diff --git a/iris.csv b/iris.csv
new file mode 100644
index 0000000..1b9d029
--- /dev/null
+++ b/iris.csv
@@ -0,0 +1,151 @@
+"sepal.length","sepal.width","petal.length","petal.width","variety"
+5.1,3.5,1.4,.2,"Setosa"
+4.9,3,1.4,.2,"Setosa"
+4.7,3.2,1.3,.2,"Setosa"
+4.6,3.1,1.5,.2,"Setosa"
+5,3.6,1.4,.2,"Setosa"
+5.4,3.9,1.7,.4,"Setosa"
+4.6,3.4,1.4,.3,"Setosa"
+5,3.4,1.5,.2,"Setosa"
+4.4,2.9,1.4,.2,"Setosa"
+4.9,3.1,1.5,.1,"Setosa"
+5.4,3.7,1.5,.2,"Setosa"
+4.8,3.4,1.6,.2,"Setosa"
+4.8,3,1.4,.1,"Setosa"
+4.3,3,1.1,.1,"Setosa"
+5.8,4,1.2,.2,"Setosa"
+5.7,4.4,1.5,.4,"Setosa"
+5.4,3.9,1.3,.4,"Setosa"
+5.1,3.5,1.4,.3,"Setosa"
+5.7,3.8,1.7,.3,"Setosa"
+5.1,3.8,1.5,.3,"Setosa"
+5.4,3.4,1.7,.2,"Setosa"
+5.1,3.7,1.5,.4,"Setosa"
+4.6,3.6,1,.2,"Setosa"
+5.1,3.3,1.7,.5,"Setosa"
+4.8,3.4,1.9,.2,"Setosa"
+5,3,1.6,.2,"Setosa"
+5,3.4,1.6,.4,"Setosa"
+5.2,3.5,1.5,.2,"Setosa"
+5.2,3.4,1.4,.2,"Setosa"
+4.7,3.2,1.6,.2,"Setosa"
+4.8,3.1,1.6,.2,"Setosa"
+5.4,3.4,1.5,.4,"Setosa"
+5.2,4.1,1.5,.1,"Setosa"
+5.5,4.2,1.4,.2,"Setosa"
+4.9,3.1,1.5,.2,"Setosa"
+5,3.2,1.2,.2,"Setosa"
+5.5,3.5,1.3,.2,"Setosa"
+4.9,3.6,1.4,.1,"Setosa"
+4.4,3,1.3,.2,"Setosa"
+5.1,3.4,1.5,.2,"Setosa"
+5,3.5,1.3,.3,"Setosa"
+4.5,2.3,1.3,.3,"Setosa"
+4.4,3.2,1.3,.2,"Setosa"
+5,3.5,1.6,.6,"Setosa"
+5.1,3.8,1.9,.4,"Setosa"
+4.8,3,1.4,.3,"Setosa"
+5.1,3.8,1.6,.2,"Setosa"
+4.6,3.2,1.4,.2,"Setosa"
+5.3,3.7,1.5,.2,"Setosa"
+5,3.3,1.4,.2,"Setosa"
+7,3.2,4.7,1.4,"Versicolor"
+6.4,3.2,4.5,1.5,"Versicolor"
+6.9,3.1,4.9,1.5,"Versicolor"
+5.5,2.3,4,1.3,"Versicolor"
+6.5,2.8,4.6,1.5,"Versicolor"
+5.7,2.8,4.5,1.3,"Versicolor"
+6.3,3.3,4.7,1.6,"Versicolor"
+4.9,2.4,3.3,1,"Versicolor"
+6.6,2.9,4.6,1.3,"Versicolor"
+5.2,2.7,3.9,1.4,"Versicolor"
+5,2,3.5,1,"Versicolor"
+5.9,3,4.2,1.5,"Versicolor"
+6,2.2,4,1,"Versicolor"
+6.1,2.9,4.7,1.4,"Versicolor"
+5.6,2.9,3.6,1.3,"Versicolor"
+6.7,3.1,4.4,1.4,"Versicolor"
+5.6,3,4.5,1.5,"Versicolor"
+5.8,2.7,4.1,1,"Versicolor"
+6.2,2.2,4.5,1.5,"Versicolor"
+5.6,2.5,3.9,1.1,"Versicolor"
+5.9,3.2,4.8,1.8,"Versicolor"
+6.1,2.8,4,1.3,"Versicolor"
+6.3,2.5,4.9,1.5,"Versicolor"
+6.1,2.8,4.7,1.2,"Versicolor"
+6.4,2.9,4.3,1.3,"Versicolor"
+6.6,3,4.4,1.4,"Versicolor"
+6.8,2.8,4.8,1.4,"Versicolor"
+6.7,3,5,1.7,"Versicolor"
+6,2.9,4.5,1.5,"Versicolor"
+5.7,2.6,3.5,1,"Versicolor"
+5.5,2.4,3.8,1.1,"Versicolor"
+5.5,2.4,3.7,1,"Versicolor"
+5.8,2.7,3.9,1.2,"Versicolor"
+6,2.7,5.1,1.6,"Versicolor"
+5.4,3,4.5,1.5,"Versicolor"
+6,3.4,4.5,1.6,"Versicolor"
+6.7,3.1,4.7,1.5,"Versicolor"
