From 51af412d7e56949689b524155380f178e33b012f Mon Sep 17 00:00:00 2001
From: Giada Sartori <sartori.giada89@gmail.com>
Date: Thu, 26 May 2022 20:17:43 +0100
Subject: [PATCH] done

---
 your-code/lab_boston_housing.ipynb | 1050 +++++++++++++++++++++++++++-
 your-code/lab_overfitting.ipynb    |  209 +++++-
 2 files changed, 1202 insertions(+), 57 deletions(-)

diff --git a/your-code/lab_boston_housing.ipynb b/your-code/lab_boston_housing.ipynb
index 3176602..824cf67 100644
--- a/your-code/lab_boston_housing.ipynb
+++ b/your-code/lab_boston_housing.ipynb
@@ -8,6 +8,23 @@
     "## Predicting Boston Housing Prices"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import imblearn #install the library directly in anaconda (conda install -c conda-forge imbalanced-learn)\n",
+    "\n",
+    "# Import libraries from sklearn package\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,11 +52,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "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",
+       "  </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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "boston_house = pd.read_csv('/Users/GiadaSartori/Documents/IRONHACK/Lab/Week7/lab-problems-in-ml/data/boston_data.csv')\n",
+    "boston_house.head()"
    ]
   },
   {
@@ -51,11 +217,171 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "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_house.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(404, 14)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston_house.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston_house.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([21.7, 19.6, 20.3, 15.4, 20.5, 34.9, 26.2, 21.6, 14.1, 17. , 10.4,\n",
+       "       23.3, 21. , 22.2,  8.7, 23.7, 12. , 21.5,  9.5, 23. , 20.8, 29.4,\n",
+       "       16.5, 16.2, 18.4, 31.1, 21.9, 18.6, 29.1, 36.2, 17.8,  5. , 23.1,\n",
+       "       50. , 29. , 12.7, 35.1, 19.9, 13.8, 23.9, 20.2, 22.6, 19.4, 15.6,\n",
+       "       43.1, 13.4, 13.1, 11.3, 31. , 42.3, 44.8, 25. , 35.2, 24. , 18.2,\n",
+       "       10.2,  7.2, 28.2, 22.9, 34.7, 22. , 22.4, 17.1, 24.1, 19.8, 33.4,\n",
+       "        8.5, 23.2, 16.8, 20.6, 16.4, 43.5, 20. , 13.5, 18.5, 23.4, 35.4,\n",
+       "       19.3, 12.6, 26.6, 32.9, 39.8, 29.6, 30.1, 14. , 24.5, 32.7, 41.3,\n",
+       "       28.7, 19. , 34.6, 20.1, 23.8, 15.1, 16.1, 21.8, 13.9, 30.8, 11.8,\n",
+       "       28.4, 18.9, 21.4, 26.4, 37.2, 24.8, 14.5, 12.3, 17.2, 10.5,  7. ,\n",
+       "       24.7, 37.9, 19.1, 24.4, 37.6, 29.8, 12.1, 27.5,  8.8,  8.4, 22.7,\n",
+       "       11.9, 21.1, 15.2, 18.7, 26.5, 11.5, 30.3, 18.8, 28.6, 27. , 33.1,\n",
+       "       26.7, 13.3,  9.7, 36.5, 33.3, 24.6, 11.7, 13. , 15.3,  8.1, 16.7,\n",
+       "       23.6, 32. , 17.3, 15. , 33.2, 20.9, 21.2, 22.3, 19.7, 20.7, 17.9,\n",
+       "       10.9, 25.1, 17.4, 24.3, 14.9, 17.5, 22.8, 14.2, 46.7, 28.1, 14.8,\n",
+       "       17.6, 22.5, 41.7,  8.3, 25.3, 14.6, 19.5, 31.5, 31.7, 42.8, 23.5,\n",
+       "       30.7, 12.5, 20.4, 22.1, 27.1,  6.3, 28.5, 13.6, 28. , 36. , 32.5,\n",
+       "        9.6, 31.2, 33. , 29.9, 15.7, 11. ,  5.6, 30.5, 14.3, 14.4, 48.3,\n",
+       "       24.2, 16.6])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your plots here"
+    "# Check which are the unique values of our target, label for prediction\n",
+    "\n",
+    "boston_house['medv'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      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  \n",
+      "0     19.2  376.94   9.88  \n",
+      "1     19.7  396.90   9.22  \n",
+      "2     18.4  396.24   9.97  \n",
+      "3     14.7  351.85  21.45  \n",
+      "4     16.8  393.97   9.43  \n",
+      "0    21.7\n",
+      "1    19.6\n",
+      "2    20.3\n",
+      "3    15.4\n",
+      "4    20.5\n",
+      "Name: medv, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Assign column/s to features and labels\n",
+    "\n",
+    "features = boston_house.drop(columns=['medv'])\n",
+    "labels = boston_house['medv']\n",
+    "\n",
+    "print(features.head())\n",
+    "print(labels.head())"
    ]
   },
   {
@@ -67,11 +393,277 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "    .dataframe thead th {\n",
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+       "</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",
+       "  </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",
+       "\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  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your response here"
