diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 1970c46..f80e119 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,11 +12,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn import datasets"
    ]
   },
   {
@@ -37,11 +39,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes = datasets.load_diabetes()"
    ]
   },
   {
@@ -53,11 +55,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes['feature_names']"
    ]
   },
   {
@@ -73,13 +106,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _diabetes_dataset:\n",
+      "\n",
+      "Diabetes dataset\n",
+      "----------------\n",
+      "\n",
+      "Ten baseline variables, age, sex, body mass index, average blood\n",
+      "pressure, and six blood serum measurements were obtained for each of n =\n",
+      "442 diabetes patients, as well as the response of interest, a\n",
+      "quantitative measure of disease progression one year after baseline.\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "  :Number of Instances: 442\n",
+      "\n",
+      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
+      "\n",
+      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
+      "\n",
+      "  :Attribute Information:\n",
+      "      - age     age in years\n",
+      "      - sex\n",
+      "      - bmi     body mass index\n",
+      "      - bp      average blood pressure\n",
+      "      - s1      tc, T-Cells (a type of white blood cells)\n",
+      "      - s2      ldl, low-density lipoproteins\n",
+      "      - s3      hdl, high-density lipoproteins\n",
+      "      - s4      tch, thyroid stimulating hormone\n",
+      "      - s5      ltg, lamotrigine\n",
+      "      - s6      glu, blood sugar level\n",
+      "\n",
+      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
+      "\n",
+      "Source URL:\n",
+      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
+      "\n",
+      "For more information see:\n",
+      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
+      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes.DESCR)"
    ]
   },
   {
@@ -97,11 +175,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# 1 - The first 10 columns are numeric predictive values. These values are measures obtained for each of 442 diabetes patients\n",
+    "# diabetes['data'] contains all the predictive values and diabetes['target'] will be a quantitative measure of the disease progression.\n",
+    "\n",
+    "# 2 - This will be the target to predict\n",
+    "\n",
+    "# 3 - There are 442 records "
    ]
   },
   {
@@ -115,11 +198,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442, 10)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes['data'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442,)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes['target'].shape"
    ]
   },
   {
@@ -156,11 +270,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "from sklearn import linear_model"
    ]
   },
   {
@@ -172,11 +286,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes_model = linear_model.LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "abc.ABCMeta"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(linear_model.LinearRegression)"
    ]
   },
   {
@@ -190,11 +324,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes_data_train = diabetes['data'][:-20]\n",
+    "diabetes_target_train = diabetes['target'][:-20]\n",
+    "diabetes_data_test = diabetes['data'][-20:]\n",
+    "diabetes_target_test = diabetes['target'][-20:]"
    ]
   },
   {
@@ -206,11 +345,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "152.76430691633442"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes_model.fit(diabetes_data_train, diabetes_target_train)\n",
+    "\n",
+    "diabetes_model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3.03499549e-01, -2.37639315e+02,  5.10530605e+02,  3.27736980e+02,\n",
+       "       -8.14131709e+02,  4.92814588e+02,  1.02848452e+02,  1.84606489e+02,\n",
+       "        7.43519617e+02,  7.60951722e+01])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes_model.coef_"
    ]
   },
   {
@@ -231,11 +405,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes_pred_train = diabetes_model.predict(diabetes_data_train)\n",
+    "diabetes_pred_test  = diabetes_model.predict(diabetes_data_test)"
    ]
   },
   {
@@ -247,11 +422,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([233.,  91., 111., 152., 120.,  67., 310.,  94., 183.,  66., 173.,\n",
+       "        72.,  49.,  64.,  48., 178., 104., 132., 220.,  57.])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes_target_test "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([198., 155., 173., 112., 165., 131., 259., 100., 117., 124., 218.,\n",
+       "        61., 132., 120.,  53., 194., 103., 124., 211.,  53.])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.round(diabetes_pred_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 35. -64. -62.  40. -45. -64.  51.  -6.  66. -58. -45.  11. -83. -56.\n",
+      "  -5. -16.   1.   8.   9.   4.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes_target_test - np.round(diabetes_pred_test) )"
    ]
   },
   {
@@ -263,11 +489,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "#They couldn't be exactly the same as the model prediction is a prediction based in the parameters of our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes_target_test == diabetes_pred_test"
    ]
   },
   {
@@ -300,15 +548,6 @@
     "<div style=\"text-align:center\"><img src=\"../images/statsmodels.png\" width=600/></div>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -326,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,11 +590,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto = pd.read_csv(r'..\\data\\auto-mpg.csv')"
