Assignment4 .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment 4 \n",
    "\n",
    "In this assignment i'm going to use the sklearn package to implement Classification and Clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.cluster import MeanShift\n",
    "\n",
    "data = pd.read_csv(\"listings.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1)\n",
    "I'm going to use sklearn to implement a naive bayes classifier, a Support Vector Machine (SVM) and KNN-classifier to classify apartments according to their latitude and longitude to a certain neighbourhood.\n",
    "To test it i'm going to train it with 1000 randomly chosen apartments and i'm going to test it with another 1000 ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First the data is going to be divided with the function train_test_split into training and testing data. The testing size is here set to 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>18666</td>\n",
       "      <td>Flat with Sunny Terrace</td>\n",
       "      <td>71615</td>\n",
       "      <td>Mireia And Maria</td>\n",
       "      <td>Sant Martí</td>\n",
       "      <td>el Camp de l'Arpa del Clot</td>\n",
       "      <td>41.40889</td>\n",
       "      <td>2.18555</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-10</td>\n",
       "      <td>0.02</td>\n",
       "      <td>30</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18674</td>\n",
       "      <td>Huge flat for 8 people close to Sagrada Familia</td>\n",
       "      <td>71615</td>\n",
       "      <td>Mireia And Maria</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>la Sagrada Família</td>\n",
       "      <td>41.40420</td>\n",
       "      <td>2.17306</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2019-10-19</td>\n",
       "      <td>0.25</td>\n",
       "      <td>30</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23197</td>\n",
       "      <td>FORUM DELUXE 5 MINS WALK  CCIB CENTER &amp; SEA!</td>\n",
       "      <td>90417</td>\n",
       "      <td>Etain (Marnie)</td>\n",
       "      <td>Sant Martí</td>\n",
       "      <td>el Besòs i el Maresme</td>\n",
       "      <td>41.41203</td>\n",
       "      <td>2.22114</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>210</td>\n",
       "      <td>3</td>\n",
       "      <td>51</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25786</td>\n",
       "      <td>NICE ROOM AVAILABLE IN THE HEART OF GRACIA</td>\n",
       "      <td>108310</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Gràcia</td>\n",
       "      <td>la Vila de Gràcia</td>\n",
       "      <td>41.40145</td>\n",
       "      <td>2.15645</td>\n",
       "      <td>Private room</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>268</td>\n",
       "      <td>2019-11-06</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31958</td>\n",
       "      <td>Gaudi Attic 1, Friendly Rentals</td>\n",
       "      <td>136853</td>\n",
       "      <td>Fidelio</td>\n",
       "      <td>Gràcia</td>\n",
       "      <td>el Camp d'en Grassot i Gràcia Nova</td>\n",
       "      <td>41.40950</td>\n",
       "      <td>2.15938</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>1.71</td>\n",
       "      <td>39</td>\n",
       "      <td>324</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20423</th>\n",
       "      <td>39968822</td>\n",
       "      <td>Bright, sunshine white sophisticated</td>\n",
       "      <td>285303484</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Horta-Guinardó</td>\n",
       "      <td>el Baix Guinardó</td>\n",
       "      <td>41.41177</td>\n",
       "      <td>2.16373</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20424</th>\n",
       "      <td>39969034</td>\n",
       "      <td>Fabulous, Modern Barcelona 2 Bedroom Apartment...</td>\n",
       "      <td>268096872</td>\n",
       "      <td>Allianz</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>41.38576</td>\n",
       "      <td>2.17438</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>508</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20425</th>\n",
       "      <td>39969285</td>\n",
       "      <td>Spectacular Luxurious 6 Bedroom Penthouse w/ P...</td>\n",
       "      <td>268096872</td>\n",
       "      <td>Allianz</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>la Sagrada Família</td>\n",
       "      <td>41.40758</td>\n",
       "      <td>2.17242</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>1031</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20426</th>\n",
       "      <td>39972885</td>\n",
       "      <td>Couples` Home with Parking</td>\n",
       "      <td>252088393</td>\n",
       "      <td>Joseph R.</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>41.39138</td>\n",
       "      <td>2.18240</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20427</th>\n",
       "      <td>39973807</td>\n",
       "      <td>Un lugar de descanso para tus vacaciones</td>\n",
       "      <td>303423208</td>\n",
       "      <td>Juanito</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>la Sagrada Família</td>\n",
       "      <td>41.40357</td>\n",
       "      <td>2.17408</td>\n",
       "      <td>Private room</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20428 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                               name    host_id  \\\n",
       "0         18666                            Flat with Sunny Terrace      71615   \n",
       "1         18674    Huge flat for 8 people close to Sagrada Familia      71615   \n",
       "2         23197       FORUM DELUXE 5 MINS WALK  CCIB CENTER & SEA!      90417   \n",
       "3         25786         NICE ROOM AVAILABLE IN THE HEART OF GRACIA     108310   \n",