+    "boston_house.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>rm</th>\n",
+       "      <th>age</th>\n",
+       "      <th>dis</th>\n",
+       "      <th>tax</th>\n",
+       "      <th>ptratio</th>\n",
+       "      <th>lstat</th>\n",
+       "      <th>medv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>crim</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.172226</td>\n",
+       "      <td>0.349288</td>\n",
+       "      <td>-0.376493</td>\n",
+       "      <td>0.565047</td>\n",
+       "      <td>0.292092</td>\n",
+       "      <td>0.428940</td>\n",
+       "      <td>-0.400956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rm</th>\n",
+       "      <td>-0.172226</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.210589</td>\n",
+       "      <td>0.178700</td>\n",
+       "      <td>-0.241733</td>\n",
+       "      <td>-0.323330</td>\n",
+       "      <td>-0.578025</td>\n",
+       "      <td>0.683541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>0.349288</td>\n",
+       "      <td>-0.210589</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.734915</td>\n",
+       "      <td>0.503830</td>\n",
+       "      <td>0.231820</td>\n",
+       "      <td>0.602001</td>\n",
+       "      <td>-0.390863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dis</th>\n",
+       "      <td>-0.376493</td>\n",
+       "      <td>0.178700</td>\n",
+       "      <td>-0.734915</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.526938</td>\n",
+       "      <td>-0.206016</td>\n",
+       "      <td>-0.500912</td>\n",
+       "      <td>0.264876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tax</th>\n",
+       "      <td>0.565047</td>\n",
+       "      <td>-0.241733</td>\n",
+       "      <td>0.503830</td>\n",
+       "      <td>-0.526938</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.465997</td>\n",
+       "      <td>0.542732</td>\n",
+       "      <td>-0.495792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ptratio</th>\n",
+       "      <td>0.292092</td>\n",
+       "      <td>-0.323330</td>\n",
+       "      <td>0.231820</td>\n",
+       "      <td>-0.206016</td>\n",
+       "      <td>0.465997</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.351408</td>\n",
+       "      <td>-0.506313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lstat</th>\n",
+       "      <td>0.428940</td>\n",
+       "      <td>-0.578025</td>\n",
+       "      <td>0.602001</td>\n",
+       "      <td>-0.500912</td>\n",
+       "      <td>0.542732</td>\n",
+       "      <td>0.351408</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.742695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>medv</th>\n",
+       "      <td>-0.400956</td>\n",
+       "      <td>0.683541</td>\n",
+       "      <td>-0.390863</td>\n",
+       "      <td>0.264876</td>\n",
+       "      <td>-0.495792</td>\n",
+       "      <td>-0.506313</td>\n",
+       "      <td>-0.742695</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             crim        rm       age       dis       tax   ptratio     lstat  \\\n",
+       "crim     1.000000 -0.172226  0.349288 -0.376493  0.565047  0.292092  0.428940   \n",
+       "rm      -0.172226  1.000000 -0.210589  0.178700 -0.241733 -0.323330 -0.578025   \n",
+       "age      0.349288 -0.210589  1.000000 -0.734915  0.503830  0.231820  0.602001   \n",
+       "dis     -0.376493  0.178700 -0.734915  1.000000 -0.526938 -0.206016 -0.500912   \n",
+       "tax      0.565047 -0.241733  0.503830 -0.526938  1.000000  0.465997  0.542732   \n",
+       "ptratio  0.292092 -0.323330  0.231820 -0.206016  0.465997  1.000000  0.351408   \n",
+       "lstat    0.428940 -0.578025  0.602001 -0.500912  0.542732  0.351408  1.000000   \n",
+       "medv    -0.400956  0.683541 -0.390863  0.264876 -0.495792 -0.506313 -0.742695   \n",
+       "\n",
+       "             medv  \n",
+       "crim    -0.400956  \n",
+       "rm       0.683541  \n",
+       "age     -0.390863  \n",
+       "dis      0.264876  \n",
+       "tax     -0.495792  \n",
+       "ptratio -0.506313  \n",
+       "lstat   -0.742695  \n",
+       "medv     1.000000  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston_house[['crim', 'rm', 'age', 'dis', 'tax', 'ptratio', 'lstat', 'medv']].corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comments from Giada\n",
+    "\n",
+    "- CRIM negative correlated\n",
+    "- RM positive correlated\n",
+    "- AGE negative correlated\n",
+    "- DIS positive correlated\n",
+    "- TAX negative correlated\n",
+    "- pratio negative correlated\n",