+6.3,2.3,4.4,1.3,"Versicolor"
+5.6,3,4.1,1.3,"Versicolor"
+5.5,2.5,4,1.3,"Versicolor"
+5.5,2.6,4.4,1.2,"Versicolor"
+6.1,3,4.6,1.4,"Versicolor"
+5.8,2.6,4,1.2,"Versicolor"
+5,2.3,3.3,1,"Versicolor"
+5.6,2.7,4.2,1.3,"Versicolor"
+5.7,3,4.2,1.2,"Versicolor"
+5.7,2.9,4.2,1.3,"Versicolor"
+6.2,2.9,4.3,1.3,"Versicolor"
+5.1,2.5,3,1.1,"Versicolor"
+5.7,2.8,4.1,1.3,"Versicolor"
+6.3,3.3,6,2.5,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+7.1,3,5.9,2.1,"Virginica"
+6.3,2.9,5.6,1.8,"Virginica"
+6.5,3,5.8,2.2,"Virginica"
+7.6,3,6.6,2.1,"Virginica"
+4.9,2.5,4.5,1.7,"Virginica"
+7.3,2.9,6.3,1.8,"Virginica"
+6.7,2.5,5.8,1.8,"Virginica"
+7.2,3.6,6.1,2.5,"Virginica"
+6.5,3.2,5.1,2,"Virginica"
+6.4,2.7,5.3,1.9,"Virginica"
+6.8,3,5.5,2.1,"Virginica"
+5.7,2.5,5,2,"Virginica"
+5.8,2.8,5.1,2.4,"Virginica"
+6.4,3.2,5.3,2.3,"Virginica"
+6.5,3,5.5,1.8,"Virginica"
+7.7,3.8,6.7,2.2,"Virginica"
+7.7,2.6,6.9,2.3,"Virginica"
+6,2.2,5,1.5,"Virginica"
+6.9,3.2,5.7,2.3,"Virginica"
+5.6,2.8,4.9,2,"Virginica"
+7.7,2.8,6.7,2,"Virginica"
+6.3,2.7,4.9,1.8,"Virginica"
+6.7,3.3,5.7,2.1,"Virginica"
+7.2,3.2,6,1.8,"Virginica"
+6.2,2.8,4.8,1.8,"Virginica"
+6.1,3,4.9,1.8,"Virginica"
+6.4,2.8,5.6,2.1,"Virginica"
+7.2,3,5.8,1.6,"Virginica"
+7.4,2.8,6.1,1.9,"Virginica"
+7.9,3.8,6.4,2,"Virginica"
+6.4,2.8,5.6,2.2,"Virginica"
+6.3,2.8,5.1,1.5,"Virginica"
+6.1,2.6,5.6,1.4,"Virginica"
+7.7,3,6.1,2.3,"Virginica"
+6.3,3.4,5.6,2.4,"Virginica"
+6.4,3.1,5.5,1.8,"Virginica"
+6,3,4.8,1.8,"Virginica"
+6.9,3.1,5.4,2.1,"Virginica"
+6.7,3.1,5.6,2.4,"Virginica"
+6.9,3.1,5.1,2.3,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+6.8,3.2,5.9,2.3,"Virginica"
+6.7,3.3,5.7,2.5,"Virginica"
+6.7,3,5.2,2.3,"Virginica"
+6.3,2.5,5,1.9,"Virginica"
+6.5,3,5.2,2,"Virginica"
+6.2,3.4,5.4,2.3,"Virginica"
+5.9,3,5.1,1.8,"Virginica"
\ No newline at end of file
diff --git a/naive_bayes.ipynb b/naive_bayes.ipynb
index fb5ceff..3579355 100644
--- a/naive_bayes.ipynb
+++ b/naive_bayes.ipynb
@@ -191,6 +191,13 @@
     "        plt.show()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pitność wody"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -659,16 +666,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fa832e363c8>"
+       "<matplotlib.collections.PathCollection at 0x7fbbadd08048>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -695,16 +702,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fa832ed0d68>"
+       "<matplotlib.collections.PathCollection at 0x7fbbad8e7358>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -722,6 +729,129 @@
