{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe05c247",
   "metadata": {},
   "source": [
    "# İstanbul Airbnb ilanları: temizleme, keşifsel analiz ve fiyat modeli\n",
    "\n",
    "**Soru:** İstanbul'da bir Airbnb ilanının gecelik fiyatını ne belirliyor?\n",
    "\n",
    "**Veri:** Inside Airbnb, İstanbul, 30 Haziran 2026 taraması (`listings.csv`, CC BY 4.0).\n",
    "\n",
    "Defter üç bölümden oluşuyor: (1) ham veriyi analize hazır hale getirmek, (2) temiz veride\n",
    "fiyatın nasıl dağıldığına bakmak, (3) fiyatı tahmin eden bir model kurup dürüstçe sınamak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45c3da7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:40.742671Z",
     "iopub.status.busy": "2026-10-06T19:58:40.742475Z",
     "iopub.status.idle": "2026-10-06T19:58:42.328497Z",
     "shell.execute_reply": "2026-10-06T19:58:42.326829Z"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import re\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import GroupKFold, KFold\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler\n",
    "\n",
    "RAW = Path(\"data/listings.csv\")\n",
    "OUT = Path(\"outputs\")\n",
    "OUT.mkdir(exist_ok=True)\n",
    "SEED = 42\n",
    "ACCENT, GRAY = \"#6d28d9\", \"#8a8799\"\n",
    "plt.rcParams.update({\"figure.dpi\": 110, \"axes.spines.top\": False, \"axes.spines.right\": False,\n",
    "                     \"axes.grid\": True, \"grid.color\": \"#e4e1ec\", \"grid.linewidth\": 0.6, \"axes.axisbelow\": True,\n",
    "                     \"font.size\": 10})\n",
    "S = {}  # rapor sayfasına gidecek özet rakamlar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad937c5d",
   "metadata": {},
   "source": [
    "## 1. Ham veriye ilk bakış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd6cdef1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:42.331068Z",
     "iopub.status.busy": "2026-10-06T19:58:42.330780Z",
     "iopub.status.idle": "2026-10-06T19:58:43.451003Z",
     "shell.execute_reply": "2026-10-06T19:58:43.449554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26631, 90)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "neighborhood_overview           100.0\n",
       "host_since                      100.0\n",
       "host_response_time              100.0\n",
       "host_thumbnail_url              100.0\n",
       "host_acceptance_rate            100.0\n",
       "host_response_rate              100.0\n",
       "host_verifications              100.0\n",
       "neighbourhood                   100.0\n",
       "host_total_listings_count       100.0\n",
       "host_neighbourhood              100.0\n",
       "calendar_updated                100.0\n",
       "instant_bookable                100.0\n",
       "neighbourhood_group_cleansed    100.0\n",
       "host_about                       58.9\n",
       "bathrooms                        45.8\n",
       "beds                             43.0\n",
       "license                          38.7\n",
       "review_scores_cleanliness        36.6\n",
       "review_scores_accuracy           36.6\n",
       "review_scores_value              36.6\n",
       "review_scores_checkin            36.6\n",
       "review_scores_rating             36.6\n",
       "reviews_per_month                36.6\n",
       "first_review                     36.6\n",
       "review_scores_communication      36.6\n",
       "review_scores_location           36.6\n",
       "last_review                      36.6\n",
       "host_location                    36.1\n",
       "bedrooms                         13.9\n",
       "price                            10.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = pd.read_csv(RAW, low_memory=False)\n",
    "print(raw.shape)\n",
    "S[\"ham\"] = {\"satir\": len(raw), \"sutun\": raw.shape[1]}\n",
    "eksik = (raw.isna().mean() * 100).round(1).sort_values(ascending=False)\n",
    "eksik[eksik > 0].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d8b0dc",
   "metadata": {},
   "source": [
    "İlk gözlemler:\n",
    "\n",
    "- 13 sütun tamamen boş.\n",
    "- `price` metin olarak gelmiş ve %10'u boş.\n",
    "- `beds` ve `bathrooms` sütunlarının neredeyse yarısı boş.\n",
    "- Değerlendirme puanlarının %37'si boş.\n",
    "\n",
    "Boşlukların rastgele olup olmadığına bakmadan hiçbirini doldurmayacağım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c722432e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.453014Z",
     "iopub.status.busy": "2026-10-06T19:58:43.452815Z",
     "iopub.status.idle": "2026-10-06T19:58:43.467148Z",
     "shell.execute_reply": "2026-10-06T19:58:43.465969Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 ['neighborhood_overview', 'host_since', 'host_response_time', 'host_response_rate', 'host_acceptance_rate', 'host_thumbnail_url', 'host_neighbourhood', 'host_total_listings_count', 'host_verifications', 'neighbourhood', 'neighbourhood_group_cleansed', 'calendar_updated', 'instant_bookable']\n"
     ]
    }
   ],
   "source": [
    "bos_sutun = [c for c in raw if raw[c].isna().all()]\n",
    "S[\"ham\"][\"bos_sutun\"] = len(bos_sutun)\n",
    "print(len(bos_sutun), bos_sutun)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a03e74",
   "metadata": {},
   "source": [
    "## 2. Temizleme\n",
    "\n",
    "### 2.1 Fiyat: metinden sayıya, dolardan liraya\n",
    "\n",
    "`price` sütunu `$4,296.25` biçiminde. Dolar işareti yanıltıcı: fiyat teklifinin ham hali\n",
    "(`price_quote_raw`) aynı tutarları `₺` ile gösteriyor. Yani birim Türk lirası."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a46fb06b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.469019Z",
     "iopub.status.busy": "2026-10-06T19:58:43.468824Z",
     "iopub.status.idle": "2026-10-06T19:58:43.522108Z",
