diff --git a/.gitignore b/.gitignore
index 46117a8..dfdfd6a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,4 +3,5 @@ venv*
 .vscode*
 __pycache__*
 music_genre.csv
-music_genre.model
\ No newline at end of file
+*.model
+.ipynb_checkpoints*
\ No newline at end of file
diff --git a/Bayes.ipynb b/Bayes.ipynb
new file mode 100644
index 0000000..b0ba291
--- /dev/null
+++ b/Bayes.ipynb
@@ -0,0 +1,1121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Klasyfikacja za pomocą naiwnej metody bayesowskiej (rozkłady ciągłe)\n",
+    "Zasady zaliczenia: 40 punktów podzielone następująco:\n",
+    "- 10 pkt - prezentacja projektu\n",
+    "- 15 pkt - implementacja, w tym:\n",
+    "- 5 pkt - zgodność z tematem,\n",
+    "- 5 pkt - jakość kodu,\n",
+    "- 5 pkt - poprawność implementacji\n",
+    "- 10 pkt - efekt \"wow\"\n",
+    "- 5 pkt - aktywność wszystkich członków grupy\n",
+    "\n",
+    "Klasyfikacja za pomocą naiwnej metody bayesowskiej (rozkłady ciągłe). Implementacja powinna założyć, że cechy są ciągłe (do wyboru rozkład normalny i jądrowe wygładzenie). Na wejściu oczekiwany jest zbiór, który zawiera p-cech ciągłych, wektor etykiet oraz wektor prawdopodobieństw a priori dla klas. Na wyjściu otrzymujemy prognozowane etykiety oraz prawdopodobieństwa a posteriori. Dodatkową wartością może być wizualizacja obszarów decyzyjnych w przypadku dwóch cech.\n",
+    "\n",
+    "```Termin oddania na Moodle: do 31 maja. Prezentacja projektów 1 czerwca na ćwiczeniach.```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pandas==1.2.4 in c:\\users\\annad\\anaconda3\\lib\\site-packages (1.2.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from pandas==1.2.4) (2.8.1)\n",
+      "Requirement already satisfied: numpy>=1.16.5 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from pandas==1.2.4) (1.20.3)\n",
+      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from pandas==1.2.4) (2020.1)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas==1.2.4) (1.15.0)\n",
+      "Requirement already satisfied: numpy==1.20.3 in c:\\users\\annad\\anaconda3\\lib\\site-packages (1.20.3)\n",
+      "Requirement already satisfied: sklearn==0.0 in c:\\users\\annad\\anaconda3\\lib\\site-packages (0.0)\n",
+      "Requirement already satisfied: scikit-learn in c:\\users\\annad\\appdata\\roaming\\python\\python38\\site-packages (from sklearn==0.0) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (0.17.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (1.20.3)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (2.1.0)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (1.5.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install pandas==1.2.4\n",
+    "!pip install numpy==1.20.3\n",
+    "!pip install sklearn==0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import typing\n",
+    "import os, pickle\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wczytywanie i normalizacja danych"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stałe\n",
+    "genre_dict = {\n",
+    "    \"blues\" : 1,\n",
+    "    \"classical\" : 2,\n",
+    "    \"country\" : 3,\n",
+    "    \"disco\" : 4,\n",
+    "    \"hiphop\" : 5,\n",
+    "    \"jazz\" : 6,\n",
+    "    \"metal\" : 7,\n",
+    "    \"pop\" : 8,\n",
+    "    \"reggae\" : 9,\n",
+    "    \"rock\" : 10\n",
+    "}\n",
+    "filename = 'music_genre.csv'\n",
+    "model_path = 'model.model'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preparing data...\n"
+     ]
+    },
+    {
+     "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></th>\n",
+       "      <th>genre</th>\n",
+       "      <th>chroma_stft_mean</th>\n",
+       "      <th>chroma_stft_var</th>\n",
+       "      <th>rms_mean</th>\n",
+       "      <th>rms_var</th>\n",
+       "      <th>spectral_centroid_mean</th>\n",
+       "      <th>spectral_centroid_var</th>\n",
+       "      <th>spectral_bandwidth_mean</th>\n",
+       "      <th>spectral_bandwidth_var</th>\n",
+       "      <th>rolloff_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>mfcc16_mean</th>\n",
+       "      <th>mfcc16_var</th>\n",
+       "      <th>mfcc17_mean</th>\n",
+       "      <th>mfcc17_var</th>\n",
+       "      <th>mfcc18_mean</th>\n",
+       "      <th>mfcc18_var</th>\n",
+       "      <th>mfcc19_mean</th>\n",
+       "      <th>mfcc19_var</th>\n",
+       "      <th>mfcc20_mean</th>\n",
+       "      <th>mfcc20_var</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.350088</td>\n",
+       "      <td>0.088757</td>\n",
+       "      <td>0.130228</td>\n",
+       "      <td>0.002827</td>\n",
+       "      <td>1784.165850</td>\n",
+       "      <td>1.297741e+05</td>\n",
+       "      <td>2002.449060</td>\n",
+       "      <td>85882.761315</td>\n",
+       "      <td>3805.839606</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.752740</td>\n",
+       "      <td>52.420910</td>\n",
+       "      <td>-1.690215</td>\n",
+       "      <td>36.524071</td>\n",
+       "      <td>-0.408979</td>\n",
