diff --git a/main.ipynb b/main.ipynb
index 24454d1a..85ca9d7e 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -22,7 +22,7 @@
     "from statistics import median\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.feature_selection import RFE\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor, plot_tree\n",
     "import numpy as np\n",
     "from sklearn.metrics import mean_squared_error\n",
     "import matplotlib.pyplot as plt\n",
@@ -1483,6 +1483,53 @@
     "display(X_test[np.abs(y_test - y_pred_test) < 1])\n",
     "display(X_test['Suma'][np.abs(y_test - y_pred_test) < 1].drop_duplicates().values)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3391 — Dochody_podatek_od_srodkow_transportowych\n",
+      "0.1816 — Dochody_podatek_od_spadkow\n",
+      "0.1590 — Powierzchnia\n",
+      "0.0714 — Wynagrodzenie_ogolem\n",
+      "0.0675 — Dochody_podatek_lesny\n",
+      "0.0377 — Dochody_razem\n",
+      "0.0312 — Dochody_podatek_rolny\n",
+      "0.0258 — Dochody_z_majatku\n",
+      "0.0220 — Dochody_podatek_PCC\n",
+      "0.0172 — Wynagrodzenie_w_relacji_do_sredniej\n",
+      "0.0172 — Dochody_podatek_od_dzialalnosci_gospodarczej\n",
+      "0.0158 — Dochody_podatek_od_nieruchomosci\n",
+      "0.0144 — Dochody_podatek_odrebne_ustawy\n"
+     ]
+    }
+   ],
+   "source": [
+    "selector = RFE(estimator=DecisionTreeRegressor(random_state=0, max_depth=best_params[0]), n_features_to_select=best_params[1])\n",
+    "selector.fit(X, y)\n",
+    "X_selected = selector.transform(X)\n",
+    "\n",
+    "model = DecisionTreeRegressor(random_state=0, max_depth=best_params[0])\n",
+    "model.fit(X_selected, y)\n",
+    "\n",
+    "feature_importance = dict(zip(feature_names, model.feature_importances_))\n",
+    "for feature, importance in sorted(feature_importance.items(), key=lambda x: x[1], reverse=True):\n",
+    "    print(f'{importance:.4f} \\u2014 {feature}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot_tree(model, fontsize=5)"
+   ]
   }
  ],
  "metadata": {