tutorials/Data Preprocessing Example I - New York City Airbnb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import insolver\n",
    "from insolver.frame import InsolverDataFrame\n",
    "from insolver.transforms import InsolverTransform\n",
    "from insolver.transforms import (\n",
    "    AutoFillNATransforms,\n",
    "    OneHotEncoderTransforms,\n",
    "    TransformMapValues,\n",
    "    EncoderTransforms,\n",
    "    DatetimeTransforms,\n",
    ")\n",
    "from insolver.feature_engineering import FeatureSelection, DimensionalityReduction, Normalization, Sampling, Smoothing\n",
    "from insolver.model_tools import download_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load a dataset with the pd.read_csv() and create InsolverDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>2787</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>2845</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>4632</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>4869</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>7192</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                              name  host_id  \\\n",
       "0  2539                Clean & quiet apt home by the park     2787   \n",
       "1  2595                             Skylit Midtown Castle     2845   \n",
       "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
       "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
       "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
       "\n",
       "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0     Private room    149               1                  9  2018-10-19   \n",
       "1  Entire home/apt    225               1                 45  2019-05-21   \n",
       "2     Private room    150               3                  0         NaN   \n",
       "3  Entire home/apt     89               1                270  2019-07-05   \n",
       "4  Entire home/apt     80              10                  9  2018-11-19   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0               0.21                               6               365  \n",
       "1               0.38                               2               355  \n",
       "2                NaN                               1               365  \n",
       "3               4.64                               1               194  \n",
       "4               0.10                               1                 0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data\n",
    "download_dataset('AB_NYC_2019')\n",
    "dataset = InsolverDataFrame(pd.read_csv(\"datasets/AB_NYC_2019.csv\"))\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information about a loaded dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'insolver.frame.frame.InsolverDataFrame'>\n",
      "RangeIndex: 48895 entries, 0 to 48894\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              48895 non-null  int64  \n",
      " 1   name                            48879 non-null  object \n",
      " 2   host_id                         48895 non-null  int64  \n",
      " 3   host_name                       48874 non-null  object \n",
      " 4   neighbourhood_group             48895 non-null  object \n",
      " 5   neighbourhood                   48895 non-null  object \n",
      " 6   latitude                        48895 non-null  float64\n",
      " 7   longitude                       48895 non-null  float64\n",
      " 8   room_type                       48895 non-null  object \n",
      " 9   price                           48895 non-null  int64  \n",
      " 10  minimum_nights                  48895 non-null  int64  \n",
      " 11  number_of_reviews               48895 non-null  int64  \n",
      " 12  last_review                     38843 non-null  object \n",
      " 13  reviews_per_month               38843 non-null  float64\n",
      " 14  calculated_host_listings_count  48895 non-null  int64  \n",
      " 15  availability_365                48895 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting useless columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(['id', 'name', 'host_id', 'host_name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'insolver.frame.frame.InsolverDataFrame'>\n",
      "RangeIndex: 48895 entries, 0 to 48894\n",
      "Data columns (total 12 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   neighbourhood_group             48895 non-null  object \n",
      " 1   neighbourhood                   48895 non-null  object \n",
      " 2   latitude                        48895 non-null  float64\n",
      " 3   longitude                       48895 non-null  float64\n",
      " 4   room_type                       48895 non-null  object \n",
      " 5   price                           48895 non-null  int64  \n",
      " 6   minimum_nights                  48895 non-null  int64  \n",
      " 7   number_of_reviews               48895 non-null  int64  \n",
      " 8   last_review                     38843 non-null  object \n",
      " 9   reviews_per_month               38843 non-null  float64\n",
      " 10  calculated_host_listings_count  48895 non-null  int64  \n",
      " 11  availability_365                48895 non-null  int64  \n",
      "dtypes: float64(3), int64(5), object(4)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group                   0\n",
       "neighbourhood                         0\n",
       "latitude                              0\n",
       "longitude                             0\n",
       "room_type                             0\n",
       "price                                 0\n",
       "minimum_nights                        0\n",
       "number_of_reviews                     0\n",
       "last_review                       10052\n",
       "reviews_per_month                 10052\n",
       "calculated_host_listings_count        0\n",
       "availability_365                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tramsform dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's tranform all object columns and autofill null values. \n",
    "\n",
    "There are 4 object columns:\n",
    "- `neighbourhood` consists of 221 unique values, for this column I use `EncoderTransforms` that encodes target labels with values between 0 and n_classes-1;\n",
    "- `neighbourhood_group` consists of 5 unique values, for this column I use `OneHotEncoderTransforms` that encodes categorical features as a one-hot numeric array.\n",
    "- `room_type` consists of 3 unique values, for this column I use `TransformMapValues` that transforms parameter values according to the given dictionary;\n",
    "- `last_review` consists of date data, for this column I use `DatetimeTransforms` that preprocesses date and date time columns, it creates new column with the selected datetime feature, unix is used by default.\n",
    "\n",
    "I use AutoFillNATransforms method to preprocess the NA values:\n",
    "- For the `numerical_method` parameter I chose 'mean' value which use mean value  \n",
    "- For the `categorical_method` I chose 'imputed_column' which fills with the frequent category and creates new bool column containing whether a value was imputed or not.\n",
    "\n",
    "The second AutoFillNATransforms is used to fill the null values created after the OneHotEncoder Transform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed = InsolverTransform(\n",
    "    dataset,\n",
    "    [\n",
    "        AutoFillNATransforms(numerical_method='mean', categorical_method='imputed_column'),\n",
    "        TransformMapValues('room_type', {'Entire home/apt': 0, 'Private room': 1, 'Shared room': 1}),\n",
    "        EncoderTransforms(['neighbourhood']),\n",
    "        OneHotEncoderTransforms(['neighbourhood_group']),\n",
    "        DatetimeTransforms(['last_review']),\n",
    "        AutoFillNATransforms(),\n",
    "    ],\n",
    ")\n",
    "transformed.ins_transform();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed.drop(['last_review'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>neighbourhood_group_Imputed</th>\n",
       "      <th>neighbourhood_Imputed</th>\n",
       "      <th>room_type_Imputed</th>\n",
       "      <th>last_review_Imputed</th>\n",
       "      <th>neighbourhood_group_Bronx</th>\n",
       "      <th>neighbourhood_group_Brooklyn</th>\n",
       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>neighbourhood_group_Queens</th>\n",
       "      <th>neighbourhood_group_Staten Island</th>\n",
       "      <th>last_review_unix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>1</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1539907200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>127</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>0</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1558396800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>4.640000</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1562284800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1542585600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48890</th>\n",
       "      <td>13</td>\n",
       "      <td>40.67853</td>\n",
       "      <td>-73.94995</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>28</td>\n",
       "      <td>40.70184</td>\n",
       "      <td>-73.93317</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>94</td>\n",
       "      <td>40.81475</td>\n",
       "      <td>-73.94867</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>95</td>\n",
       "      <td>40.75751</td>\n",
       "      <td>-73.99112</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>95</td>\n",
       "      <td>40.76404</td>\n",
       "      <td>-73.98933</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type  price  minimum_nights  \\\n",
       "0                108  40.64749  -73.97237          1    149               1   \n",
       "1                127  40.75362  -73.98377          0    225               1   \n",
       "2                 94  40.80902  -73.94190          1    150               3   \n",
       "3                 41  40.68514  -73.95976          0     89               1   \n",
       "4                 61  40.79851  -73.94399          0     80              10   \n",
       "...              ...       ...        ...        ...    ...             ...   \n",
       "48890             13  40.67853  -73.94995          1     70               2   \n",
       "48891             28  40.70184  -73.93317          1     40               4   \n",
       "48892             94  40.81475  -73.94867          0    115              10   \n",
       "48893             95  40.75751  -73.99112          1     55               1   \n",
       "48894             95  40.76404  -73.98933          1     90               7   \n",
       "\n",
       "       number_of_reviews  reviews_per_month  calculated_host_listings_count  \\\n",
       "0                      9           0.210000                               6   \n",
       "1                     45           0.380000                               2   \n",
       "2                      0           1.373221                               1   \n",
       "3                    270           4.640000                               1   \n",
       "4                      9           0.100000                               1   \n",
       "...                  ...                ...                             ...   \n",
       "48890                  0           1.373221                               2   \n",
       "48891                  0           1.373221                               2   \n",
       "48892                  0           1.373221                               1   \n",
       "48893                  0           1.373221                               6   \n",
       "48894                  0           1.373221                               1   \n",
       "\n",
       "       availability_365  neighbourhood_group_Imputed  neighbourhood_Imputed  \\\n",
       "0                   365                            0                      0   \n",
