diff --git a/lections/OATD_lec_2.pdf b/lections/OATD_lec_2.pdf
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diff --git a/lections/OATD_lec_2.pptx b/lections/OATD_lec_2.pptx
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diff --git a/lections/notebooks/lec2_preprocess.ipynb b/lections/notebooks/lec2_preprocess.ipynb
new file mode 100644
index 0000000..4a0cf51
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+++ b/lections/notebooks/lec2_preprocess.ipynb
@@ -0,0 +1,634 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import sklearn.metrics.pairwise as pw\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = [[3, 4, 5]]\n",
+    "B = [[3, 5, 4]]\n",
+    "C = [[1, 2, 1]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Euclidean: \t [[1.41421356]]\n",
+      "Cosine: \t [[0.98]]\n",
+      "Manhattan: \t [[2.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Euclidean: \\t',pw.euclidean_distances(A, B))\n",
+    "print('Cosine: \\t',pw.cosine_similarity(A, B))\n",
+    "print('Manhattan: \\t',pw.manhattan_distances(A, B))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Euclidean: \t [[4.89897949]]\n",
+      "Cosine: \t [[0.92376043]]\n",
+      "Manhattan: \t [[8.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Euclidean: \\t',pw.euclidean_distances(A, C))\n",
+    "print('Cosine: \\t',pw.cosine_similarity(A, C))\n",
+    "print('Manhattan: \\t',pw.manhattan_distances(A, C))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Euclidean E-D: \t [[3.31662479]]\n",
+      "Euclidean E-F: \t [[2.82842712]]\n",
+      "\n",
+      "Cosine: E-D \t [[0.99014754]]\n",
+      "Cosine E-F: \t [[0.7592566]]\n",
+      "\n",
+      "Manhattan: E-D \t [[5.]]\n",
+      "Manhattan E-F: \t [[6.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sklearn.metrics.pairwise as pw\n",
+    "\n",
+    "D = [[6,0,0,3,3]]\n",
+    "E = [[3,0,0,2,2]]\n",
+    "F = [[1,1,1,1,1]]\n",
+    "\n",
+    "print('Euclidean E-D: \\t',pw.euclidean_distances(D, E))\n",
+    "print('Euclidean E-F: \\t',pw.euclidean_distances(E, F))\n",
+    "\n",
+    "print('\\nCosine: E-D \\t',pw.cosine_similarity(D, E))\n",
+    "print('Cosine E-F: \\t',pw.cosine_similarity(E, F))\n",
+    "\n",
+    "print('\\nManhattan: E-D \\t',pw.manhattan_distances(D, E))\n",
+    "print('Manhattan E-F: \\t',pw.manhattan_distances(E, F))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>red</td>\n",
+       "      <td>3300</td>\n",
+       "      <td>MSK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>red</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>SPB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>4600</td>\n",
+       "      <td>EKB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>green</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>MSK</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A       B     C    D\n",
+       "0  1.0     red  3300  MSK\n",
+       "1  2.0     red  1250  SPB\n",
+       "2  NaN  yellow  4600  EKB\n",
+       "3  2.0   green  4500  MSK"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "dataset = pd.DataFrame({'A': [1 , 2, None, 2], \n",
+    "                        'B': ['red', 'red', 'yellow', 'green'], \n",
+    "                        'C': [3300, 1250, 4600, 4500],\n",
+    "                        'D': ['MSK', 'SPB', 'EKB', 'MSK']})\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>A</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>B_green</th>\n",
+       "      <th>B_red</th>\n",
+       "      <th>B_yellow</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3300</td>\n",
+       "      <td>MSK</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>SPB</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4600</td>\n",
+       "      <td>EKB</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>MSK</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A     C    D  B_green  B_red  B_yellow\n",
+       "0  1.0  3300  MSK        0      1         0\n",
+       "1  2.0  1250  SPB        0      1         0\n",
+       "2  NaN  4600  EKB        0      0         1\n",
+       "3  2.0  4500  MSK        1      0         0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# OHE encoding\n",
+    "dataset = pd.get_dummies(dataset, columns = ['B'])\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LabelEncoder()"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Label encoding\n",
+    "from sklearn import preprocessing\n",
+    "le = preprocessing.LabelEncoder()\n",
+    "le.fit(dataset['D'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "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>A</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>B_green</th>\n",
+       "      <th>B_red</th>\n",
+       "      <th>B_yellow</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3300</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A     C  D  B_green  B_red  B_yellow\n",
+       "0  1.0  3300  1        0      1         0\n",
+       "1  2.0  1250  2        0      1         0\n",
+       "2  NaN  4600  0        0      0         1\n",
+       "3  2.0  4500  1        1      0         0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset['D'] = le.transform(dataset['D'])\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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>A</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>B_green</th>\n",
+       "      <th>B_red</th>\n",
+       "      <th>B_yellow</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3300</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.666667</td>\n",
+       "      <td>4600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A     C  D  B_green  B_red  B_yellow\n",
+       "0  1.000000  3300  1        0      1         0\n",
+       "1  2.000000  1250  2        0      1         0\n",
+       "2  1.666667  4600  0        0      0         1\n",
+       "3  2.000000  4500  1        1      0         0"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Заполняем пропущенные данные\n",
+    "dataset['A'] = dataset['A'].fillna(np.mean(dataset['A']))\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "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>A</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>B_green</th>\n",
+       "      <th>B_red</th>\n",
+       "      <th>B_yellow</th>\n",
+       "      <th>C_normalized</th>\n",
+       "      <th>C_standardized</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3300</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.611940</td>\n",
+       "      <td>-0.072209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.388018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.666667</td>\n",
+       "      <td>4600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.762206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.970149</td>\n",
+       "      <td>0.698021</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A     C  D  B_green  B_red  B_yellow  C_normalized  C_standardized\n",
+       "0  1.000000  3300  1        0      1         0      0.611940       -0.072209\n",
+       "1  2.000000  1250  2        0      1         0      0.000000       -1.388018\n",
+       "2  1.666667  4600  0        0      0         1      1.000000        0.762206\n",
+       "3  2.000000  4500  1        1      0         0      0.970149        0.698021"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset['C_normalized'] = (dataset['C'] - dataset['C'].min()) / (dataset['C'].max() - dataset['C'].min())\n",
+    "dataset['C_standardized'] = (dataset['C'] - dataset['C'].mean()) / dataset['C'].std()\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
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+}