From 696d3aefac84312b611ce38ca6eb3e95c6aa2def Mon Sep 17 00:00:00 2001
From: thbeca-30 <nmcvetkov@yandex.ru>
Date: Tue, 15 Jun 2021 18:20:00 +0300
Subject: [PATCH 1/2] finish 4 lab

---
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Цветков Николай Максимович, Вариант №4\n",
+    "### Лабораторная работа №4. Регрессионный анализ данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import sklearn as sk\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from scipy import interp \n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.neighbors import RadiusNeighborsClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import (auc, roc_curve, \n",
+    "                             precision_recall_curve, \n",
+    "                             average_precision_score)\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from sklearn.linear_model import Perceptron\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn import neighbors\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
+    "from sklearn.metrics import precision_recall_curve, classification_report\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.metrics import roc_curve, auc\n",
+    "from matplotlib.pylab import rc, plot\n",
+    "\n",
+    "df = pd.read_csv('kc_house_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.Изучить набор данных. Создать описание набора данных на русском языке. Описать признаки, используемые в наборе данных (включить полученные описания в отчёт)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Этот набор данных содержит цены продажи домов для округа Кинг, включая Сиэтл. Сюда входят дома, проданные в период с мая 2014 года по май 2015 года.\n",
+    "\n",
+    "id - Уникальный идентификатор для каждого проданного дома\n",
+    "\n",
+    "date - Дата продажи дома\n",
+    "\n",
+    "цена - Цена каждого проданного дома\n",
+    "\n",
+    "спальни - Количество спален\n",
+    "\n",
+    "ванные комнаты - Количество ванных комнат, где 0,5 составляет комнату с туалетом, но без душа.\n",
+    "\n",
+    "sqft_living - Метраж внутренней жилой площади квартиры\n",
+    "\n",
+    "sqft_lot - метраж земельного участка\n",
+    "\n",
+    "floors - Количество этажей\n",
+    "\n",
+    "Waterfront - фиктивная переменная, определяющая, выходила ли квартира на набережную или нет.\n",
+    "\n",
+    "view - индекс от 0 до 4 того, насколько хорош был вид собственности.\n",
+    "\n",
+    "Состояние - Индекс от 1 до 5 от состояния квартиры,\n",
+    "\n",
+    "оценка - индекс от 1 до 13, где 1–3 не соответствует уровню строительства и проектирования, 7 - среднему уровню строительства и проектирования, а 11–13 - высокому уровню качества строительства и проектирования.\n",
+    "\n",
+    "sqft_above - квадратные метры внутреннего жилого пространства, которое находится над уровнем земли.\n",
+    "\n",
+    "sqft_basement - квадратные метры внутреннего жилого пространства, которое находится ниже уровня земли.\n",
+    "\n",
+    "yr_built - год постройки дома\n",
+    "\n",
+    "yr_renovated - Год последней реновации дома\n",
+    "\n",
+    "zipcode - В каком районе с почтовым индексом находится дом\n",
+    "\n",
+    "lat - Широта\n",
+    "\n",
+    "long - долгота\n",
+    "\n",
+    "sqft_living15 - Площадь жилой площади внутри жилья для ближайших 15 соседей.\n",
+    "\n",
+    "sqft_lot15 - Площадь земельных участков ближайших 15 соседей"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.Удалите дубликаты строк в наборе данных; приведите размер набора данных до и после данной операции"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21613, 21)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21613 entries, 0 to 21612\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   id             21613 non-null  int64  \n",
+      " 1   date           21613 non-null  object \n",
+      " 2   price          21613 non-null  float64\n",
+      " 3   bedrooms       21613 non-null  int64  \n",
+      " 4   bathrooms      21613 non-null  float64\n",
+      " 5   sqft_living    21613 non-null  int64  \n",
+      " 6   sqft_lot       21613 non-null  int64  \n",
+      " 7   floors         21613 non-null  float64\n",
+      " 8   waterfront     21613 non-null  int64  \n",
+      " 9   view           21613 non-null  int64  \n",