     "shell.execute_reply": "2026-10-06T19:58:43.520443Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eksik': 2688, 'eksik_yuzde': np.float64(10.1), 'teklifte_tl_yuzde': np.float64(98.0), 'teklifte_dolar': 0}\n",
      "['$1,356.82', '$2,583.67', '$11,484.50'] -> [1356.82, 2583.67, 11484.5]\n"
     ]
    }
   ],
   "source": [
    "df = raw.drop(columns=bos_sutun).copy()\n",
    "df[\"fiyat\"] = df[\"price\"].str.replace(r\"[$,]\", \"\", regex=True).astype(float)\n",
    "quote = df[\"price_quote_raw\"].dropna()\n",
    "S[\"fiyat\"] = {\"eksik\": int(df[\"fiyat\"].isna().sum()), \"eksik_yuzde\": round(df[\"fiyat\"].isna().mean() * 100, 1),\n",
    "              \"teklifte_tl_yuzde\": round(quote.str.contains(\"₺\").mean() * 100, 1),\n",
    "              \"teklifte_dolar\": int(quote.str.contains(r\"\\$\").sum())}\n",
    "print(S[\"fiyat\"])\n",
    "print(raw[\"price\"].dropna().head(3).tolist(), \"->\", df[\"fiyat\"].dropna().head(3).tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6eac9d",
   "metadata": {},
   "source": [
    "### 2.2 Eksik fiyat rastgele değil\n",
    "\n",
    "Inside Airbnb her ilanın `source` sütununda bilginin bu taramadan mı (`city scrape`) yoksa\n",
    "önceki taramadan mı (`previous scrape`) geldiğini yazıyor. Eksikler neredeyse tamamen ikinci grupta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57df1cf2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.524042Z",
     "iopub.status.busy": "2026-10-06T19:58:43.523824Z",
     "iopub.status.idle": "2026-10-06T19:58:43.549278Z",
     "shell.execute_reply": "2026-10-06T19:58:43.548266Z"
    }
   },
   "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>ilan</th>\n",
       "      <th>fiyat_eksik</th>\n",
       "      <th>yatak_eksik</th>\n",
       "      <th>banyo_eksik</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>city scrape</th>\n",
       "      <td>15395</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous scrape</th>\n",
       "      <td>11236</td>\n",
       "      <td>23.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ilan  fiyat_eksik  yatak_eksik  banyo_eksik\n",
       "source                                                       \n",
       "city scrape      15395          0.5          1.4          6.2\n",
       "previous scrape  11236         23.2        100.0        100.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaynak = df.groupby(\"source\").agg(ilan=(\"id\", \"size\"), fiyat_eksik=(\"fiyat\", lambda s: s.isna().mean() * 100),\n",
    "                                  yatak_eksik=(\"beds\", lambda s: s.isna().mean() * 100),\n",
    "                                  banyo_eksik=(\"bathrooms\", lambda s: s.isna().mean() * 100)).round(1)\n",
    "S[\"kaynak\"] = {k: {a: (int(b) if a == \"ilan\" else float(b)) for a, b in v.items()} for k, v in kaynak.to_dict(\"index\").items()}\n",
    "kaynak"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44506bf",
   "metadata": {},
   "source": [
    "Önceki taramadan taşınan ilanlarda yatak ve banyo sayısı hiç yok, fiyatın dörtte biri eksik.\n",
    "Bu yüzden eksik fiyatı tahminle doldurmuyorum; fiyatı olmayan ilanlar fiyat analizine girmiyor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b140e6cb",
   "metadata": {},
   "source": [
    "### 2.3 En önemli tuzak: fiyat her ilanda aynı şeyi ölçmüyor\n",
    "\n",
    "Fiyat, ilanın kabul ettiği en kısa konaklama için alınmış bir tekliften hesaplanıyor.\n",
    "Teklifin giriş ve çıkış tarihleri veride var; aradaki gece sayısına bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "502e5409",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.551049Z",
     "iopub.status.busy": "2026-10-06T19:58:43.550875Z",
     "iopub.status.idle": "2026-10-06T19:58:43.570013Z",
     "shell.execute_reply": "2026-10-06T19:58:43.568934Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teklif_gece\n",
      "100.0    8851\n",
      "1.0      7700\n",
      "2.0      4235\n",
      "3.0      2082\n",
      "NaN      1616\n",
      "4.0       429\n",
      "5.0       412\n",
      "101.0     371\n",
      "Name: count, dtype: int64\n",
      "minimum_nights\n",
      "100.0    9776\n",
      "1.0      8727\n",
      "2.0      4154\n",
      "3.0      1800\n",
      "101.0     438\n",
      "5.0       375\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "giris = pd.to_datetime(df[\"price_quote_checkin_date\"])\n",
    "cikis = pd.to_datetime(df[\"price_quote_checkout_date\"])\n",
    "df[\"teklif_gece\"] = (cikis - giris).dt.days\n",
    "df[\"giris_gunu\"] = giris.dt.dayofweek\n",
    "print(df[\"teklif_gece\"].value_counts(dropna=False).head(8))\n",
    "print(df[\"minimum_nights\"].value_counts().head(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e017b7fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.571571Z",
     "iopub.status.busy": "2026-10-06T19:58:43.571384Z",
     "iopub.status.idle": "2026-10-06T19:58:43.819822Z",
     "shell.execute_reply": "2026-10-06T19:58:43.818481Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1': np.float64(32.8), '2_27': np.float64(26.4), '28_99': np.float64(0.9), '100_ustu': np.float64(40.0), 'tam_100': 9776}\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 770x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn = df[\"minimum_nights\"]\n",
    "S[\"min_gece\"] = {\"1\": round((mn == 1).mean() * 100, 1), \"2_27\": round(mn.between(2, 27).mean() * 100, 1),\n",
    "                 \"28_99\": round(mn.between(28, 99).mean() * 100, 1), \"100_ustu\": round((mn >= 100).mean() * 100, 1),\n",
    "                 \"tam_100\": int((mn == 100).sum())}\n",
    "print(S[\"min_gece\"])\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "kutu = pd.cut(mn, [0, 1, 2, 3, 7, 27, 99, 2000], labels=[\"1\", \"2\", \"3\", \"4–7\", \"8–27\", \"28–99\", \"100+\"])\n",
    "kutu.value_counts(sort=False).plot.bar(ax=ax, color=ACCENT, width=0.6)\n",
