/2NODnwAyK1vzvexyyPPDrVH//FuAbrTq3EEKoImdLX9LYNLFltqSjazy+jKSfp8fvlLR+jvfQ0imbtk8GTl7EIdu16lxCCKFemVZLRNIQ4AfA7hSLpE+RNMn2faXDDqVYffA9kj4JnE6GNUcW/5JxIYTQIhmnbG4HzLb9sO3XgZ8Be/c4Zm9gYrp9BbCr0mK5zWj7xVnNmHjkWtliffV7s7PF+vtZH8kW6zcnPpMt1gvn7JMt1jlnPJstFsA/v5SvVHPOGvi/n3lhtlhdzzyRLdb888/OFmvP/1sqW6wbDlkxWyyAAy59JVusa/o+pE/1FFwrTy9PJtiekG6vBTxWemwO8L4eId44Ji2k/jzwTmBuXSfdw2Kd9EMIoZXqmYqZEvyEXh6u1WLv+ZlS5Zi6RdIPIYSKFjbfu9JtDlBe/mxtoOdXwe5j5qQ1xVcEmv7qH336IYRQUcaLs6YAG0naQNLSFNUKel6wOgk4JN3eH7jRbn7BxmjphxBCRblm76Q++sOB3wJDgAtt3yvpRGCq7UnA/wCXSJpN0cL/ZI7X7tikL2kMcKTtPdt9LiGEAGQtpJYuRL2mx77jSrdfBT6e7QWTlif9NOVItju5PEUIIbxNLJdYUbqS7FrgJmB7YIakLYBlgStsfy8dNxb4T4opSdNacW4hhFBVru6ddmrlQO4mwMW2twL+1fZoYCSwk6SRkoYC51PU5NkBWL1WEEnjJU2VNPXC2+6rdUgIIfSLhXVsnaqVSf+vtu9Itw+QNA2YDmwGjAA2BR6x/VAaof5JrSDl0sqf++CIlpx4CCFA0dKvunWqVvbpvwQgaQPgSGBb289KuggYmo4ZCF1mIYQBaiAMRLZjnv4KFB8Az0taDdgj7X8A2EDShun+gW04txBC6NVAWESl5bN3bN8taTpwL/AwcFva/2qqVfEbSXOBPwCbt/r8QgihNwNgXfTWJH3bj1JK4LbH9XLcdRR9+yGE0HE6eXGUqjr24qwQQug0A2HQMZJ+CCFU1MmzcqpavJP+y/lqbS+VcUxbq+er8/+68tWt17Bh2WI9q6ezxQLQsktni7XA+WZJ56yBv8TwNbPF8vx8HQ3KWFpAyy2bLRbAswvzrSeRQycP0Fa1eCf9EEJooUj6IYQwiESffgghDCILok8/hBAGj4HQ0m/JFbmSVpJ0WCteK4QQ+ksXrrw1Q9JwSb+T9FD6d+Uax4ySdLukeyXNlPSJKrFbVYZhJSCSfghhsdbCMgxHA5NtbwRMTvd7ehk42PZmwFjgPyWt1FfgViX904ANJc2QdI6kyZKmSZolaW8ASdumT6uhkpZLn15RhiGE0DFcx9akvYGJ6fZE4GNvOxf7z7YfSrefAJ4CVu0rcKv69I8GNrc9Kq3q/g7bL0haBbhD0iTbUyRNAv6dYnGVn9i+p2egVJ9nPMB/7bM9n3vfJi16CyGEwa6eFnw5VyUTbE+o+PTVbD8JYPtJSe/q47W2A5YG/tJX4HYM5Ao4RdKOFD/DtYDVgL8DJ1KsEv8qcEStJ6cf2gSAl0//7EAYVwkhLCYWqHrKKeeqWiTdQO3Foo6p55wkrQFcAhxSZRnadiT9T1F8BdnG9nxJj/JmPf3hwDBgqbTvpTacXwgh1JSzlWl7t94ek/QPSWukVv4aFF03tY5bAfgN8N3SIlWL1Ko+/XnA8un2isBTKeHvDKxXOm4CcCxwKXB6i84thBAqaeFA7iTgkHT7EOBXPQ+QtDRwJcUytJdXDdySpG/7aeA2SfcAo4DRkqZStPofAJB0MLDA9k8pBn63lbRLK84vhBCqaNWUTYocuLukh4Dd030kjZZ0QTrmAGBHYFyaJDND0qi+Arese8f2QX0c8ihwcTp2IfC+/j6nEEKoR6sGEVNDedca+6cCn0+3f0Iva4kvSlyRG0IIFS0YANfkLtZJf961j2SLNdcrZov1ysXXZov1zJB1ssX6ywlvmwHbsKdZJlssgLnXvpAt1thl8v3M5p9/drZYOcshD/3uudli7XTlsdlivTblsWyxANxh6xMu/il/MU/6IYTQSlFaOYQQBhEPgLZ+JP0QQqgoWvohhDCIZJiK2XYNz9OX9HVJ72jgeeMkrVm6f4GkEY2eRwghtMpCXHnrVM1cnPV1oGbSlzRkEc8bB7yR9G1/3vZ9TZxHCCG0RAuvyO03fSZ9SetLekDSxFT6+ApJR1Ak7psk3ZSOe1HSiZLuBLaXdJykKZLukTRBhf2B0cCl6eqxZSXdLGl0inFgKrd8j6QowxBC6Ciu479OVbWlvwlFWdCRwAsUJTyfAHa2vXM6ZjngHtvvs/0H4Dzb29renKJU8p62rwCmAp+yPcr2K90vkLp8Tgd2oSjVsK2kt9WQljRe0lRJUy954omG3nQIITRiULT0k8ds35Zu/wT4UI1jFgK/KN3fWdKdkmZRJPLN+niNbYGbbf/T9gKKoms79jzI9gTbo22P/syaa74tSAgh9JeB0NKvOnun5zuo9Y5eTTVzkDQU+G9gtO3HJB3Pm+WTe9NZl96FEEIPndyCr6pqS39dSdun2wcCf+Ct5ZJ76k7wcyUNA/YvPdbb8+4EdpK0ShoIPhC4peL5hRBCv1toV946VdWW/v3AIZJ+DDwE/BB4HbhW0pOlfn0AbD8n6XxgFkX1zCmlhy8CfiTpFWD70nOelPRt4CaKVv81tt9WQzqEENplIMzTr5r0u2x/qce+/0obALaHlR+0/V3guz0D2f4Fb+37H1N67KfATyueUwghtFQn99VXFVfkhhBCRYOiT9/2o2naZQghDGqtWjlL0nBJv5P0UPp35UUcu4KkxyWdVyX2Yt3S/8ojK2SLddLS87PFGj8tX23+8a/nq8F+rPJNkDrW+c4L4KznhmeLddyGj2eLtef/LZUtljJOUMtZA/+Yu07KFusT23w9WyyAa7fPt85CDi0sr3A0MNn2aZKOTveP6uXYk6hj0kurFkYPIYTFnu3KW5P2Biam2xOBt12oCiBpG2A14PqqgSPphxBCRfV075SrB6RtfB0vtZrtJ6GY2Qi8q+cBkpYAzgb+rZ73sFh374QQQivVM5BrewIwobfHJd0ArF7joWMqvsRhFFPbH1MdXbeR9EMIoaKcUzZt79bbY5L+IWmNdP3SGsBTNQ7bHthB0mHAMGBpSS/aPnpRr9tU906qwPm21bbLlTPrjDeu6gh0CCG0Wqtm7wCTgEPS7UOAt12oavtTtte1vT5wJHBxXwkfok8/hBAqa2EZhtOA3SU9BOye7iNptKQLmgmcI+kv2aPW/lsWVpH0wzSIca+kE0r7t5X0R0l3S/qTpOV7PO8jkm6XtEqGcwwhhKa1qsqm7adt72p7o/TvM2n/VNufr3H8RbYPrxI7R9LvWWv/sB6PH2N7NDCSoqDaSElLAz8HvmZ7S2A3oFxbfx+Keakftj23HKw8Iv7wi3/NcPohhFBNC7t3+k2OpN9Xrf0DJE0DplPU1B9B8UHxpO0pALZfSDX0AXamuAjhI7af7fli5Xr67x62XobTDyGEalo4T7/f5Ej6vdbal7QBxQDDrumbwG8oyi6rxvO6PUxRennjDOcWQgjZREu/UKvWfrcVgJeA5yWtBuyR9j8ArClpWwBJy0vqnj76V2Bf4GJJfa22FUIILbPQXZW3TpUj6XfX2p8JDKeotQ+A7bspunXuBS4Ebkv7Xwc+AfyXpLuB31FaWcv2g8CngMslbZjhHEMIoWmuY+tUTV2cZftRij76nsaUjhnXy3OnAO/vsfuitGF7ei+xQwihLTq526aquCI3hBAqiqTfZheOfS1brIOuzfejuOyodbPFuvLk57LFuvSba2aLtfUp07LFAph27LbZYu1y6j+yxbrhkHxlsrXcstlivTblsWyxcpZD/vld/5ktFsBya+2YLdbrGWJ08qycqhbrpB9CCK0ULf0QQhhEujp4Vk5VkfRDCKGiaOmHEMIgMhD69LNW2ZT0Yh+Pf6dinErHhRBCK8UVufWrmswj6YcQOk6rqmz2p35J+pLWkHSrpBmS7pG0g6TTgGXTvkvTcVdJuiuVXR6f9r3tuBBC6ARdduWtU/VXS/8g4Le2RwFbAjPSii6v2B5l+1PpuM/Z3gYYDRwh6Z29HPeGcmnl/71/Tj+dfgghvF3U3undFOCzko4HtrA9r5fjjki1d+4A1gE26itwubTyZ9+7drYTDiGEvrSqe0fScEm/k/RQ+nflXo5bV9L1ku6XdJ+k9fuK3S9J3/atwI7A48Alkg7ueYykMRSLp2yfFlKZTqnoWgghdJoWdu8cDUy2vREwOd2v5WLgTNvvBbaj9gLqb9FfffrrAU/ZPh/4H2Dr9NB8SUul2ysCz9p+WdKmvLX4Wvm4EELoCC0cyN0bmJhuTwQ+1vMASSOAJW3/DsD2i7Zf7itwf3XvjAFmSJoO7Aecm/ZPAGamAdrrKNbXnQmcRNHFQ43jQgihI9TT0i+PP6ZtfB0vtZrtJwHSv++qcczGwHOSfilpuqQzJQ3pK3DWi7NsD0v/TuTNT8vlBR4AABCySURBVKny40dRLIXYbY+ex/RyXAghtF2XF1Y+1vYEigZsTZJuAFav8dAxFV9iSWAHYCvgbxTrjo+j6F1Z5JNCCCFUkPOiK9u79faYpH9IWsP2k5LWoHZf/Rxguu2H03OuougmX2TSb/XFWSGEsNhq4cLok4BD0u1DgF/VOGYKsLKkVdP9XYD7+gqsxbmWxN7r7pnt5P93m95mldbvw3cqW6wfLbVCtljf6spRUbzwqzPely0WwEFH5avPf95aL2WL9YXH800oe3bhK9li5fy7vXb7vo+parVfz84XDHjp8VuzxVpqlXc3/Ye59vDNK//g5zxzT8OvJ+mdwGXAuhRdNx+3/Yyk0cCXbH8+Hbc7cDYg4C5gfFqOtlfRvRNCCBW1qpFs+2lg1xr7pwKfL93/HTCyntiR9EMIoaJOLq9QVST9EEKoKBZRCSGEQaSTSyZX1fbZO5JuToMTIYTQ0Vo4e6ffREs/hBAqGgh9+g219CWtL+kBSRekevmXStpN0m2pKtx2kpaTdKGkKekS4b3Tc5eV9DNJMyX9HFg27f+ypDNKrzFO0n9leZchhJDBQGjpN9O98x6KmjojgU0pauh/CDiSYuWrY4AbbW8L7AycKWk54MvAy7ZHAicD26R4VwD7luJ/guKy4rco17N49MW/NXH6IYRQn4GwXGIz3TuP2J4FIOleijKgljQLWB9YG9hL0pHp+KEUFxrsCHwfwPbMVHAN2/+U9LCk9wMPAZsAt/V80XI9i5wXZ4UQQl8Wdg3u2TuvlW53le53pbgLgf1sP1h+kiSg14/BnwMHAA8AV7qTvyOFEAadTl77tqr+nL3zW+CrSlle0lZp/63Ap9K+zXnr1WS/pKgbfSA1unZCCKGdYo3cRTsJWIqiLv496T7AD4FhqVvnW8Cfup9g+1mKgkHr2f4TIYTQQQbCQG5D3Tu2HwU2L90f18tjX6zx3FeATy4i9p6NnFMIIfS3gdC9E/P0Qwihoq5BPpAbQgiDyuLfzqe+PqrFdaOoMR2x2hCrk88tYg2MWP0RbyBvba+90yL1LEgcsfLGyh0vYkWsVsQbsAZL0g8hhEAk/RBCGFQGS9KfELHaFit3vIgVsVoRb8BarBdGDyGEUJ/B0tIPIYRAJP0QQhhUIumHEMIgEkk/LBbSAjyhxSQNr7Fvg3acS8hjwCZ9SSMl7SVp3+6twTirSvqOpAlp+ccLJV3YxHl9XNLy6fZ3Jf1S0tYdEOskSUuW7q8g6X8bjLVnWiLzGUkvSJon6YUGY31A0n3A/en+lpL+u8FYZ6T3tZSkyZLmSvp0A3GuljSpt62BeCNq7BtTb5wez99L0llp+2gToa6WtEIp7gjg6gbP6ZIq+yrGOlfSBxp57mA3IJN+SsoXAvsBH01bo9U7fwWsCNwA/Ka0NepY2/MkfQj4f8BEinLT7Y61JHBn+rD8F2AKcFeDsf4TOAR4p+0VbC9ve4W+ntSLcyje29MAtu+mWH2tEf9i+wWK34U5wMbAvzUQ5yzgbOAR4BXg/LS9CNzTQLzLJB2lwrJpbehTG4gDgKRTga9RlCm/Dzgi7WvEKRSJf5ikbYDLgbo/KJPNepznEN5cLrVe04DvSpot6UxJoxuMM/i0uw5Ef2zAfRljzch8btPTv6cCB5X3tTNWeu5uFEnsCeA9TcS5CVgi08/rzp7vC7i7wVj3pn/PB8Y2Eys999Yq+yrEWQ44D7id4kPj2838/ICZ5ecDQ4CZTcT7GPBHYBawUQPP/zYwD1gAvJC2eRQf5Kc2+fsxHPgCMBl4KMfv3EDfBmqVzdsljbB9X4ZYv5b0YdvXZIgF8LikH1Mk2NMlLUPj37iyxZK0I8VC9ycCWwDnSfqc7ScaCPct4BpJt1BaVtP2fzQQ67H0Nd6SlgaOIHX1NOBqSQ9QfLAdJmlV4NUGYwGsKundth+GN/q6V20gzvx0TstSrCX9iO1ma/iuBDyTbq9Y75PTt43yRTwrAA9TrIaH7SOqxrJ9KnCqpFNtf7vec+nDe4BNKdblzvH3PuANyIuzUgK7Gvg7RdIRYNsjF/nE2rHmAe+g+MN8vRSroe4KSe8AxgKzbD8kaQ1gC9vXtznWn4Bx3R+UaQzkFNubNhDreoqujlkUayYDYPuEBmKtQvFhtBvFz/564Gu2n643Voq3MvCC7YXp57eC7b83GGssxZWgD6dd6wNftP3bOuPcTdGNeBLwTuDHwHzb+zd4XgcCp1F84xJFd9i3bf+sjhiHLOpx2xMbPLeVgY0oPty6Y93aQJzTgX2BvwCXAb+0/Vwj5zTYDNSkPxv4Jm9POn9tINaNwNm2f1Pad77tLzR4buvW2m/7b43ESzHfxVv/iOqOJWmI7YU99r2zkeQqaartjuljlbSL7Rt7G8y3/csmYi9D0dIEeMD2a4s6vpcYo21P7bHvM7YbGuRMz18D2JYi6d/ZxAfbcsCr3b8bqR9+GdsvNxDr8xRjDWsDM4D3A7fb3qWBWF8CrrA9t97nDnYDNenf2MgvUi+xHgYeAybbPjHtm2a70Vkysyi+NosiUW8APGh7s0U+sXasvSgGFNcEngLWpUg8jcRaETieolVo4BbgRNvPNxDrNODGRr5x1Ij1/Rq7nwem2v5VxRjH2z4+zUbq/tm/8a/tzzVxfpsDI3jrh+7FDcZq6sNb0qa2H+htBpftaQ2c0x3AbrZfTPeHAdfbrnvmTPrd3xa4w/YoSZsCJ9j+RB0xFvl318h7HGwGap/+A5J+StHFU+5TbqRF9xywK/B9SVfT+MyF7nPYonw//RK/bS3hik6iaC3dYHsrSTsDBzYY60KKQcSPp/ufAf6X4it0ZZJE0af/LUmvUXSLNdMlNpSiJX15ur8fcC9wqKSdbX+9Qox5kr5J8f66kz00uRCSpO8BYyiS/jXAHsAfgLqSfppS+R+8+eG9HsW4Rb0f3t+kqCt/do3HDDTSEBranfABbL+YusUa8artVyUhaZn0AbVJnTG639tQYDRwN8X/z5HAncCHGjy3QWOgJv1lKZL9v5T2GWgk6cv2AoqBv3EUf9QrN32G3SdlT5O0bYNPn2/7aUlLSFrC9k2pr7MRG9rer3T/BEkz6g1i25JmNPpNqIb3ALuk/wdI+iFFv/7uFN13VQxL/25C0dL8FUWi+ChQd39yyf7AlhQziz4raTXgggbi/DsZPrxtdy8ksofttwxQSxpa4ylVvCRp6+4WdJq2+UqDseZIWgm4CvidpGcpZopVZnvndB4/o1gta1a6vzlwZIPnNagMyKRv+7MZw/2oFPei9BX1K40GSy3ObksAWwP/bDDcc+nr9q3ApZKeopgW14hXJH3I9h/SeX6Qxv+4b5e0re0pDT6/bC2KKY3d3UzLAWumgdhK/efdA8hpgHlr2/PS/eN58xtEI1613SVpgYoLmJ4C3t1AnJwf3lBMr+z5oVtrXxVfBy6X1J2c1wAqd8eU2d4n3Txe0k0Us4qubSQWsGl3wk+x75E0qsFYg8qASvqSvmX7jBrTzQDqmmZWes6Pe9y/C2i4DxhYvnR7AcWFXr9oMNbeFFMOvwF8iuKP6MQGY30ZmJj69gGepbjAqhE7A1+S9CjwEk3MngLOAGZIupk3Z6KckgYYb6gz1roUM7C6vU4x46ZuqRtrZmq5nk9xIduLwJ8aCFfrw3t+A+e0OsWH5LKStuLNbqwVKGag1c32lNT3vkmK94Dtus8tnd8ltj+T4t7SvY+iK7Fe90u6APgJxd/6p2l8Ku+gMqAGciV91PbVvU03a3Sa2WCQZqHsD2xIMcf7eYpEXfeHiKT1au1vZPZUircmRWJ4gKKlP6fBaX7HAAcAV1Ikin2An6d55I2c1122t0m316eY/jmzgThnU1wZvARvfnhvafvQOuMcAoyj6OsuzwaaB1zU6CylXIPVPSdApJlAs2y/rQxFhVhDKRoq3Vdn3wr8sGe3Vni7AZX04Y1fpNNsN3J5fb9Jg8C9/rBt79VAzH2B04F3UbTCGh4wlXQdxaD1NOCNqZu2aw0KtkzOaX4p3tbADunurbanN3FuP6BIpk11Y9WaDSZpZoPfjJC0n+1Gvz32jFVzsLqeawgkfRv4DsVYW/dUT1F805rgBi/YkrQssK7tBxt5/mA14JI+5J2ymYukndLNfYHVKb6WQjFg96jt7zQQczbwUdtNf62VdI/tzZuNk1uOaX79RUUhuI2Bv9JAN5akLwOHUXy7ml16aHngNtsNzxST9BGK2T/l1nkj39pm8eZg9Zbdg9W26y7ipoxX5KbpymcCS9veIPXnn9hI42mwGVB9+iXTVVQ7vJzijxFo7iKcZpX6ME+yXS4YdrWkRmeQ/CNHwk/+KGmL8uBYh8gxza+/7NHk839KMZB5KnB0af8828/UfkrfJP2Iog9/Z4rZRPvT2FgDwCuZBquhKGmynO2XVFQ33Ro4t8Fuv+8B2wE3A9iekbrYQh8GatIfTlHMqdzab3TKZm5N12vRm1eWTpX0c4opcM1ej/AhYJykR2iydEVmTU/z6y+NjlGUnv88xdhJo9dW9OYDtkemLqIT0phBo7/7UzMNVkNRAXZLSVtSXMvxPxTXNOy0yGfVtsD288V4eqjHQE36S1DUZ3kO3qj30da+6ZJvADeruNIXUr2WOmN0f7U2RR9pjusRmm219otepvld18ZTWhx0D2a+nAbBn6a48rtutg9LN3+Uxn0aGqxOFqTrOPamaOH/T2+TLiq4R9JBwBBJG1EU4vtjg7EGlYGa9Ee6VHzJ9rNpClvb2b4u/ZI2XK+l+zoESRPJ9OHWbKu1Fbq7yEKfrk6t8zMpBuZN0VKvTIsod1C+WKtO89Kg7qeBHdOki6UaiAPwVeAYim+lPwV+S3GFeujDQE36S0ha2fazACqWfOuk97oNRQt/SYqvu43Wa+nYD7fQHpKWoKgT9RzwC0m/piilUG8NpXLjoTzbo7tuUSMTJT4BHAQcavvvKooPntlAHChmE42g+BtakuKalb0oyjGERRios3cOpli44QqKX9ADgJPdRNXCXNLFKBtSTD/snhrpRi4cU1GSd0yPD7db3KO+TxhcJN1ue/tMsZalmGH0IYq/pd/TAfPhJT1IUXbhHpqspDvYDMikD6BiLc9dKFomk51nQZWmSbofGOEMP/hO/nAL7SPpBIrVs37Z7O+ZpMsoVrq6NO06EFjJ9gF1xJhH7WtUmrmu5A+2o7haAwZs0u9Uki4HjrD9ZKZ4HfnhFtonJdnlKMp8vEpzyfVu21v2ta/VJO1K8QE0meZnrg0qndTPPVisAtynYqWq8i9rQxeVpCQfiT68wfbyfR9V2XRJ77d9B4Ck9wG3ZYzfqM9STIZYije7dzplWnZHi5Z+i5WuzH2LmJkScpE02faufe3rI0b3Yj9LURRb+1u6vx5wX7uv3pY0K8auGhMt/RaL5B76SypC9g5glTR9t1xlc806w+2Z89z6wR2SRkR3Zv2ipd8i3QNPNQa1mlpoPYRukr5GUf9+TeDx0kPzgPNtn9eWE+sHaULEhkCnXUHe8SLphzDASPoqsDQdNs0yp9zluweTSPohDDBphtjzNDHNMgxckfRDGGA6dZpl6AxLtPsEQgjZTZf0/u47HTTNMnSAaOmHMMCkQc7uaZZQrA18P8V89hjsHOQi6YcwwPQ2yNktBjsHt0j6IYQwiESffgghDCKR9EMIYRCJpB9CCINIJP0QQhhE/j/6RQmS+Rqq6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(boston.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a39124250>"
