diff --git a/dwarves/Mining_HQ.ipynb b/dwarves/Mining_HQ.ipynb
index 44e61ce..27d2690 100644
--- a/dwarves/Mining_HQ.ipynb
+++ b/dwarves/Mining_HQ.ipynb
@@ -1068,79 +1068,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<bound method NDFrame.head of           Rank                                          Name  \\\n",
-      "19        20.0                            Grand Theft Auto V   \n",
-      "20        21.0                            Grand Theft Auto V   \n",
-      "21        22.0  Brain Age: Train Your Brain in Minutes a Day   \n",
-      "24        25.0                                  Mario Kart 7   \n",
-      "25        26.0               Pokemon Diamond / Pearl Version   \n",
-      "...        ...                                           ...   \n",
-      "55090  55091.0                                        Strafe   \n",
-      "55423  55424.0                                       Thumper   \n",
-      "55490  55491.0          Travis Strikes Again: No More Heroes   \n",
-      "55528  55529.0                                    TumbleSeed   \n",
-      "55653  55654.0                             Wheels of Aurelia   \n",
-      "\n",
-      "                  Genre ESRB_Rating Platform                     Publisher  \\\n",
-      "19               Action           M      PS3                Rockstar Games   \n",
-      "20               Action           M      PS4                Rockstar Games   \n",
-      "21                 Misc           E       DS                      Nintendo   \n",
-      "24               Racing           E      3DS                      Nintendo   \n",
-      "25         Role-Playing           E       DS                      Nintendo   \n",
-      "...                 ...         ...      ...                           ...   \n",
-      "55090           Shooter           M       PC              Devolver Digital   \n",
-      "55423             Music         E10       NS                         Drool   \n",
-      "55490            Action           M       NS  Grasshopper Manufacture Inc.   \n",
-      "55528  Action-Adventure           E       NS                        aeiowu   \n",
-      "55653      Visual Novel           T      PS4                  Mixedbag Srl   \n",
-      "\n",
-      "                          Developer  Critic_Score  User_Score  Total_Shipped  \\\n",
-      "19                   Rockstar North           1.0    9.507143      20.076667   \n",
-      "20                   Rockstar North           1.0    9.528571      19.543333   \n",
-      "21                     Nintendo SDD           1.0    9.550000      19.010000   \n",
-      "24     Nintendo EAD / Retro Studios           1.0    9.614286      18.110000   \n",
-      "25                       Game Freak           1.0    9.635714      17.670000   \n",
-      "...                             ...           ...         ...            ...   \n",
-      "55090                  Pixel Titans           1.0    8.000000       0.030000   \n",
-      "55423                         Drool           1.0    9.300000       0.030000   \n",
-      "55490       Grasshopper Manufacture           1.0    8.309565       0.030000   \n",
-      "55528               Team TumbleSeed           1.0    7.747826       0.030000   \n",
-      "55653             Santa Ragione Srl           1.0    8.550000       0.030000   \n",
-      "\n",
-      "       NA_Sales  PAL_Sales    Year bin_Critic_Score  bin_value  \\\n",
-      "19        6.370      9.850  2013.0             larg        8.5   \n",
-      "20        6.060      9.710  2014.0             larg        8.5   \n",
-      "21        6.295      9.288  2006.0             larg        8.5   \n",
-      "24        7.000      8.022  2011.0             larg        8.5   \n",
-      "25        7.235      7.600  2007.0             larg        8.5   \n",
-      "...         ...        ...     ...              ...        ...   \n",
-      "55090     0.000      0.000  2017.0             larg        8.5   \n",
-      "55423     0.000      0.000  2017.0             larg        8.5   \n",
-      "55490     0.000      0.000  2019.0             larg        8.5   \n",
-      "55528     0.000      0.000  2017.0             epik        5.5   \n",
-      "55653     0.000      0.000  2016.0             larg        8.5   \n",
-      "\n",
-      "       Critic_Score_Norm  Kmean_Labels  my_channel  \n",
-      "19              1.830687             4         NaN  \n",
-      "20              2.083244             4         NaN  \n",
-      "21              0.736276             4         NaN  \n",
-      "24              0.820462             4         NaN  \n",