    "ax.set(xlabel=\"En az konaklama (gece)\", ylabel=\"İlan\", title=\"İlanların en az konaklama koşulu\")\n",
    "ax.tick_params(axis=\"x\", rotation=0)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2368182c",
   "metadata": {},
   "source": [
    "İlanların %40'ı en az 100 gece koşulu koymuş. Bu ilanlarda fiyat teklifi 100 gecelik bir\n",
    "konaklama için alınmış ve geceye bölünmüş; yani gecelik fiyat yerine aylık kiranın günlüğü.\n",
    "\n",
    "İki grubun \"gecelik fiyatı\" aynı sütunda duruyor ama kıyaslanamaz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65fb0fa1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.821588Z",
     "iopub.status.busy": "2026-10-06T19:58:43.821357Z",
     "iopub.status.idle": "2026-10-06T19:58:43.913679Z",
     "shell.execute_reply": "2026-10-06T19:58:43.912688Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'kisa_ilan': 15226, 'uzun_ilan': 8717, 'kisa_medyan': 4302.0, 'uzun_medyan': 670.0, 'hepsi_medyan': 2853.0, 'ev_kisa_medyan': 4715.0, 'ev_uzun_medyan': 868.0}\n"
     ]
    },
    {
     "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>ilan</th>\n",
       "      <th>medyan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uzun</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>15226</td>\n",
       "      <td>4302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>8717</td>\n",
       "      <td>670.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ilan  medyan\n",
       "uzun                \n",
       "False  15226  4302.0\n",
       "True    8717   670.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiyatli = df[df[\"fiyat\"].notna()].copy()\n",
    "fiyatli[\"uzun\"] = fiyatli[\"teklif_gece\"] >= 28\n",
    "kars = fiyatli.groupby(\"uzun\")[\"fiyat\"].agg(ilan=\"size\", medyan=\"median\").round(0)\n",
    "ev = fiyatli[fiyatli[\"room_type\"] == \"Entire home/apt\"].groupby(\"uzun\")[\"fiyat\"].median().round(0)\n",
    "S[\"iki_pazar\"] = {\"kisa_ilan\": int(kars.loc[False, \"ilan\"]), \"uzun_ilan\": int(kars.loc[True, \"ilan\"]),\n",
    "                  \"kisa_medyan\": float(kars.loc[False, \"medyan\"]), \"uzun_medyan\": float(kars.loc[True, \"medyan\"]),\n",
    "                  \"hepsi_medyan\": float(round(fiyatli[\"fiyat\"].median())),\n",
    "                  \"ev_kisa_medyan\": float(ev[False]), \"ev_uzun_medyan\": float(ev[True])}\n",
    "print(S[\"iki_pazar\"])\n",
    "kars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5caf7d07",
   "metadata": {},
   "source": [
    "Kısa konaklamada medyan ₺4.302, uzun konaklamada ₺670. Hepsini birlikte alınca çıkan ₺2.853\n",
    "hiçbir ilan grubunu temsil etmiyor. Analizin geri kalanını **kısa konaklama** ilanlarıyla\n",
    "(teklifi 28 geceden kısa olanlar) yapıyorum.\n",
    "\n",
    "### 2.4 100 gece koşulu nereden geliyor?\n",
    "\n",
    "Türkiye'de 1 Ocak 2024'te yürürlüğe giren 7464 sayılı kanun, 100 gün ve daha kısa süreli konut\n",
    "kiralamaları için Kültür ve Turizm Bakanlığı'ndan izin belgesi istiyor. `license` sütunu serbest\n",
    "metin; önce onu sınıflandırıyorum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d4a70a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.915416Z",
     "iopub.status.busy": "2026-10-06T19:58:43.915199Z",
     "iopub.status.idle": "2026-10-06T19:58:43.953224Z",
     "shell.execute_reply": "2026-10-06T19:58:43.951970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'12-3456' yazan ilan: 149\n"
     ]
    },
    {
     "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>minimum_nights</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>100+ gece payı %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>izin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34-… biçiminde numara</th>\n",
       "      <td>9441</td>\n",
       "      <td>201</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boş</th>\n",
       "      <td>112</td>\n",
       "      <td>10181</td>\n",
       "      <td>98.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diğer</th>\n",
       "      <td>4170</td>\n",
       "      <td>160</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>konut dışı (otel vb.)</th>\n",
       "      <td>2055</td>\n",
       "      <td>27</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>muaf</th>\n",
       "      <td>211</td>\n",
       "      <td>73</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "minimum_nights         False   True  100+ gece payı %\n",
       "izin                                                 \n",
       "34-… biçiminde numara   9441    201               2.1\n",
       "boş                      112  10181              98.9\n",
       "diğer                   4170    160               3.7\n",
       "konut dışı (otel vb.)   2055     27               1.3\n",
       "muaf                     211     73              25.7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def izin_turu(s):\n",
    "    if pd.isna(s):\n",
    "        return \"boş\"\n",
    "    if \"Non-real\" in s:\n",
    "        return \"konut dışı (otel vb.)\"\n",
    "    if \"xempt\" in s:\n",
    "        return \"muaf\"\n",
    "    if re.fullmatch(r\"(\\d{4}-)?34-\\d+\", s.strip()):\n",
    "        return \"34-… biçiminde numara\"\n",
    "    return \"diğer\"\n",
    "\n",
    "\n",
    "df[\"izin\"] = df[\"license\"].map(izin_turu)\n",
    "izin = pd.crosstab(df[\"izin\"], mn >= 100)\n",
    "izin[\"100+ gece payı %\"] = (izin[True] / izin.sum(axis=1) * 100).round(1)\n",
    "S[\"izin\"] = {k: {\"ilan\": int(r[False] + r[True]), \"yuz_gece_yuzde\": float(r[\"100+ gece payı %\"])} for k, r in izin.iterrows()}\n",
    "S[\"izin_yer_tutucu\"] = int((df[\"license\"] == \"12-3456\").sum())\n",
    "print(\"'12-3456' yazan ilan:\", S[\"izin_yer_tutucu\"])\n",
    "izin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce8c2a3",
   "metadata": {},
