diff --git a/zajecia3/sklearn cz. 1.ipynb b/zajecia3/sklearn cz. 1.ipynb
index 9a5ffa4..16881a9 100644
--- a/zajecia3/sklearn cz. 1.ipynb	
+++ b/zajecia3/sklearn cz. 1.ipynb	
@@ -183,22 +183,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='gdp', ylabel='life_expectancy'>"
+       "<Axes: xlabel='gdp_log', ylabel='gdp'>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -208,7 +208,7 @@
     }
    ],
    "source": [
-    "df.plot.scatter('gdp', 'life_expectancy')"
+    "df.plot.scatter('gdp_log', 'gdp')"
    ]
   },
   {
diff --git a/zajecia3/sklearn cz. 2.ipynb b/zajecia3/sklearn cz. 2.ipynb
index 8b0447d..593cf53 100644
--- a/zajecia3/sklearn cz. 2.ipynb	
+++ b/zajecia3/sklearn cz. 2.ipynb	
@@ -313,10 +313,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.average(df['Sepal length'])\n",
+    "df.groupby('Class')['Sepal length'].mean()\n",
+    "len(df.index.drop_duplicates())\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -327,12 +344,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
+    "data = pd.read_csv(\n",
+    "    './iris.data', \n",
+    "    names=[\n",
+    "        'sepal_length', \n",
+    "        'sepal_width', \n",
+    "        'petal_length', \n",
+    "        'petal_width',\n",
+    "        'class'\n",
+    "    ])\n",
+    "\n",
     "X = data.loc[:, 'sepal_length':'petal_width']\n",
     "Y = data['class']\n",
     "\n",
@@ -348,9 +375,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "\n",
@@ -367,9 +408,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zaklasyfikowane: Iris-versicolor, Orginalne: Iris-versicolor\n",
+      "Zaklasyfikowane: Iris-setosa, Orginalne: Iris-setosa\n",
+      "Zaklasyfikowane: Iris-virginica, Orginalne: Iris-virginica\n",
+      "Zaklasyfikowane: Iris-versicolor, Orginalne: Iris-versicolor\n",
+      "Zaklasyfikowane: Iris-versicolor, Orginalne: Iris-versicolor\n",
+      "Zaklasyfikowane: Iris-setosa, Orginalne: Iris-setosa\n",
+      "Zaklasyfikowane: Iris-versicolor, Orginalne: Iris-versicolor\n",
+      "Zaklasyfikowane: Iris-virginica, Orginalne: Iris-virginica\n",
+      "Zaklasyfikowane: Iris-versicolor, Orginalne: Iris-versicolor\n",
+      "Zaklasyfikowane: Iris-versicolor, Orginalne: Iris-versicolor\n"
+     ]
+    }
+   ],
    "source": [
     "predicted = model.predict(test_X)\n",
     "\n",
@@ -386,9 +444,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.98\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -404,10 +470,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.98\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "model = KNeighborsClassifier(n_neighbors=20)\n",
+    "model.fit(train_X, train_Y)\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "print(accuracy_score(test_Y, predicted))\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -418,10 +501,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "model = KNeighborsClassifier(n_neighbors=1)\n",
+    "model.fit(train_X, train_Y)\n",
+    "\n",
+    "predicted = model.predict(train_X)\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "print(accuracy_score(train_Y, predicted))\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -472,10 +573,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9800000000000001"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "neighbors = list(range(2,52,2))\n",
+    "\n",
+    "cv_scores = list()\n",
+    "\n",
+    "for i in neighbors:\n",
+    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
+    "    cv_scores.append(cross_val_score(knn, X, Y, cv=10, scoring='accuracy').mean())\n",
+    "\n",
+    "np.max(cv_scores)\n",
+    "\n",
+    "    \n",
+    "\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -486,9 +613,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "# changing to misclassification error\n",
@@ -515,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -542,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -552,7 +690,7 @@
        "       'this'], dtype=object)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -563,7 +701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -573,7 +711,7 @@
        "\twith 21 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -584,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -600,7 +738,7 @@
        "         0.        , 0.38408524, 0.        , 0.38408524]])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -634,7 +772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -643,7 +781,7 @@
        "array([0, 0, 1, 0])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -657,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -669,7 +807,7 @@
        "       [0.51514316, 0.48485684]])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,10 +847,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>Sepal length</th>\n",
+       "      <th>Sepal width</th>\n",
+       "      <th>Petal length</th>\n",
+       "      <th>Petal width</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Iris-setosa</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-setosa</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-setosa</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-setosa</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-setosa</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-virginica</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-virginica</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-virginica</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-virginica</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iris-virginica</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Sepal length  Sepal width  Petal length  Petal width\n",
+       "Class                                                               \n",
+       "Iris-setosa              5.1          3.5           1.4          0.2\n",
+       "Iris-setosa              4.9          3.0           1.4          0.2\n",
+       "Iris-setosa              4.7          3.2           1.3          0.2\n",
+       "Iris-setosa              4.6          3.1           1.5          0.2\n",
+       "Iris-setosa              5.0          3.6           1.4          0.2\n",
+       "...                      ...          ...           ...          ...\n",
+       "Iris-virginica           6.7          3.0           5.2          2.3\n",
+       "Iris-virginica           6.3          2.5           5.0          1.9\n",
+       "Iris-virginica           6.5          3.0           5.2          2.0\n",
+       "Iris-virginica           6.2          3.4           5.4          2.3\n",
+       "Iris-virginica           5.9          3.0           5.1          1.8\n",
+       "\n",
+       "[150 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn \n",
+    "\n",
+    "df = pd.read_csv(\n",
+    "    './iris.data', \n",
+    "    index_col=4, \n",
+    "    names=[\n",
+    "        'Sepal length', \n",
+    "        'Sepal width', \n",
+    "        'Petal length', \n",
+    "        'Petal width',\n",
+    "        'Class'\n",
+    "    ])\n",
+    "df"
+   ]
   }
  ],
  "metadata": {