       "1                   355                            0                      0   \n",
       "2                   365                            0                      0   \n",
       "3                   194                            0                      0   \n",
       "4                     0                            0                      0   \n",
       "...                 ...                          ...                    ...   \n",
       "48890                 9                            0                      0   \n",
       "48891                36                            0                      0   \n",
       "48892                27                            0                      0   \n",
       "48893                 2                            0                      0   \n",
       "48894                23                            0                      0   \n",
       "\n",
       "       room_type_Imputed  last_review_Imputed  neighbourhood_group_Bronx  \\\n",
       "0                      0                    0                        0.0   \n",
       "1                      0                    0                        0.0   \n",
       "2                      0                    1                        0.0   \n",
       "3                      0                    0                        0.0   \n",
       "4                      0                    0                        0.0   \n",
       "...                  ...                  ...                        ...   \n",
       "48890                  0                    1                        0.0   \n",
       "48891                  0                    1                        0.0   \n",
       "48892                  0                    1                        0.0   \n",
       "48893                  0                    1                        0.0   \n",
       "48894                  0                    1                        0.0   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "0                               1.0                            0.0   \n",
       "1                               0.0                            1.0   \n",
       "2                               0.0                            1.0   \n",
       "3                               1.0                            0.0   \n",
       "4                               0.0                            1.0   \n",
       "...                             ...                            ...   \n",
       "48890                           1.0                            0.0   \n",
       "48891                           1.0                            0.0   \n",
       "48892                           0.0                            1.0   \n",
       "48893                           0.0                            1.0   \n",
       "48894                           0.0                            1.0   \n",
       "\n",
       "       neighbourhood_group_Queens  neighbourhood_group_Staten Island  \\\n",
       "0                             0.0                                0.0   \n",
       "1                             0.0                                0.0   \n",
       "2                             0.0                                0.0   \n",
       "3                             0.0                                0.0   \n",
       "4                             0.0                                0.0   \n",
       "...                           ...                                ...   \n",
       "48890                         0.0                                0.0   \n",
       "48891                         0.0                                0.0   \n",
       "48892                         0.0                                0.0   \n",
       "48893                         0.0                                0.0   \n",
       "48894                         0.0                                0.0   \n",
       "\n",
       "       last_review_unix  \n",
       "0            1539907200  \n",
       "1            1558396800  \n",
       "2            1561248000  \n",
       "3            1562284800  \n",
       "4            1542585600  \n",
       "...                 ...  \n",
       "48890        1561248000  \n",
       "48891        1561248000  \n",
       "48892        1561248000  \n",
       "48893        1561248000  \n",
       "48894        1561248000  \n",
       "\n",
       "[48895 rows x 20 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the feature selection I will use `FeatureSelection` class that allows you to compute features importances using selected method. \n",
    "\n",
    "Let's use two different selection methods: `random_forest`(used by default), `mutual_inf` and `lasso`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models and plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_selection = FeatureSelection(y_column='price', task='reg')\n",
    "feature_selection.create_model(transformed)\n",
    "feature_selection.plot_importance(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using method plot_importance you can see the most important features in created model. You can change plot size with the `figsize` parameter. It will create (5,5) plot by default."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mutual Information**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_selection = FeatureSelection(method='mutual_inf', y_column='price', task='reg')\n",
    "feature_selection.create_model(transformed)\n",
    "feature_selection.plot_importance(figsize=(7, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lasso**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_selection = FeatureSelection(method='lasso', y_column='price', task='reg')\n",
    "feature_selection.create_model(transformed)\n",
    "feature_selection.plot_importance(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create plot with the `importance_threshold` parameter and it will create plot only with the values bigger then `importance_threshold`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_selection.plot_importance(importance_threshold=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create new dataset with the selected features only using `threshold = 3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>last_review_Imputed</th>\n",
       "      <th>neighbourhood_group_Brooklyn</th>\n",
       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>neighbourhood_group_Staten Island</th>\n",
       "      <th>last_review_unix</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1539907200</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>127</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1558396800</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1562284800</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1542585600</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48890</th>\n",
       "      <td>13</td>\n",
       "      <td>40.67853</td>\n",
       "      <td>-73.94995</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>28</td>\n",
       "      <td>40.70184</td>\n",
       "      <td>-73.93317</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>94</td>\n",
       "      <td>40.81475</td>\n",
       "      <td>-73.94867</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>95</td>\n",
       "      <td>40.75751</td>\n",
       "      <td>-73.99112</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>95</td>\n",
       "      <td>40.76404</td>\n",
       "      <td>-73.98933</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type  number_of_reviews  \\\n",
       "0                108  40.64749  -73.97237          1                  9   \n",
       "1                127  40.75362  -73.98377          0                 45   \n",
       "2                 94  40.80902  -73.94190          1                  0   \n",
       "3                 41  40.68514  -73.95976          0                270   \n",
       "4                 61  40.79851  -73.94399          0                  9   \n",
       "...              ...       ...        ...        ...                ...   \n",
       "48890             13  40.67853  -73.94995          1                  0   \n",
       "48891             28  40.70184  -73.93317          1                  0   \n",
       "48892             94  40.81475  -73.94867          0                  0   \n",
       "48893             95  40.75751  -73.99112          1                  0   \n",
       "48894             95  40.76404  -73.98933          1                  0   \n",
       "\n",
       "       calculated_host_listings_count  availability_365  last_review_Imputed  \\\n",
       "0                                   6               365                    0   \n",
       "1                                   2               355                    0   \n",
       "2                                   1               365                    1   \n",
       "3                                   1               194                    0   \n",
       "4                                   1                 0                    0   \n",
       "...                               ...               ...                  ...   \n",
       "48890                               2                 9                    1   \n",
       "48891                               2                36                    1   \n",
       "48892                               1                27                    1   \n",
       "48893                               6                 2                    1   \n",
       "48894                               1                23                    1   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "0                               1.0                            0.0   \n",
       "1                               0.0                            1.0   \n",
       "2                               0.0                            1.0   \n",
       "3                               1.0                            0.0   \n",
       "4                               0.0                            1.0   \n",
       "...                             ...                            ...   \n",
       "48890                           1.0                            0.0   \n",
       "48891                           1.0                            0.0   \n",
       "48892                           0.0                            1.0   \n",
       "48893                           0.0                            1.0   \n",
       "48894                           0.0                            1.0   \n",
       "\n",
       "       neighbourhood_group_Staten Island  last_review_unix  price  \n",
       "0                                    0.0        1539907200    149  \n",
       "1                                    0.0        1558396800    225  \n",
       "2                                    0.0        1561248000    150  \n",
       "3                                    0.0        1562284800     89  \n",
       "4                                    0.0        1542585600     80  \n",
       "...                                  ...               ...    ...  \n",
       "48890                                0.0        1561248000     70  \n",
       "48891                                0.0        1561248000     40  \n",
       "48892                                0.0        1561248000    115  \n",
       "48893                                0.0        1561248000     55  \n",
       "48894                                0.0        1561248000     90  \n",
       "\n",
       "[48895 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = feature_selection.create_new_dataset(threshold=3)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also create new dataset with the 'mean' (used by default) or 'median' `threshold`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>availability_365</th>\n",
       "      <th>last_review_Imputed</th>\n",