+    "- Istat negative correlated"
    ]
   },
   {
@@ -83,11 +675,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your response here"
+    "#1. First heatmap\n",
+    "\n",
+    "corr = boston_house.corr()\n",
+    "ax = sns.heatmap(\n",
+    "    corr, \n",
+    "    vmin=-1, vmax=1, center=0,\n",
+    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
+    "    square=True\n",
+    ")\n",
+    "ax.set_xticklabels(\n",
+    "    ax.get_xticklabels(),\n",
+    "    rotation=45,\n",
+    "    horizontalalignment='right'\n",
+    ")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/vy/72w35wd12ms4_h7n214795k40000gn/T/ipykernel_23749/673952724.py:7: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype=np.bool)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x1008 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#2. Second heatmap\n",
+    "\n",
+    "boston_data = pd.concat([features, labels], axis=1)\n",
+    "\n",
+    "corr = boston_data.corr()\n",
+    "\n",
+    "mask = np.zeros_like(corr, dtype=np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(14, 14))\n",
+    "\n",
+    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
+    "\n",
+    "sns.heatmap(corr, mask=mask,  vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -100,11 +766,226 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "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>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.00000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</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>0.556710</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>0.254290</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>0.006320</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.460000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.392000</td>\n",
+       "      <td>3.56100</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>1.169100</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>187.000000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>1.730000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.082382</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.453000</td>\n",
+       "      <td>5.90275</td>\n",
+       "      <td>45.800000</td>\n",
+       "      <td>2.087875</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>281.000000</td>\n",
+       "      <td>17.375000</td>\n",
+       "      <td>374.710000</td>\n",
+       "      <td>7.135000</td>\n",
+       "      <td>17.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.253715</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.795000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.538000</td>\n",
+       "      <td>6.23050</td>\n",
+       "      <td>76.600000</td>\n",
+       "      <td>3.207450</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>330.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>391.065000</td>\n",
+       "      <td>11.265000</td>\n",
+       "      <td>21.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.053158</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>18.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.631000</td>\n",
+       "      <td>6.62925</td>\n",
+       "      <td>94.150000</td>\n",
+       "      <td>5.222125</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>666.000000</td>\n",
+       "      <td>20.200000</td>\n",
+       "      <td>396.007500</td>\n",
+       "      <td>16.910000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>88.976200</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>27.740000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.871000</td>\n",
+       "      <td>8.78000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>12.126500</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>711.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>34.370000</td>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             crim          zn       indus        chas         nox         rm  \\\n",
+       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.00000   \n",
+       "mean     3.730912   10.509901   11.189901    0.069307    0.556710    6.30145   \n",
+       "std      8.943922   22.053733    6.814909    0.254290    0.117321    0.67583   \n",
+       "min      0.006320    0.000000    0.460000    0.000000    0.392000    3.56100   \n",
+       "25%      0.082382    0.000000    5.190000    0.000000    0.453000    5.90275   \n",
+       "50%      0.253715    0.000000    9.795000    0.000000    0.538000    6.23050   \n",
+       "75%      4.053158   12.500000   18.100000    0.000000    0.631000    6.62925   \n",
+       "max     88.976200   95.000000   27.740000    1.000000    0.871000    8.78000   \n",
+       "\n",
+       "              age         dis         rad         tax     ptratio       black  \\\n",
+       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
+       "mean    68.601733    3.799666    9.836634  411.688119   18.444554  355.068243   \n",
+       "std     28.066143    2.109916    8.834741  171.073553    2.150295   94.489572   \n",
+       "min      2.900000    1.169100    1.000000  187.000000   12.600000    0.320000   \n",
+       "25%     45.800000    2.087875    4.000000  281.000000   17.375000  374.710000   \n",
+       "50%     76.600000    3.207450    5.000000  330.000000   19.000000  391.065000   \n",
+       "75%     94.150000    5.222125   24.000000  666.000000   20.200000  396.007500   \n",
+       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
+       "\n",
+       "            lstat        medv  \n",
+       "count  404.000000  404.000000  \n",
+       "mean    12.598936   22.312376  \n",
+       "std      6.925173    8.837019  \n",
+       "min      1.730000    5.000000  \n",
+       "25%      7.135000   17.100000  \n",
+       "50%     11.265000   21.400000  \n",
+       "75%     16.910000   25.000000  \n",
+       "max     34.370000   50.000000  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "boston_house.describe()"
    ]
   },
   {
@@ -126,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +1016,9 @@
     "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)"
    ]
   },
   {
@@ -143,16 +1026,23 @@
    "metadata": {},
    "source": [
     "### Implementation: Shuffle and Split Data\n",
-    "Split the data into the testing and training datasets. Shuffle the data as well to remove any bias in selecting the traing and test. "
+    "Split the data into the testing and training datasets. Shuffle the data as well to remove any bias in selecting the traing and test. \n",
+    "\n",
+    "#### Steps:\n",
+    "1. Do the train-test split, with a default test size of 25% (since it is not specified)\n",
+    "2. Choose a model, and create an empty model to train\n",
+    "3. Evaluate the model with score, then do it with prediction (just in case)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, labels)"
    ]
   },
   {
@@ -175,11 +1065,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Five separate RFR here with the given max depths"
+    "from sklearn.ensemble import RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(max_depth=10)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Five separate RFR here with the given max depths\n",
+    "\n",
+    "forest_2 = RandomForestRegressor(max_depth=2)\n",
+    "forest_4 = RandomForestRegressor(max_depth=4)\n",
+    "forest_6 = RandomForestRegressor(max_depth=6)\n",
+    "forest_8 = RandomForestRegressor(max_depth=8)\n",
+    "forest_10 = RandomForestRegressor(max_depth=10)\n",
+    "\n",
+    "forest_2.fit(X_train, y_train)\n",
+    "forest_4.fit(X_train, y_train)\n",
+    "forest_6.fit(X_train, y_train)\n",
+    "forest_8.fit(X_train, y_train)\n",
+    "forest_10.fit(X_train, y_train)"
    ]
   },
   {
@@ -191,13 +1113,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "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",
+    "\n",
+    "#plt.bar(x, height = h, color = c)\n",
+    "\n",
+    "test_score = [forest_2.score(X_test, y_test), forest_4.score(X_test, y_test), forest_6.score(X_test, y_test), forest_8.score(X_test, y_test), forest_10.score(X_test, y_test)]\n",
+    "train_score = [forest_2.score(X_train, y_train), forest_4.score(X_train, y_train), forest_6.score(X_train, y_train), forest_8.score(X_train, y_train), forest_10.score(X_train, y_train)]\n",
+    "\n",
+    "depth = [2,4,6,8,10] # --> x\n",
+    "\n",
+    "color_test_score = 'orange'\n",
+    "color_train_score = 'green'\n",
+    "\n",
+    "plt.bar(depth, test_score, color = color_test_score)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.bar(depth, train_score, color = color_train_score)\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -208,12 +1171,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your response here"
+    "# Comments from Giada:\n",
+    "\n",
+    "We see that the bigger the depth of the training set, the more accurate and better the accuracy of the model\n"
    ]
   },
   {
@@ -238,7 +1201,9 @@
    "metadata": {},
    "source": [
     "### Best-Guess Optimal Model\n",
-    "What is the max_depth parameter that you think would optimize the model? Run your model and explain its performance."