    ]
   },
   {
@@ -367,11 +606,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 62,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>car_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"chevrolet chevelle malibu\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"buick skylark 320\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"plymouth satellite\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"amc rebel sst\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"ford torino\"</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "0  18.0          8         307.0        130.0    3504          12.0   \n",
+       "1  15.0          8         350.0        165.0    3693          11.5   \n",
+       "2  18.0          8         318.0        150.0    3436          11.0   \n",
+       "3  16.0          8         304.0        150.0    3433          12.0   \n",
+       "4  17.0          8         302.0        140.0    3449          10.5   \n",
+       "\n",
+       "   model_year                       car_name  \n",
+       "0          70  \\t\"chevrolet chevelle malibu\"  \n",
+       "1          70          \\t\"buick skylark 320\"  \n",
+       "2          70         \\t\"plymouth satellite\"  \n",
+       "3          70              \\t\"amc rebel sst\"  \n",
+       "4          70                \\t\"ford torino\"  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.head()"
    ]
   },
   {
@@ -383,11 +734,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             float64\n",
+       "cylinders         int64\n",
+       "displacement    float64\n",
+       "horse_power     float64\n",
+       "weight            int64\n",
+       "acceleration    float64\n",
+       "model_year        int64\n",
+       "car_name         object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.dtypes"
    ]
   },
   {
@@ -399,11 +769,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "82"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "auto['model_year'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto['model_year'].min()"
    ]
   },
   {
@@ -415,11 +816,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             0\n",
+       "cylinders       0\n",
+       "displacement    0\n",
+       "horse_power     6\n",
+       "weight          0\n",
+       "acceleration    0\n",
+       "model_year      0\n",
+       "car_name        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(398, 8)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto = auto.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(392, 8)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.shape"
    ]
   },
   {
@@ -431,11 +900,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    199\n",
+       "8    103\n",
+       "6     83\n",
+       "3      4\n",
+       "5      3\n",
+       "Name: cylinders, dtype: int64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto['cylinders'].value_counts()    # There are 5 possible values for cylinders"
    ]
   },
   {
@@ -451,11 +936,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto = auto.drop(columns='car_name')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = auto.drop('mpg',axis=1)\n",
+    "y = auto['mpg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)"
    ]
   },
   {
@@ -469,11 +975,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "auto_model = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto_model.fit(X_train,y_train)"
    ]
   },
   {
@@ -503,11 +1031,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7511000138029085"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = auto_model.predict(X_train)\n",
+    "\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "r2_score(y_pred,y_train)"
    ]
   },
   {
@@ -523,11 +1066,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7231744043324384"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test_pred = auto_model.predict(X_test)\n",
+    "\n",
+    "r2_score(y_test_pred,y_test)"
    ]
   },
   {
@@ -541,11 +1097,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your answer here"
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7511000138029085"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred= auto_model.predict(X_train)\n",
+    "\n",
+    "r2_score(y_pred,y_train)                         # There is a better score for the training set"
    ]
   },
   {
@@ -561,11 +1130,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "X_train09, X_test09, y_train09, y_test09 = train_test_split(X, y, train_size=0.9)"
    ]
   },
   {
@@ -577,11 +1146,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto_model09 = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto_model09.fit(X_train09,y_train09)"
    ]
   },
   {
@@ -593,18 +1182,222 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7614508161583322"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred09 = auto_model09.predict(X_train09)\n",
+    "\n",
+    "r2_score(y_pred09,y_train09)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# your code here"
+    "Compute the r squared score for the smaller test set. Is there an improvement in the test r squared?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8052542480262883"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred09_test = auto_model09.predict(X_test09)\n",
+    "\n",
+    "r2_score(y_pred09_test,y_test09)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Compute the r squared score for the smaller test set. Is there an improvement in the test r squared?"