+      ]
+     },
+     "execution_count": 23,
+     "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": [
+    "sns.scatterplot(data = boston, x=\"crim\", y=\"medv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2c751710>"
+      ]
+     },
+     "execution_count": 31,
+     "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": [
+    "sns.scatterplot(x=X_cols[\"age\"], y=y_col)"
    ]
   },
   {
@@ -68,11 +443,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "#Yeah kinda although I am much more happier using the heatmaps instead of the pairplot.\n",
+    "# Anyway according to the heatmap there doesn't seem to be a high collinearity between any two columns"
    ]
   },
   {
@@ -84,11 +461,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# please see my answer above"
    ]
   },
   {
@@ -102,11 +480,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    404.000000\n",
+       "mean      22.312376\n",
+       "std        8.837019\n",
+       "min        5.000000\n",
+       "25%       17.100000\n",
+       "50%       21.400000\n",
+       "75%       25.000000\n",
+       "max       50.000000\n",
+       "Name: medv, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "boston[\"medv\"].describe()"
    ]
   },
   {
@@ -128,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +535,9 @@
     "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",
+    "    \n",
+    "    return r2_score(y_true, y_predict)"
    ]
   },
   {
@@ -150,11 +550,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = boston.drop([\"medv\"], axis=1)\n",
+    "y = boston[\"medv\"]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=124)"
    ]
   },
   {
@@ -177,11 +583,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Five separate RFR here with the given max depths\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "reg = RandomForestRegressor()\n",
+    "\n",
+    "param_grid = {\n",
+    "    \"max_depth\":[1, 2, 4, 6, 8, 10]\n",
+    "}\n",
+    "\n",
+    "RFR_search = GridSearchCV(estimator=reg,\n",
+    "                         param_grid=param_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=None, error_score=nan,\n",
+       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
+       "                                             criterion='mse', max_depth=None,\n",
+       "                                             max_features='auto',\n",
+       "                                             max_leaf_nodes=None,\n",