       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>194</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80</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>48890</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       availability_365  last_review_Imputed  neighbourhood_group_Manhattan  \\\n",
       "0                   365                    0                            0.0   \n",
       "1                   355                    0                            1.0   \n",
       "2                   365                    1                            1.0   \n",
       "3                   194                    0                            0.0   \n",
       "4                     0                    0                            1.0   \n",
       "...                 ...                  ...                            ...   \n",
       "48890                 9                    1                            0.0   \n",
       "48891                36                    1                            0.0   \n",
       "48892                27                    1                            1.0   \n",
       "48893                 2                    1                            1.0   \n",
       "48894                23                    1                            1.0   \n",
       "\n",
       "       price  \n",
       "0        149  \n",
       "1        225  \n",
       "2        150  \n",
       "3         89  \n",
       "4         80  \n",
       "...      ...  \n",
       "48890     70  \n",
       "48891     40  \n",
       "48892    115  \n",
       "48893     55  \n",
       "48894     90  \n",
       "\n",
       "[48895 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset_median = feature_selection.create_new_dataset(threshold='median')\n",
    "new_dataset_median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the dimensionality reduction task I will use the `DimensionalityReduction` class. It has several different models that can be used for this task.\n",
    "\n",
    "Matrix decomposition is represented by methods such as:\n",
    "* `pca` - Principal Component Analysis, PCA;\n",
    "* `svd` - truncated Singular Value Decomposition, SVD;\n",
    "* `fa` - Factor Analysis, FA;\n",
    "* `nmf` - Non-Negative Matrix Factorization, NMF. \n",
    "\n",
    "Discriminant Analysis is represented by methods such as:\n",
    "* `lda` - Linear Discriminant Analysis, LDA.\n",
    "\n",
    "Manifold learning is represented by methods such as:\n",
    "* `lle` - Locally Linear Embedding, LLE;\n",
    "* `isomap` - Isomap Embedding;\n",
    "* `t_sne` - T-distributed Stochastic Neighbor Embedding, T-SNE.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's decompose this transformed dataset containing 20 columns using the PCA method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.362869e+06</td>\n",
       "      <td>11.901710</td>\n",
       "      <td>-253.605888</td>\n",
       "      <td>-7.148001</td>\n",
       "      <td>-22.170715</td>\n",
       "      <td>-21.493989</td>\n",
       "      <td>-12.246664</td>\n",
       "      <td>-0.931738</td>\n",
       "      <td>0.769059</td>\n",
       "      <td>0.307488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.512673e+07</td>\n",
       "      <td>84.519733</td>\n",
       "      <td>-221.053152</td>\n",
       "      <td>-24.481916</td>\n",
       "      <td>12.032628</td>\n",
       "      <td>-18.993786</td>\n",
       "      <td>-9.761820</td>\n",
       "      <td>-1.583632</td>\n",
       "      <td>-0.722675</td>\n",
       "      <td>-0.428359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.797793e+07</td>\n",
       "      <td>9.627487</td>\n",
       "      <td>-230.921884</td>\n",
       "      <td>5.372608</td>\n",
       "      <td>-34.733556</td>\n",
       "      <td>-28.745311</td>\n",
       "      <td>-9.731255</td>\n",
       "      <td>0.095528</td>\n",
       "      <td>-0.679259</td>\n",
       "      <td>0.523109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.901473e+07</td>\n",
       "      <td>-65.001532</td>\n",
       "      <td>-78.749730</td>\n",
       "      <td>67.840818</td>\n",
       "      <td>233.596790</td>\n",
       "      <td>34.806700</td>\n",
       "      <td>5.953084</td>\n",
       "      <td>-0.917157</td>\n",
       "      <td>0.445035</td>\n",
       "      <td>-0.523183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.844693e+05</td>\n",
       "      <td>-79.991686</td>\n",
       "      <td>107.036182</td>\n",
       "      <td>47.022149</td>\n",
       "      <td>-10.883226</td>\n",
       "      <td>-0.174703</td>\n",
       "      <td>5.712553</td>\n",
       "      <td>-1.039901</td>\n",
       "      <td>-0.882975</td>\n",
       "      <td>-0.429759</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48890</th>\n",
       "      <td>-1.797793e+07</td>\n",
       "      <td>-92.965534</td>\n",
       "      <td>116.368154</td>\n",
       "      <td>93.258025</td>\n",
       "      <td>-24.828780</td>\n",
       "      <td>-2.562457</td>\n",
       "      <td>-2.229234</td>\n",
       "      <td>0.052157</td>\n",
       "      <td>0.546221</td>\n",
       "      <td>0.396200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>-1.797793e+07</td>\n",
       "      <td>-120.999952</td>\n",
       "      <td>88.070391</td>\n",
       "      <td>76.966127</td>\n",
       "      <td>-25.913251</td>\n",
       "      <td>-4.280991</td>\n",
       "      <td>-0.905671</td>\n",
       "      <td>0.062559</td>\n",
       "      <td>0.564634</td>\n",
       "      <td>0.379879</td>\n",
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       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>-1.797793e+07</td>\n",
       "      <td>-45.408512</td>\n",
       "      <td>103.018507</td>\n",
       "      <td>12.770417</td>\n",
       "      <td>-22.978482</td>\n",
       "      <td>-4.863794</td>\n",
       "      <td>4.750554</td>\n",
       "      <td>0.112832</td>\n",
       "      <td>-0.804983</td>\n",
       "      <td>-0.366151</td>\n",
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       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>-1.797793e+07</td>\n",
       "      <td>-106.749160</td>\n",
       "      <td>124.296085</td>\n",
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       "      <td>-23.102742</td>\n",
       "      <td>1.242234</td>\n",
       "      <td>-4.008324</td>\n",
       "      <td>0.091221</td>\n",
       "      <td>-0.696836</td>\n",
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       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>-1.797793e+07</td>\n",
       "      <td>-70.589356</td>\n",
       "      <td>105.618620</td>\n",
       "      <td>11.374890</td>\n",
       "      <td>-22.910813</td>\n",
       "      <td>-4.744280</td>\n",
       "      <td>1.881798</td>\n",
       "      <td>0.117496</td>\n",
       "      <td>-0.690959</td>\n",
       "      <td>0.586998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0           1           2          3           4          5  \\\n",
       "0      3.362869e+06   11.901710 -253.605888  -7.148001  -22.170715 -21.493989   \n",
       "1     -1.512673e+07   84.519733 -221.053152 -24.481916   12.032628 -18.993786   \n",
       "2     -1.797793e+07    9.627487 -230.921884   5.372608  -34.733556 -28.745311   \n",
       "3     -1.901473e+07  -65.001532  -78.749730  67.840818  233.596790  34.806700   \n",
       "4      6.844693e+05  -79.991686  107.036182  47.022149  -10.883226  -0.174703   \n",
       "...             ...         ...         ...        ...         ...        ...   \n",
       "48890 -1.797793e+07  -92.965534  116.368154  93.258025  -24.828780  -2.562457   \n",
       "48891 -1.797793e+07 -120.999952   88.070391  76.966127  -25.913251  -4.280991   \n",
       "48892 -1.797793e+07  -45.408512  103.018507  12.770417  -22.978482  -4.863794   \n",
       "48893 -1.797793e+07 -106.749160  124.296085  11.148555  -23.102742   1.242234   \n",
       "48894 -1.797793e+07  -70.589356  105.618620  11.374890  -22.910813  -4.744280   \n",
       "\n",
       "               6         7         8         9  \n",
       "0     -12.246664 -0.931738  0.769059  0.307488  \n",
       "1      -9.761820 -1.583632 -0.722675 -0.428359  \n",
       "2      -9.731255  0.095528 -0.679259  0.523109  \n",
       "3       5.953084 -0.917157  0.445035 -0.523183  \n",
       "4       5.712553 -1.039901 -0.882975 -0.429759  \n",
       "...          ...       ...       ...       ...  \n",
       "48890  -2.229234  0.052157  0.546221  0.396200  \n",
       "48891  -0.905671  0.062559  0.564634  0.379879  \n",
       "48892   4.750554  0.112832 -0.804983 -0.366151  \n",
       "48893  -4.008324  0.091221 -0.696836  0.574723  \n",
       "48894   1.881798  0.117496 -0.690959  0.586998  \n",
       "\n",
       "[48895 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = DimensionalityReduction(method='pca').transform(X=transformed, n_components=10)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of how to plot created model using the y column. *(a random column was chosen as y)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "isomap = DimensionalityReduction(method='svd')\n",
    "isomap.transform(X=transformed, n_components=2)\n",
    "isomap.plot_transformed(y=transformed['room_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If `n_components` is greater than 3, `sns.pairplot` will be created. You can also change the plot settings. *(a random column was chosen as y)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600.625x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "isomap = DimensionalityReduction(method='svd')\n",
    "isomap.transform(X=transformed, n_components=3)\n",
    "isomap.plot_transformed(y=transformed['room_type'], figsize=(5, 5), palette='Set2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the normalization reduction task I will use the `Normalization` class.\n",
    "\n",
    "Normalization is represented by methods such as:\n",
    "* `standard` - StandardScaler standardizes features by removing the mean and scaling to unit variance;\n",
    "* `minmax` - MinMaxScaler transforms features by scaling each feature to a given range;\n",
    "* `robust` - RobustScaler scales features using statistics that are robust to outliers;\n",
    "* `normalizer` - Normalizer normalizes samples individually to unit norm;\n",
    "* `yeo-johnson` - PowerTransformer(method=’yeo-johnson’) applies a power transform featurewise to make data more Gaussian-like, supports both positive or negative data;\n",
    "* `box-cox` - PowerTransformer(method=’box-cox’) applies a power transform featurewise to make data more Gaussian-like, requires input data to be strictly positive;\n",
    "* `log` - logarithm of the values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's normalize transformed dataset using the `standard` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>neighbourhood_group_Imputed</th>\n",
       "      <th>neighbourhood_Imputed</th>\n",
       "      <th>room_type_Imputed</th>\n",
       "      <th>last_review_Imputed</th>\n",
       "      <th>neighbourhood_group_Bronx</th>\n",
       "      <th>neighbourhood_group_Brooklyn</th>\n",
       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>neighbourhood_group_Queens</th>\n",
       "      <th>neighbourhood_group_Staten Island</th>\n",
       "      <th>last_review_unix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.012762</td>\n",
       "      <td>-1.493849</td>\n",
       "      <td>-0.437652</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.015493</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>-0.320414</td>\n",
       "      <td>-0.776641</td>\n",
       "      <td>-0.034716</td>\n",
       "      <td>1.916250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>1.196705</td>\n",
       "      <td>-0.891833</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>-0.101411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.289156</td>\n",
       "      <td>0.452436</td>\n",