+    "# Extra Lab - Tree models & fine-tune the parameters\n",
+    "\n",
+    "You should visualize the trees. Add feature selection strategies (VarianceThreshold, SelectBestK, RFE etc.)  and check if this improves the models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "model = DecisionTreeRegressor(max_depth=3)\n",
+    "\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_train = model.predict(X_train)\n",
+    "y_pred_test  = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def performance_model(y_train, y_test, y_pred_train, y_pred_test):\n",
+    "\n",
+    "    ME_train = np.mean(y_train-y_pred_train)\n",
+    "    ME_test  = np.mean(y_test-y_pred_test)\n",
+    "\n",
+    "    MAE_train = mean_absolute_error(y_train,y_pred_train)\n",
+    "    MAE_test  = mean_absolute_error(y_test,y_pred_test)\n",
+    "\n",
+    "    MSE_train = mean_squared_error(y_train,y_pred_train)\n",
+    "    MSE_test  = mean_squared_error(y_test,y_pred_test)\n",
+    "\n",
+    "    RMSE_train = np.sqrt(MSE_train)\n",
+    "    RMSE_test  = np.sqrt(MSE_test)\n",
+    "\n",
+    "    MAPE_train = np.mean((np.abs(y_train-y_pred_train) / y_train)* 100.)\n",
+    "    MAPE_test  = np.mean((np.abs(y_test-y_pred_test) / y_test)* 100.)\n",
+    "\n",
+    "    R2_train = r2_score(y_train,y_pred_train)\n",
+    "    R2_test  = r2_score(y_test,y_pred_test)\n",
+    "\n",
+    "    performance = pd.DataFrame({'Error_metric': ['Mean error','Mean absolute error','Mean squared error',\n",
+    "                                             'Root mean squared error','Mean absolute percentual error',\n",
+    "                                             'R2'],\n",
+    "                            'Train': [ME_train, MAE_train, MSE_train, RMSE_train, MAPE_train, R2_train],\n",
+    "                            'Test' : [ME_test, MAE_test , MSE_test, RMSE_test, MAPE_test, R2_test]})\n",
+    "\n",
+    "    pd.options.display.float_format = '{:.2f}'.format\n",
+    "\n",
+    "\n",
+    "    df_train = pd.DataFrame({'Real_value': y_train, 'Predicted_value': y_pred_train})\n",
+    "    df_test  = pd.DataFrame({'Real_value': y_test,  'PRedicted_value': y_pred_test})\n",
+    "\n",
+    "    return performance, df_train, df_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "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>Error_metric</th>\n",
+       "      <th>Train</th>\n",
+       "      <th>Test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mean error</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mean absolute error</td>\n",
+       "      <td>2.16</td>\n",
+       "      <td>2.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mean squared error</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>15.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Root mean squared error</td>\n",
+       "      <td>2.83</td>\n",
+       "      <td>3.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Mean absolute percentual error</td>\n",
+       "      <td>9.68</td>\n",
+       "      <td>10.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>R2</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Error_metric  Train  Test\n",
+       "0                      Mean error   0.00  0.69\n",
+       "1             Mean absolute error   2.16  2.64\n",
+       "2              Mean squared error   8.00 15.23\n",
+       "3         Root mean squared error   2.83  3.90\n",
+       "4  Mean absolute percentual error   9.68 10.98\n",
+       "5                              R2   0.87  0.75"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance, _ ,_ = performance_model(y_train, y_test, y_pred_train, y_pred_test)\n",
+    "performance"
    ]
   },
   {
@@ -613,7 +1406,35 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# Better R2 score with the DecisionTreeRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.tree import plot_tree\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n",
+    "\n",
+    "plot_tree(model,filled = True, rounded=True)\n",
+    "plt.show()  "
    ]
   },
   {
@@ -737,7 +1558,49 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,