diff --git a/notebooks/lab4.ipynb b/notebooks/lab4.ipynb
index 8dead69..2bea38a 100644
--- a/notebooks/lab4.ipynb
+++ b/notebooks/lab4.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 533,
+   "execution_count": 833,
    "metadata": {},
    "outputs": [
     {
@@ -325,7 +325,7 @@
        "[19237 rows x 17 columns]"
       ]
      },
-     "execution_count": 533,
+     "execution_count": 833,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 534,
+   "execution_count": 834,
    "metadata": {},
    "outputs": [
     {
@@ -401,7 +401,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 534,
+     "execution_count": 834,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 535,
+   "execution_count": 835,
    "metadata": {},
    "outputs": [
     {
@@ -426,7 +426,7 @@
        "        6.8,  4.5,  7.3,  0.1,  3.1,  6.4,  3.9,  0.9,  5.2,  5.8])"
       ]
      },
-     "execution_count": 535,
+     "execution_count": 835,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 536,
+   "execution_count": 836,
    "metadata": {},
    "outputs": [
     {
@@ -448,7 +448,7 @@
        "array([186005, 192000, 200000, ..., 140607, 307325, 186923])"
       ]
      },
-     "execution_count": 536,
+     "execution_count": 836,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 537,
+   "execution_count": 837,
    "metadata": {},
    "outputs": [
     {
@@ -532,7 +532,7 @@
        "        1901])"
       ]
      },
-     "execution_count": 537,
+     "execution_count": 837,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -545,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 538,
+   "execution_count": 838,
    "metadata": {},
    "outputs": [
     {
@@ -554,7 +554,7 @@
        "array([ 6,  4,  8,  1, 12,  3,  2, 16,  5,  7,  9, 10, 14])"
       ]
      },
-     "execution_count": 538,
+     "execution_count": 838,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -566,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 539,
+   "execution_count": 839,
    "metadata": {},
    "outputs": [
     {
@@ -575,7 +575,7 @@
        "array(['04-May', '02-Mar', '>5'], dtype=object)"
       ]
      },
-     "execution_count": 539,
+     "execution_count": 839,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -586,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 540,
+   "execution_count": 840,
    "metadata": {},
    "outputs": [
     {
@@ -595,7 +595,7 @@
        "array(['Четырехдверный', 'Двухдверный', 'Многодверный'], dtype=object)"
       ]
      },
-     "execution_count": 540,
+     "execution_count": 840,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -609,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 541,
+   "execution_count": 841,
    "metadata": {},
    "outputs": [
     {
@@ -618,7 +618,7 @@
        "array([       1,        3,        6, ...,   627220,   872946, 26307500])"
       ]
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-     "execution_count": 541,
+     "execution_count": 841,
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@@ -630,7 +630,7 @@
   },
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-   "execution_count": 542,
+   "execution_count": 842,
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    "outputs": [
     {
@@ -650,7 +650,7 @@
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   {
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-   "execution_count": 543,
+   "execution_count": 843,
    "metadata": {},
    "outputs": [
     {
@@ -659,7 +659,7 @@
        "array([     500,      549,      600, ...,   627220,   872946, 26307500])"
       ]
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-     "execution_count": 543,
+     "execution_count": 843,
      "metadata": {},
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@@ -671,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 544,
+   "execution_count": 844,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +684,7 @@
        "       2014, 2015, 2016, 2017, 2018, 2019, 2020])"
       ]
      },
-     "execution_count": 544,
+     "execution_count": 844,
      "metadata": {},
      "output_type": "execute_result"
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@@ -696,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 545,
+   "execution_count": 845,
    "metadata": {},
    "outputs": [
     {
@@ -1021,7 +1021,7 @@
        "[17574 rows x 17 columns]"
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-     "execution_count": 545,
+     "execution_count": 845,
      "metadata": {},
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@@ -1039,7 +1039,7 @@
   },
   {
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-   "execution_count": 546,
+   "execution_count": 846,
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
        "np.int64(2773)"
       ]
      },
-     "execution_count": 546,
+     "execution_count": 846,
      "metadata": {},