+       "      <td>41.597103</td>\n",
+       "      <td>-2.303523</td>\n",
+       "      <td>55.062923</td>\n",
+       "      <td>1.221291</td>\n",
+       "      <td>46.936035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.340914</td>\n",
+       "      <td>0.094980</td>\n",
+       "      <td>0.095948</td>\n",
+       "      <td>0.002373</td>\n",
+       "      <td>1530.176679</td>\n",
+       "      <td>3.758501e+05</td>\n",
+       "      <td>2039.036516</td>\n",
+       "      <td>213843.755497</td>\n",
+       "      <td>3550.522098</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.927998</td>\n",
+       "      <td>55.356403</td>\n",
+       "      <td>-0.731125</td>\n",
+       "      <td>60.314529</td>\n",
+       "      <td>0.295073</td>\n",
+       "      <td>48.120598</td>\n",
+       "      <td>-0.283518</td>\n",
+       "      <td>51.106190</td>\n",
+       "      <td>0.531217</td>\n",
+       "      <td>45.786282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.363637</td>\n",
+       "      <td>0.085275</td>\n",
+       "      <td>0.175570</td>\n",
+       "      <td>0.002746</td>\n",
+       "      <td>1552.811865</td>\n",
+       "      <td>1.564676e+05</td>\n",
+       "      <td>1747.702312</td>\n",
+       "      <td>76254.192257</td>\n",
+       "      <td>3042.260232</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.451690</td>\n",
+       "      <td>40.598766</td>\n",
+       "      <td>-7.729093</td>\n",
+       "      <td>47.639427</td>\n",
+       "      <td>-1.816407</td>\n",
+       "      <td>52.382141</td>\n",
+       "      <td>-3.439720</td>\n",
+       "      <td>46.639660</td>\n",
+       "      <td>-2.231258</td>\n",
+       "      <td>30.573025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.404785</td>\n",
+       "      <td>0.093999</td>\n",
+       "      <td>0.141093</td>\n",
+       "      <td>0.006346</td>\n",
+       "      <td>1070.106615</td>\n",
+       "      <td>1.843559e+05</td>\n",
+       "      <td>1596.412872</td>\n",
+       "      <td>166441.494769</td>\n",
+       "      <td>2184.745799</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.780874</td>\n",
+       "      <td>44.427753</td>\n",
+       "      <td>-3.319597</td>\n",
+       "      <td>50.206673</td>\n",
+       "      <td>0.636965</td>\n",
+       "      <td>37.319130</td>\n",
+       "      <td>-0.619121</td>\n",
+       "      <td>37.259739</td>\n",
+       "      <td>-3.407448</td>\n",
+       "      <td>31.949339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.308526</td>\n",
+       "      <td>0.087841</td>\n",
+       "      <td>0.091529</td>\n",
+       "      <td>0.002303</td>\n",
+       "      <td>1835.004266</td>\n",
+       "      <td>3.433999e+05</td>\n",
+       "      <td>1748.172116</td>\n",
+       "      <td>88445.209036</td>\n",
+       "      <td>3579.757627</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-4.520576</td>\n",
+       "      <td>86.099236</td>\n",
+       "      <td>-5.454034</td>\n",
+       "      <td>75.269707</td>\n",
+       "      <td>-0.916874</td>\n",
+       "      <td>53.613918</td>\n",
+       "      <td>-4.404827</td>\n",
+       "      <td>62.910812</td>\n",
+       "      <td>-11.703234</td>\n",
+       "      <td>55.195160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302456</td>\n",
+       "      <td>0.087532</td>\n",
+       "      <td>0.103494</td>\n",
+       "      <td>0.003981</td>\n",
+       "      <td>1831.993940</td>\n",
+       "      <td>1.030482e+06</td>\n",
+       "      <td>1729.653287</td>\n",
+       "      <td>201910.508633</td>\n",
+       "      <td>3481.517592</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.576589</td>\n",
+       "      <td>72.549225</td>\n",
+       "      <td>-1.838263</td>\n",
+       "      <td>68.702026</td>\n",
+       "      <td>-2.783800</td>\n",
+       "      <td>42.447453</td>\n",
+       "      <td>-3.047909</td>\n",
+       "      <td>39.808784</td>\n",
+       "      <td>-8.109991</td>\n",
+       "      <td>46.311005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.291328</td>\n",
+       "      <td>0.093981</td>\n",
+       "      <td>0.141874</td>\n",
+       "      <td>0.008803</td>\n",
+       "      <td>1459.366472</td>\n",
+       "      <td>4.378594e+05</td>\n",
+       "      <td>1389.009131</td>\n",
+       "      <td>185023.239545</td>\n",
+       "      <td>2795.610963</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-10.068051</td>\n",
+       "      <td>83.248245</td>\n",
+       "      <td>-10.913176</td>\n",
+       "      <td>56.902153</td>\n",
+       "      <td>-6.971336</td>\n",
+       "      <td>38.231800</td>\n",
+       "      <td>-3.436505</td>\n",
+       "      <td>48.235741</td>\n",
+       "      <td>-6.483466</td>\n",
+       "      <td>70.170364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.307955</td>\n",
+       "      <td>0.092903</td>\n",
+       "      <td>0.131822</td>\n",
+       "      <td>0.005531</td>\n",