+      " 10  condition      21613 non-null  int64  \n",
+      " 11  grade          21613 non-null  int64  \n",
+      " 12  sqft_above     21613 non-null  int64  \n",
+      " 13  sqft_basement  21613 non-null  int64  \n",
+      " 14  yr_built       21613 non-null  int64  \n",
+      " 15  yr_renovated   21613 non-null  int64  \n",
+      " 16  zipcode        21613 non-null  int64  \n",
+      " 17  lat            21613 non-null  float64\n",
+      " 18  long           21613 non-null  float64\n",
+      " 19  sqft_living15  21613 non-null  int64  \n",
+      " 20  sqft_lot15     21613 non-null  int64  \n",
+      "dtypes: float64(5), int64(15), object(1)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>20141013T000000</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>20150225T000000</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>20150218T000000</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
+       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
+       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
+       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
+       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
+       "\n",
+       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
+       "0      5650     1.0           0     0  ...      7        1180              0   \n",
+       "1      7242     2.0           0     0  ...      7        2170            400   \n",
+       "2     10000     1.0           0     0  ...      6         770              0   \n",
+       "3      5000     1.0           0     0  ...      7        1050            910   \n",
+       "4      8080     1.0           0     0  ...      8        1680              0   \n",
+       "\n",
+       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "0      1955             0    98178  47.5112 -122.257           1340   \n",
+       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
+       "2      1933             0    98028  47.7379 -122.233           2720   \n",
+       "3      1965             0    98136  47.5208 -122.393           1360   \n",
+       "4      1987             0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "   sqft_lot15  \n",
+       "0        5650  \n",
+       "1        7639  \n",
+       "2        8062  \n",
+       "3        5000  \n",
+       "4        7503  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>7</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21611</th>\n",
+       "      <td>291310100</td>\n",
+       "      <td>20150116T000000</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21612</th>\n",
+       "      <td>1523300157</td>\n",
+       "      <td>20141015T000000</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id             date     price  bedrooms  bathrooms  \\\n",
+       "21608   263000018  20140521T000000  360000.0         3       2.50   \n",
+       "21609  6600060120  20150223T000000  400000.0         4       2.50   \n",
+       "21610  1523300141  20140623T000000  402101.0         2       0.75   \n",
+       "21611   291310100  20150116T000000  400000.0         3       2.50   \n",
+       "21612  1523300157  20141015T000000  325000.0         2       0.75   \n",
+       "\n",
+       "       sqft_living  sqft_lot  floors  waterfront  view  ...  grade  \\\n",
+       "21608         1530      1131     3.0           0     0  ...      8   \n",
+       "21609         2310      5813     2.0           0     0  ...      8   \n",
+       "21610         1020      1350     2.0           0     0  ...      7   \n",
+       "21611         1600      2388     2.0           0     0  ...      8   \n",
+       "21612         1020      1076     2.0           0     0  ...      7   \n",
+       "\n",
+       "       sqft_above  sqft_basement  yr_built  yr_renovated  zipcode      lat  \\\n",
+       "21608        1530              0      2009             0    98103  47.6993   \n",
+       "21609        2310              0      2014             0    98146  47.5107   \n",
+       "21610        1020              0      2009             0    98144  47.5944   \n",
+       "21611        1600              0      2004             0    98027  47.5345   \n",
+       "21612        1020              0      2008             0    98144  47.5941   \n",