+    "What is the max_depth parameter that you think would optimize the model? Run your model and explain its performance.\n",
+    "\n",
+    "Max_depth indicates how deep the tree can be. The deeper the tree, the more splits it has and it captures more information about the data."
    ]
   },
   {
@@ -264,19 +1229,32 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your response here"
+    "### Comments\n",
+    "\n",
+    "- The data from 1978 is not quite meaningful for a prediction since it is too long backdated. It is rather useful to compare how prices have changed compared to today's average price\n",
+    "- The data should have been updated and considered prices dating back to 2010 at least. , only for a comparison to todays prices\n",
+    "- Also the indicators included in the dataset for prediction of the current average price of houses should be modernized:\n",
+    "    - removal of black for discriminatory reasons\n",
+    "    - number of schools around the area is relevant\n",
+    "    - distance to the companies employing the most amount of inhabitants can be interesting "
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "interpreter": {
+   "hash": "308f656801bdbd0b2d403197f25506e92fef2516366746747ba806d876d6bcae"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.12 ('ironhack')",
    "language": "python",
    "name": "python3"
   },
@@ -290,7 +1268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/your-code/lab_overfitting.ipynb b/your-code/lab_overfitting.ipynb
index 3776411..9b4f18a 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": [
@@ -48,18 +48,29 @@
    "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 changign 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": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = [0.001, 1, 20]\n",
+    "\n",
+    "classifiers = [SVC(gamma=g) for g in gamma]"
    ]
   },
   {
@@ -71,9 +82,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
@@ -146,11 +168,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.93"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "svc = SVC(gamma=0.7)\n",
+    "\n",
+    "svc.fit(X,y)\n",
+    "\n",
+    "svc.score(X,y)"
    ]
   },
   {
@@ -162,11 +199,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9733333333333334\n",
+      "0.92\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
+    "\n",
+    "svc = SVC(gamma=20)\n",
+    "\n",
+    "svc.fit(X,y)\n",
+    "\n",
+    "print(svc.score(X_train,y_train))\n",
+    "print(svc.score(X_test,y_test))"
    ]
   },
   {
@@ -178,11 +233,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9066666666666666\n",
+      "0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "svc = SVC(gamma=4)\n",
+    "\n",
+    "svc.fit(X,y)\n",
+    "\n",
+    "print(svc.score(X_train,y_train))\n",
+    "print(svc.score(X_test,y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9466666666666667\n",
+      "0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "svc = SVC(gamma=8)\n",
+    "\n",
+    "svc.fit(X,y)\n",
+    "\n",
+    "print(svc.score(X_train,y_train))\n",
+    "print(svc.score(X_test,y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9466666666666667\n",
+      "0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "svc = SVC(gamma=10)\n",
+    "\n",
+    "svc.fit(X,y)\n",
+    "\n",
+    "print(svc.score(X_train,y_train))\n",
+    "print(svc.score(X_test,y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9866666666666667\n",
+      "0.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "svc = SVC(gamma=35)\n",
+    "\n",
+    "svc.fit(X,y)\n",
+    "\n",
+    "print(svc.score(X_train,y_train))\n",
+    "print(svc.score(X_test,y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "0.96\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "svc = SVC(gamma=75)\n",
+    "\n",
+    "svc.fit(X,y)\n",
+    "\n",
+    "print(svc.score(X_train,y_train))\n",
+    "print(svc.score(X_test,y_test))"
    ]
   },
   {
@@ -193,18 +354,24 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your response here"
+    "Models with gamma > 20 are overfitting, because the Train score is always higher than the Test score."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "308f656801bdbd0b2d403197f25506e92fef2516366746747ba806d876d6bcae"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.12 ('ironhack')",
    "language": "python",
    "name": "python3"
   },
@@ -218,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,