      "output_type": "execute_result"
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@@ -1059,7 +1059,7 @@
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-   "execution_count": 547,
+   "execution_count": 847,
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@@ -1081,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 548,
+   "execution_count": 848,
    "metadata": {},
    "outputs": [
     {
@@ -1107,7 +1107,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 548,
+     "execution_count": 848,
      "metadata": {},
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@@ -1125,7 +1125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 549,
+   "execution_count": 849,
    "metadata": {},
    "outputs": [
     {
@@ -1151,7 +1151,7 @@
        "dtype: object"
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-     "execution_count": 549,
+     "execution_count": 849,
      "metadata": {},
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@@ -1162,7 +1162,7 @@
   },
   {
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-   "execution_count": 550,
+   "execution_count": 850,
    "metadata": {},
    "outputs": [
     {
@@ -1224,7 +1224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 551,
+   "execution_count": 851,
    "metadata": {},
    "outputs": [
     {
@@ -1255,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 552,
+   "execution_count": 852,
    "metadata": {},
    "outputs": [
     {
@@ -1310,7 +1310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 553,
+   "execution_count": 853,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1326,7 +1326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 554,
+   "execution_count": 854,
    "metadata": {},
    "outputs": [
     {
@@ -1352,7 +1352,7 @@
        "dtype: object"
       ]
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-     "execution_count": 554,
+     "execution_count": 854,
      "metadata": {},
      "output_type": "execute_result"
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@@ -1363,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 555,
+   "execution_count": 855,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1467,7 +1467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 556,
+   "execution_count": 856,
    "metadata": {},
    "outputs": [
     {
@@ -1866,7 +1866,7 @@
        "[12597 rows x 83 columns]"
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-     "execution_count": 556,
+     "execution_count": 856,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1890,7 +1890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 557,
+   "execution_count": 857,
    "metadata": {},
    "outputs": [
     {
@@ -1915,7 +1915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 558,
+   "execution_count": 858,
    "metadata": {},
    "outputs": [
     {
@@ -2314,7 +2314,7 @@
        "[8817 rows x 82 columns]"
       ]
      },
-     "execution_count": 558,
+     "execution_count": 858,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2337,7 +2337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 559,
+   "execution_count": 859,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2377,7 +2377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 560,
+   "execution_count": 860,
    "metadata": {},
    "outputs": [
     {
@@ -2417,21 +2417,7 @@
       "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names\n",
       "  warnings.warn(\n",
       "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but KNeighborsRegressor was fitted without feature names\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: decision_tree\n",
-      "Model: knn\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "  warnings.warn(\n",
       "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but KNeighborsRegressor was fitted without feature names\n",
       "  warnings.warn(\n"
      ]
@@ -2440,6 +2426,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: decision_tree\n",
+      "Model: knn\n",
       "Model: random_forest\n"
      ]
     },
@@ -2490,112 +2478,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 561,
+   "execution_count": 861,
    "metadata": {},
    "outputs": [
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      "output_type": "execute_result"
     }
@@ -2611,7 +2599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 562,
+   "execution_count": 862,
    "metadata": {},
    "outputs": [
     {
@@ -2639,7 +2627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 563,
+   "execution_count": 863,
    "metadata": {},
    "outputs": [
     {
@@ -2663,25 +2651,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Leather interior_Yes</th>\n",
-       "      <th>Category_Coupe</th>\n",
-       "      <th>Category_Goods wagon</th>\n",
-       "      <th>Category_Hatchback</th>\n",
-       "      <th>Category_Jeep</th>\n",