+       "\n",
+       "          long  sqft_living15  sqft_lot15  \n",
+       "21608 -122.346           1530        1509  \n",
+       "21609 -122.362           1830        7200  \n",
+       "21610 -122.299           1020        2007  \n",
+       "21611 -122.069           1410        1287  \n",
+       "21612 -122.299           1020        1357  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21613, 21)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop_duplicates()\n",
+    "df = df.dropna()\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21613 entries, 0 to 21612\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   id             21613 non-null  int64  \n",
+      " 1   date           21613 non-null  object \n",
+      " 2   price          21613 non-null  float64\n",
+      " 3   bedrooms       21613 non-null  int64  \n",
+      " 4   bathrooms      21613 non-null  float64\n",
+      " 5   sqft_living    21613 non-null  int64  \n",
+      " 6   sqft_lot       21613 non-null  int64  \n",
+      " 7   floors         21613 non-null  float64\n",
+      " 8   waterfront     21613 non-null  int64  \n",
+      " 9   view           21613 non-null  int64  \n",
+      " 10  condition      21613 non-null  int64  \n",
+      " 11  grade          21613 non-null  int64  \n",
+      " 12  sqft_above     21613 non-null  int64  \n",
+      " 13  sqft_basement  21613 non-null  int64  \n",
+      " 14  yr_built       21613 non-null  int64  \n",
+      " 15  yr_renovated   21613 non-null  int64  \n",
+      " 16  zipcode        21613 non-null  int64  \n",
+      " 17  lat            21613 non-null  float64\n",
+      " 18  long           21613 non-null  float64\n",
+      " 19  sqft_living15  21613 non-null  int64  \n",
+      " 20  sqft_lot15     21613 non-null  int64  \n",
+      "dtypes: float64(5), int64(15), object(1)\n",
+      "memory usage: 3.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>20141013T000000</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>20150225T000000</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>20150218T000000</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
+       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
+       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
+       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
+       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
+       "\n",
+       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
+       "0      5650     1.0           0     0  ...      7        1180              0   \n",
+       "1      7242     2.0           0     0  ...      7        2170            400   \n",
+       "2     10000     1.0           0     0  ...      6         770              0   \n",
+       "3      5000     1.0           0     0  ...      7        1050            910   \n",
+       "4      8080     1.0           0     0  ...      8        1680              0   \n",
+       "\n",
+       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "0      1955             0    98178  47.5112 -122.257           1340   \n",
+       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
+       "2      1933             0    98028  47.7379 -122.233           2720   \n",
+       "3      1965             0    98136  47.5208 -122.393           1360   \n",
+       "4      1987             0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "   sqft_lot15  \n",
+       "0        5650  \n",
+       "1        7639  \n",
+       "2        8062  \n",
+       "3        5000  \n",
+       "4        7503  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.Выполните масштабирование количественных признаков; Постройте диаграммы BoxPlot для признаков до и после масштабирования. Выберите способ масштабирования (например, нормализацию или стандартизацию)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "numerical:  ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15']\n",
+      "categorial:  ['date']\n"
+     ]
+    }
+   ],
+   "source": [
+    "numerical_columns = [i for i in df.columns if df[i].dtype.name != 'object']\n",
+    "categorial_columns = [i for i in df.columns if df[i].dtype.name == 'object']\n",
+    "\n",
+    "print('numerical: ', numerical_columns)\n",
