diff --git a/your-code/lab_boston_housing.ipynb b/your-code/lab_boston_housing.ipynb
index 3176602..98b028b 100644
--- a/your-code/lab_boston_housing.ipynb
+++ b/your-code/lab_boston_housing.ipynb
@@ -17,6 +17,29 @@
     "We will build models with several different parameters, which will change the goodness of fit for each. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Usuario\\anaconda3\\New folder\\envs\\machine_learning\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
+      "c:\\Users\\Usuario\\anaconda3\\New folder\\envs\\machine_learning\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
+      "c:\\Users\\Usuario\\anaconda3\\New folder\\envs\\machine_learning\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n",
+      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,11 +58,161 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "boston_house = pd.read_csv(r'C:\\Users\\Usuario\\Desktop\\Week 7\\lab-problems-in-ml\\data\\boston_data.csv')\n",
+    "boston_house.head()"
    ]
   },
   {
@@ -51,11 +224,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your plots here\n",
+    "boston_house.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(404, 14)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston_house.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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": [
-    "# Your plots here"
+    "features = boston_house.drop(columns=['medv'])\n",
+    "labels = boston_house['medv']\n",
+    "\n",
+    "print(features.head())\n",
+    "print(labels.head())"
    ]
   },
   {
@@ -67,13 +325,172 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "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>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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "boston_house[['crim', 'rm', 'age', 'dis', 'tax', 'ptratio', 'lstat', 'medv']].corr()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -83,11 +500,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "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"
+    "# Your response here\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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_3420\\2191265260.py:5: 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": [
+    "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 +588,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "boston_house.describe()"
    ]
   },
   {
@@ -126,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +839,9 @@
     "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)"
    ]
   },
   {
@@ -148,11 +854,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "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(features, labels)"
    ]
   },
   {
@@ -175,11 +884,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Five separate RFR here with the given max depths"
+    "# Five separate RFR here with the given max depths\n",
+    "from sklearn.ensemble import RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(max_depth=10)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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 +931,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "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",
+    "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()"
    ]
   },
   {
@@ -209,11 +988,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "\n",
+    "## I can see that when the depth is bigger of the training more accurate it is the model"
    ]
   },
   {
@@ -226,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +1024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,8 +1056,11 @@
  ],
  "metadata": {
   "anaconda-cloud": {},
+  "interpreter": {
+   "hash": "4cf47c1695b8dc71c4dc45cfe109041c2bf14a1931a8c7ea2256e653f1582b6e"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.12 ('machine_learning')",
    "language": "python",
    "name": "python3"
   },
@@ -290,7 +1074,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,