+       "      <td>1451.667066</td>\n",
+       "      <td>4.495682e+05</td>\n",
+       "      <td>1577.270941</td>\n",
+       "      <td>168211.938804</td>\n",
+       "      <td>2954.836760</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-8.426083</td>\n",
+       "      <td>70.438438</td>\n",
+       "      <td>-10.568935</td>\n",
+       "      <td>52.090893</td>\n",
+       "      <td>-10.784515</td>\n",
+       "      <td>60.461330</td>\n",
+       "      <td>-4.690678</td>\n",
+       "      <td>65.547516</td>\n",
+       "      <td>-8.630722</td>\n",
+       "      <td>56.401436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.408879</td>\n",
+       "      <td>0.086512</td>\n",
+       "      <td>0.142416</td>\n",
+       "      <td>0.001507</td>\n",
+       "      <td>1719.368948</td>\n",
+       "      <td>1.632828e+05</td>\n",
+       "      <td>2031.740381</td>\n",
+       "      <td>105542.718193</td>\n",
+       "      <td>3782.316288</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.452559</td>\n",
+       "      <td>50.563751</td>\n",
+       "      <td>-7.041824</td>\n",
+       "      <td>28.894934</td>\n",
+       "      <td>2.695248</td>\n",
+       "      <td>36.889568</td>\n",
+       "      <td>3.412305</td>\n",
+       "      <td>33.698597</td>\n",
+       "      <td>-2.715692</td>\n",
+       "      <td>36.418430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.273950</td>\n",
+       "      <td>0.092316</td>\n",
+       "      <td>0.081314</td>\n",
+       "      <td>0.004347</td>\n",
+       "      <td>1817.150863</td>\n",
+       "      <td>2.982361e+05</td>\n",
+       "      <td>1973.773306</td>\n",
+       "      <td>114070.112591</td>\n",
+       "      <td>3943.490565</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.179920</td>\n",
+       "      <td>59.314602</td>\n",
+       "      <td>-1.916804</td>\n",
+       "      <td>58.418438</td>\n",
+       "      <td>-2.292661</td>\n",
+       "      <td>83.205231</td>\n",
+       "      <td>2.881967</td>\n",
+       "      <td>77.082222</td>\n",
+       "      <td>-4.235203</td>\n",
+       "      <td>91.468811</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   genre  chroma_stft_mean  chroma_stft_var  rms_mean   rms_var  \\\n",
+       "0      1          0.350088         0.088757  0.130228  0.002827   \n",
+       "1      1          0.340914         0.094980  0.095948  0.002373   \n",
+       "2      1          0.363637         0.085275  0.175570  0.002746   \n",
+       "3      1          0.404785         0.093999  0.141093  0.006346   \n",
+       "4      1          0.308526         0.087841  0.091529  0.002303   \n",
+       "5      1          0.302456         0.087532  0.103494  0.003981   \n",
+       "6      1          0.291328         0.093981  0.141874  0.008803   \n",
+       "7      1          0.307955         0.092903  0.131822  0.005531   \n",
+       "8      1          0.408879         0.086512  0.142416  0.001507   \n",
+       "9      1          0.273950         0.092316  0.081314  0.004347   \n",
+       "\n",
+       "   spectral_centroid_mean  spectral_centroid_var  spectral_bandwidth_mean  \\\n",
+       "0             1784.165850           1.297741e+05              2002.449060   \n",
+       "1             1530.176679           3.758501e+05              2039.036516   \n",
+       "2             1552.811865           1.564676e+05              1747.702312   \n",
+       "3             1070.106615           1.843559e+05              1596.412872   \n",
+       "4             1835.004266           3.433999e+05              1748.172116   \n",
+       "5             1831.993940           1.030482e+06              1729.653287   \n",
+       "6             1459.366472           4.378594e+05              1389.009131   \n",
+       "7             1451.667066           4.495682e+05              1577.270941   \n",
+       "8             1719.368948           1.632828e+05              2031.740381   \n",
+       "9             1817.150863           2.982361e+05              1973.773306   \n",
+       "\n",
+       "   spectral_bandwidth_var  rolloff_mean  ...  mfcc16_mean  mfcc16_var  \\\n",
+       "0            85882.761315   3805.839606  ...     0.752740   52.420910   \n",
+       "1           213843.755497   3550.522098  ...     0.927998   55.356403   \n",
+       "2            76254.192257   3042.260232  ...     2.451690   40.598766   \n",
+       "3           166441.494769   2184.745799  ...     0.780874   44.427753   \n",
+       "4            88445.209036   3579.757627  ...    -4.520576   86.099236   \n",
+       "5           201910.508633   3481.517592  ...    -5.576589   72.549225   \n",
+       "6           185023.239545   2795.610963  ...   -10.068051   83.248245   \n",
+       "7           168211.938804   2954.836760  ...    -8.426083   70.438438   \n",
+       "8           105542.718193   3782.316288  ...    -1.452559   50.563751   \n",