+       "                                             max_samples=None,\n",
+       "                                             min_impurity_decrease=0.0,\n",
+       "                                             min_impurity_split=None,\n",
+       "                                             min_samples_leaf=1,\n",
+       "                                             min_samples_split=2,\n",
+       "                                             min_weight_fraction_leaf=0.0,\n",
+       "                                             n_estimators=100, n_jobs=None,\n",
+       "                                             oob_score=False, random_state=None,\n",
+       "                                             verbose=0, warm_start=False),\n",
+       "             iid='deprecated', n_jobs=None,\n",
+       "             param_grid={'max_depth': [1, 2, 4, 6, 8, 10]},\n",
+       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
+       "             scoring=None, verbose=0)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RFR_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean_fit_time': array([0.15618834, 0.16395273, 0.17513647, 0.19443407, 0.26387148,\n",
+       "        0.243401  ]),\n",
+       " 'std_fit_time': array([0.01305163, 0.01639515, 0.00326966, 0.00454551, 0.0252861 ,\n",
+       "        0.01787362]),\n",
+       " 'mean_score_time': array([0.00955462, 0.00965862, 0.00880117, 0.009021  , 0.01307921,\n",
+       "        0.01105127]),\n",
+       " 'std_score_time': array([0.00159988, 0.0016749 , 0.000288  , 0.00026678, 0.00158436,\n",
+       "        0.00141289]),\n",
+       " 'param_max_depth': masked_array(data=[1, 2, 4, 6, 8, 10],\n",
+       "              mask=[False, False, False, False, False, False],\n",
+       "        fill_value='?',\n",
+       "             dtype=object),\n",
+       " 'params': [{'max_depth': 1},\n",
+       "  {'max_depth': 2},\n",
+       "  {'max_depth': 4},\n",
+       "  {'max_depth': 6},\n",
+       "  {'max_depth': 8},\n",
+       "  {'max_depth': 10}],\n",
+       " 'split0_test_score': array([0.62145039, 0.78789564, 0.86452857, 0.86903376, 0.8675503 ,\n",
+       "        0.8845941 ]),\n",
+       " 'split1_test_score': array([0.59856109, 0.79405093, 0.8860993 , 0.8984155 , 0.89204173,\n",
+       "        0.90500374]),\n",
+       " 'split2_test_score': array([0.42474958, 0.58291323, 0.69469556, 0.74936489, 0.73746308,\n",
+       "        0.75231451]),\n",
+       " 'split3_test_score': array([0.67139195, 0.69310164, 0.73389378, 0.77152452, 0.77404786,\n",