       "      <td>-0.684639</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>0.300974</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>0.487665</td>\n",
       "      <td>-0.663138</td>\n",
       "      <td>-0.156104</td>\n",
       "      <td>1.840275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.456164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.190897</td>\n",
       "      <td>1.468399</td>\n",
       "      <td>0.222497</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.011329</td>\n",
       "      <td>-0.196484</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>1.916250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.961892</td>\n",
       "      <td>-0.803398</td>\n",
       "      <td>-0.164450</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>-0.265335</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>5.538156</td>\n",
       "      <td>2.181110</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>0.617065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>1.196705</td>\n",
       "      <td>-0.891833</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.573411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.670950</td>\n",
       "      <td>1.275660</td>\n",
       "      <td>0.177216</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>-0.302811</td>\n",
       "      <td>0.144807</td>\n",
       "      <td>-0.320414</td>\n",
       "      <td>-0.850084</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>-0.856865</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>-0.020641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48890</th>\n",
       "      <td>-1.369209</td>\n",
       "      <td>-0.924616</td>\n",
       "      <td>0.048089</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.344452</td>\n",
       "      <td>-0.245240</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.156104</td>\n",
       "      <td>-0.788486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>1.196705</td>\n",
       "      <td>-0.891833</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>-1.151003</td>\n",
       "      <td>-0.497141</td>\n",
       "      <td>0.411637</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.469373</td>\n",
       "      <td>-0.147729</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.156104</td>\n",
       "      <td>-0.583352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>1.196705</td>\n",
       "      <td>-0.891833</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>-0.190897</td>\n",
       "      <td>1.573480</td>\n",
       "      <td>0.075821</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>-0.157070</td>\n",
       "      <td>0.144807</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>-0.651730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>-0.176350</td>\n",
       "      <td>0.523774</td>\n",
       "      <td>-0.843881</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.406912</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.034716</td>\n",
       "      <td>-0.841669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>-0.176350</td>\n",
       "      <td>0.643525</td>\n",
       "      <td>-0.805100</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.261171</td>\n",
       "      <td>-0.001461</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>-0.682120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type     price  \\\n",
       "0           0.012762 -1.493849  -0.437652   1.040134 -0.015493   \n",
       "1           0.289156  0.452436  -0.684639  -0.961415  0.300974   \n",
       "2          -0.190897  1.468399   0.222497   1.040134 -0.011329   \n",
       "3          -0.961892 -0.803398  -0.164450  -0.961415 -0.265335   \n",
       "4          -0.670950  1.275660   0.177216  -0.961415 -0.302811   \n",
       "...              ...       ...        ...        ...       ...   \n",
       "48890      -1.369209 -0.924616   0.048089   1.040134 -0.344452   \n",
       "48891      -1.151003 -0.497141   0.411637   1.040134 -0.469373   \n",
       "48892      -0.190897  1.573480   0.075821  -0.961415 -0.157070   \n",
       "48893      -0.176350  0.523774  -0.843881   1.040134 -0.406912   \n",
       "48894      -0.176350  0.643525  -0.805100   1.040134 -0.261171   \n",
       "\n",
       "       minimum_nights  number_of_reviews  reviews_per_month  \\\n",
       "0           -0.293996          -0.320414          -0.776641   \n",
       "1           -0.293996           0.487665          -0.663138   \n",
       "2           -0.196484          -0.522433           0.000000   \n",
       "3           -0.293996           5.538156           2.181110   \n",
       "4            0.144807          -0.320414          -0.850084   \n",
       "...               ...                ...                ...   \n",
       "48890       -0.245240          -0.522433           0.000000   \n",
       "48891       -0.147729          -0.522433           0.000000   \n",
       "48892        0.144807          -0.522433           0.000000   \n",
       "48893       -0.293996          -0.522433           0.000000   \n",
       "48894       -0.001461          -0.522433           0.000000   \n",
       "\n",
       "       calculated_host_listings_count  availability_365  \\\n",
       "0                           -0.034716          1.916250   \n",
       "1                           -0.156104          1.840275   \n",
       "2                           -0.186451          1.916250   \n",
       "3                           -0.186451          0.617065   \n",
       "4                           -0.186451         -0.856865   \n",
       "...                               ...               ...   \n",
       "48890                       -0.156104         -0.788486   \n",
       "48891                       -0.156104         -0.583352   \n",
       "48892                       -0.186451         -0.651730   \n",
       "48893                       -0.034716         -0.841669   \n",
       "48894                       -0.186451         -0.682120   \n",
       "\n",
       "       neighbourhood_group_Imputed  neighbourhood_Imputed  room_type_Imputed  \\\n",
       "0                              0.0                    0.0                0.0   \n",
       "1                              0.0                    0.0                0.0   \n",
       "2                              0.0                    0.0                0.0   \n",
       "3                              0.0                    0.0                0.0   \n",
       "4                              0.0                    0.0                0.0   \n",
       "...                            ...                    ...                ...   \n",
       "48890                          0.0                    0.0                0.0   \n",
       "48891                          0.0                    0.0                0.0   \n",
       "48892                          0.0                    0.0                0.0   \n",
       "48893                          0.0                    0.0                0.0   \n",
       "48894                          0.0                    0.0                0.0   \n",
       "\n",
       "       last_review_Imputed  neighbourhood_group_Bronx  \\\n",
       "0                -0.508710                  -0.151071   \n",
       "1                -0.508710                  -0.151071   \n",
       "2                 1.965758                  -0.151071   \n",
       "3                -0.508710                  -0.151071   \n",
       "4                -0.508710                  -0.151071   \n",
       "...                    ...                        ...   \n",
       "48890             1.965758                  -0.151071   \n",
       "48891             1.965758                  -0.151071   \n",
       "48892             1.965758                  -0.151071   \n",
       "48893             1.965758                  -0.151071   \n",
       "48894             1.965758                  -0.151071   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "0                          1.196705                      -0.891833   \n",
       "1                         -0.835628                       1.121286   \n",
       "2                         -0.835628                       1.121286   \n",
       "3                          1.196705                      -0.891833   \n",
       "4                         -0.835628                       1.121286   \n",
       "...                             ...                            ...   \n",
       "48890                      1.196705                      -0.891833   \n",
       "48891                      1.196705                      -0.891833   \n",
       "48892                     -0.835628                       1.121286   \n",
       "48893                     -0.835628                       1.121286   \n",
       "48894                     -0.835628                       1.121286   \n",
       "\n",
       "       neighbourhood_group_Queens  neighbourhood_group_Staten Island  \\\n",
       "0                       -0.362035                          -0.087677   \n",
       "1                       -0.362035                          -0.087677   \n",
       "2                       -0.362035                          -0.087677   \n",
       "3                       -0.362035                          -0.087677   \n",
       "4                       -0.362035                          -0.087677   \n",
       "...                           ...                                ...   \n",
       "48890                   -0.362035                          -0.087677   \n",
       "48891                   -0.362035                          -0.087677   \n",
       "48892                   -0.362035                          -0.087677   \n",
       "48893                   -0.362035                          -0.087677   \n",
       "48894                   -0.362035                          -0.087677   \n",
       "\n",
       "       last_review_unix  \n",
       "0             -0.101411  \n",
       "1              0.456164  \n",
       "2              0.542145  \n",
       "3              0.573411  \n",
       "4             -0.020641  \n",
       "...                 ...  \n",
       "48890          0.542145  \n",
       "48891          0.542145  \n",
       "48892          0.542145  \n",
       "48893          0.542145  \n",
       "48894          0.542145  \n",
       "\n",
       "[48895 rows x 20 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = Normalization(method='standard').transform(transformed)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can normalize only a selected list of column using the `column_names` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>neighbourhood_group_Imputed</th>\n",
       "      <th>neighbourhood_Imputed</th>\n",
       "      <th>room_type_Imputed</th>\n",
       "      <th>last_review_Imputed</th>\n",
       "      <th>neighbourhood_group_Bronx</th>\n",
       "      <th>neighbourhood_group_Brooklyn</th>\n",
       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>neighbourhood_group_Queens</th>\n",
       "      <th>neighbourhood_group_Staten Island</th>\n",
       "      <th>last_review_unix</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>127</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
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       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.663138</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>94</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.011329</td>\n",
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       "      <td>365</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1562284800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.302811</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.850084</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1542585600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48890</th>\n",
       "      <td>13</td>\n",
       "      <td>40.67853</td>\n",