   "source": [
    "İzin numarası boş olan ilanların %99'u en az 100 gece koşulu koymuş; 34 ile başlayan numarası\n",
    "olanlarda bu oran %2. Veri nedeni söylemiyor, ama örüntü kanunla uyumlu: izin belgesi olmayan\n",
    "ilanlar kapsam dışında kalacak kadar uzun kiralamaya geçmiş görünüyor.\n",
    "\n",
    "Serbest metin olduğu için alan temiz değil: 149 ilanda numara olarak `12-3456` yazılmış."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db683e6",
   "metadata": {},
   "source": [
    "### 2.5 Metin sütunlarını sayıya çevirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f67b99d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:43.955151Z",
     "iopub.status.busy": "2026-10-06T19:58:43.954985Z",
     "iopub.status.idle": "2026-10-06T19:58:44.469424Z",
     "shell.execute_reply": "2026-10-06T19:58:44.467617Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sayisal_eksik_yuzde': np.float64(45.8), 'metinden_sonra_eksik_yuzde': np.float64(0.5), 'metin_cesidi': 58}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'farkli': 4372, 'medyan': 29.0} {'o_klima': 0.625, 'o_mutfak': 0.783, 'o_camasir_mak': 0.562, 'o_asansor': 0.282, 'o_otopark': 0.323, 'o_havuz': 0.113, 'o_jakuzi': 0.053, 'o_deniz_manzara': 0.049, 'o_bulasik_mak': 0.295, 'o_evcil': 0.261, 'o_kendi_giris': 0.397}\n"
     ]
    }
   ],
   "source": [
    "# Banyo: \"1.5 shared baths\", \"Half-bath\", \"Private half-bath\" ...\n",
    "bt = df[\"bathrooms_text\"].str.lower()\n",
    "df[\"banyo\"] = pd.to_numeric(bt.str.extract(r\"(\\d+\\.?\\d*)\")[0], errors=\"coerce\")\n",
    "df.loc[bt.str.contains(\"half\", na=False) & df[\"banyo\"].isna(), \"banyo\"] = 0.5\n",
    "df[\"ortak_banyo\"] = bt.str.contains(\"shared\", na=False).astype(int)\n",
    "S[\"banyo\"] = {\"sayisal_eksik_yuzde\": round(raw[\"bathrooms\"].isna().mean() * 100, 1),\n",
    "              \"metinden_sonra_eksik_yuzde\": round(df[\"banyo\"].isna().mean() * 100, 1),\n",
    "              \"metin_cesidi\": int(df[\"bathrooms_text\"].nunique())}\n",
    "print(S[\"banyo\"])\n",
    "\n",
    "# Evet/hayır sütunları\n",
    "for c in [\"host_is_superhost\", \"host_has_profile_pic\", \"host_identity_verified\", \"has_availability\"]:\n",
    "    df[c] = df[c].map({\"t\": 1, \"f\": 0})\n",
    "\n",
    "# Olanaklar: metin içinde JSON listesi\n",
    "olanak = df[\"amenities\"].map(json.loads)\n",
    "df[\"olanak_sayisi\"] = olanak.map(len)\n",
    "tum = pd.Series([a for l in olanak for a in l])\n",
    "S[\"olanak\"] = {\"farkli\": int(tum.nunique()), \"medyan\": float(df[\"olanak_sayisi\"].median())}\n",
    "SECILI = {\"klima\": \"Air conditioning\", \"mutfak\": \"Kitchen\", \"camasir_mak\": \"Washer\", \"asansor\": \"Elevator\",\n",
    "          \"otopark\": \"Free parking on premises\", \"havuz\": \"pool\", \"jakuzi\": \"hot tub\", \"deniz_manzara\": \"Sea view\",\n",
    "          \"bulasik_mak\": \"Dishwasher\", \"evcil\": \"Pets allowed\", \"kendi_giris\": \"Self check-in\"}\n",
    "for ad, anahtar in SECILI.items():\n",
    "    if anahtar[0].islower():  # \"pool\" gibi alt metin: \"Private pool\", \"Shared outdoor pool\" ...\n",
    "        df[\"o_\" + ad] = olanak.map(lambda l: int(any(anahtar in a.lower() for a in l)))\n",
    "    else:\n",
    "        df[\"o_\" + ad] = olanak.map(lambda l: int(anahtar in l))\n",
    "print(S[\"olanak\"], df[[c for c in df if c.startswith(\"o_\")]].mean().round(3).to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f8c2dc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:44.471628Z",
     "iopub.status.busy": "2026-10-06T19:58:44.471438Z",
     "iopub.status.idle": "2026-10-06T19:58:44.529736Z",
     "shell.execute_reply": "2026-10-06T19:58:44.528696Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'farkli_tip': 90, 'gruplar': {'daire': 17328, 'otel/pansiyon': 5410, 'müstakil ev/villa': 2129, 'rezidans': 1210, 'diğer': 554}}\n",
      "{'İstanbul, Turkey': 6884, 'İstanbul, Türkiye': 5533, 'Istanbul, Turkey': 1777, 'Istanbul, Türkiye': 1085}\n"
     ]
    }
   ],
   "source": [
    "# Mülk tipi: 90 farklı değer -> 5 grup\n",
    "def mulk_grubu(p):\n",
    "    p = p.lower()\n",
    "    if \"hotel\" in p or \"hostel\" in p or \"bed and breakfast\" in p or \"aparthotel\" in p:\n",
    "        return \"otel/pansiyon\"\n",
    "    if \"serviced apartment\" in p:\n",
    "        return \"rezidans\"\n",
    "    if \"villa\" in p or p in (\"entire home\", \"private room in home\", \"entire townhouse\", \"entire guesthouse\", \"entire cottage\", \"entire bungalow\"):\n",
    "        return \"müstakil ev/villa\"\n",
    "    if \"rental unit\" in p or \"condo\" in p or \"loft\" in p:\n",
    "        return \"daire\"\n",
    "    return \"diğer\"\n",
    "\n",
    "\n",
    "df[\"mulk\"] = df[\"property_type\"].map(mulk_grubu)\n",
    "S[\"mulk\"] = {\"farkli_tip\": int(df[\"property_type\"].nunique()), \"gruplar\": df[\"mulk\"].value_counts().to_dict()}\n",
    "print(S[\"mulk\"])\n",
    "\n",
    "# İlçe adları Türkçe karakterlerle\n",
    "ILCE = {\"Beyoglu\": \"Beyoğlu\", \"Sisli\": \"Şişli\", \"Kadikoy\": \"Kadıköy\", \"Besiktas\": \"Beşiktaş\", \"Uskudar\": \"Üsküdar\",\n",
    "        \"Kagithane\": \"Kağıthane\", \"Bagcilar\": \"Bağcılar\", \"Sile\": \"Şile\", \"Atasehir\": \"Ataşehir\", \"Arnavutkoy\": \"Arnavutköy\",\n",
    "        \"Avcilar\": \"Avcılar\", \"Bakirkoy\": \"Bakırköy\", \"Umraniye\": \"Ümraniye\", \"Basaksehir\": \"Başakşehir\",\n",
    "        \"Kucukcekmece\": \"Küçükçekmece\", \"Beylikduzu\": \"Beylikdüzü\", \"Sariyer\": \"Sarıyer\", \"Eyup\": \"Eyüpsultan\",\n",
    "        \"Buyukcekmece\": \"Büyükçekmece\", \"Gaziosmanpasa\": \"Gaziosmanpaşa\", \"Cekmekoy\": \"Çekmeköy\", \"Gungoren\": \"Güngören\",\n",
    "        \"Bayrampasa\": \"Bayrampaşa\", \"Catalca\": \"Çatalca\", \"Bahcelievler\": \"Bahçelievler\"}\n",