+    "print('categorial: ', categorial_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.boxplot(column='bedrooms',by='floors',rot=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cast(obj):\n",
+    "    if type(obj) == str:\n",
+    "        return float(obj.replace(',', '.'))\n",
+    "    else:\n",
+    "        return obj\n",
+    "\n",
+    "df['bathrooms'] = df['bathrooms'].apply(lambda x: cast(x))\n",
+    "df['floors'] = df['floors'].apply(lambda x: cast(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "numerical:  ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15']\n",
+      "categorial:  ['date']\n"
+     ]
+    }
+   ],
+   "source": [
+    "numerical_columns = [i for i in df.columns if df[i].dtype.name != 'object']\n",
+    "categorial_columns = [i for i in df.columns if df[i].dtype.name == 'object']\n",
+    "\n",
+    "print('numerical: ', numerical_columns)\n",
+    "print('categorial: ', categorial_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21613 entries, 0 to 21612\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   id             21613 non-null  int64  \n",
+      " 1   date           21613 non-null  object \n",
+      " 2   price          21613 non-null  float64\n",
+      " 3   bedrooms       21613 non-null  int64  \n",
+      " 4   bathrooms      21613 non-null  float64\n",
+      " 5   sqft_living    21613 non-null  int64  \n",
+      " 6   sqft_lot       21613 non-null  int64  \n",
+      " 7   floors         21613 non-null  float64\n",
+      " 8   waterfront     21613 non-null  int64  \n",
+      " 9   view           21613 non-null  int64  \n",
+      " 10  condition      21613 non-null  int64  \n",
+      " 11  grade          21613 non-null  int64  \n",
+      " 12  sqft_above     21613 non-null  int64  \n",
+      " 13  sqft_basement  21613 non-null  int64  \n",
+      " 14  yr_built       21613 non-null  int64  \n",
+      " 15  yr_renovated   21613 non-null  int64  \n",
+      " 16  zipcode        21613 non-null  int64  \n",
+      " 17  lat            21613 non-null  float64\n",
+      " 18  long           21613 non-null  float64\n",
+      " 19  sqft_living15  21613 non-null  int64  \n",
+      " 20  sqft_lot15     21613 non-null  int64  \n",
+      "dtypes: float64(5), int64(15), object(1)\n",
+      "memory usage: 3.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.720103</td>\n",
+       "      <td>20141013T000000</td>\n",
+       "      <td>0.019266</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.12500</td>\n",
+       "      <td>0.067170</td>\n",
+       "      <td>0.003108</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.097588</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.478261</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.893939</td>\n",
+       "      <td>0.571498</td>\n",
+       "      <td>0.217608</td>\n",
+       "      <td>0.161934</td>\n",
+       "      <td>0.005742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.647853</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>0.060721</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.28125</td>\n",
+       "      <td>0.172075</td>\n",
+       "      <td>0.004072</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.206140</td>\n",
+       "      <td>0.082988</td>\n",
+       "      <td>0.443478</td>\n",
+       "      <td>0.988089</td>\n",
+       "      <td>0.626263</td>\n",
+       "      <td>0.908959</td>\n",
+       "      <td>0.166113</td>\n",
+       "      <td>0.222165</td>\n",
+       "      <td>0.008027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.568795</td>\n",
+       "      <td>20150225T000000</td>\n",
+       "      <td>0.013770</td>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.12500</td>\n",
+       "      <td>0.036226</td>\n",
+       "      <td>0.005743</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0.052632</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.286957</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.136364</td>\n",
+       "      <td>0.936143</td>\n",
+       "      <td>0.237542</td>\n",
+       "      <td>0.399415</td>\n",
+       "      <td>0.008513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.251157</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>0.069377</td>\n",