+       "9           114070.112591   3943.490565  ...    -1.179920   59.314602   \n",
+       "\n",
+       "   mfcc17_mean  mfcc17_var  mfcc18_mean  mfcc18_var  mfcc19_mean  mfcc19_var  \\\n",
+       "0    -1.690215   36.524071    -0.408979   41.597103    -2.303523   55.062923   \n",
+       "1    -0.731125   60.314529     0.295073   48.120598    -0.283518   51.106190   \n",
+       "2    -7.729093   47.639427    -1.816407   52.382141    -3.439720   46.639660   \n",
+       "3    -3.319597   50.206673     0.636965   37.319130    -0.619121   37.259739   \n",
+       "4    -5.454034   75.269707    -0.916874   53.613918    -4.404827   62.910812   \n",
+       "5    -1.838263   68.702026    -2.783800   42.447453    -3.047909   39.808784   \n",
+       "6   -10.913176   56.902153    -6.971336   38.231800    -3.436505   48.235741   \n",
+       "7   -10.568935   52.090893   -10.784515   60.461330    -4.690678   65.547516   \n",
+       "8    -7.041824   28.894934     2.695248   36.889568     3.412305   33.698597   \n",
+       "9    -1.916804   58.418438    -2.292661   83.205231     2.881967   77.082222   \n",
+       "\n",
+       "   mfcc20_mean  mfcc20_var  \n",
+       "0     1.221291   46.936035  \n",
+       "1     0.531217   45.786282  \n",
+       "2    -2.231258   30.573025  \n",
+       "3    -3.407448   31.949339  \n",
+       "4   -11.703234   55.195160  \n",
+       "5    -8.109991   46.311005  \n",
+       "6    -6.483466   70.170364  \n",
+       "7    -8.630722   56.401436  \n",
+       "8    -2.715692   36.418430  \n",
+       "9    -4.235203   91.468811  \n",
+       "\n",
+       "[10 rows x 58 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if os.path.isfile(filename):\n",
+    "    print(\"Loading prepared data...\")\n",
+    "    data = pd.read_csv(filename)\n",
+    "else:\n",
+    "    print(\"Preparing data...\")\n",
+    "    data = pd.read_csv('music_genre_raw.csv')\n",
+    "    column = data[\"label\"].apply(lambda x: genre_dict[x])\n",
+    "    data.insert(0, 'genre', column, 'int')\n",
+    "    data = data.drop(columns=['filename', 'label', 'length'])\n",
+    "    data.to_csv(filename, index=False)\n",
+    "display(data.head(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Podział danych na zbiory train i test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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></th>\n",
+       "      <th>chroma_stft_mean</th>\n",
+       "      <th>chroma_stft_var</th>\n",
+       "      <th>rms_mean</th>\n",
+       "      <th>rms_var</th>\n",
+       "      <th>spectral_centroid_mean</th>\n",
+       "      <th>spectral_centroid_var</th>\n",
+       "      <th>spectral_bandwidth_mean</th>\n",
+       "      <th>spectral_bandwidth_var</th>\n",
+       "      <th>rolloff_mean</th>\n",
+       "      <th>rolloff_var</th>\n",
+       "      <th>...</th>\n",
+       "      <th>mfcc16_mean</th>\n",
+       "      <th>mfcc16_var</th>\n",
+       "      <th>mfcc17_mean</th>\n",
+       "      <th>mfcc17_var</th>\n",
+       "      <th>mfcc18_mean</th>\n",
+       "      <th>mfcc18_var</th>\n",
+       "      <th>mfcc19_mean</th>\n",
+       "      <th>mfcc19_var</th>\n",
+       "      <th>mfcc20_mean</th>\n",
+       "      <th>mfcc20_var</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>0.516547</td>\n",
+       "      <td>0.072241</td>\n",
+       "      <td>0.267380</td>\n",
+       "      <td>0.001175</td>\n",
+       "      <td>3338.581900</td>\n",
+       "      <td>172002.893292</td>\n",
+       "      <td>2697.128636</td>\n",
+       "      <td>45771.294278</td>\n",
+       "      <td>6670.863091</td>\n",
+       "      <td>3.556853e+05</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.023183</td>\n",
+       "      <td>37.339474</td>\n",
+       "      <td>-8.121326</td>\n",
+       "      <td>33.968277</td>\n",
+       "      <td>4.910113</td>\n",
+       "      <td>42.063385</td>\n",
+       "      <td>-2.474697</td>\n",
+       "      <td>35.162354</td>\n",
+       "      <td>3.192656</td>\n",
+       "      <td>36.478157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.344511</td>\n",
+       "      <td>0.085002</td>\n",
+       "      <td>0.046747</td>\n",
+       "      <td>0.001542</td>\n",
+       "      <td>1503.869486</td>\n",
+       "      <td>554576.511533</td>\n",
+       "      <td>1754.216082</td>\n",
+       "      <td>283554.933422</td>\n",
+       "      <td>2799.283099</td>\n",
+       "      <td>2.685679e+06</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.957420</td>\n",
+       "      <td>50.311016</td>\n",
+       "      <td>-1.503434</td>\n",
+       "      <td>41.141155</td>\n",
+       "      <td>0.221949</td>\n",
+       "      <td>55.707256</td>\n",
+       "      <td>-1.991485</td>\n",
+       "      <td>50.006485</td>\n",
+       "      <td>-3.353825</td>\n",
+       "      <td>49.906403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>0.368345</td>\n",
+       "      <td>0.090390</td>\n",
+       "      <td>0.111073</td>\n",