    "df[\"ilce\"] = df[\"neighbourhood_cleansed\"].replace(ILCE)\n",
    "\n",
    "# Ev sahibinin konumu: aynı şehir dört ayrı yazılmış\n",
    "print(df[\"host_location\"].value_counts().head(4).to_dict())\n",
    "hl = df[\"host_location\"].str.lower().str.replace(\"i̇\", \"i\")\n",
    "df[\"ev_sahibi_istanbulda\"] = np.where(hl.isna(), np.nan, hl.str.contains(\"stanbul\", na=False).astype(float))\n",
    "S[\"konum_yazimi\"] = int(df.loc[hl.str.contains(\"stanbul\", na=False), \"host_location\"].nunique())\n",
    "\n",
    "# Taksim Meydanı'na kuş uçuşu uzaklık (km)\n",
    "lat0, lon0 = np.radians(41.0370), np.radians(28.9850)\n",
    "la, lo = np.radians(df[\"latitude\"]), np.radians(df[\"longitude\"])\n",
    "df[\"taksim_km\"] = 2 * 6371 * np.arcsin(np.sqrt(np.sin((la - lat0) / 2) ** 2 + np.cos(lat0) * np.cos(la) * np.sin((lo - lon0) / 2) ** 2))\n",
    "df[\"yorum_var\"] = (df[\"number_of_reviews\"] > 0).astype(int)\n",
    "assert (df[\"review_scores_rating\"].isna() == (df[\"number_of_reviews\"] == 0)).all()  # puan yoksa yorum da yok"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21cf49c6",
   "metadata": {},
   "source": [
    "Değerlendirme puanlarındaki %37 boşluk da rastgele değil: puanı olmayan her ilan, hiç yorumu\n",
    "olmayan bir ilan. Bunları ortalamayla doldurmak yeni ilanlara sahte bir puan vermek olurdu;\n",
    "boş bırakıp ayrı bir \"yorumu var mı\" sütunu ekledim.\n",
    "\n",
    "### 2.6 Analiz kümesi: kısa konaklama, tekrarsız, uç değerler ayıklanmış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b61d5032",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:44.531652Z",
     "iopub.status.busy": "2026-10-06T19:58:44.531472Z",
     "iopub.status.idle": "2026-10-06T19:58:44.675907Z",
     "shell.execute_reply": "2026-10-06T19:58:44.674626Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alt': 1137.0, 'ust': 40000.0, 'ham_maks': 1706000.0} {'bedrooms': 9, 'beds': 70, 'banyo': 7}\n"
     ]
    },
    {
     "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>Adım</th>\n",
       "      <th>Kalan ilan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ham veri</td>\n",
       "      <td>26631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fiyatı olmayanlar çıkarıldı</td>\n",
       "      <td>23943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uzun konaklama teklifleri (28+ gece) ayrıldı</td>\n",
       "      <td>15226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aynı ev sahibinin birebir aynı ilanları teke i...</td>\n",
       "      <td>14923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fiyatın en uç %1'i çıkarıldı</td>\n",
       "      <td>14774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Adım  Kalan ilan\n",
       "0                                           Ham veri       26631\n",
       "1                        Fiyatı olmayanlar çıkarıldı       23943\n",
       "2       Uzun konaklama teklifleri (28+ gece) ayrıldı       15226\n",
       "3  Aynı ev sahibinin birebir aynı ilanları teke i...       14923\n",
       "4                       Fiyatın en uç %1'i çıkarıldı       14774"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adim = [(\"Ham veri\", len(df))]\n",
    "k = df[df[\"fiyat\"].notna()]\n",
    "adim.append((\"Fiyatı olmayanlar çıkarıldı\", len(k)))\n",
    "k = k[k[\"teklif_gece\"] < 28]\n",
    "adim.append((\"Uzun konaklama teklifleri (28+ gece) ayrıldı\", len(k)))\n",
    "k = k.drop_duplicates([\"host_id\", \"name\", \"room_type\", \"accommodates\", \"fiyat\"])\n",
    "adim.append((\"Aynı ev sahibinin birebir aynı ilanları teke indirildi\", len(k)))\n",
    "alt, ust = k[\"fiyat\"].quantile([0.005, 0.995])\n",
    "k = k[k[\"fiyat\"].between(alt, ust)].copy()\n",
    "adim.append((\"Fiyatın en uç %1'i çıkarıldı\", len(k)))\n",
    "S[\"adimlar\"] = [{\"adim\": a, \"ilan\": int(n)} for a, n in adim]\n",
    "S[\"fiyat_sinir\"] = {\"alt\": float(round(alt)), \"ust\": float(round(ust)), \"ham_maks\": float(round(df[\"fiyat\"].max()))}\n",
    "for c in [\"bedrooms\", \"beds\", \"banyo\"]:\n",
    "    S.setdefault(\"kirpilan\", {})[c] = int((k[c] > 10).sum())\n",
    "    k[c] = k[c].clip(upper=10)\n",
    "print(S[\"fiyat_sinir\"], S[\"kirpilan\"])\n",
    "pd.DataFrame(adim, columns=[\"Adım\", \"Kalan ilan\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45d6359b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:44.677634Z",
     "iopub.status.busy": "2026-10-06T19:58:44.677438Z",
     "iopub.status.idle": "2026-10-06T19:58:45.041986Z",
     "shell.execute_reply": "2026-10-06T19:58:45.040950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x330 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n",
    "axes[0].hist(np.log10(fiyatli[\"fiyat\"]), bins=80, color=GRAY)\n",
    "axes[0].set(title=\"Önce: tüm fiyatlar (log ölçek)\", xlabel=\"log10(₺)\", ylabel=\"İlan\")\n",
    "axes[1].hist(np.log10(k[\"fiyat\"]), bins=60, color=ACCENT)\n",
    "axes[1].set(title=\"Sonra: kısa konaklama, temizlenmiş\", xlabel=\"log10(₺)\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9eabd0b",
   "metadata": {},
   "source": [
    "Soldaki iki tepeli dağılım iki ayrı pazarın üst üste binmiş hali. Temizlikten sonra tek tepe kalıyor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cd2246",
   "metadata": {},
   "source": [
    "## 3. Keşifsel analiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d2d5ae79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:45.044141Z",
     "iopub.status.busy": "2026-10-06T19:58:45.043960Z",
     "iopub.status.idle": "2026-10-06T19:58:45.064518Z",
     "shell.execute_reply": "2026-10-06T19:58:45.063267Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ilan': 14774, 'medyan': 4299.594999999999, 'q1': 2851.6325, 'q3': 6499.0, 'ev_sahibi': 3387}\n"
     ]
    },
    {
     "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>ilan</th>\n",