       "4         31958                    Gaudi Attic 1, Friendly Rentals     136853   \n",
       "...         ...                                                ...        ...   \n",
       "20423  39968822               Bright, sunshine white sophisticated  285303484   \n",
       "20424  39969034  Fabulous, Modern Barcelona 2 Bedroom Apartment...  268096872   \n",
       "20425  39969285  Spectacular Luxurious 6 Bedroom Penthouse w/ P...  268096872   \n",
       "20426  39972885                         Couples` Home with Parking  252088393   \n",
       "20427  39973807           Un lugar de descanso para tus vacaciones  303423208   \n",
       "\n",
       "              host_name neighbourhood_group  \\\n",
       "0      Mireia And Maria          Sant Martí   \n",
       "1      Mireia And Maria            Eixample   \n",
       "2        Etain (Marnie)          Sant Martí   \n",
       "3                 Pedro              Gràcia   \n",
       "4               Fidelio              Gràcia   \n",
       "...                 ...                 ...   \n",
       "20423               Ann      Horta-Guinardó   \n",
       "20424           Allianz        Ciutat Vella   \n",
       "20425           Allianz            Eixample   \n",
       "20426         Joseph R.        Ciutat Vella   \n",
       "20427           Juanito            Eixample   \n",
       "\n",
       "                               neighbourhood  latitude  longitude  \\\n",
       "0                 el Camp de l'Arpa del Clot  41.40889    2.18555   \n",
       "1                         la Sagrada Família  41.40420    2.17306   \n",
       "2                      el Besòs i el Maresme  41.41203    2.22114   \n",
       "3                          la Vila de Gràcia  41.40145    2.15645   \n",
       "4         el Camp d'en Grassot i Gràcia Nova  41.40950    2.15938   \n",
       "...                                      ...       ...        ...   \n",
       "20423                       el Baix Guinardó  41.41177    2.16373   \n",
       "20424                         el Barri Gòtic  41.38576    2.17438   \n",
       "20425                     la Sagrada Família  41.40758    2.17242   \n",
       "20426  Sant Pere, Santa Caterina i la Ribera  41.39138    2.18240   \n",
       "20427                     la Sagrada Família  41.40357    2.17408   \n",
       "\n",
       "             room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0      Entire home/apt    130               3                  1  2015-10-10   \n",
       "1      Entire home/apt     60               1                 20  2019-10-19   \n",
       "2      Entire home/apt    210               3                 51  2019-09-29   \n",
       "3         Private room     32               1                268  2019-11-06   \n",
       "4      Entire home/apt     60               1                182  2019-10-16   \n",
       "...                ...    ...             ...                ...         ...   \n",
       "20423  Entire home/apt     50              32                  0         NaN   \n",
       "20424  Entire home/apt    508               3                  0         NaN   \n",
       "20425  Entire home/apt   1031               3                  0         NaN   \n",
       "20426  Entire home/apt     89               3                  0         NaN   \n",
       "20427     Private room     50               1                  0         NaN   \n",
       "\n",
       "       reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0                   0.02                              30               304  \n",
       "1                   0.25                              30               312  \n",
       "2                   0.48                               2               359  \n",
       "3                   2.38                               1                46  \n",
       "4                   1.71                              39               324  \n",
       "...                  ...                             ...               ...  \n",
       "20423                NaN                               1               349  \n",
       "20424                NaN                              11               362  \n",
       "20425                NaN                              11               362  \n",
       "20426                NaN                               1                22  \n",
       "20427                NaN                               3               362  \n",
       "\n",
       "[20428 rows x 16 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['neighbourhood_group']\n",
    "X = data[['latitude', 'longitude']]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1000, random_state=0)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing a naive bayes classifier with GaussianNB(). We assume the predictors to have a gaussian distribution and are independent from each other.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 1000 points : 142\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "model = gnb.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"\n",
    "       % (X_test.shape[0], (y_test != y_pred).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing a Support Vector Machine. \n",
    "This algorithm outputs an optimal hyperplane to categorize new examples.\n",
    "It has a lot of tuning parameters to modify the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 1000 points : 673\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC()\n",
    "model = clf.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"\n",
