diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 8818016..de3b0ba 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -19,7 +19,8 @@
     "# Libraries\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "from sklearn import datasets\n"
+    "from sklearn import datasets\n",
+    "import sklearn"
    ]
   },
   {
@@ -44,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "diabetes = sklearn.datasets.load_diabetes()"
    ]
   },
   {
@@ -71,7 +72,8 @@
     }
    ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "diabetes.keys()"
    ]
   },
   {
@@ -89,7 +91,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
@@ -138,7 +140,7 @@
     }
    ],
    "source": [
-    "# your code here\n"
+    "print(diabetes[\"DESCR\"])"
    ]
   },
   {
@@ -158,9 +160,24 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442, 10)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here \n"
+    "# 1. There are 7 attributes:\n",
+    "len(diabetes.keys())\n",
+    "# 2. \"data\" is the independent feature and \"target\" the dependent feat.\n",
+    "# 3. There are 442 records in the data\n",
+    "diabetes[\"data\"].shape"
    ]
   },
   {
@@ -177,13 +194,6 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of 'data' :\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -196,7 +206,8 @@
     }
    ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "diabetes[\"data\"].shape"
    ]
   },
   {
@@ -204,13 +215,6 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of 'target' :\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -222,7 +226,9 @@
      "output_type": "execute_result"
     }
    ],
-   "source": []
+   "source": [
+    "diabetes[\"target\"].shape"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -262,7 +268,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split"
    ]
   },
   {
@@ -278,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "diabetes_model = LinearRegression()"
    ]
   },
   {
@@ -296,7 +303,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "X = diabetes[\"data\"]\n",
+    "y = diabetes[\"target\"]\n",
+    "\n",
+    "diabetes_data_train = X[:-20,:]\n",
+    "diabetes_data_test = X[-20:,:]\n",
+    "diabetes_target_train = y[:-20]\n",
+    "diabetes_target_test = y[-20:]"
    ]
   },
   {
@@ -308,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -317,46 +330,53 @@
        "LinearRegression()"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here\n"
+    "diabetes_model.fit(diabetes_data_train, diabetes_target_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Intercept: 152.76430691633442\n"
+      "152.76430691633442\n"
      ]
     }
    ],
-   "source": []
+   "source": [
+    "# Intercept: 152.76430691633442\n",
+    "print(diabetes_model.intercept_)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Coefficients: [ 3.03499549e-01 -2.37639315e+02  5.10530605e+02  3.27736980e+02\n",
+      "[ 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]\n"
      ]
     }
    ],
-   "source": []
+   "source": [
+    "# Coefficients: [ 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  7.43519617e+02  7.60951722e+01]\n",
+    "print(diabetes_model.coef_)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -376,11 +396,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([197.61846908, 155.43979328, 172.88665147, 111.53537279,\n",
+       "       164.80054784, 131.06954875, 259.12237761, 100.47935157,\n",
+       "       117.0601052 , 124.30503555, 218.36632793,  61.19831284,\n",
+       "       132.25046751, 120.3332925 ,  52.54458691, 194.03798088,\n",
+       "       102.57139702, 123.56604987, 211.0346317 ,  52.60335674])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "diabetes_model.predict(diabetes_data_test)"
    ]
   },
   {
@@ -392,16 +427,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "diabetes_target_test:\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -409,27 +437,22 @@
        "        72.,  49.,  64.,  48., 178., 104., 132., 220.,  57.])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here\n"
+    "# array([233.,  91., 111., 152., 120.,  67., 310.,  94., 183.,  66., 173.,\n",
+    "#         72.,  49.,  64.,  48., 178., 104., 132., 220.,  57.])\n",
+    "diabetes_target_test"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "test prediction:\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -440,12 +463,19 @@
        "       102.57139702, 123.56604987, 211.0346317 ,  52.60335674])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": []
+   "source": [
+    "# array([197.61846908, 155.43979328, 172.88665147, 111.53537279,\n",
+    "#        164.80054784, 131.06954875, 259.12237761, 100.47935157,\n",
+    "#        117.0601052 , 124.30503555, 218.36632793,  61.19831284,\n",
+    "#        132.25046751, 120.3332925 ,  52.54458691, 194.03798088,\n",
+    "#        102.57139702, 123.56604987, 211.0346317 ,  52.60335674])\n",
+    "diabetes_model.predict(diabetes_data_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -456,11 +486,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here \n"
+    "# No. It's different. The test size is very small in order to do a better prediction."