       "      <td>-73.94995</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.344452</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>28</td>\n",
       "      <td>40.70184</td>\n",
       "      <td>-73.93317</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.469373</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>94</td>\n",
       "      <td>40.81475</td>\n",
       "      <td>-73.94867</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.157070</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>95</td>\n",
       "      <td>40.75751</td>\n",
       "      <td>-73.99112</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.406912</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>95</td>\n",
       "      <td>40.76404</td>\n",
       "      <td>-73.98933</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.261171</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type     price  \\\n",
       "0                108  40.64749  -73.97237          1 -0.015493   \n",
       "1                127  40.75362  -73.98377          0  0.300974   \n",
       "2                 94  40.80902  -73.94190          1 -0.011329   \n",
       "3                 41  40.68514  -73.95976          0 -0.265335   \n",
       "4                 61  40.79851  -73.94399          0 -0.302811   \n",
       "...              ...       ...        ...        ...       ...   \n",
       "48890             13  40.67853  -73.94995          1 -0.344452   \n",
       "48891             28  40.70184  -73.93317          1 -0.469373   \n",
       "48892             94  40.81475  -73.94867          0 -0.157070   \n",
       "48893             95  40.75751  -73.99112          1 -0.406912   \n",
       "48894             95  40.76404  -73.98933          1 -0.261171   \n",
       "\n",
       "       minimum_nights  number_of_reviews  reviews_per_month  \\\n",
       "0                   1                  9          -0.776641   \n",
       "1                   1                 45          -0.663138   \n",
       "2                   3                  0           0.000000   \n",
       "3                   1                270           2.181110   \n",
       "4                  10                  9          -0.850084   \n",
       "...               ...                ...                ...   \n",
       "48890               2                  0           0.000000   \n",
       "48891               4                  0           0.000000   \n",
       "48892              10                  0           0.000000   \n",
       "48893               1                  0           0.000000   \n",
       "48894               7                  0           0.000000   \n",
       "\n",
       "       calculated_host_listings_count  availability_365  \\\n",
       "0                                   6               365   \n",
       "1                                   2               355   \n",
       "2                                   1               365   \n",
       "3                                   1               194   \n",
       "4                                   1                 0   \n",
       "...                               ...               ...   \n",
       "48890                               2                 9   \n",
       "48891                               2                36   \n",
       "48892                               1                27   \n",
       "48893                               6                 2   \n",
       "48894                               1                23   \n",
       "\n",
       "       neighbourhood_group_Imputed  neighbourhood_Imputed  room_type_Imputed  \\\n",
       "0                                0                      0                  0   \n",
       "1                                0                      0                  0   \n",
       "2                                0                      0                  0   \n",
       "3                                0                      0                  0   \n",
       "4                                0                      0                  0   \n",
       "...                            ...                    ...                ...   \n",
       "48890                            0                      0                  0   \n",
       "48891                            0                      0                  0   \n",
       "48892                            0                      0                  0   \n",
       "48893                            0                      0                  0   \n",
       "48894                            0                      0                  0   \n",
       "\n",
       "       last_review_Imputed  neighbourhood_group_Bronx  \\\n",
       "0                        0                        0.0   \n",
       "1                        0                        0.0   \n",
       "2                        1                        0.0   \n",
       "3                        0                        0.0   \n",
       "4                        0                        0.0   \n",
       "...                    ...                        ...   \n",
       "48890                    1                        0.0   \n",
       "48891                    1                        0.0   \n",
       "48892                    1                        0.0   \n",
       "48893                    1                        0.0   \n",
       "48894                    1                        0.0   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "0                               1.0                            0.0   \n",
       "1                               0.0                            1.0   \n",
       "2                               0.0                            1.0   \n",
       "3                               1.0                            0.0   \n",
       "4                               0.0                            1.0   \n",
       "...                             ...                            ...   \n",
       "48890                           1.0                            0.0   \n",
       "48891                           1.0                            0.0   \n",
       "48892                           0.0                            1.0   \n",
       "48893                           0.0                            1.0   \n",
       "48894                           0.0                            1.0   \n",
       "\n",
       "       neighbourhood_group_Queens  neighbourhood_group_Staten Island  \\\n",
       "0                             0.0                                0.0   \n",
       "1                             0.0                                0.0   \n",
       "2                             0.0                                0.0   \n",
       "3                             0.0                                0.0   \n",
       "4                             0.0                                0.0   \n",
       "...                           ...                                ...   \n",
       "48890                         0.0                                0.0   \n",
       "48891                         0.0                                0.0   \n",
       "48892                         0.0                                0.0   \n",
       "48893                         0.0                                0.0   \n",
       "48894                         0.0                                0.0   \n",
       "\n",
       "       last_review_unix  \n",
       "0            1539907200  \n",
       "1            1558396800  \n",
       "2            1561248000  \n",
       "3            1562284800  \n",
       "4            1542585600  \n",
       "...                 ...  \n",
       "48890        1561248000  \n",
       "48891        1561248000  \n",
       "48892        1561248000  \n",
       "48893        1561248000  \n",
       "48894        1561248000  \n",
       "\n",
       "[48895 rows x 20 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = Normalization(method='standard', column_names=['price', 'reviews_per_month']).transform(transformed)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to transform some columns using the selected methods, use the `column_method` parameter and initialize the columns and methods as a dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>neighbourhood_group_Imputed</th>\n",
       "      <th>neighbourhood_Imputed</th>\n",
       "      <th>room_type_Imputed</th>\n",
       "      <th>last_review_Imputed</th>\n",
       "      <th>neighbourhood_group_Bronx</th>\n",
       "      <th>neighbourhood_group_Brooklyn</th>\n",
       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>neighbourhood_group_Queens</th>\n",
       "      <th>neighbourhood_group_Staten Island</th>\n",
       "      <th>last_review_unix</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.012762</td>\n",
       "      <td>-1.493849</td>\n",
       "      <td>-0.437652</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>0.405660</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>-0.320414</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>-0.034716</td>\n",
       "      <td>1.916250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>1.196705</td>\n",
       "      <td>-0.891833</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>-0.101411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.289156</td>\n",
       "      <td>0.452436</td>\n",
       "      <td>-0.684639</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>1.122642</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>0.487665</td>\n",
       "      <td>0.006326</td>\n",
       "      <td>-0.156104</td>\n",
       "      <td>1.840275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.456164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.190897</td>\n",
       "      <td>1.468399</td>\n",
       "      <td>0.222497</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>-0.196484</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.023307</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>1.916250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.961892</td>\n",
       "      <td>-0.803398</td>\n",
       "      <td>-0.164450</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>-0.160377</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>5.538156</td>\n",
       "      <td>0.079159</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>0.617065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>1.196705</td>\n",
       "      <td>-0.891833</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.573411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.670950</td>\n",
       "      <td>1.275660</td>\n",
       "      <td>0.177216</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>-0.245283</td>\n",
       "      <td>0.144807</td>\n",
       "      <td>-0.320414</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>-0.856865</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.508710</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>-0.020641</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.048089</td>\n",
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       "      <td>-0.339623</td>\n",
       "      <td>-0.245240</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.023307</td>\n",
       "      <td>-0.156104</td>\n",
       "      <td>-0.788486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>1.196705</td>\n",
       "      <td>-0.891833</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
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       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>-1.151003</td>\n",
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       "      <td>-0.522433</td>\n",
       "      <td>0.023307</td>\n",
       "      <td>-0.156104</td>\n",
       "      <td>-0.583352</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>-0.190897</td>\n",
       "      <td>1.573480</td>\n",
       "      <td>0.075821</td>\n",
       "      <td>-0.961415</td>\n",
       "      <td>0.084906</td>\n",