-       "      <th>Category_Limousine</th>\n",
-       "      <th>Category_Microbus</th>\n",
-       "      <th>Category_Minivan</th>\n",
-       "      <th>Category_Pickup</th>\n",
-       "      <th>Category_Sedan</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Manufacturer_VAZ</th>\n",
-       "      <th>Manufacturer_VOLKSWAGEN</th>\n",
-       "      <th>Manufacturer_VOLVO</th>\n",
-       "      <th>Manufacturer_სხვა</th>\n",
-       "      <th>Levy</th>\n",
-       "      <th>Prod. year</th>\n",
-       "      <th>Engine volume</th>\n",
-       "      <th>Airbags</th>\n",
        "      <th>Price</th>\n",
        "      <th>PricePred</th>\n",
        "    </tr>\n",
@@ -2689,176 +2658,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>15146</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.187596</td>\n",
-       "      <td>1.943625</td>\n",
-       "      <td>-0.487669</td>\n",
-       "      <td>-0.673150</td>\n",
        "      <td>0.144553</td>\n",
        "      <td>0.603431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14145</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.187596</td>\n",
-       "      <td>-0.874572</td>\n",
-       "      <td>-0.900100</td>\n",
-       "      <td>-1.179883</td>\n",
        "      <td>-0.665312</td>\n",
        "      <td>-0.619034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8943</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.081225</td>\n",
-       "      <td>0.642919</td>\n",
-       "      <td>-0.487669</td>\n",
-       "      <td>0.847049</td>\n",
        "      <td>-0.144678</td>\n",
        "      <td>-0.179776</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17889</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.187596</td>\n",
-       "      <td>-1.524925</td>\n",
-       "      <td>0.474671</td>\n",
-       "      <td>-0.419784</td>\n",
        "      <td>-0.462869</td>\n",
        "      <td>-0.566201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9515</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.695495</td>\n",
-       "      <td>1.510056</td>\n",
-       "      <td>0.474671</td>\n",
-       "      <td>-0.673150</td>\n",
        "      <td>2.765339</td>\n",
        "      <td>2.675833</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 84 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Leather interior_Yes  Category_Coupe  Category_Goods wagon  \\\n",
-       "15146                   0.0             0.0                   0.0   \n",
-       "14145                   0.0             0.0                   0.0   \n",
-       "8943                    0.0             0.0                   0.0   \n",
-       "17889                   1.0             0.0                   0.0   \n",
-       "9515                    1.0             0.0                   0.0   \n",
-       "\n",
-       "       Category_Hatchback  Category_Jeep  Category_Limousine  \\\n",
-       "15146                 0.0            0.0                 0.0   \n",
-       "14145                 0.0            0.0                 0.0   \n",
-       "8943                  0.0            0.0                 0.0   \n",
-       "17889                 0.0            1.0                 0.0   \n",
-       "9515                  0.0            0.0                 0.0   \n",
-       "\n",
-       "       Category_Microbus  Category_Minivan  Category_Pickup  Category_Sedan  \\\n",
-       "15146                0.0               0.0              0.0             1.0   \n",
-       "14145                0.0               0.0              0.0             1.0   \n",
-       "8943                 0.0               0.0              0.0             1.0   \n",
-       "17889                0.0               0.0              0.0             0.0   \n",
-       "9515                 0.0               0.0              0.0             0.0   \n",
-       "\n",
-       "       ...  Manufacturer_VAZ  Manufacturer_VOLKSWAGEN  Manufacturer_VOLVO  \\\n",
-       "15146  ...               0.0                      0.0                 0.0   \n",
-       "14145  ...               0.0                      0.0                 0.0   \n",
-       "8943   ...               0.0                      0.0                 0.0   \n",
-       "17889  ...               0.0                      0.0                 0.0   \n",
-       "9515   ...               0.0                      0.0                 0.0   \n",
-       "\n",
-       "       Manufacturer_სხვა      Levy  Prod. year  Engine volume   Airbags  \\\n",
-       "15146                0.0 -1.187596    1.943625      -0.487669 -0.673150   \n",
-       "14145                0.0 -1.187596   -0.874572      -0.900100 -1.179883   \n",
-       "8943                 0.0  0.081225    0.642919      -0.487669  0.847049   \n",
-       "17889                0.0 -1.187596   -1.524925       0.474671 -0.419784   \n",