+       "      <td>0.004402</td>\n",
+       "      <td>2446.919077</td>\n",
+       "      <td>490397.099115</td>\n",
+       "      <td>2449.159840</td>\n",
+       "      <td>215375.540632</td>\n",
+       "      <td>4958.057490</td>\n",
+       "      <td>2.650020e+06</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.122951</td>\n",
+       "      <td>78.892769</td>\n",
+       "      <td>-1.054999</td>\n",
+       "      <td>79.877068</td>\n",
+       "      <td>4.496278</td>\n",
+       "      <td>112.834435</td>\n",
+       "      <td>-0.978958</td>\n",
+       "      <td>75.059898</td>\n",
+       "      <td>-5.256925</td>\n",
+       "      <td>120.275269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>979</th>\n",
+       "      <td>0.360042</td>\n",
+       "      <td>0.083953</td>\n",
+       "      <td>0.116724</td>\n",
+       "      <td>0.000789</td>\n",
+       "      <td>2148.410463</td>\n",
+       "      <td>253618.158995</td>\n",
+       "      <td>2107.165355</td>\n",
+       "      <td>72155.551685</td>\n",
+       "      <td>4479.264304</td>\n",
+       "      <td>9.787046e+05</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.621152</td>\n",
+       "      <td>37.060532</td>\n",
+       "      <td>-13.479134</td>\n",
+       "      <td>50.848667</td>\n",
+       "      <td>3.308529</td>\n",
+       "      <td>47.726006</td>\n",
+       "      <td>-3.704957</td>\n",
+       "      <td>56.781952</td>\n",
+       "      <td>1.085497</td>\n",
+       "      <td>54.243389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>817</th>\n",
+       "      <td>0.425788</td>\n",
+       "      <td>0.091852</td>\n",
+       "      <td>0.139799</td>\n",
+       "      <td>0.003601</td>\n",
+       "      <td>1803.774378</td>\n",
+       "      <td>659241.158049</td>\n",
+       "      <td>1973.418903</td>\n",
+       "      <td>201432.199120</td>\n",
+       "      <td>3777.969679</td>\n",
+       "      <td>2.632339e+06</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.633915</td>\n",
+       "      <td>64.068756</td>\n",
+       "      <td>-2.219202</td>\n",
+       "      <td>99.249870</td>\n",
+       "      <td>5.304260</td>\n",
+       "      <td>64.088127</td>\n",
+       "      <td>-6.597187</td>\n",
+       "      <td>62.661850</td>\n",
+       "      <td>-2.923168</td>\n",
+       "      <td>67.490440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>0.495959</td>\n",
+       "      <td>0.072854</td>\n",
+       "      <td>0.117362</td>\n",
+       "      <td>0.000867</td>\n",
+       "      <td>2657.912854</td>\n",
+       "      <td>189139.438926</td>\n",
+       "      <td>2345.662472</td>\n",
+       "      <td>32730.579626</td>\n",
+       "      <td>5358.261979</td>\n",
+       "      <td>5.918222e+05</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.089191</td>\n",
+       "      <td>27.937113</td>\n",
+       "      <td>-10.676390</td>\n",
+       "      <td>26.519361</td>\n",
+       "      <td>3.875155</td>\n",
+       "      <td>25.613684</td>\n",
+       "      <td>-4.943561</td>\n",
+       "      <td>24.334734</td>\n",
+       "      <td>3.255899</td>\n",
+       "      <td>25.199259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>814</th>\n",
+       "      <td>0.395137</td>\n",
+       "      <td>0.093939</td>\n",
+       "      <td>0.114246</td>\n",
+       "      <td>0.004025</td>\n",
+       "      <td>1716.249594</td>\n",
+       "      <td>920189.339374</td>\n",
+       "      <td>2062.885827</td>\n",
+       "      <td>358557.016423</td>\n",
+       "      <td>3790.901258</td>\n",
+       "      <td>4.734865e+06</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.066329</td>\n",
+       "      <td>66.090370</td>\n",
+       "      <td>-4.590122</td>\n",
+       "      <td>72.595345</td>\n",
+       "      <td>4.261040</td>\n",
+       "      <td>63.185764</td>\n",
+       "      <td>-2.127876</td>\n",
+       "      <td>50.693245</td>\n",
+       "      <td>-3.665569</td>\n",
+       "      <td>89.750290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.249535</td>\n",
+       "      <td>0.087563</td>\n",
+       "      <td>0.060560</td>\n",
+       "      <td>0.001276</td>\n",
+       "      <td>1465.857446</td>\n",
+       "      <td>143302.098295</td>\n",
+       "      <td>1738.858902</td>\n",
+       "      <td>58868.399307</td>\n",
+       "      <td>2822.406728</td>\n",
+       "      <td>7.392007e+05</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.887793</td>\n",
+       "      <td>109.811813</td>\n",
+       "      <td>-0.027696</td>\n",
+       "      <td>113.660950</td>\n",
+       "      <td>2.098475</td>\n",
+       "      <td>160.025497</td>\n",
+       "      <td>1.109709</td>\n",
+       "      <td>136.810165</td>\n",
+       "      <td>2.935807</td>\n",
+       "      <td>95.914490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>0.353474</td>\n",
+       "      <td>0.087755</td>\n",
+       "      <td>0.052264</td>\n",