       "      <td>0.144807</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.023307</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>-0.651730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>-0.176350</td>\n",
       "      <td>0.523774</td>\n",
       "      <td>-0.843881</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.481132</td>\n",
       "      <td>-0.293996</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.023307</td>\n",
       "      <td>-0.034716</td>\n",
       "      <td>-0.841669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>-0.176350</td>\n",
       "      <td>0.643525</td>\n",
       "      <td>-0.805100</td>\n",
       "      <td>1.040134</td>\n",
       "      <td>-0.150943</td>\n",
       "      <td>-0.001461</td>\n",
       "      <td>-0.522433</td>\n",
       "      <td>0.023307</td>\n",
       "      <td>-0.186451</td>\n",
       "      <td>-0.682120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965758</td>\n",
       "      <td>-0.151071</td>\n",
       "      <td>-0.835628</td>\n",
       "      <td>1.121286</td>\n",
       "      <td>-0.362035</td>\n",
       "      <td>-0.087677</td>\n",
       "      <td>0.542145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type     price  \\\n",
       "0           0.012762 -1.493849  -0.437652   1.040134  0.405660   \n",
       "1           0.289156  0.452436  -0.684639  -0.961415  1.122642   \n",
       "2          -0.190897  1.468399   0.222497   1.040134  0.415094   \n",
       "3          -0.961892 -0.803398  -0.164450  -0.961415 -0.160377   \n",
       "4          -0.670950  1.275660   0.177216  -0.961415 -0.245283   \n",
       "...              ...       ...        ...        ...       ...   \n",
       "48890      -1.369209 -0.924616   0.048089   1.040134 -0.339623   \n",
       "48891      -1.151003 -0.497141   0.411637   1.040134 -0.622642   \n",
       "48892      -0.190897  1.573480   0.075821  -0.961415  0.084906   \n",
       "48893      -0.176350  0.523774  -0.843881   1.040134 -0.481132   \n",
       "48894      -0.176350  0.643525  -0.805100   1.040134 -0.150943   \n",
       "\n",
       "       minimum_nights  number_of_reviews  reviews_per_month  \\\n",
       "0           -0.293996          -0.320414           0.003419   \n",
       "1           -0.293996           0.487665           0.006326   \n",
       "2           -0.196484          -0.522433           0.023307   \n",
       "3           -0.293996           5.538156           0.079159   \n",
       "4            0.144807          -0.320414           0.001539   \n",
       "...               ...                ...                ...   \n",
       "48890       -0.245240          -0.522433           0.023307   \n",
       "48891       -0.147729          -0.522433           0.023307   \n",
       "48892        0.144807          -0.522433           0.023307   \n",
       "48893       -0.293996          -0.522433           0.023307   \n",
       "48894       -0.001461          -0.522433           0.023307   \n",
       "\n",
       "       calculated_host_listings_count  availability_365  \\\n",
       "0                           -0.034716          1.916250   \n",
       "1                           -0.156104          1.840275   \n",
       "2                           -0.186451          1.916250   \n",
       "3                           -0.186451          0.617065   \n",
       "4                           -0.186451         -0.856865   \n",
       "...                               ...               ...   \n",
       "48890                       -0.156104         -0.788486   \n",
       "48891                       -0.156104         -0.583352   \n",
       "48892                       -0.186451         -0.651730   \n",
       "48893                       -0.034716         -0.841669   \n",
       "48894                       -0.186451         -0.682120   \n",
       "\n",
       "       neighbourhood_group_Imputed  neighbourhood_Imputed  room_type_Imputed  \\\n",
       "0                              0.0                    0.0                0.0   \n",
       "1                              0.0                    0.0                0.0   \n",
       "2                              0.0                    0.0                0.0   \n",
       "3                              0.0                    0.0                0.0   \n",
       "4                              0.0                    0.0                0.0   \n",
       "...                            ...                    ...                ...   \n",
       "48890                          0.0                    0.0                0.0   \n",
       "48891                          0.0                    0.0                0.0   \n",
       "48892                          0.0                    0.0                0.0   \n",
       "48893                          0.0                    0.0                0.0   \n",
       "48894                          0.0                    0.0                0.0   \n",
       "\n",
       "       last_review_Imputed  neighbourhood_group_Bronx  \\\n",
       "0                -0.508710                  -0.151071   \n",
       "1                -0.508710                  -0.151071   \n",
       "2                 1.965758                  -0.151071   \n",
       "3                -0.508710                  -0.151071   \n",
       "4                -0.508710                  -0.151071   \n",
       "...                    ...                        ...   \n",
       "48890             1.965758                  -0.151071   \n",
       "48891             1.965758                  -0.151071   \n",
       "48892             1.965758                  -0.151071   \n",
       "48893             1.965758                  -0.151071   \n",
       "48894             1.965758                  -0.151071   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "0                          1.196705                      -0.891833   \n",
       "1                         -0.835628                       1.121286   \n",
       "2                         -0.835628                       1.121286   \n",
       "3                          1.196705                      -0.891833   \n",
       "4                         -0.835628                       1.121286   \n",
       "...                             ...                            ...   \n",
       "48890                      1.196705                      -0.891833   \n",
       "48891                      1.196705                      -0.891833   \n",
       "48892                     -0.835628                       1.121286   \n",
       "48893                     -0.835628                       1.121286   \n",
       "48894                     -0.835628                       1.121286   \n",
       "\n",
       "       neighbourhood_group_Queens  neighbourhood_group_Staten Island  \\\n",
       "0                       -0.362035                          -0.087677   \n",
       "1                       -0.362035                          -0.087677   \n",
       "2                       -0.362035                          -0.087677   \n",
       "3                       -0.362035                          -0.087677   \n",
       "4                       -0.362035                          -0.087677   \n",
       "...                           ...                                ...   \n",
       "48890                   -0.362035                          -0.087677   \n",
       "48891                   -0.362035                          -0.087677   \n",
       "48892                   -0.362035                          -0.087677   \n",
       "48893                   -0.362035                          -0.087677   \n",
       "48894                   -0.362035                          -0.087677   \n",
       "\n",
       "       last_review_unix  \n",
       "0             -0.101411  \n",
       "1              0.456164  \n",
       "2              0.542145  \n",
       "3              0.573411  \n",
       "4             -0.020641  \n",
       "...                 ...  \n",
       "48890          0.542145  \n",
       "48891          0.542145  \n",
       "48892          0.542145  \n",
       "48893          0.542145  \n",
       "48894          0.542145  \n",
       "\n",
       "[48895 rows x 20 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = Normalization(\n",
    "    method='standard',\n",
    "    column_method={'reviews_per_month': 'minmax', 'price': 'robust'},\n",
    ").transform(transformed)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is as example of the distribution plot created using the `plot_transformed` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 416.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm = Normalization(method='log', column_names='price')\n",
    "new = norm.transform(transformed)\n",
    "norm.plot_transformed(column='price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sampling task I will use the `Sampling` class.\n",
    "\n",
    "Sampling is represented by methods such as:\n",
    "* `simple` sampling is a technique in which a subset is randomly selected number from a set;\n",
    "* `systematic` sampling is a technique in which a subset is selected from a set using a defined step;\n",
    "* `cluster` sampling is a technique in which a set is divided into clusters, then the set is determined by a randomly selected number of clusters;\n",
    "* `stratified` sampling is a technique in which a set is divided into clusters, then the set is determined by a randomly selected number of units from each cluster.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a simple sampling with 1000 samples to keep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>neighbourhood_group_Imputed</th>\n",
       "      <th>neighbourhood_Imputed</th>\n",
       "      <th>room_type_Imputed</th>\n",
       "      <th>last_review_Imputed</th>\n",
       "      <th>neighbourhood_group_Bronx</th>\n",
       "      <th>neighbourhood_group_Brooklyn</th>\n",
       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>neighbourhood_group_Queens</th>\n",
       "      <th>neighbourhood_group_Staten Island</th>\n",
       "      <th>last_review_unix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37306</th>\n",
       "      <td>178</td>\n",
       "      <td>40.72243</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>13</td>\n",
       "      <td>40.68570</td>\n",
       "      <td>-73.95595</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>282</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1560556800</td>\n",
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       "    <tr>\n",
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       "      <td>40.74480</td>\n",
       "      <td>-73.92364</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1508112000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14535</th>\n",
       "      <td>68</td>\n",
       "      <td>40.73958</td>\n",
       "      <td>-73.87288</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
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       "    <tr>\n",
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       "      <td>40.66714</td>\n",
       "      <td>-73.98401</td>\n",
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       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>2</td>\n",
       "      <td>347</td>\n",
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       "      <td>1558224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21432</th>\n",
       "      <td>35</td>\n",
       "      <td>40.71449</td>\n",
       "      <td>-73.99094</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.373221</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1561248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6002</th>\n",
       "      <td>79</td>\n",
       "      <td>40.71320</td>\n",
       "      <td>-73.84901</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>5</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1439164800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25937</th>\n",