+       "        0.79866399]),\n",
+       " 'split4_test_score': array([0.48417609, 0.67286357, 0.71961886, 0.72960593, 0.73053738,\n",
+       "        0.73495977]),\n",
+       " 'mean_test_score': array([0.56006582, 0.706165  , 0.77976721, 0.80358892, 0.80032807,\n",
+       "        0.81510722]),\n",
+       " 'std_test_score': array([0.09133805, 0.0785805 , 0.07931001, 0.06740469, 0.06699794,\n",
+       "        0.06862468]),\n",
+       " 'rank_test_score': array([6, 5, 4, 2, 3, 1], dtype=int32)}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RFR_search.cv_results_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_depth_l = np.array([1, 2, 4, 6, 8, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Five separate RFR here with the given max depths"
+    "scores = RFR_search.cv_results_[\"mean_test_score\"]"
    ]
   },
   {
@@ -193,13 +718,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a300b5210>"
+      ]
+     },
+     "execution_count": 61,
+     "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": [
-    "# 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",
+    "sns.lineplot(x=max_depth_l, y=scores)"
    ]
   },
   {
@@ -211,11 +760,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# after a certain depth the increase in model performance is marginal. \n",
+    "#In this case, after max depth = 4 there is a marginal increase"
    ]
   },
   {
@@ -232,7 +783,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# Yes with a maxdepth of 1 the model suffers from high variance \n",
+    "# whereas at a maxdepth of 10 it will suffer from high bias."
    ]
   },
   {
@@ -245,11 +798,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
+       "                      max_depth=4, max_features='auto', max_leaf_nodes=None,\n",
+       "                      max_samples=None, min_impurity_decrease=0.0,\n",
+       "                      min_impurity_split=None, min_samples_leaf=1,\n",
+       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
+       "                      random_state=None, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your response here\n",
+    "reg_optimal = RandomForestRegressor(max_depth=4)\n",
+    "\n",
+    "reg_optimal.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "y_train_preds = reg_optimal.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9159119501750652"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_train_preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8269369396882951"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test_preds = reg_optimal.predict(X_test)\n",