       "      <th>medyan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oda</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Evin tamamı</th>\n",
       "      <td>10759</td>\n",
       "      <td>4688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Özel oda</th>\n",
       "      <td>3644</td>\n",
       "      <td>3007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Otel odası</th>\n",
       "      <td>269</td>\n",
       "      <td>5699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paylaşımlı oda</th>\n",
       "      <td>102</td>\n",
       "      <td>2729.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ilan  medyan\n",
       "oda                          \n",
       "Evin tamamı     10759  4688.0\n",
       "Özel oda         3644  3007.0\n",
       "Otel odası        269  5699.0\n",
       "Paylaşımlı oda    102  2729.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S[\"ozet\"] = {\"ilan\": len(k), \"medyan\": float(k[\"fiyat\"].median()), \"q1\": float(k[\"fiyat\"].quantile(0.25)),\n",
    "             \"q3\": float(k[\"fiyat\"].quantile(0.75)), \"ev_sahibi\": int(k[\"host_id\"].nunique())}\n",
    "ODA = {\"Entire home/apt\": \"Evin tamamı\", \"Private room\": \"Özel oda\", \"Hotel room\": \"Otel odası\", \"Shared room\": \"Paylaşımlı oda\"}\n",
    "k[\"oda\"] = k[\"room_type\"].map(ODA)\n",
    "oda = k.groupby(\"oda\")[\"fiyat\"].agg(ilan=\"size\", medyan=\"median\").sort_values(\"ilan\", ascending=False)\n",
    "S[\"oda\"] = [{\"ad\": a, \"ilan\": int(r.ilan), \"medyan\": float(round(r.medyan))} for a, r in oda.iterrows()]\n",
    "print(S[\"ozet\"])\n",
    "oda.round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cde34cb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:45.066647Z",
     "iopub.status.busy": "2026-10-06T19:58:45.066371Z",
     "iopub.status.idle": "2026-10-06T19:58:45.482870Z",
     "shell.execute_reply": "2026-10-06T19:58:45.481899Z"
    }
   },
   "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>ilan</th>\n",
       "      <th>medyan</th>\n",
       "      <th>kisi</th>\n",
       "      <th>kisi_basi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ilce</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adalar</th>\n",
       "      <td>150</td>\n",
       "      <td>8050.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Şile</th>\n",
       "      <td>270</td>\n",
       "      <td>6013.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kağıthane</th>\n",
       "      <td>106</td>\n",
       "      <td>5554.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Şişli</th>\n",
       "      <td>1795</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bağcılar</th>\n",
       "      <td>211</td>\n",
       "      <td>5273.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Üsküdar</th>\n",
       "      <td>176</td>\n",
       "      <td>4955.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ataşehir</th>\n",
       "      <td>153</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beşiktaş</th>\n",
       "      <td>745</td>\n",
       "      <td>4371.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beyoğlu</th>\n",
       "      <td>5329</td>\n",
       "      <td>4356.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ümraniye</th>\n",
       "      <td>107</td>\n",
       "      <td>4165.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1522.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bakırköy</th>\n",
       "      <td>153</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kadıköy</th>\n",
       "      <td>921</td>\n",
       "      <td>3876.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maltepe</th>\n",
       "      <td>108</td>\n",
       "      <td>3643.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahçelievler</th>\n",
       "      <td>379</td>\n",
       "      <td>3616.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fatih</th>\n",
       "      <td>2637</td>\n",
       "      <td>3469.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Esenyurt</th>\n",
       "      <td>292</td>\n",
       "      <td>3321.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arnavutköy</th>\n",
       "      <td>202</td>\n",
       "      <td>3304.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pendik</th>\n",
       "      <td>158</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avcılar</th>\n",
       "      <td>101</td>\n",
       "      <td>3008.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ilan  medyan  kisi  kisi_basi\n",
       "ilce                                       \n",
       "Adalar         150  8050.0   2.0     2902.0\n",
       "Şile           270  6013.0   3.0     1997.0\n",
       "Kağıthane      106  5554.0   4.0     1438.0\n",
       "Şişli         1795  5400.0   4.0     1390.0\n",
       "Bağcılar       211  5273.0   3.0     1496.0\n",
       "Üsküdar        176  4955.0   4.0     1639.0\n",
       "Ataşehir       153  4850.0   3.0     1694.0\n",
       "Beşiktaş       745  4371.0   3.0     1568.0\n",
       "Beyoğlu       5329  4356.0   3.0     1237.0\n",
       "Ümraniye       107  4165.0   3.0     1522.0\n",
       "Bakırköy       153  3990.0   3.0     1348.0\n",
       "Kadıköy        921  3876.0   2.0     1405.0\n",
       "Maltepe        108  3643.0   3.0     1350.0\n",
       "Bahçelievler   379  3616.0   3.0     1047.0\n",
       "Fatih         2637  3469.0   3.0     1140.0\n",
       "Esenyurt       292  3321.0   4.0      945.0\n",
       "Arnavutköy     202  3304.0   2.0     1398.0\n",
       "Pendik         158  3240.0   3.0     1166.0\n",
       "Avcılar        101  3008.0   2.0     1100.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 770x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ilce = k.groupby(\"ilce\").agg(ilan=(\"id\", \"size\"), medyan=(\"fiyat\", \"median\"), kisi=(\"accommodates\", \"median\"),\n",