    ]
   },
   {
@@ -495,50 +525,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                      y   R-squared:                       0.512\n",
-      "Model:                            OLS   Adj. R-squared:                  0.500\n",
-      "Method:                 Least Squares   F-statistic:                     43.16\n",
-      "Date:                Mon, 10 May 2021   Prob (F-statistic):           4.64e-58\n",
-      "Time:                        17:52:26   Log-Likelihood:                -2281.1\n",
-      "No. Observations:                 422   AIC:                             4584.\n",
-      "Df Residuals:                     411   BIC:                             4629.\n",
-      "Df Model:                          10                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "x1             0.3035     61.286      0.005      0.996    -120.169     120.776\n",
-      "x2          -237.6393     62.837     -3.782      0.000    -361.162    -114.117\n",
-      "x3           510.5306     68.156      7.491      0.000     376.553     644.508\n",
-      "x4           327.7370     66.876      4.901      0.000     196.275     459.199\n",
-      "x5          -814.1317    424.044     -1.920      0.056   -1647.697      19.434\n",
-      "x6           492.8146    344.227      1.432      0.153    -183.850    1169.480\n",
-      "x7           102.8485    219.463      0.469      0.640    -328.561     534.258\n",
-      "x8           184.6065    167.336      1.103      0.271    -144.334     513.547\n",
-      "x9           743.5196    175.359      4.240      0.000     398.807    1088.232\n",
-      "x10           76.0952     68.293      1.114      0.266     -58.152     210.343\n",
-      "const        152.7643      2.658     57.469      0.000     147.539     157.990\n",
-      "==============================================================================\n",
-      "Omnibus:                        1.544   Durbin-Watson:                   2.026\n",
-      "Prob(Omnibus):                  0.462   Jarque-Bera (JB):                1.421\n",
-      "Skew:                           0.004   Prob(JB):                        0.491\n",
-      "Kurtosis:                       2.716   Cond. No.                         224.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# your code here\n",
     "import statsmodels.api as sm\n",
@@ -562,11 +551,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here\n"
+    "# your answer here"
    ]
   },
   {
@@ -587,11 +576,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "auto = pd.read_csv(r\"C:\\Users\\trito.DESKTOP-V9IEUOR\\Desktop\\IronHack Data Analyst\\Labs\\week7labs\\lab-supervised-learning-sklearn\\data\\auto-mpg.csv\")"
    ]
   },
   {
@@ -603,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -713,13 +702,13 @@
        "4          70                \\t\"ford torino\"  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here\n"
+    "auto.head()"
    ]
   },
   {
@@ -731,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -752,12 +741,28 @@
       " 6   model_year    398 non-null    int64  \n",
       " 7   car_name      398 non-null    object \n",
       "dtypes: float64(4), int64(3), object(1)\n",
-      "memory usage: 25.0+ KB\n"
+      "memory usage: 25.0+ KB\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 398 entries, 0 to 397\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   mpg           398 non-null    float64\n",
+      " 1   cylinders     398 non-null    int64  \n",
+      " 2   displacement  398 non-null    float64\n",
+      " 3   horse_power   392 non-null    float64\n",
+      " 4   weight        398 non-null    int64  \n",
+      " 5   acceleration  398 non-null    float64\n",
+      " 6   model_year    398 non-null    int64  \n",
+      "dtypes: float64(4), int64(3)\n",
+      "memory usage: 21.9 KB\n"
      ]
     }
    ],
    "source": [
-    "# your code here\n"
+    "auto.info() # car_name is not numeric.\n",
+    "auto  = auto.drop(columns=[\"car_name\"])\n",
+    "auto.info()"
    ]
   },
   {
@@ -769,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -778,19 +783,20 @@
        "70"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# your code here\n",
-    "# OLDEST MODEL\n"
+    "# OLDEST MODEL 70\n",
+    "auto[\"model_year\"].min()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -799,13 +805,14 @@
        "82"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# NEWEST MODEL \n"
+    "# NEWEST MODEL 82\n",
+    "auto[\"model_year\"].max()"
    ]
   },
   {
@@ -817,11 +824,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "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",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             0\n",
+       "cylinders       0\n",
+       "displacement    0\n",
+       "horse_power     0\n",
+       "weight          0\n",
+       "acceleration    0\n",
+       "model_year      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "auto = auto.dropna()\n",
+    "auto.isnull().sum()"
    ]
   },
   {
@@ -833,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -847,13 +902,13 @@
        "Name: cylinders, dtype: int64"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here \n"
+    "auto[\"cylinders\"].value_counts() # THere are 5 possible values."
    ]
   },
   {
@@ -867,13 +922,28 @@
     "*Hint: To separate data for training and test, use the `train_test_split` method we used in previous labs.*"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "auto.columns\n",
+    "X = auto.iloc[:,1:]\n",
+    "y = auto.iloc[:,0]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=8)"
    ]
   },
   {
@@ -887,11 +957,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "auto_model = LinearRegression()\n",
+    "auto_model.fit(X_train,y_train)"
    ]
   },
   {
@@ -921,22 +1003,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8198690008457218"
+       "0.8081736333891638"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here\n"
+    "y_pred = auto_model.predict(X_train)\n",
+    "\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "r2_score(y_train, y_pred)"
    ]
   },
   {
@@ -952,22 +1038,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7507754274816084"
+       "0.8085475967919091"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here\n"
+    "y_test_pred = auto_model.predict(X_test)\n",
+    "\n",
+    "r2_score(y_test,y_test_pred)"
    ]
   },
   {
@@ -985,7 +1073,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here\n"
+    "# Both R2 values are pretty similar, this meaning that the model is well fitted. "
    ]
   },
   {
@@ -1001,11 +1089,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "X_train09, X_test09, y_train09, y_test09 = train_test_split(X, y, test_size=0.90, random_state=1)"
    ]
   },
   {
@@ -1017,11 +1105,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "auto_model09 = LinearRegression()\n",
+    "auto_model09.fit(X_train09,y_train09)"
    ]
   },
   {
@@ -1033,22 +1133,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_train_09 = auto_model09.predict(X_train09)\n",
+    "y_pred_test_09 = auto_model09.predict(X_test09)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8109551916128583"
+       "0.9181704244987553"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here\n"
+    "r2_score(y_train09,y_pred_train_09)"
    ]
   },
   {
@@ -1060,22 +1170,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7913151386161112"
+       "0.7608096464950158"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# your code here\n"
+    "r2_score(y_test09,y_pred_test_09) # There is an improvement."
    ]
   },
   {
@@ -1217,7 +1327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,