    "       % (X_test.shape[0], (y_test != y_pred).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing a KNN-classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 1000 points : 18\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=len(y.unique()))\n",
    "model = classifier.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"\n",
    "       % (X_test.shape[0], (y_test != y_pred).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The support vector machine classifies the worst. This could maybe be fixed by conducting a parameter tuning before fitting the model. The naive bayes classifier classified much better and the knn-classifier classified really good. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2)\n",
    "Now i'm going to clusterize the apartments according to their price and room type by K-means clustering and agglomerative clustering.\n",
    "\n",
    "K-means clustering is trying to seperate the samples by identifying the centroids and then allocating every data point to the nearest cluster. In our case we want to clusterize by room type and price of the apartments. Room type is a categorical value which is why we have to transform it into a numerical value to be able to calculate the euclidean distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['price','room_type']]\n",
    "# transform room_types into numbers from 1 to 4\n",
    "X = X.replace(to_replace=\"Shared room\", value=1).replace(to_replace=\"Private room\", value=2).replace(to_replace=\"Hotel room\", value=3).replace(to_replace=\"Entire home/apt\", value=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trying different numbers of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f30dcb8c8>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# trying with 5 clusters\n",
    "kmeans = KMeans(n_clusters=5, random_state=0).fit(X)\n",
    "labels = kmeans.labels_\n",
    "centers = kmeans.cluster_centers_\n",
    "data[\"labels\"]=labels\n",
    "\n",
    "# plot the clusterization\n",
    "seaborn.scatterplot(x=\"price\",y=\"room_type\", hue=\"labels\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f32162d88>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# trying 2 clusters\n",
    "kmeans = KMeans(n_clusters=2, random_state=0).fit(X)\n",
    "labels = kmeans.labels_\n",
    "centers = kmeans.cluster_centers_\n",
    "data[\"labels\"]=labels\n",
    "\n",
    "# plot the clusterization\n",
    "seaborn.scatterplot(x=\"price\",y=\"room_type\", hue=\"labels\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f321c5f08>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# trying 3 clusters\n",
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(X)\n",
    "labels = kmeans.labels_\n",
    "centers = kmeans.cluster_centers_\n",
    "data[\"labels\"]=labels\n",
    "\n",
    "# plot the clusterization\n",
    "seaborn.scatterplot(x=\"price\",y=\"room_type\", hue=\"labels\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the clustering depends more on the price than on the room_type. For all, 2, 3 and for 5 clusters the apartments are clustered by price (probably because room_type is a categorical variable).\n",
    "In general we can see that the rooms with higher prices are entire homes as well as hotel rooms; private rooms and shared rooms are cheaper.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting for different features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f30d2aa48>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting according to price and neighbourhood_group\n",
    "\n",
    "# plot the clusterization\n",
    "plt.figure(figsize=(15,5))\n",
    "seaborn.scatterplot(x=\"neighbourhood_group\",y=\"price\", hue=\"labels\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some neighbourhoods like Eixanple, Grácia, Ciutat Vella and Sants-Montjuic seem to have higher prices than other neighborhoods like Nou Barris, Sant Andreu or Les Corts. In these cheaper neighbourhoods we can find more apartments belonging to cluster with label \"0\". That tend to be more private rooms or shared rooms. More touristy areas like Eixanple, Grácia, Ciutat Vella apparently tend to have more hotel rooms, or whole apartments (labels \"1\" or \"2\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f32230d48>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting according to availability and room_type\n",
    "seaborn.scatterplot(x=\"availability_365\",y=\"room_type\", hue=\"labels\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f30c07308>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting according to availability and room_type\n",
    "plt.figure(figsize=(20,5))\n",
    "seaborn.scatterplot(x=\"neighbourhood_group\",y=\"minimum_nights\", hue=\"labels\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently we almost just find minimum nights for apartments from cluster with label \"0\" which are as we saw before mostly shared flats and private rooms. Apparently we don't find minimum night data for the other room types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering with Agglomerative Clustering \n",
    "In agglomerative Clustering the pair of clusters that minimally increases a given linkage distance is recursively merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering = AgglomerativeClustering().fit(X)\n",
    "clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23f32d76848>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"labels2\"]=clustering.labels_\n",
    "# plot the clusterization\n",
    "seaborn.scatterplot(x=\"price\",y=\"room_type\", hue=\"labels2\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The clustering with agglomerative clustering chooses two clusters and looks really similar to the clusterization of k-means for two clusters."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}