+    "\n",
+    "r2_score(y_test, y_test_preds)"
    ]
   },
   {
@@ -292,7 +916,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/your-code/lab_overfitting.ipynb b/your-code/lab_overfitting.ipynb
index 9739877..6a46430 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": [
@@ -56,11 +56,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "classifiers = [SVC(gamma=.001), SVC(gamma=1), SVC(gamma=20)]"
    ]
   },
   {
@@ -72,9 +75,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
@@ -86,7 +100,8 @@
     "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
     "                     np.arange(y_min, y_max, h))\n",
     "cm = plt.cm.RdBu\n",
-    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\nnames = ['gamma = 0.001', 'gamma = 1', 'gamma = 20']\n",
+    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "names = ['gamma = 0.001', 'gamma = 1', 'gamma = 20']\n",
     "\n",
     "# iterate over classifiers\n",
     "for name, clf in zip(names, classifiers):\n",
@@ -133,7 +148,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# The gamma of 20 fits best, but it seeems to fit too well, in other words overfitted.\n",
+    "# The optimal gamma is at 1 where there are some errors but overall it performs better"
    ]
   },
   {
@@ -146,11 +163,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "    decision_function_shape='ovr', degree=3, gamma=0.7, kernel='rbf',\n",
+       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "    tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "svc_test1 = SVC(gamma=.7)\n",
+    "svc_test1.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.93"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "svc_test1.score(X,y)"
    ]
   },
   {
@@ -162,11 +215,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.95"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "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, test_size=.2, random_state=124)\n",
+    "\n",
+    "svc_test2 = SVC(gamma=20)\n",
+    "svc_test2.fit(X_train, y_train)\n",
+    "svc_test2.score(X_test, y_test)"
    ]
   },
   {
@@ -178,11 +249,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.95"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "svc_test3 = SVC(gamma=.5)\n",
+    "svc_test3.fit(X_train, y_train)\n",
+    "svc_test3.score(X_test, y_test)"
    ]
   },
   {
@@ -194,11 +279,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "# A gamma of 0.5 works just as well"
    ]
   }
  ],
@@ -218,7 +304,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,