+       "      <td>0.121212</td>\n",
+       "      <td>0.37500</td>\n",
+       "      <td>0.126038</td>\n",
+       "      <td>0.002714</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.188797</td>\n",
+       "      <td>0.565217</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.586939</td>\n",
+       "      <td>0.104651</td>\n",
+       "      <td>0.165376</td>\n",
+       "      <td>0.004996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.197333</td>\n",
+       "      <td>20150218T000000</td>\n",
+       "      <td>0.057049</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>0.104906</td>\n",
+       "      <td>0.004579</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.152412</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.756522</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.368687</td>\n",
+       "      <td>0.741354</td>\n",
+       "      <td>0.393688</td>\n",
+       "      <td>0.241094</td>\n",
+       "      <td>0.007871</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "0  0.720103  20141013T000000  0.019266  0.090909    0.12500     0.067170   \n",
+       "1  0.647853  20141209T000000  0.060721  0.090909    0.28125     0.172075   \n",
+       "2  0.568795  20150225T000000  0.013770  0.060606    0.12500     0.036226   \n",
+       "3  0.251157  20141209T000000  0.069377  0.121212    0.37500     0.126038   \n",
+       "4  0.197333  20150218T000000  0.057049  0.090909    0.25000     0.104906   \n",
+       "\n",
+       "   sqft_lot  floors  waterfront  view  ...     grade  sqft_above  \\\n",
+       "0  0.003108     0.0         0.0   0.0  ...  0.500000    0.097588   \n",
+       "1  0.004072     0.4         0.0   0.0  ...  0.500000    0.206140   \n",
+       "2  0.005743     0.0         0.0   0.0  ...  0.416667    0.052632   \n",
+       "3  0.002714     0.0         0.0   0.0  ...  0.500000    0.083333   \n",
+       "4  0.004579     0.0         0.0   0.0  ...  0.583333    0.152412   \n",
+       "\n",
+       "   sqft_basement  yr_built  yr_renovated   zipcode       lat      long  \\\n",
+       "0       0.000000  0.478261      0.000000  0.893939  0.571498  0.217608   \n",
+       "1       0.082988  0.443478      0.988089  0.626263  0.908959  0.166113   \n",
+       "2       0.000000  0.286957      0.000000  0.136364  0.936143  0.237542   \n",
+       "3       0.188797  0.565217      0.000000  0.681818  0.586939  0.104651   \n",
+       "4       0.000000  0.756522      0.000000  0.368687  0.741354  0.393688   \n",
+       "\n",
+       "   sqft_living15  sqft_lot15  \n",
+       "0       0.161934    0.005742  \n",
+       "1       0.222165    0.008027  \n",
+       "2       0.399415    0.008513  \n",
+       "3       0.165376    0.004996  \n",
+       "4       0.241094    0.007871  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# нормализуем\n",
+    "scaler = MinMaxScaler()\n",
+    "df[numerical_columns] = scaler.fit_transform(df[numerical_columns])\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.boxplot(column='bedrooms',by='floors',rot=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.Выполните замену категориальных признаков; выберите и обоснуйте способ замены"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Категориальных признаков нет"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5)Оцените корреляцию между признаков и удалите те признаки, которые коррелируют с наибольшим числом других"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  price  bedrooms  bathrooms  sqft_living  sqft_lot    floors  \\\n",
+      "price          1.000000  0.308350   0.525138     0.702035  0.089661  0.256794   \n",
+      "bedrooms       0.308350  1.000000   0.515884     0.576671  0.031703  0.175429   \n",
+      "bathrooms      0.525138  0.515884   1.000000     0.754665  0.087740  0.500653   \n",
+      "sqft_living    0.702035  0.576671   0.754665     1.000000  0.172826  0.353949   \n",
+      "sqft_lot       0.089661  0.031703   0.087740     0.172826  1.000000 -0.005201   \n",
+      "floors         0.256794  0.175429   0.500653     0.353949 -0.005201  1.000000   \n",
+      "condition      0.036362  0.028472  -0.124982    -0.058753 -0.008958 -0.263768   \n",
+      "grade          0.667434  0.356967   0.664983     0.762704  0.113621  0.458183   \n",