+       "      <td>0.000316</td>\n",
+       "      <td>1993.352766</td>\n",
+       "      <td>64753.479332</td>\n",
+       "      <td>2127.165109</td>\n",
+       "      <td>36027.039069</td>\n",
+       "      <td>4248.194549</td>\n",
+       "      <td>3.987029e+05</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.366530</td>\n",
+       "      <td>57.230133</td>\n",
+       "      <td>-1.110214</td>\n",
+       "      <td>48.080849</td>\n",
+       "      <td>-0.784249</td>\n",
+       "      <td>57.033504</td>\n",
+       "      <td>-2.984207</td>\n",
+       "      <td>55.737625</td>\n",
+       "      <td>0.350456</td>\n",
+       "      <td>64.126846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>940</th>\n",
+       "      <td>0.416089</td>\n",
+       "      <td>0.087772</td>\n",
+       "      <td>0.142935</td>\n",
+       "      <td>0.003150</td>\n",
+       "      <td>3009.958707</td>\n",
+       "      <td>435134.775688</td>\n",
+       "      <td>2778.049758</td>\n",
+       "      <td>135548.871316</td>\n",
+       "      <td>6131.200719</td>\n",
+       "      <td>1.788624e+06</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.717880</td>\n",
+       "      <td>42.315434</td>\n",
+       "      <td>-3.953057</td>\n",
+       "      <td>48.761936</td>\n",
+       "      <td>-3.092345</td>\n",
+       "      <td>49.514446</td>\n",
+       "      <td>-2.731183</td>\n",
+       "      <td>58.219994</td>\n",
+       "      <td>-0.909785</td>\n",
+       "      <td>63.111858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 57 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     chroma_stft_mean  chroma_stft_var  rms_mean   rms_var  \\\n",
+       "687          0.516547         0.072241  0.267380  0.001175   \n",
+       "500          0.344511         0.085002  0.046747  0.001542   \n",
+       "332          0.368345         0.090390  0.111073  0.004402   \n",
+       "979          0.360042         0.083953  0.116724  0.000789   \n",
+       "817          0.425788         0.091852  0.139799  0.003601   \n",
+       "620          0.495959         0.072854  0.117362  0.000867   \n",
+       "814          0.395137         0.093939  0.114246  0.004025   \n",
+       "516          0.249535         0.087563  0.060560  0.001276   \n",
+       "518          0.353474         0.087755  0.052264  0.000316   \n",
+       "940          0.416089         0.087772  0.142935  0.003150   \n",
+       "\n",
+       "     spectral_centroid_mean  spectral_centroid_var  spectral_bandwidth_mean  \\\n",
+       "687             3338.581900          172002.893292              2697.128636   \n",
+       "500             1503.869486          554576.511533              1754.216082   \n",
+       "332             2446.919077          490397.099115              2449.159840   \n",
+       "979             2148.410463          253618.158995              2107.165355   \n",
+       "817             1803.774378          659241.158049              1973.418903   \n",
+       "620             2657.912854          189139.438926              2345.662472   \n",
+       "814             1716.249594          920189.339374              2062.885827   \n",
+       "516             1465.857446          143302.098295              1738.858902   \n",
+       "518             1993.352766           64753.479332              2127.165109   \n",
+       "940             3009.958707          435134.775688              2778.049758   \n",
+       "\n",
+       "     spectral_bandwidth_var  rolloff_mean   rolloff_var  ...  mfcc16_mean  \\\n",
+       "687            45771.294278   6670.863091  3.556853e+05  ...     8.023183   \n",
+       "500           283554.933422   2799.283099  2.685679e+06  ...    -1.957420   \n",
+       "332           215375.540632   4958.057490  2.650020e+06  ...     0.122951   \n",
+       "979            72155.551685   4479.264304  9.787046e+05  ...    -0.621152   \n",
+       "817           201432.199120   3777.969679  2.632339e+06  ...     3.633915   \n",
+       "620            32730.579626   5358.261979  5.918222e+05  ...     5.089191   \n",
+       "814           358557.016423   3790.901258  4.734865e+06  ...     3.066329   \n",
+       "516            58868.399307   2822.406728  7.392007e+05  ...     2.887793   \n",
+       "518            36027.039069   4248.194549  3.987029e+05  ...    12.366530   \n",
+       "940           135548.871316   6131.200719  1.788624e+06  ...    -5.717880   \n",
+       "\n",
+       "     mfcc16_var  mfcc17_mean  mfcc17_var  mfcc18_mean  mfcc18_var  \\\n",
+       "687   37.339474    -8.121326   33.968277     4.910113   42.063385   \n",
+       "500   50.311016    -1.503434   41.141155     0.221949   55.707256   \n",
+       "332   78.892769    -1.054999   79.877068     4.496278  112.834435   \n",
+       "979   37.060532   -13.479134   50.848667     3.308529   47.726006   \n",
+       "817   64.068756    -2.219202   99.249870     5.304260   64.088127   \n",
