AirBnB-Seattle-Data-Analysis-Question2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d3aa613",
   "metadata": {},
   "source": [
    "# AirBnB Seattle Data Analysis (continued)\n",
    "For the Udacity Data Scientist Blog Post an analysis of dataset has to be done.\n",
    "This notebook is used to analyze the data from AirBnB Seattle provided in Kaggle:\n",
    "\n",
    "* https://www.kaggle.com/datasets/airbnb/seattle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a52a1f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "df_cal = pd.read_csv('./data/calendar.csv')\n",
    "df_list = pd.read_csv('./data/listings.csv')\n",
    "df_rev = pd.read_csv('./data/reviews.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adfe04b",
   "metadata": {},
   "source": [
    "# Question 2 - Is the number of reviews related to the customer satisfaction?\n",
    "\n",
    "From question 1 we now that the customer satisfaction can be measured using `df_list[\"review_score_rating\"]`\n",
    "\n",
    "There is a field reviews_per_month in the \"listings\" dataset. We want to cross check, if this matches the actual reviews, which we get from the \"reviews\" dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "091d1c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3191"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[\"reviews_per_month\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "32f8e97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3191.000000\n",
       "mean        2.078919\n",
       "std         1.822348\n",
       "min         0.020000\n",
       "25%         0.695000\n",
       "50%         1.540000\n",
       "75%         3.000000\n",
       "max        12.150000\n",
       "Name: reviews_per_month, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_list[\"reviews_per_month\"].hist(bins=50)\n",
    "df_list[\"reviews_per_month\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b23b1b64",
   "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>count_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4291</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5682</th>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6606</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7369</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9419</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995551</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10012724</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10020221</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10118341</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10248139</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3191 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            count_reviews\n",
       "listing_id               \n",
       "4291                   35\n",
       "5682                  297\n",
       "6606                   52\n",
       "7369                   40\n",
       "9419                   79\n",
       "...                   ...\n",
       "9995551                 1\n",
       "10012724                2\n",
       "10020221                1\n",
       "10118341                1\n",
       "10248139                1\n",
       "\n",
       "[3191 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_per_listing = df_rev[[\"listing_id\", \"id\"]].groupby(\"listing_id\").count()\n",
    "reviews_per_listing.rename(columns = {\"id\":\"count_reviews\"}, inplace=True)\n",
    "reviews_per_listing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b1e6d3",
   "metadata": {},
   "source": [
    "lets take a look at the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07c3d1d6",
   "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>count_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.590097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.840788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>474.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count_reviews\n",
       "count    3191.000000\n",
       "mean       26.590097\n",
       "std        39.840788\n",
       "min         1.000000\n",
       "25%         4.000000\n",
       "50%        13.000000\n",
       "75%        31.000000\n",
       "max       474.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(reviews_per_listing).hist(bins=50)\n",
    "reviews_per_listing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef7709c",
   "metadata": {},
   "source": [
    "This means, that 50% of the listings have 13 or less reviews, but there is a max listing of 474 reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8d33dc",
   "metadata": {},
   "source": [
    "Now, where we have both mesures for the review, let´s check the correlation to the rating.\n",
    "Therefore, we merge the values together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6bd5f6c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>count_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.07</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>953595</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3308979</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>278830</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5956968</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>17</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>3186</th>\n",
       "      <td>262764</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.56</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3187</th>\n",
       "      <td>8578490</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>3383329</td>\n",
       "      <td>96.0</td>\n",
       "      <td>4.01</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>8101950</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3190</th>\n",
       "      <td>8902327</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3171 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  review_scores_rating  reviews_per_month  count_reviews\n",
       "0      241032                  95.0               4.07            207\n",
       "1      953595                  96.0               1.48             43\n",
       "2     3308979                  97.0               1.15             20\n",
       "3      278830                  92.0               0.89             38\n",
       "4     5956968                  95.0               2.45             17\n",
       "...       ...                   ...                ...            ...\n",
       "3186   262764                  92.0               1.56              5\n",
       "3187  8578490                 100.0               0.63              2\n",
       "3188  3383329                  96.0               4.01             73\n",
       "3189  8101950                  80.0               0.30              1\n",
       "3190  8902327                 100.0               2.00              2\n",
       "\n",
       "[3171 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge = df_list[[\"id\",\"review_scores_rating\", \"reviews_per_month\"]].copy()\n",
    "df_merge = df_merge.merge(reviews_per_listing.reset_index(names=[\"id\"]), left_on='id', right_on='id')\n",
    "df_merge = df_merge.dropna()\n",
    "df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e4703e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.set_theme(style=\"white\")\n",
    "corr = df_merge.drop([\"id\"], axis=1).corr()\n",
    "heatmap = sns.heatmap(corr, annot=True, cmap=\"Blues\", fmt='.1g')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ec6584",
   "metadata": {},
   "source": [
    "Huh, this was also unexpected. \n",
    "reviews_per_month are not as strong correlated to count_reviews, as I thought. I expected those values to be nearly linear equal.\n",
    "This might be another analysis ouside of question 2.\n",
    "\n",
    "But for Question 2 it does not matter, because for both correlations we have an"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c2e701",
   "metadata": {},
   "source": [
    "# Result\n",
    "\n",
    "The number of reviews does not change significant with the customer satisfaction. So, bad ratings have the same amount of reviews as good ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1981c682",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}