       "      <td>35</td>\n",
       "      <td>40.71365</td>\n",
       "      <td>-73.99730</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1556582400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25362</th>\n",
       "      <td>51</td>\n",
       "      <td>40.67423</td>\n",
       "      <td>-73.95737</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1556150400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>168</td>\n",
       "      <td>40.58615</td>\n",
       "      <td>-73.81245</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>1</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1442793600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type  price  minimum_nights  \\\n",
       "37306            178  40.72243  -74.00027          1     80               5   \n",
       "10122             13  40.68570  -73.95595          1     89               5   \n",
       "26186            189  40.74480  -73.92364          1     47               2   \n",
       "14535             68  40.73958  -73.87288          1    100               1   \n",
       "5409             182  40.66714  -73.98401          1     75               3   \n",
       "...              ...       ...        ...        ...    ...             ...   \n",
       "21432             35  40.71449  -73.99094          1     70               5   \n",
       "6002              79  40.71320  -73.84901          1     50               1   \n",
       "25937             35  40.71365  -73.99730          0    180               5   \n",
       "25362             51  40.67423  -73.95737          0    110               2   \n",
       "367              168  40.58615  -73.81245          1     70              27   \n",
       "\n",
       "       number_of_reviews  reviews_per_month  calculated_host_listings_count  \\\n",
       "37306                  1           0.240000                               1   \n",
       "10122                 28           0.600000                               3   \n",
       "26186                  2           0.090000                               2   \n",
       "14535                  0           1.373221                               1   \n",
       "5409                  14           0.240000                               2   \n",
       "...                  ...                ...                             ...   \n",
       "21432                  0           1.373221                               1   \n",
       "6002                   1           0.020000                               5   \n",
       "25937                 11           0.560000                               1   \n",
       "25362                 31           1.360000                               1   \n",
       "367                   13           0.130000                               1   \n",
       "\n",
       "       availability_365  neighbourhood_group_Imputed  neighbourhood_Imputed  \\\n",
       "37306                 0                            0                      0   \n",
       "10122               282                            0                      0   \n",
       "26186                 0                            0                      0   \n",
       "14535                 0                            0                      0   \n",
       "5409                347                            0                      0   \n",
       "...                 ...                          ...                    ...   \n",
       "21432                 0                            0                      0   \n",
       "6002                183                            0                      0   \n",
       "25937               171                            0                      0   \n",
       "25362                 4                            0                      0   \n",
       "367                 335                            0                      0   \n",
       "\n",
       "       room_type_Imputed  last_review_Imputed  neighbourhood_group_Bronx  \\\n",
       "37306                  0                    0                        0.0   \n",
       "10122                  0                    0                        0.0   \n",
       "26186                  0                    0                        0.0   \n",
       "14535                  0                    1                        0.0   \n",
       "5409                   0                    0                        0.0   \n",
       "...                  ...                  ...                        ...   \n",
       "21432                  0                    1                        0.0   \n",
       "6002                   0                    0                        0.0   \n",
       "25937                  0                    0                        0.0   \n",
       "25362                  0                    0                        0.0   \n",
       "367                    0                    0                        0.0   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "37306                           0.0                            1.0   \n",
       "10122                           1.0                            0.0   \n",
       "26186                           0.0                            0.0   \n",
       "14535                           0.0                            0.0   \n",
       "5409                            1.0                            0.0   \n",
       "...                             ...                            ...   \n",
       "21432                           0.0                            1.0   \n",
       "6002                            0.0                            0.0   \n",
       "25937                           0.0                            1.0   \n",
       "25362                           1.0                            0.0   \n",
       "367                             0.0                            0.0   \n",
       "\n",
       "       neighbourhood_group_Queens  neighbourhood_group_Staten Island  \\\n",
       "37306                         0.0                                0.0   \n",
       "10122                         0.0                                0.0   \n",
       "26186                         1.0                                0.0   \n",
       "14535                         1.0                                0.0   \n",
       "5409                          0.0                                0.0   \n",
       "...                           ...                                ...   \n",
       "21432                         0.0                                0.0   \n",
       "6002                          1.0                                0.0   \n",
       "25937                         0.0                                0.0   \n",
       "25362                         0.0                                0.0   \n",
       "367                           1.0                                0.0   \n",
       "\n",
       "       last_review_unix  \n",
       "37306        1551744000  \n",
       "10122        1560556800  \n",
       "26186        1508112000  \n",
       "14535        1561248000  \n",
       "5409         1558224000  \n",
       "...                 ...  \n",
       "21432        1561248000  \n",
       "6002         1439164800  \n",
       "25937        1556582400  \n",
       "25362        1556150400  \n",
       "367          1442793600  \n",
       "\n",
       "[1000 rows x 20 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = Sampling(method='simple', n=1000).sample_dataset(transformed)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a cluster sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood</th>\n",
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       "      <th>9782</th>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>29334</th>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1514764800</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>29336</th>\n",
       "      <td>201</td>\n",
       "      <td>40.76396</td>\n",
       "      <td>-73.96365</td>\n",
       "      <td>0</td>\n",
       "      <td>350</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1515024000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19558 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type  price  minimum_nights  \\\n",
       "9779              51  40.67065  -73.93544          1     40               8   \n",
       "9780              34  40.73940  -73.99838          0    210               1   \n",
       "9781             204  40.70146  -73.98188          0    225              30   \n",
       "9782             127  40.75737  -73.96916          1     70               1   \n",
       "9783              41  40.69188  -73.96070          1     45               5   \n",
       "...              ...       ...        ...        ...    ...             ...   \n",
       "29332             95  40.77122  -73.99234          0     80               1   \n",
       "29333             64  40.72432  -73.98916          1    250               1   \n",
       "29334             64  40.72472  -73.98881          0    100               2   \n",
       "29335             61  40.78538  -73.94121          1     60               2   \n",
       "29336            201  40.76396  -73.96365          0    350               3   \n",
       "\n",
       "       number_of_reviews  reviews_per_month  calculated_host_listings_count  \\\n",
       "9779                   1           0.020000                               1   \n",
       "9780                  10           0.220000                               1   \n",
       "9781                  23           0.490000                               1   \n",
       "9782                   1           0.020000                               1   \n",
       "9783                   0           1.373221                               1   \n",
       "...                  ...                ...                             ...   \n",
       "29332                  1           0.050000                               1   \n",
       "29333                  0           1.373221                               2   \n",
       "29334                  0           1.373221                               2   \n",
       "29335                  1           0.050000                               2   \n",
       "29336                  1           0.050000                               1   \n",
       "\n",
       "       availability_365  ...  neighbourhood_Imputed  room_type_Imputed  \\\n",
       "9779                  0  ...                      0                  0   \n",
       "9780                  6  ...                      0                  0   \n",
       "9781                268  ...                      0                  0   \n",
       "9782                  0  ...                      0                  0   \n",
       "9783                  0  ...                      0                  0   \n",
       "...                 ...  ...                    ...                ...   \n",
       "29332                 0  ...                      0                  0   \n",
       "29333                 0  ...                      0                  0   \n",
       "29334                22  ...                      0                  0   \n",
       "29335                 0  ...                      0                  0   \n",
       "29336                 5  ...                      0                  0   \n",
       "\n",
       "       last_review_Imputed  neighbourhood_group_Bronx  \\\n",
       "9779                     0                        0.0   \n",
       "9780                     0                        0.0   \n",
       "9781                     0                        0.0   \n",
       "9782                     0                        0.0   \n",
       "9783                     1                        0.0   \n",
       "...                    ...                        ...   \n",
       "29332                    0                        0.0   \n",
