diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index f1794a8..0b74c27 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,10 +52,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=111)\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -66,10 +69,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "lr = LinearRegression()\n",
+    "lr.fit(X_train, y_train)\n",
+    "y_train_pred = lr.predict(X_train).round(1)\n",
+    "y_test_pred = lr.predict(X_test).round(1)\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -80,10 +89,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn import metrics\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.737\n",
+      "test:  0.745\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",metrics.r2_score(y_train, y_train_pred).round(3))\n",
+    "print(\"test: \",metrics.r2_score(y_test, y_test_pred).round(3))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -94,10 +125,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  21.775\n",
+      "test:  23.105\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",metrics.mean_squared_error(y_train, y_train_pred).round(3))\n",
+    "print(\"test: \",metrics.mean_squared_error(y_test, y_test_pred).round(3))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -108,10 +151,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  3.225\n",
+      "test:  3.382\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",metrics.mean_absolute_error(y_train, y_train_pred).round(3))\n",
+    "print(\"test: \",metrics.mean_absolute_error(y_test, y_test_pred).round(3))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -122,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,10 +202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=141)\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -161,10 +218,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/alejandro/.local/lib/python3.8/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  return f(*args, **kwargs)\n",
+      "/home/alejandro/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log = LogisticRegression()\n",
+    "log.fit(\n",
+    "    X_train, \n",
+    "    y_train\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_pred = log.predict(X_train)\n",
+    "y_test_pred = log.predict(X_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -175,10 +284,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.9833333333333333\n",
+      "test:  0.9666666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",accuracy_score(y_train, y_train_pred))\n",
+    "print(\"test: \",accuracy_score(y_test, y_test_pred))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -189,10 +319,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.metrics import balanced_accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.981981981981982\n",
+      "test:  0.9583333333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",balanced_accuracy_score(y_train, y_train_pred))\n",
+    "print(\"test: \",balanced_accuracy_score(y_test, y_test_pred))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -203,10 +354,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.metrics import precision_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.9840909090909091\n",
+      "test:  0.9690476190476189\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",precision_score(y_train, y_train_pred, average=\"weighted\"))\n",
+    "print(\"test: \",precision_score(y_test, y_test_pred, average=\"weighted\"))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -217,10 +389,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.metrics import recall_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.9833333333333333\n",
+      "test:  0.9666666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",recall_score(y_train, y_train_pred, average=\"weighted\"))\n",
+    "print(\"test: \",recall_score(y_test, y_test_pred, average=\"weighted\"))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -231,10 +424,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.metrics import f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.9832956503014643\n",
+      "test:  0.9661728395061729\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"train: \",f1_score(y_train, y_train_pred, average=\"weighted\"))\n",
+    "print(\"test: \",f1_score(y_test, y_test_pred, average=\"weighted\"))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -245,17 +459,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 42,
+   "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>col_0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "col_0   0   1   2\n",
+       "class            \n",
+       "0      41   0   0\n",
+       "1       0  35   2\n",
+       "2       0   0  42"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(\n",
+    "    y_train[\"class\"],\n",
+    "    y_train_pred\n",
+    ")"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 43,
+   "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>col_0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "col_0  0   1  2\n",
+       "class          \n",
+       "0      9   0  0\n",
+       "1      0  13  0\n",
+       "2      0   1  7"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(\n",
+    "    y_test[\"class\"],\n",
+    "    y_test_pred\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -267,7 +627,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -281,7 +641,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,