-      "25              1.157204             4         NaN  \n",
-      "...                  ...           ...         ...  \n",
-      "55090          -0.021392             6         NaN  \n",
-      "55423           1.493945             8         NaN  \n",
-      "55490           0.652091             6         NaN  \n",
-      "55528          -0.189763             6         NaN  \n",
-      "55653           0.652091             6         NaN  \n",
-      "\n",
-      "[6116 rows x 18 columns]>\n",
       "                  Genre  ESRB_Rating  Platform  NA_Sales\n",
       "19               Action            3        17       750\n",
       "20               Action            3        18       742\n",
@@ -1161,19 +1095,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\hellom\\AppData\\Local\\Temp\\ipykernel_12888\\1695520614.py:18: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_8738/2203680914.py:18: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  to_be_nodes[\"NA_Sales\"] = le.fit_transform(gammas[\"NA_Sales\"])\n",
-      "C:\\Users\\hellom\\AppData\\Local\\Temp\\ipykernel_12888\\1695520614.py:19: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_8738/2203680914.py:19: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  to_be_nodes[\"ESRB_Rating\"] = le.fit_transform(gammas[\"ESRB_Rating\"])\n",
-      "C:\\Users\\hellom\\AppData\\Local\\Temp\\ipykernel_12888\\1695520614.py:20: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_8738/2203680914.py:20: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1189,7 +1123,7 @@
     "gammas.loc[gammas[\"Critic_Score\"] < 5, 'Critic_Score'] = 0\n",
     "gammas.loc[gammas[\"Critic_Score\"] > 5, 'Critic_Score'] = 1\n",
     "\n",
-    "print(gammas.head)\n",
+    "# print(gammas.head)\n",
     "\n",
     "# Columns to be considered as nodes\n",
     "to_be_nodes = gammas[node_cols]\n",
@@ -1197,9 +1131,9 @@
     "\n",
     "le = preprocessing.LabelEncoder()\n",
     "# Attribute to be predicted\n",
-    "predikt_col = le.fit_transform(gammas[\"Genre\"])\n",
+    "predikt_col = le.fit_transform(gammas[\"Critic_Score\"])\n",
     "\n",
-    "# to_be_nodes[\"Genre\"] = le.fit_transform(gammas[\"Genre\"])\n",
+    "to_be_nodes[\"Genre\"] = le.fit_transform(gammas[\"Genre\"])\n",
     "to_be_nodes[\"NA_Sales\"] = le.fit_transform(gammas[\"NA_Sales\"])\n",
     "to_be_nodes[\"ESRB_Rating\"] = le.fit_transform(gammas[\"ESRB_Rating\"])\n",
     "to_be_nodes[\"Platform\"] = le.fit_transform(gammas[\"Platform\"])\n",
@@ -1231,18 +1165,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'DecisionTreeClassifier' is not defined",
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: 'Action-Adventure'",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m classifier_obj \u001b[39m=\u001b[39m DecisionTreeClassifier()\n\u001b[0;32m      3\u001b[0m classifier_obj \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39mfit(node_train, predikt_train)\n\u001b[0;32m      5\u001b[0m predikt_result \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39mpredict(node_test)\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'DecisionTreeClassifier' is not defined"
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[54], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m classifier_obj \u001b[39m=\u001b[39m DecisionTreeClassifier()\n\u001b[0;32m----> 3\u001b[0m classifier_obj \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39;49mfit(node_train, predikt_train)\n\u001b[1;32m      5\u001b[0m predikt_result \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39mpredict(node_test)\n\u001b[1;32m      7\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mACCURACY FOR MODEL PRE: \u001b[39m\u001b[39m\"\u001b[39m, metrics\u001b[39m.\u001b[39maccuracy_score(predikt_test, predikt_result))\n",
+      "File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/tree/_classes.py:889\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    859\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, check_input\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m    860\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[1;32m    861\u001b[0m \n\u001b[1;32m    862\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    886\u001b[0m \u001b[39m        Fitted estimator.\u001b[39;00m\n\u001b[1;32m    887\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 889\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit(\n\u001b[1;32m    890\u001b[0m         X,\n\u001b[1;32m    891\u001b[0m         y,\n\u001b[1;32m    892\u001b[0m         sample_weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[1;32m    893\u001b[0m         check_input\u001b[39m=\u001b[39;49mcheck_input,\n\u001b[1;32m    894\u001b[0m     )\n\u001b[1;32m    895\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