+      "sqft_above     0.605567  0.477600   0.685342     0.876597  0.183512  0.523885   \n",
+      "sqft_basement  0.323816  0.303093   0.283770     0.435043  0.015286 -0.245705   \n",
+      "yr_built       0.054012  0.154178   0.506019     0.318049  0.053080  0.489319   \n",
+      "yr_renovated   0.126434  0.018841   0.050739     0.055363  0.007644  0.006338   \n",
+      "sqft_living15  0.585379  0.391638   0.568634     0.756420  0.144608  0.279885   \n",
+      "sqft_lot15     0.082447  0.029244   0.087175     0.183286  0.718557 -0.011269   \n",
+      "\n",
+      "               condition     grade  sqft_above  sqft_basement  yr_built  \\\n",
+      "price           0.036362  0.667434    0.605567       0.323816  0.054012   \n",
+      "bedrooms        0.028472  0.356967    0.477600       0.303093  0.154178   \n",
+      "bathrooms      -0.124982  0.664983    0.685342       0.283770  0.506019   \n",
+      "sqft_living    -0.058753  0.762704    0.876597       0.435043  0.318049   \n",
+      "sqft_lot       -0.008958  0.113621    0.183512       0.015286  0.053080   \n",
+      "floors         -0.263768  0.458183    0.523885      -0.245705  0.489319   \n",
+      "condition       1.000000 -0.144674   -0.158214       0.174105 -0.361417   \n",
+      "grade          -0.144674  1.000000    0.755923       0.168392  0.446963   \n",
+      "sqft_above     -0.158214  0.755923    1.000000      -0.051943  0.423898   \n",
+      "sqft_basement   0.174105  0.168392   -0.051943       1.000000 -0.133124   \n",
+      "yr_built       -0.361417  0.446963    0.423898      -0.133124  1.000000   \n",
+      "yr_renovated   -0.060618  0.014414    0.023285       0.071323 -0.224874   \n",
+      "sqft_living15  -0.092824  0.713202    0.731870       0.200355  0.326229   \n",
+      "sqft_lot15     -0.003406  0.119248    0.194050       0.017276  0.070958   \n",
+      "\n",
+      "               yr_renovated  sqft_living15  sqft_lot15  \n",
+      "price              0.126434       0.585379    0.082447  \n",
+      "bedrooms           0.018841       0.391638    0.029244  \n",
+      "bathrooms          0.050739       0.568634    0.087175  \n",
+      "sqft_living        0.055363       0.756420    0.183286  \n",
+      "sqft_lot           0.007644       0.144608    0.718557  \n",
+      "floors             0.006338       0.279885   -0.011269  \n",
+      "condition         -0.060618      -0.092824   -0.003406  \n",
+      "grade              0.014414       0.713202    0.119248  \n",
+      "sqft_above         0.023285       0.731870    0.194050  \n",
+      "sqft_basement      0.071323       0.200355    0.017276  \n",
+      "yr_built          -0.224874       0.326229    0.070958  \n",
+      "yr_renovated       1.000000      -0.002673    0.007854  \n",
+      "sqft_living15     -0.002673       1.000000    0.183192  \n",
+      "sqft_lot15         0.007854       0.183192    1.000000  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr_matrix = df.drop(['id','date','waterfront','view','zipcode','lat','long'], axis=1).corr()\n",
+    "sns.heatmap(corr_matrix);\n",
+    "print(corr_matrix)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6)Заполните пропущенные значения в данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21613 entries, 0 to 21612\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   id             21613 non-null  float64\n",
+      " 1   date           21613 non-null  object \n",
+      " 2   price          21613 non-null  float64\n",
+      " 3   bedrooms       21613 non-null  float64\n",
+      " 4   bathrooms      21613 non-null  float64\n",
+      " 5   sqft_living    21613 non-null  float64\n",
+      " 6   sqft_lot       21613 non-null  float64\n",
+      " 7   floors         21613 non-null  float64\n",
+      " 8   waterfront     21613 non-null  float64\n",
+      " 9   view           21613 non-null  float64\n",
+      " 10  condition      21613 non-null  float64\n",
+      " 11  grade          21613 non-null  float64\n",
+      " 12  sqft_above     21613 non-null  float64\n",
+      " 13  sqft_basement  21613 non-null  float64\n",
+      " 14  yr_built       21613 non-null  float64\n",
+      " 15  yr_renovated   21613 non-null  float64\n",