-       "9515                 0.0  1.695495    1.510056       0.474671 -0.673150   \n",
-       "\n",
-       "          Price  PricePred  \n",
-       "15146  0.144553   0.603431  \n",
-       "14145 -0.665312  -0.619034  \n",
-       "8943  -0.144678  -0.179776  \n",
-       "17889 -0.462869  -0.566201  \n",
-       "9515   2.765339   2.675833  \n",
-       "\n",
-       "[5 rows x 84 columns]"
+       "          Price  PricePred\n",
+       "15146  0.144553   0.603431\n",
+       "14145 -0.665312  -0.619034\n",
+       "8943  -0.144678  -0.179776\n",
+       "17889 -0.462869  -0.566201\n",
+       "9515   2.765339   2.675833"
       ]
      },
-     "execution_count": 563,
+     "execution_count": 863,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2866,7 +2702,6 @@
    "source": [
     "pd.concat(\n",
     "    [\n",
-    "        train_df,\n",
     "        price_y_train,\n",
     "        pd.Series(\n",
     "            models[best_model][\"train_preds\"],\n",
@@ -2880,7 +2715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 564,
+   "execution_count": 864,
    "metadata": {},
    "outputs": [
     {
@@ -2904,25 +2739,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Leather interior_Yes</th>\n",
-       "      <th>Category_Coupe</th>\n",
-       "      <th>Category_Goods wagon</th>\n",
-       "      <th>Category_Hatchback</th>\n",
-       "      <th>Category_Jeep</th>\n",
-       "      <th>Category_Limousine</th>\n",
-       "      <th>Category_Microbus</th>\n",
-       "      <th>Category_Minivan</th>\n",
-       "      <th>Category_Pickup</th>\n",
-       "      <th>Category_Sedan</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Manufacturer_VAZ</th>\n",
-       "      <th>Manufacturer_VOLKSWAGEN</th>\n",
-       "      <th>Manufacturer_VOLVO</th>\n",
-       "      <th>Manufacturer_სხვა</th>\n",
-       "      <th>Levy</th>\n",
-       "      <th>Prod. year</th>\n",
-       "      <th>Engine volume</th>\n",
-       "      <th>Airbags</th>\n",
        "      <th>Price</th>\n",
        "      <th>PricePred</th>\n",
        "    </tr>\n",
@@ -2930,176 +2746,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10968</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.055651</td>\n",
-       "      <td>-0.007434</td>\n",
-       "      <td>-1.037578</td>\n",
-       "      <td>1.353782</td>\n",
        "      <td>-1.316082</td>\n",
        "      <td>-1.213541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1121</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.331577</td>\n",
-       "      <td>0.642919</td>\n",
-       "      <td>0.474671</td>\n",
-       "      <td>1.353782</td>\n",
        "      <td>-1.489657</td>\n",
        "      <td>-0.753720</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4355</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.398430</td>\n",
-       "      <td>1.076487</td>\n",
-       "      <td>-0.762623</td>\n",
-       "      <td>-0.673150</td>\n",
        "      <td>0.771343</td>\n",
        "      <td>0.798361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17702</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.528788</td>\n",
-       "      <td>-0.441003</td>\n",
-       "      <td>-0.762623</td>\n",
-       "      <td>-0.673150</td>\n",
        "      <td>-0.679792</td>\n",
        "      <td>-0.531793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6167</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.719981</td>\n",
-       "      <td>-0.441003</td>\n",
-       "      <td>-0.487669</td>\n",
-       "      <td>0.340316</td>\n",
        "      <td>-0.173638</td>\n",
        "      <td>-0.322582</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 84 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Leather interior_Yes  Category_Coupe  Category_Goods wagon  \\\n",
-       "10968                   1.0             0.0                   0.0   \n",
-       "1121                    1.0             0.0                   0.0   \n",
-       "4355                    1.0             0.0                   0.0   \n",
-       "17702                   1.0             0.0                   0.0   \n",
-       "6167                    0.0             0.0                   0.0   \n",
-       "\n",
-       "       Category_Hatchback  Category_Jeep  Category_Limousine  \\\n",
-       "10968                 1.0            0.0                 0.0   \n",
-       "1121                  0.0            0.0                 0.0   \n",
-       "4355                  0.0            0.0                 0.0   \n",
-       "17702                 1.0            0.0                 0.0   \n",
-       "6167                  0.0            0.0                 0.0   \n",
-       "\n",
-       "       Category_Microbus  Category_Minivan  Category_Pickup  Category_Sedan  \\\n",
-       "10968                0.0               0.0              0.0             0.0   \n",
-       "1121                 0.0               0.0              0.0             1.0   \n",
-       "4355                 0.0               0.0              0.0             1.0   \n",
-       "17702                0.0               0.0              0.0             0.0   \n",
-       "6167                 0.0               0.0              0.0             1.0   \n",
-       "\n",
-       "       ...  Manufacturer_VAZ  Manufacturer_VOLKSWAGEN  Manufacturer_VOLVO  \\\n",
-       "10968  ...               0.0                      0.0                 0.0   \n",
-       "1121   ...               0.0                      0.0                 0.0   \n",
-       "4355   ...               0.0                      0.0                 0.0   \n",
-       "17702  ...               0.0                      0.0                 0.0   \n",
-       "6167   ...               0.0                      0.0                 0.0   \n",
-       "\n",
-       "       Manufacturer_სხვა      Levy  Prod. year  Engine volume   Airbags  \\\n",
-       "10968                0.0 -0.055651   -0.007434      -1.037578  1.353782   \n",
-       "1121                 0.0 -0.331577    0.642919       0.474671  1.353782   \n",
-       "4355                 0.0  0.398430    1.076487      -0.762623 -0.673150   \n",
-       "17702                0.0  0.528788   -0.441003      -0.762623 -0.673150   \n",
-       "6167                 0.0  0.719981   -0.441003      -0.487669  0.340316   \n",
-       "\n",
-       "          Price  PricePred  \n",
-       "10968 -1.316082  -1.213541  \n",
-       "1121  -1.489657  -0.753720  \n",
-       "4355   0.771343   0.798361  \n",
-       "17702 -0.679792  -0.531793  \n",
-       "6167  -0.173638  -0.322582  \n",
-       "\n",
-       "[5 rows x 84 columns]"
+       "          Price  PricePred\n",
+       "10968 -1.316082  -1.213541\n",
+       "1121  -1.489657  -0.753720\n",
+       "4355   0.771343   0.798361\n",
+       "17702 -0.679792  -0.531793\n",
+       "6167  -0.173638  -0.322582"
       ]
      },
-     "execution_count": 564,
+     "execution_count": 864,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3107,7 +2790,6 @@
    "source": [
     "pd.concat(\n",
     "    [\n",
-    "        test_df,\n",
     "        price_y_test,\n",
     "        pd.Series(\n",
     "            models[best_model][\"preds\"],\n",
@@ -3118,6 +2800,142 @@
     "    axis=1,\n",
     ").head(5)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Подбор гиперпараметров методом поиска по сетке"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 865,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'max_depth': 20, 'n_estimators': 70}"
+      ]
+     },
+     "execution_count": 865,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "optimized_model_type = \"random_forest\"\n",
+    "\n",
+    "random_forest_model = models[optimized_model_type][\"fitted\"]\n",
+    "\n",
+    "param_grid = {\n",
+    "    \"n_estimators\": [\n",
+    "        40,\n",
+    "        50,\n",
+    "        60,\n",
+    "        70,\n",
+    "        80,\n",
+    "    ],\n",
+    "    \"max_depth\": [\n",
+    "        10,\n",
+    "        20,\n",
+    "        30,\n",
+    "        40,\n",
+    "        50,\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "gs_optomizer = GridSearchCV(\n",
+    "    estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
+    ")\n",
+    "gs_optomizer.fit(train_df, price_y_train.values.ravel())\n",
+    "gs_optomizer.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 866,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_estimators': 75}"
+      ]
+     },
+     "execution_count": 866,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "optimized_model_type = \"random_forest\"\n",
+    "\n",
+    "random_forest_model = models[optimized_model_type][\"fitted\"]\n",
+    "\n",
+    "param_grid = {\n",
+    "    \"n_estimators\": [\n",
+    "        70,\n",
+    "        71,\n",
+    "        72,\n",
+    "        73,\n",
+    "        74,\n",
+    "        75,\n",
+    "        76,\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "gs_optomizer = GridSearchCV(\n",
+    "    estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
+    ")\n",
+    "gs_optomizer.fit(train_df, price_y_train.values.ravel())\n",
+    "gs_optomizer.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 868,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'max_depth': 17}"
+      ]
+     },
+     "execution_count": 868,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "optimized_model_type = \"random_forest\"\n",
+    "\n",
+    "random_forest_model = models[optimized_model_type][\"fitted\"]\n",
+    "\n",
+    "param_grid = {\n",
+    "    \"max_depth\": [\n",
+    "        16,\n",
+    "        17,\n",
+    "        18,\n",
+    "        19,\n",