+       "620   27.937113   -10.676390   26.519361     3.875155   25.613684   \n",
+       "814   66.090370    -4.590122   72.595345     4.261040   63.185764   \n",
+       "516  109.811813    -0.027696  113.660950     2.098475  160.025497   \n",
+       "518   57.230133    -1.110214   48.080849    -0.784249   57.033504   \n",
+       "940   42.315434    -3.953057   48.761936    -3.092345   49.514446   \n",
+       "\n",
+       "     mfcc19_mean  mfcc19_var  mfcc20_mean  mfcc20_var  \n",
+       "687    -2.474697   35.162354     3.192656   36.478157  \n",
+       "500    -1.991485   50.006485    -3.353825   49.906403  \n",
+       "332    -0.978958   75.059898    -5.256925  120.275269  \n",
+       "979    -3.704957   56.781952     1.085497   54.243389  \n",
+       "817    -6.597187   62.661850    -2.923168   67.490440  \n",
+       "620    -4.943561   24.334734     3.255899   25.199259  \n",
+       "814    -2.127876   50.693245    -3.665569   89.750290  \n",
+       "516     1.109709  136.810165     2.935807   95.914490  \n",
+       "518    -2.984207   55.737625     0.350456   64.126846  \n",
+       "940    -2.731183   58.219994    -0.909785   63.111858  \n",
+       "\n",
+       "[10 rows x 57 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = data.drop([\"genre\"], axis=1)\n",
+    "Y = data[\"genre\"]\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.20, random_state = False)\n",
+    "display(X_train.head(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ilość krotek dla poszczególnych gatunków z podziałem na test/train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "blues\ttest: 15\ttrain: 85\tall: 100\n",
+      "classical\ttest: 11\ttrain: 89\tall: 100\n",
+      "country\ttest: 27\ttrain: 73\tall: 100\n",
+      "disco\ttest: 22\ttrain: 78\tall: 100\n",
+      "hiphop\ttest: 23\ttrain: 77\tall: 100\n",
+      "jazz\ttest: 18\ttrain: 82\tall: 100\n",
+      "metal\ttest: 20\ttrain: 80\tall: 100\n",
+      "pop\ttest: 24\ttrain: 76\tall: 100\n",
+      "reggae\ttest: 15\ttrain: 85\tall: 100\n",
+      "rock\ttest: 25\ttrain: 75\tall: 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in genre_dict.keys():\n",
+    "    count = len(data[data[\"genre\"]==genre_dict[key]])\n",
+    "    count_train = len(X_train[Y_train==genre_dict[key]])\n",
+    "    count_test = len(X_test[Y_test==genre_dict[key]])\n",
+    "    print(f\"{key}\\ttest: {count_test}\\ttrain: {count_train}\\tall: {count}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Wczytywanie modelu (utworzenie nowego w przypadku jego braku)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating model\n",
+      "Model saved (model.model)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if os.path.isfile(model_path):\n",
+    "    print(\"Loading model\")\n",
+    "    with open(model_path, 'rb') as file:\n",
+    "        model = pickle.load(file)\n",
+    "    print(f\"Model loaded ({model_path})\")\n",
+    "else:\n",
+    "    print(\"Creating model\")\n",
+    "    model = GaussianNB()\n",
+    "    model.fit(X_train, Y_train)\n",
+    "    with open(model_path, 'wb') as file:\n",
+    "        pickle.dump(model, file)\n",
+    "    print(f\"Model saved ({model_path})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ewaluacja"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Zbiór trenujący"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(Train data) Confusion matrix:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[27,  1, 13,  1,  0,  2, 32,  0,  9,  0],\n",
+       "       [ 0, 82,  0,  0,  0,  0,  3,  0,  3,  1],\n",
+       "       [ 8,  0, 37, 11,  1,  0,  8,  1,  6,  1],\n",
+       "       [ 1,  0,  3, 35,  5,  0, 23,  3,  3,  5],\n",
+       "       [ 1,  0,  5, 14, 28,  0, 12,  7,  8,  2],\n",
+       "       [12, 18,  2, 11,  1, 17, 14,  1,  0,  6],\n",
+       "       [ 0,  0,  1,  8,  0,  0, 70,  0,  0,  1],\n",
+       "       [ 0,  0,  2, 20,  4,  1,  0, 42,  5,  2],\n",
+       "       [ 5,  0, 12,  4,  6,  0,  4,  7, 45,  2],\n",
+       "       [ 1,  0, 10, 17,  2,  0, 29,  1,  6,  9]], dtype=int64)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(Train data) Accuracy:\n",
+      "0.49\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y_train_predicted = model.predict(X_train)\n",
+    "cm = confusion_matrix(Y_train, Y_train_predicted)\n",
+    "ac = accuracy_score(Y_train, Y_train_predicted)\n",
+    "print(\"(Train data) Confusion matrix:\")\n",
+    "display(cm)\n",
+    "print(\"(Train data) Accuracy:\")\n",
+    "print(ac)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Zbiór testowy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion matrix:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3,  0,  2,  0,  0,  0,  7,  0,  3,  0],\n",
+       "       [ 1, 10,  0,  0,  0,  0,  0,  0,  0,  0],\n",
+       "       [ 6,  0,  9,  4,  0,  0,  4,  0,  2,  2],\n",
+       "       [ 1,  0,  1,  6,  3,  0,  4,  0,  3,  4],\n",
+       "       [ 1,  0,  3,  2,  5,  0,  6,  1,  4,  1],\n",