+      " 16  zipcode        21613 non-null  float64\n",
+      " 17  lat            21613 non-null  float64\n",
+      " 18  long           21613 non-null  float64\n",
+      " 19  sqft_living15  21613 non-null  float64\n",
+      " 20  sqft_lot15     21613 non-null  float64\n",
+      "dtypes: float64(20), object(1)\n",
+      "memory usage: 3.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Пропущенных значений нет"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7)Решите поставленную задачу регрессии в соответствии с заданием. При подборе параметров метода принятия решения используйте перекрёстную проверку (изучите возможные для изменения параметры метода регрессии). Вычислите точность решения задачи, вычислив разницу между реальным значением и предсказанным. Вычислите коэффициент корреляции (Пирсона, Спирмена) между реальным значением и предсказаннымс учётом p-value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ridge(alpha=0.5)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from  sklearn import linear_model\n",
+    "# линейная регрессия, RIDGE регуляризатор.\n",
+    "\n",
+    "X, y = df.drop(['price','date'], axis=1), df['price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 123)\n",
+    "\n",
+    "reg = linear_model.Ridge (alpha = .5)\n",
+    "reg.fit (X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.00193748, -0.13540374,  0.04129303,  0.11038102,  0.02741847,\n",
+       "        0.00124632,  0.08238055,  0.02730064,  0.01342247,  0.15861042,\n",
+       "        0.13349222,  0.05085051, -0.03985562,  0.00523935, -0.01478764,\n",
+       "        0.04892132, -0.03391988,  0.01578631, -0.04172803])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.07406577 0.05182631 0.1123396  0.15140513 0.02083593 0.02279907\n",
+      " 0.07315432 0.05148704 0.07626971 0.07923561]\n"
+     ]
+    }
+   ],
+   "source": [
+    "xt = reg.predict(X_test)\n",
+    "print(xt[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5506     0.060000\n",
+      "9279     0.043934\n",
+      "16034    0.092787\n",
+      "6608     0.120656\n",
+      "20359    0.026754\n",
+      "8798     0.013115\n",
+      "10035    0.080525\n",
+      "13321    0.026230\n",
+      "15842    0.051148\n",
+      "12119    0.056525\n",
+      "Name: price, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_test[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5506     0.014066\n",
+      "9279     0.007892\n",
+      "16034    0.019553\n",
+      "6608     0.030749\n",
+      "20359   -0.005918\n",
+      "8798     0.009684\n",
+      "10035   -0.007370\n",
+      "13321    0.025258\n",
+      "15842    0.025122\n",
+      "12119    0.022711\n",
+      "Name: price, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(xt[:10]-y_test[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.07059632503296992"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from  sklearn import linear_model\n",
+    "# линейная регрессия без использования регуляризаторов;\n",
+    "\n",
+    "X, y = df.drop(['price','date'], axis=1), df['price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 123)\n",
+    "\n",
+    "reg = linear_model.LinearRegression()\n",
+    "\n",
+    "reg.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.00194496, -0.14592851,  0.04155688,  0.11204693,  0.03233074,\n",
+       "        0.00104583,  0.08272934,  0.02706334,  0.01346456,  0.1589406 ,\n",
+       "        0.13549138,  0.05164739, -0.04000652,  0.00515634, -0.01487619,\n",
+       "        0.04888636, -0.03401542,  0.01486874, -0.04690618])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.06988863157025132"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.84251977295736\n",
+      "0.8579427865393281\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.stats import *\n",
+    "corr={}\n",
+    "corr['pearson'], p_value_p = stats.pearsonr(xt,y_test)\n",
+    "corr['spearman'], p_value_s = stats.spearmanr(xt,y_test)\n",
+    "print(corr['pearson'])\n",
+    "print(corr['spearman'])"
+   ]
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From b97015d1060f83cf6c6bf79d5f2746216550be3a Mon Sep 17 00:00:00 2001
From: thbeca-30 <nmcvetkov@yandex.ru>
Date: Wed, 23 Jun 2021 10:29:37 +0300
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