+    "        20,\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "gs_optomizer = GridSearchCV(\n",
+    "    estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
+    ")\n",
+    "gs_optomizer.fit(train_df, price_y_train.values.ravel())\n",
+    "gs_optomizer.best_params_"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/lab4_pipeline.ipynb b/notebooks/lab4_pipeline.ipynb
index b091041..df7229d 100644
--- a/notebooks/lab4_pipeline.ipynb
+++ b/notebooks/lab4_pipeline.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 577,
    "metadata": {},
    "outputs": [
     {
@@ -334,7 +334,7 @@
        "[12597 rows x 17 columns]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 577,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 578,
    "metadata": {},
    "outputs": [
     {
@@ -402,7 +402,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 578,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -420,13 +420,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 579,
    "metadata": {},
    "outputs": [],
    "source": [
     "columns_to_drop = [\n",
     "    \"Model\",\n",
-    "    # \"Manufacturer\",\n",
     "    \"Color\",\n",
     "    \"Doors\",\n",
     "    \"Cylinders\",\n",
@@ -512,7 +511,7 @@
     "        (\"features_preprocessing\", features_preprocessing),\n",
     "        (\"drop_columns\", drop_columns),\n",
     "    ]\n",
-    ")"
+    ", memory=None)"
    ]
   },
   {
@@ -524,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 580,
    "metadata": {},
    "outputs": [
     {
@@ -923,7 +922,7 @@
        "[12597 rows x 83 columns]"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 580,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -947,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 581,
    "metadata": {},
    "outputs": [
     {
@@ -970,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 582,
    "metadata": {},
    "outputs": [
     {
@@ -1369,7 +1368,7 @@
        "[8817 rows x 82 columns]"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 582,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1392,17 +1391,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 583,
    "metadata": {},
    "outputs": [],
    "source": [
     "models = {\n",
     "    \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
     "    \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
-    "    \"decision_tree\": {\"model\": tree.DecisionTreeRegressor(random_state=random_state)},\n",
-    "    \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
-    "    \"random_forest\": {\n",
-    "        \"model\": ensemble.RandomForestRegressor(random_state=random_state, n_jobs=-1)\n",
+    "    \"decision_tree\": {\n",
+    "        \"model\": tree.DecisionTreeRegressor(max_depth=35, random_state=random_state)\n",
+    "    },\n",
+    "    \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=6, n_jobs=-1)},\n",
+    "    \"random_forest1\": {\n",
+    "        \"model\": ensemble.RandomForestRegressor(\n",
+    "            random_state=random_state, n_jobs=-1,\n",
+    "        )\n",
+    "    },\n",
+    "    \"random_forest2\": {\n",
+    "        \"model\": ensemble.RandomForestRegressor(\n",
+    "            random_state=random_state, n_jobs=-1, max_depth=17, n_estimators=75\n",
+    "        )\n",
     "    },\n",
     "}"
    ]
@@ -1416,7 +1424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 584,
    "metadata": {},
    "outputs": [
     {
@@ -1467,7 +1475,24 @@
      "text": [
       "Model: decision_tree\n",
       "Model: knn\n",
-      "Model: random_forest\n"
+      "Model: random_forest1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: random_forest2\n"
      ]
     },
     {
@@ -1517,112 +1542,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 585,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "<style type=\"text/css\">\n",
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-       "  background-color: #238a8d;\n",
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        "</style>\n",
-       "<table id=\"T_04a5e\">\n",
+       "<table id=\"T_f9ae5\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_04a5e_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
-       "      <th id=\"T_04a5e_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
-       "      <th id=\"T_04a5e_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
-       "      <th id=\"T_04a5e_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
+       "      <th id=\"T_f9ae5_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
+       "      <th id=\"T_f9ae5_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
+       "      <th id=\"T_f9ae5_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
+       "      <th id=\"T_f9ae5_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_04a5e_level0_row0\" class=\"row_heading level0 row0\" >random_forest</th>\n",