    "                             kisi_basi=(\"fiyat\", lambda s: (s / k.loc[s.index, \"accommodates\"]).median()))\n",
    "buyuk = ilce[ilce[\"ilan\"] >= 100].sort_values(\"medyan\", ascending=False)\n",
    "S[\"ilce\"] = [{\"ad\": a, \"ilan\": int(r.ilan), \"medyan\": float(round(r.medyan)), \"kisi\": float(r.kisi), \"kisi_basi\": float(round(r.kisi_basi))}\n",
    "             for a, r in buyuk.iterrows()]\n",
    "S[\"ilce_pay\"] = {\"ilk3\": round(ilce[\"ilan\"].nlargest(3).sum() / len(k) * 100, 1), \"ilk3_ad\": ilce[\"ilan\"].nlargest(3).index.tolist()}\n",
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "buyuk[\"medyan\"].sort_values().plot.barh(ax=ax, color=ACCENT, width=0.6)\n",
    "ax.set(xlabel=\"Medyan gecelik fiyat (₺)\", ylabel=\"\", title=\"İlçelere göre medyan fiyat (en az 100 ilan)\")\n",
    "plt.tight_layout()\n",
    "buyuk.round(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96baca16",
   "metadata": {},
   "source": [
    "Adalar ve Şile açık ara önde; ikisi de sayfiye. Geri kalanında ilçe farkının bir kısmı büyüklükten\n",
    "geliyor: Şişli'nin medyanı Kadıköy'den ₺1.500 yüksek, ama Şişli'de tipik ilan 4 kişilik, Kadıköy'de\n",
    "2 kişilik. Kişi başına bakınca ikisi aynı (₺1.390 ve ₺1.405). İlçe tek başına okunursa büyüklük\n",
    "etkisini de taşıyor; bu yüzden etkileri ayırmak için modele ihtiyaç var."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fbd44775",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:45.485604Z",
     "iopub.status.busy": "2026-10-06T19:58:45.485308Z",
     "iopub.status.idle": "2026-10-06T19:58:45.507800Z",
     "shell.execute_reply": "2026-10-06T19:58:45.506292Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'tek_ilanli_yuzde': np.float64(39.9), 'on_ustu_ilan_payi': np.float64(43.8), 'en_buyuk': 325}\n"
     ]
    },
    {
     "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>ilan</th>\n",
       "      <th>medyan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kisi</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>269</td>\n",
       "      <td>1865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5088</td>\n",
       "      <td>2921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2801</td>\n",
       "      <td>3836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2869</td>\n",
       "      <td>4580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1192</td>\n",
       "      <td>5845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1217</td>\n",
       "      <td>7006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>408</td>\n",
       "      <td>7706.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>930</td>\n",
       "      <td>10562.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ilan   medyan\n",
       "kisi               \n",
       "1      269   1865.0\n",
       "2     5088   2921.0\n",
       "3     2801   3836.0\n",
       "4     2869   4580.0\n",
       "5     1192   5845.0\n",
       "6     1217   7006.0\n",
       "7      408   7706.0\n",
       "8      930  10562.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kap = k.assign(kisi=k[\"accommodates\"].clip(upper=8)).groupby(\"kisi\")[\"fiyat\"].agg(ilan=\"size\", medyan=\"median\")\n",
    "S[\"kapasite\"] = [{\"kisi\": int(a), \"ilan\": int(r.ilan), \"medyan\": float(round(r.medyan))} for a, r in kap.iterrows()]\n",
    "hs = k.groupby(\"host_id\").size()\n",
    "S[\"ev_sahibi\"] = {\"tek_ilanli_yuzde\": round((hs == 1).mean() * 100, 1), \"on_ustu_ilan_payi\": round(hs[hs >= 10].sum() / len(k) * 100, 1),\n",
    "                  \"en_buyuk\": int(hs.max())}\n",
    "print(S[\"ev_sahibi\"])\n",
    "kap.round(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5daf0dfe",
   "metadata": {},
   "source": [
    "Fiyat kapasiteyle düzenli artıyor: 2 kişilik ilanın medyanı ₺2.921, 6 kişiliğin ₺7.006.\n",
    "\n",
    "Kısa konaklama ilanlarının %44'ü, on ve daha fazla ilanı olan ev sahiplerine ait; en büyük ev\n",
    "sahibinin 325 ilanı var. Aynı ev sahibinin ilanları birbirine çok benziyor (aynı bina, aynı\n",
    "fiyatlama). Bu, modelin nasıl sınanacağını belirliyor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355b7aab",
   "metadata": {},
   "source": [
    "## 4. Fiyat modeli\n",
    "\n",
    "Hedef: gecelik fiyatın logaritması. Fiyattan türeyen sütunları (`estimated_revenue_l365d`,\n",
    "teklif toplamı) modele almıyorum; alırsam model cevabı görmüş olur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "242a75d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:45.510139Z",
     "iopub.status.busy": "2026-10-06T19:58:45.509878Z",
     "iopub.status.idle": "2026-10-06T19:58:45.528910Z",
     "shell.execute_reply": "2026-10-06T19:58:45.527816Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14774, 36) eksik: {'bedrooms': 0.069, 'beds': 0.021, 'banyo': 0.005, 'ev_sahibi_istanbulda': 0.338, 'reviews_per_month': 0.214, 'review_scores_rating': 0.214}\n"
     ]
    }
   ],
   "source": [
    "KAT = [\"ilce\", \"room_type\", \"mulk\", \"izin\"]\n",
    "SAY = [\"accommodates\", \"bedrooms\", \"beds\", \"banyo\", \"ortak_banyo\", \"latitude\", \"longitude\", \"taksim_km\",\n",
    "       \"olanak_sayisi\"] + [c for c in k if c.startswith(\"o_\")] + [\n",
    "       \"host_is_superhost\", \"host_listings_count\", \"hosts_time_as_host_years\", \"ev_sahibi_istanbulda\",\n",
    "       \"minimum_nights\", \"teklif_gece\", \"giris_gunu\", \"availability_365\",\n",
    "       \"number_of_reviews\", \"reviews_per_month\", \"review_scores_rating\", \"yorum_var\"]\n",
    "X, y, grup = k[KAT + SAY].copy(), np.log(k[\"fiyat\"]), k[\"host_id\"]\n",
    "print(X.shape, \"eksik:\", X.isna().mean()[lambda s: s > 0].round(3).to_dict())\n",
    "\n",
    "dogrusal = make_pipeline(\n",