       "29333                    1                        0.0   \n",
       "29334                    1                        0.0   \n",
       "29335                    0                        0.0   \n",
       "29336                    0                        0.0   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "9779                            1.0                            0.0   \n",
       "9780                            0.0                            1.0   \n",
       "9781                            1.0                            0.0   \n",
       "9782                            0.0                            1.0   \n",
       "9783                            1.0                            0.0   \n",
       "...                             ...                            ...   \n",
       "29332                           0.0                            1.0   \n",
       "29333                           0.0                            1.0   \n",
       "29334                           0.0                            1.0   \n",
       "29335                           0.0                            1.0   \n",
       "29336                           0.0                            1.0   \n",
       "\n",
       "       neighbourhood_group_Queens  neighbourhood_group_Staten Island  \\\n",
       "9779                          0.0                                0.0   \n",
       "9780                          0.0                                0.0   \n",
       "9781                          0.0                                0.0   \n",
       "9782                          0.0                                0.0   \n",
       "9783                          0.0                                0.0   \n",
       "...                           ...                                ...   \n",
       "29332                         0.0                                0.0   \n",
       "29333                         0.0                                0.0   \n",
       "29334                         0.0                                0.0   \n",
       "29335                         0.0                                0.0   \n",
       "29336                         0.0                                0.0   \n",
       "\n",
       "       last_review_unix  cluster_id  \n",
       "9779         1439078400           2  \n",
       "9780         1546300800           2  \n",
       "9781         1560211200           2  \n",
       "9782         1440374400           2  \n",
       "9783         1561248000           2  \n",
       "...                 ...         ...  \n",
       "29332        1514937600           3  \n",
       "29333        1561248000           3  \n",
       "29334        1561248000           3  \n",
       "29335        1514764800           3  \n",
       "29336        1515024000           3  \n",
       "\n",
       "[19558 rows x 21 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = Sampling(method='cluster', n=2, n_clusters=5).sample_dataset(transformed)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clusters can be created using the column specified in the `cluster_column` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
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       "      <th>neighbourhood_group_Manhattan</th>\n",
       "      <th>neighbourhood_group_Queens</th>\n",
       "      <th>neighbourhood_group_Staten Island</th>\n",
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       "      <td>116</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>91</td>\n",
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       "      <td>-73.94177</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>0.110000</td>\n",
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       "      <td>1540944000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       neighbourhood  latitude  longitude  room_type  price  minimum_nights  \\\n",
       "13486            169  40.76947  -73.94303          1     80               1   \n",
       "20004            127  40.76365  -73.98149          1    350               4   \n",
       "19328             13  40.68989  -73.95878          1     45               7   \n",
       "26524            202  40.77895  -73.98332          1    145               1   \n",
       "27190            214  40.71227  -73.93618          1     58               2   \n",
       "...              ...       ...        ...        ...    ...             ...   \n",
       "43584             64  40.72236  -73.97689          0    140              26   \n",
       "24578            202  40.78443  -73.98256          0    135              90   \n",
       "1158              35  40.71668  -73.99073          0    150               2   \n",
       "2242             201  40.77440  -73.95578          0    199               1   \n",
       "2782              91  40.72686  -73.94177          0    149              30   \n",
       "\n",
       "       number_of_reviews  reviews_per_month  calculated_host_listings_count  \\\n",
       "13486                  1           0.020000                               2   \n",
       "20004                  0           1.373221                               1   \n",
       "19328                 14           0.420000                               3   \n",
       "26524                 43           2.010000                               1   \n",
       "27190                 11           0.730000                               1   \n",
       "...                  ...                ...                             ...   \n",
       "43584                  0           1.373221                               2   \n",
       "24578                  0           1.373221                               8   \n",
       "1158                 162           1.920000                               1   \n",
       "2242                 116           1.540000                               1   \n",
       "2782                   7           0.110000                              52   \n",
       "\n",
       "       availability_365  ...  neighbourhood_Imputed  room_type_Imputed  \\\n",
       "13486                 0  ...                      0                  0   \n",
       "20004                 0  ...                      0                  0   \n",
       "19328               364  ...                      0                  0   \n",
       "26524               197  ...                      0                  0   \n",
       "27190                88  ...                      0                  0   \n",
       "...                 ...  ...                    ...                ...   \n",
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       "24578               125  ...                      0                  0   \n",
       "1158                231  ...                      0                  0   \n",
       "2242                300  ...                      0                  0   \n",
       "2782                281  ...                      0                  0   \n",
       "\n",
       "       last_review_Imputed  neighbourhood_group_Bronx  \\\n",
       "13486                    0                        0.0   \n",
       "20004                    1                        0.0   \n",
       "19328                    0                        0.0   \n",
       "26524                    0                        0.0   \n",
       "27190                    0                        0.0   \n",
       "...                    ...                        ...   \n",
       "43584                    1                        0.0   \n",
       "24578                    1                        0.0   \n",
       "1158                     0                        0.0   \n",
       "2242                     0                        0.0   \n",
       "2782                     0                        0.0   \n",
       "\n",
       "       neighbourhood_group_Brooklyn  neighbourhood_group_Manhattan  \\\n",
       "13486                           0.0                            1.0   \n",
       "20004                           0.0                            1.0   \n",
       "19328                           1.0                            0.0   \n",
       "26524                           0.0                            1.0   \n",
       "27190                           1.0                            0.0   \n",
       "...                             ...                            ...   \n",
       "43584                           0.0                            1.0   \n",
       "24578                           0.0                            1.0   \n",
       "1158                            0.0                            1.0   \n",
       "2242                            0.0                            1.0   \n",
       "2782                            1.0                            0.0   \n",
       "\n",
       "       neighbourhood_group_Queens  neighbourhood_group_Staten Island  \\\n",
       "13486                         0.0                                0.0   \n",
       "20004                         0.0                                0.0   \n",
       "19328                         0.0                                0.0   \n",
       "26524                         0.0                                0.0   \n",
       "27190                         0.0                                0.0   \n",
       "...                           ...                                ...   \n",
       "43584                         0.0                                0.0   \n",
       "24578                         0.0                                0.0   \n",
       "1158                          0.0                                0.0   \n",
       "2242                          0.0                                0.0   \n",
       "2782                          0.0                                0.0   \n",
       "\n",
       "       last_review_unix  cluster_id  \n",
       "13486        1451952000           1  \n",
       "20004        1561248000           1  \n",
       "19328        1538265600           1  \n",
       "26524        1562025600           1  \n",
       "27190        1558224000           1  \n",
       "...                 ...         ...  \n",
       "43584        1561248000           0  \n",
       "24578        1561248000           0  \n",
       "1158         1560643200           0  \n",
       "2242         1560729600           0  \n",
       "2782         1540944000           0  \n",
       "\n",
       "[2000 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = Sampling(n=1000, cluster_column='room_type', method='stratified').sample_dataset(transformed)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Smoothing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the smoothing task I will use the `Smoothing` class.\n",
    "\n",
    "You can select the method by changing the `method` parameter:\n",
    "* `moving_average` is a calculation to analyze data points by creating a series of averages of different subsets of the full data set, uses pandas.DataFrame.rolling().mean() method;\n",
    "* `lowess` - Locally Weighted Scatterplot Smoothing is a generalization of moving average and polynomial regression, uses statsmodels.api.nonparametric.lowess;\n",
    "* `s_g_filter` - Savitzky–Golay filter is achieved by fitting successive sub-sets of adjacent data points with a low-degree polynomial by the method of linear least squares, uses scipy.signal.savgol_filter;\n",
    "* `fft` - Fast Fourier transform is an algorithm that computes the discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT), uses scipy.fft.rfft and scipy.fft.irfft.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's smooth the `reviews_per_month` column and plot the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smoothing = Smoothing(method='moving_average', x_column='reviews_per_month')\n",
    "smoothing.transform(data=transformed)\n",
    "smoothing.plot_transformed()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "384px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}