-       "      <td id=\"T_04a5e_row0_col0\" class=\"data row0 col0\" >0.324521</td>\n",
-       "      <td id=\"T_04a5e_row0_col1\" class=\"data row0 col1\" >0.548105</td>\n",
-       "      <td id=\"T_04a5e_row0_col2\" class=\"data row0 col2\" >0.602703</td>\n",
-       "      <td id=\"T_04a5e_row0_col3\" class=\"data row0 col3\" >0.702688</td>\n",
+       "      <th id=\"T_f9ae5_level0_row0\" class=\"row_heading level0 row0\" >random_forest2</th>\n",
+       "      <td id=\"T_f9ae5_row0_col0\" class=\"data row0 col0\" >0.355556</td>\n",
+       "      <td id=\"T_f9ae5_row0_col1\" class=\"data row0 col1\" >0.544928</td>\n",
+       "      <td id=\"T_f9ae5_row0_col2\" class=\"data row0 col2\" >0.603362</td>\n",
+       "      <td id=\"T_f9ae5_row0_col3\" class=\"data row0 col3\" >0.706124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_04a5e_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
-       "      <td id=\"T_04a5e_row1_col0\" class=\"data row1 col0\" >0.529359</td>\n",
-       "      <td id=\"T_04a5e_row1_col1\" class=\"data row1 col1\" >0.594354</td>\n",
-       "      <td id=\"T_04a5e_row1_col2\" class=\"data row1 col2\" >0.635191</td>\n",
-       "      <td id=\"T_04a5e_row1_col3\" class=\"data row1 col3\" >0.650397</td>\n",
+       "      <th id=\"T_f9ae5_level0_row1\" class=\"row_heading level0 row1\" >random_forest1</th>\n",
+       "      <td id=\"T_f9ae5_row1_col0\" class=\"data row1 col0\" >0.324521</td>\n",
+       "      <td id=\"T_f9ae5_row1_col1\" class=\"data row1 col1\" >0.548105</td>\n",
+       "      <td id=\"T_f9ae5_row1_col2\" class=\"data row1 col2\" >0.602703</td>\n",
+       "      <td id=\"T_f9ae5_row1_col3\" class=\"data row1 col3\" >0.702688</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_04a5e_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
-       "      <td id=\"T_04a5e_row2_col0\" class=\"data row2 col0\" >0.281112</td>\n",
-       "      <td id=\"T_04a5e_row2_col1\" class=\"data row2 col1\" >0.659240</td>\n",
-       "      <td id=\"T_04a5e_row2_col2\" class=\"data row2 col2\" >0.654294</td>\n",
-       "      <td id=\"T_04a5e_row2_col3\" class=\"data row2 col3\" >0.569896</td>\n",
+       "      <th id=\"T_f9ae5_level0_row2\" class=\"row_heading level0 row2\" >knn</th>\n",
+       "      <td id=\"T_f9ae5_row2_col0\" class=\"data row2 col0\" >0.520051</td>\n",
+       "      <td id=\"T_f9ae5_row2_col1\" class=\"data row2 col1\" >0.591774</td>\n",
+       "      <td id=\"T_f9ae5_row2_col2\" class=\"data row2 col2\" >0.632628</td>\n",
+       "      <td id=\"T_f9ae5_row2_col3\" class=\"data row2 col3\" >0.653426</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_04a5e_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
-       "      <td id=\"T_04a5e_row3_col0\" class=\"data row3 col0\" >0.753637</td>\n",
-       "      <td id=\"T_04a5e_row3_col1\" class=\"data row3 col1\" >0.759154</td>\n",
-       "      <td id=\"T_04a5e_row3_col2\" class=\"data row3 col2\" >0.758869</td>\n",
-       "      <td id=\"T_04a5e_row3_col3\" class=\"data row3 col3\" >0.429644</td>\n",
+       "      <th id=\"T_f9ae5_level0_row3\" class=\"row_heading level0 row3\" >decision_tree</th>\n",
+       "      <td id=\"T_f9ae5_row3_col0\" class=\"data row3 col0\" >0.281113</td>\n",
+       "      <td id=\"T_f9ae5_row3_col1\" class=\"data row3 col1\" >0.659013</td>\n",
+       "      <td id=\"T_f9ae5_row3_col2\" class=\"data row3 col2\" >0.655231</td>\n",
+       "      <td id=\"T_f9ae5_row3_col3\" class=\"data row3 col3\" >0.570193</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_04a5e_level0_row4\" class=\"row_heading level0 row4\" >linear</th>\n",
-       "      <td id=\"T_04a5e_row4_col0\" class=\"data row4 col0\" >0.752569</td>\n",
-       "      <td id=\"T_04a5e_row4_col1\" class=\"data row4 col1\" >0.759341</td>\n",
-       "      <td id=\"T_04a5e_row4_col2\" class=\"data row4 col2\" >0.758784</td>\n",
-       "      <td id=\"T_04a5e_row4_col3\" class=\"data row4 col3\" >0.429364</td>\n",
+       "      <th id=\"T_f9ae5_level0_row4\" class=\"row_heading level0 row4\" >ridge</th>\n",
+       "      <td id=\"T_f9ae5_row4_col0\" class=\"data row4 col0\" >0.753637</td>\n",
+       "      <td id=\"T_f9ae5_row4_col1\" class=\"data row4 col1\" >0.759154</td>\n",
+       "      <td id=\"T_f9ae5_row4_col2\" class=\"data row4 col2\" >0.758869</td>\n",
+       "      <td id=\"T_f9ae5_row4_col3\" class=\"data row4 col3\" >0.429644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f9ae5_level0_row5\" class=\"row_heading level0 row5\" >linear</th>\n",
+       "      <td id=\"T_f9ae5_row5_col0\" class=\"data row5 col0\" >0.752569</td>\n",
+       "      <td id=\"T_f9ae5_row5_col1\" class=\"data row5 col1\" >0.759341</td>\n",
+       "      <td id=\"T_f9ae5_row5_col2\" class=\"data row5 col2\" >0.758784</td>\n",