    "source": [
     "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_test, s=50, cmap='viridis')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Irysy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(105, 4) (105,)\n",
+      "(45, 4) (45,)\n",
+      "0.9333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Preprocessing danych\n",
+    "\n",
+    "\n",
+    "# Wczytywanie danych\n",
+    "df = pd.read_csv(\"iris.csv\")\n",
+    "\n",
+    "# Zrandomizowanie kolejności danych w datasecie\n",
+    "df = df.sample(frac=1, random_state=10).reset_index(drop=True)\n",
+    "\n",
+    "# Podział na atrybuty i przewidywane wartości\n",
+    "X, y = df.iloc[:, :-1], df.iloc[:, -1]\n",
+    "\n",
+    "# Normalizacja i skalowanie danych\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "sc = StandardScaler()\n",
+    "X = sc.fit_transform(X.to_numpy())\n",
+    "X = pd.DataFrame(X, columns=df.columns.values.tolist()[:-1])\n",
+    "\n",
+    "# Podział na dane trenujące i testowe, z uwzględnieniem równego rozłożenia danych\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=1)\n",
+    "\n",
+    "print(X_train.shape, y_train.shape)\n",
+    "print(X_test.shape, y_test.shape)\n",
+    "\n",
+    "\n",
+    "# Trenowanie modelu klasyfikatora\n",
+    "x = NaiveBayesClassifier()\n",
+    "x.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "# Predykcja wartości dla danych testowych\n",
+    "predictions = x.predict(X_test)\n",
+    "\n",
+    "# Prawdopodobieństwa kolejnych predykcji\n",
+    "probabilities = [p[1] for p in predictions]\n",
+    "\n",
+    "# Przewidziana wartość\n",
+    "predictions = [p[0] for p in predictions]\n",
+    "\n",
+    "\n",
+    "# Wyliczenie accuracy modelu\n",
+    "print(x.accuracy(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9326599326599326"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_test, predictions, average=\"macro\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fbbad7b1358>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca = PCA(n_components=2)\n",
+    "pca.fit(X_test)\n",
+    "X_pca = pca.transform(X_test)\n",
+    "\n",
+    "df_pred = pd.DataFrame(predictions).replace({'Virginica': 0, 'Versicolor': 1, \"Setosa\": 2}, regex=True)\n",
+    "df_pred = np.array(df_pred).reshape(1, -1)\n",
+    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=df_pred[0], s=50, cmap='viridis')"
+   ]
   }
  ],
  "metadata": {