+      "File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/tree/_classes.py:186\u001b[0m, in \u001b[0;36mBaseDecisionTree.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    184\u001b[0m check_X_params \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(dtype\u001b[39m=\u001b[39mDTYPE, accept_sparse\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcsc\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    185\u001b[0m check_y_params \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(ensure_2d\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, dtype\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[0;32m--> 186\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[1;32m    187\u001b[0m     X, y, validate_separately\u001b[39m=\u001b[39;49m(check_X_params, check_y_params)\n\u001b[1;32m    188\u001b[0m )\n\u001b[1;32m    189\u001b[0m \u001b[39mif\u001b[39;00m issparse(X):\n\u001b[1;32m    190\u001b[0m     X\u001b[39m.\u001b[39msort_indices()\n",
+      "File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/base.py:549\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    547\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mestimator\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m check_X_params:\n\u001b[1;32m    548\u001b[0m     check_X_params \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mdefault_check_params, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_X_params}\n\u001b[0;32m--> 549\u001b[0m X \u001b[39m=\u001b[39m check_array(X, input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_X_params)\n\u001b[1;32m    550\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mestimator\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m check_y_params:\n\u001b[1;32m    551\u001b[0m     check_y_params \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mdefault_check_params, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params}\n",
+      "File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/utils/validation.py:877\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    875\u001b[0m         array \u001b[39m=\u001b[39m xp\u001b[39m.\u001b[39mastype(array, dtype, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m    876\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 877\u001b[0m         array \u001b[39m=\u001b[39m _asarray_with_order(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype, xp\u001b[39m=\u001b[39;49mxp)\n\u001b[1;32m    878\u001b[0m \u001b[39mexcept\u001b[39;00m ComplexWarning \u001b[39mas\u001b[39;00m complex_warning:\n\u001b[1;32m    879\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    880\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mComplex data not supported\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(array)\n\u001b[1;32m    881\u001b[0m     ) \u001b[39mfrom\u001b[39;00m \u001b[39mcomplex_warning\u001b[39;00m\n",
+      "File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/utils/_array_api.py:185\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    182\u001b[0m     xp, _ \u001b[39m=\u001b[39m get_namespace(array)\n\u001b[1;32m    183\u001b[0m \u001b[39mif\u001b[39;00m xp\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m \u001b[39min\u001b[39;00m {\u001b[39m\"\u001b[39m\u001b[39mnumpy\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnumpy.array_api\u001b[39m\u001b[39m\"\u001b[39m}:\n\u001b[1;32m    184\u001b[0m     \u001b[39m# Use NumPy API to support order\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m     array \u001b[39m=\u001b[39m numpy\u001b[39m.\u001b[39;49masarray(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype)\n\u001b[1;32m    186\u001b[0m     \u001b[39mreturn\u001b[39;00m xp\u001b[39m.\u001b[39masarray(array, copy\u001b[39m=\u001b[39mcopy)\n\u001b[1;32m    187\u001b[0m \u001b[39melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/pandas/core/generic.py:2070\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   2069\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array__\u001b[39m(\u001b[39mself\u001b[39m, dtype: npt\u001b[39m.\u001b[39mDTypeLike \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m np\u001b[39m.\u001b[39mndarray:\n\u001b[0;32m-> 2070\u001b[0m     \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39;49masarray(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_values, dtype\u001b[39m=\u001b[39;49mdtype)\n",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Action-Adventure'"
      ]
     }
    ],