+       "      <td id=\"T_f9ae5_row5_col3\" class=\"data row5 col3\" >0.429364</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x155e0cc85f0>"
+       "<pandas.io.formats.style.Styler at 0x155ed72f5f0>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 585,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1638,13 +1686,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 586,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'random_forest'"
+       "'random_forest2'"
       ]
      },
      "metadata": {},
@@ -1666,7 +1714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 587,
    "metadata": {},
    "outputs": [
     {
@@ -1736,7 +1784,7 @@
        "      <td>-0.487669</td>\n",
        "      <td>-0.673150</td>\n",
        "      <td>0.144553</td>\n",
-       "      <td>0.603431</td>\n",
+       "      <td>0.496276</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9482</th>\n",
@@ -1760,7 +1808,7 @@
        "      <td>-0.900100</td>\n",
        "      <td>-1.179883</td>\n",
        "      <td>-0.665312</td>\n",
-       "      <td>-0.619034</td>\n",
+       "      <td>-0.598765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6177</th>\n",
@@ -1784,7 +1832,7 @@
        "      <td>-0.487669</td>\n",
        "      <td>0.847049</td>\n",
        "      <td>-0.144678</td>\n",
-       "      <td>-0.179776</td>\n",
+       "      <td>-0.075680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11756</th>\n",
@@ -1808,7 +1856,7 @@
        "      <td>0.474671</td>\n",
        "      <td>-0.419784</td>\n",
        "      <td>-0.462869</td>\n",
-       "      <td>-0.566201</td>\n",
+       "      <td>-0.564690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6557</th>\n",
@@ -1832,7 +1880,7 @@
        "      <td>0.474671</td>\n",
        "      <td>-0.673150</td>\n",
        "      <td>2.765339</td>\n",
-       "      <td>2.675833</td>\n",
+       "      <td>2.638053</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1876,16 +1924,16 @@
        "6557                 0.0  1.695495    1.510056       0.474671 -0.673150   \n",
        "\n",
        "          Price  PricePred  \n",
-       "10083  0.144553   0.603431  \n",
-       "9482  -0.665312  -0.619034  \n",
-       "6177  -0.144678  -0.179776  \n",
-       "11756 -0.462869  -0.566201  \n",
-       "6557   2.765339   2.675833  \n",
+       "10083  0.144553   0.496276  \n",
+       "9482  -0.665312  -0.598765  \n",
+       "6177  -0.144678  -0.075680  \n",
+       "11756 -0.462869  -0.564690  \n",
+       "6557   2.765339   2.638053  \n",
        "\n",
        "[5 rows x 84 columns]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 587,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1907,7 +1955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 588,
    "metadata": {},
    "outputs": [
     {
@@ -1977,7 +2025,7 @@
        "      <td>-1.037578</td>\n",
        "      <td>1.353782</td>\n",
        "      <td>-1.316082</td>\n",
-       "      <td>-1.213541</td>\n",
+       "      <td>-1.121364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>850</th>\n",
@@ -2001,7 +2049,7 @@
        "      <td>0.474671</td>\n",
        "      <td>1.353782</td>\n",
        "      <td>-1.489657</td>\n",
-       "      <td>-0.753720</td>\n",
+       "      <td>-0.675715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3126</th>\n",
@@ -2025,7 +2073,7 @@
        "      <td>-0.762623</td>\n",
        "      <td>-0.673150</td>\n",
        "      <td>0.771343</td>\n",
-       "      <td>0.798361</td>\n",
+       "      <td>0.795271</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11638</th>\n",
@@ -2049,7 +2097,7 @@
        "      <td>-0.762623</td>\n",
        "      <td>-0.673150</td>\n",
        "      <td>-0.679792</td>\n",
-       "      <td>-0.531793</td>\n",
+       "      <td>-0.537321</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4367</th>\n",
@@ -2073,7 +2121,7 @@
        "      <td>-0.487669</td>\n",
        "      <td>0.340316</td>\n",
        "      <td>-0.173638</td>\n",
-       "      <td>-0.322582</td>\n",
+       "      <td>-0.317791</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2117,16 +2165,16 @@
        "4367                 0.0  0.719981   -0.441003      -0.487669  0.340316   \n",
        "\n",
        "          Price  PricePred  \n",
-       "7472  -1.316082  -1.213541  \n",
-       "850   -1.489657  -0.753720  \n",
-       "3126   0.771343   0.798361  \n",
-       "11638 -0.679792  -0.531793  \n",
-       "4367  -0.173638  -0.322582  \n",
+       "7472  -1.316082  -1.121364  \n",
+       "850   -1.489657  -0.675715  \n",
+       "3126   0.771343   0.795271  \n",
+       "11638 -0.679792  -0.537321  \n",
+       "4367  -0.173638  -0.317791  \n",
        "\n",
        "[5 rows x 84 columns]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 588,
      "metadata": {},
      "output_type": "execute_result"
     }