+       "       [ 1,  4,  2,  2,  0,  4,  3,  1,  1,  0],\n",
+       "       [ 0,  0,  1,  4,  1,  0, 14,  0,  0,  0],\n",
+       "       [ 0,  0,  1,  4,  2,  0,  1, 13,  1,  2],\n",
+       "       [ 1,  0,  1,  1,  2,  0,  0,  1,  7,  2],\n",
+       "       [ 1,  1,  5,  6,  1,  1,  9,  0,  0,  1]], dtype=int64)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:\n",
+      "0.36\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y_test_predicted = model.predict(X_test)\n",
+    "cm = confusion_matrix(Y_test, Y_test_predicted)\n",
+    "ac = accuracy_score(Y_test, Y_test_predicted)\n",
+    "print(\"Confusion matrix:\")\n",
+    "display(cm)\n",
+    "print(\"Accuracy:\")\n",
+    "print(ac)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Przykładowe porównania"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Y: 10\tPredicted: 7\n",
+      "Y: 9\tPredicted: 9\n",
+      "Y: 3\tPredicted: 3\n",
+      "Y: 6\tPredicted: 6\n",
+      "Y: 7\tPredicted: 7\n",
+      "Y: 10\tPredicted: 7\n",
+      "Y: 1\tPredicted: 1\n",
+      "Y: 3\tPredicted: 10\n",
+      "Y: 4\tPredicted: 7\n",
+      "Y: 8\tPredicted: 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(10):\n",
+    "    print(f\"Y: {Y_test.to_numpy()[i]}\\tPredicted: {Y_test_predicted[i]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Porównanie accuracy na zbiorach train/test dla różnych modeli typu NB\n",
+    "\n",
+    "| Class | Acc_Train | Acc_Test | Uwagi | \n",
+    "|---|---|---|---|\n",
+    "| GaussianNB | 0.49 | 0.36 | - |\n",
+    "| MultinomialNB | - | - | Brak obsługi ujemnego inputu |\n",
+    "| ComplementNB | - | - | Brak obsługi ujemnego inputu |\n",
+    "| BernoulliNB | 0.35125 | 0.305 | - |\n",
+    "| CategoricalNB  | - | - | Brak obsługi ujemnego inputu |"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Readme.md b/Readme.md
index 7dafd35..8785370 100644
--- a/Readme.md
+++ b/Readme.md
@@ -10,4 +10,12 @@ Zasady zaliczenia: 40 punktów podzielone następująco:
 
 Klasyfikacja za pomocą naiwnej metody bayesowskiej (rozkłady ciągłe). Implementacja powinna założyć, że cechy są ciągłe (do wyboru rozkład normalny i jądrowe wygładzenie). Na wejściu oczekiwany jest zbiór, który zawiera p-cech ciągłych, wektor etykiet oraz wektor prawdopodobieństw a priori dla klas. Na wyjściu otrzymujemy prognozowane etykiety oraz prawdopodobieństwa a posteriori. Dodatkową wartością może być wizualizacja obszarów decyzyjnych w przypadku dwóch cech.
 
-```Termin oddania na Moodle: do 31 maja. Prezentacja projektów 1 czerwca na ćwiczeniach.```
\ No newline at end of file
+```Termin oddania na Moodle: do 31 maja. Prezentacja projektów 1 czerwca na ćwiczeniach.```
+
+| Class | Acc_Train | Acc_Test | Uwagi | 
+|---|---|---|---|
+| GaussianNB | 0.49 | 0.36 | - |
+| MultinomialNB | - | - | Bez ujemnego inputu |
+| ComplementNB | - | - | Bez ujemnego inputu |
+| BernoulliNB | 0.35125 | 0.305 | - |
+| CategoricalNB  | - | - | Bez ujemnego inputu |
\ No newline at end of file
diff --git a/datapreparator.py b/datapreparator.py
index 408a72b..8c20e86 100644
--- a/datapreparator.py
+++ b/datapreparator.py
@@ -21,12 +21,19 @@ class DataPreparator:
     def prepare_data(df: pd.DataFrame) -> pd.DataFrame:
         data = deepcopy(df)
         column = df["label"].apply(lambda x: DataPreparator.genre_dict[x])
-        data.insert(0, 'genre', column, 'float')
+        data.insert(0, 'genre', column, 'int')
         data = data.drop(columns=['filename', 'label', 'length'])
         return data
 
     
     def train_test_split(df: pd.DataFrame) -> typing.Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:
-        X = df.drop(["genre"], axis=1)
+        #X = df.drop(["genre"], axis=1)
+        X = df[["chroma_stft_mean","chroma_stft_var","rms_mean"]]
         Y = df["genre"]
-        return train_test_split(X, Y, test_size = 0.20, random_state = False)
\ No newline at end of file
+        return train_test_split(X, Y, test_size = 0.20, random_state = False)
+
+
+    def print_df_info(df: pd.DataFrame) -> None:
+        for key in DataPreparator.genre_dict.keys():
+            count = len(df[df["genre"]==DataPreparator.genre_dict[key]])
+            print(f"Key: {key}\tCount: {count}")
\ No newline at end of file
diff --git a/main.py b/main.py
index be5f612..616be32 100644
--- a/main.py
+++ b/main.py
@@ -13,11 +13,10 @@ else:
 
 X_train, X_test, Y_train, Y_test = DataPreparator.train_test_split(data)
 
-bayes = Bayes('music_genre.model')
+bayes = Bayes('_model.model')
 if(not bayes.model_exists):
     bayes.train(X_train, Y_train)
 
-
 Y_predicted = bayes.predict(X_train)
 eval_result = bayes.eval(Y_train, Y_predicted)
 print("Train:")
@@ -25,5 +24,11 @@ print(eval_result[1])
 
 Y_predicted = bayes.predict(X_test)
 eval_result = bayes.eval(Y_test, Y_predicted)
+
 print("Test:")
 print(eval_result[1])
+
+#Result preview
+# for i in range(100):
+#     print(f"Expected: {Y_test.to_numpy()[i]}\tPred: {Y_predicted[i]}")
+DataPreparator.print_df_info(data)
\ No newline at end of file