    "    ColumnTransformer([(\"kat\", OneHotEncoder(handle_unknown=\"ignore\", min_frequency=20), KAT),\n",
    "                       (\"say\", make_pipeline(SimpleImputer(strategy=\"median\", add_indicator=True), StandardScaler()), SAY)]),\n",
    "    Ridge(alpha=3.0))\n",
    "agac = make_pipeline(\n",
    "    ColumnTransformer([(\"kat\", OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=-1), KAT)],\n",
    "                      remainder=\"passthrough\", verbose_feature_names_out=False),\n",
    "    HistGradientBoostingRegressor(max_iter=600, learning_rate=0.05, max_leaf_nodes=31, min_samples_leaf=20,\n",
    "                                  l2_regularization=1.0, categorical_features=list(range(len(KAT))), random_state=SEED))\n",
    "\n",
    "\n",
    "class Taban:\n",
    "    \"\"\"İlçe × oda tipinin medyan fiyatı; grup görülmediyse oda tipinin medyanı.\"\"\"\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        d = X.assign(y=y)\n",
    "        self.g = d.groupby([\"ilce\", \"room_type\"])[\"y\"].median()\n",
    "        self.o = d.groupby(\"room_type\")[\"y\"].median()\n",
    "        self.m = y.median()\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        p = pd.Series(list(zip(X[\"ilce\"], X[\"room_type\"])), index=X.index).map(self.g)\n",
    "        return p.fillna(X[\"room_type\"].map(self.o)).fillna(self.m).to_numpy()\n",
    "\n",
    "\n",
    "def olc(y, p):\n",
    "    hata = np.abs(np.exp(p - y) - 1)\n",
    "    return {\"r2\": r2_score(y, p), \"medyan_hata\": float(np.median(hata) * 100), \"yuzde25_icinde\": float((hata <= 0.25).mean() * 100)}\n",
    "\n",
    "\n",
    "def cv(model, bolme, gruplar=None):\n",
    "    tahmin = pd.Series(index=y.index, dtype=float)\n",
    "    for tr, te in bolme.split(X, y, gruplar):\n",
    "        model.fit(X.iloc[tr], y.iloc[tr])\n",
    "        tahmin.iloc[te] = model.predict(X.iloc[te])\n",
    "    return olc(y, tahmin), tahmin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff1a014",
   "metadata": {},
   "source": [
    "### Doğrulama: rastgele bölme mi, ev sahibine göre bölme mi?\n",
    "\n",
    "Rastgele bölmede aynı ev sahibinin benzer ilanları hem eğitimde hem testte olur; model fiyatı\n",
    "tahmin etmek yerine ev sahibini tanır. Gerçek soru \"hiç görmediğim bir ev sahibinin ilanını\n",
    "fiyatlayabilir miyim?\" olduğu için ilanları ev sahibine göre gruplayarak bölüyorum (5 kat)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d514cd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:58:45.530894Z",
     "iopub.status.busy": "2026-10-06T19:58:45.530638Z",
     "iopub.status.idle": "2026-10-06T19:59:06.100932Z",
     "shell.execute_reply": "2026-10-06T19:59:06.099547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "taban {'r2': 0.097, 'medyan_hata': 35.276, 'yuzde25_icinde': 37.228}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dogrusal {'r2': 0.495, 'medyan_hata': 26.076, 'yuzde25_icinde': 48.044}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "agac {'r2': 0.64, 'medyan_hata': 21.593, 'yuzde25_icinde': 56.153}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "agac, rastgele bölme: {'r2': 0.759, 'medyan_hata': 16.115, 'yuzde25_icinde': 67.497}\n"
     ]
    },
    {
     "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>r2</th>\n",
       "      <th>medyan_hata</th>\n",
       "      <th>yuzde25_icinde</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>taban</th>\n",
       "      <td>0.097</td>\n",
       "      <td>35.276</td>\n",
       "      <td>37.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dogrusal</th>\n",
       "      <td>0.495</td>\n",
       "      <td>26.076</td>\n",
       "      <td>48.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agac</th>\n",
       "      <td>0.640</td>\n",
       "      <td>21.593</td>\n",
       "      <td>56.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agac_rastgele</th>\n",
       "      <td>0.759</td>\n",
       "      <td>16.115</td>\n",
       "      <td>67.497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  r2  medyan_hata  yuzde25_icinde\n",
       "taban          0.097       35.276          37.228\n",
       "dogrusal       0.495       26.076          48.044\n",
       "agac           0.640       21.593          56.153\n",
       "agac_rastgele  0.759       16.115          67.497"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gk, rk = GroupKFold(n_splits=5), KFold(n_splits=5, shuffle=True, random_state=SEED)\n",
    "sonuc, tahminler = {}, {}\n",
    "for ad, m in [(\"taban\", Taban()), (\"dogrusal\", dogrusal), (\"agac\", agac)]:\n",
    "    sonuc[ad], tahminler[ad] = cv(m, gk, grup)\n",
    "    print(ad, {a: round(b, 3) for a, b in sonuc[ad].items()})\n",
    "rastgele, _ = cv(agac, rk)\n",
    "print(\"agac, rastgele bölme:\", {a: round(b, 3) for a, b in rastgele.items()})\n",
    "S[\"model\"] = {ad: {a: round(float(b), 3) for a, b in v.items()} for ad, v in sonuc.items()}\n",
    "S[\"model\"][\"agac_rastgele\"] = {a: round(float(b), 3) for a, b in rastgele.items()}\n",
    "pd.DataFrame(S[\"model\"]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23271f03",
   "metadata": {},
   "source": [
    "Üç şey görülüyor:\n",
    "\n",
    "- İlçe ve oda tipinin medyanını söyleyen taban, fiyatı neredeyse hiç açıklamıyor (R² 0,10).\n",
    "- Gradyan artırmalı ağaçlar doğrusal modelden belirgin iyi (R² 0,64 ve 0,49); tahminlerin yarısı\n",
    "  gerçek fiyatın ±%22'si içinde.\n",
    "- Aynı model rastgele bölmeyle ölçülünce R² 0,76 çıkıyor. Aradaki fark modelin başarısı değil;\n",
    "  ev sahibini tanımasından geliyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8dd70f71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:59:06.102868Z",
     "iopub.status.busy": "2026-10-06T19:59:06.102671Z",
     "iopub.status.idle": "2026-10-06T19:59:06.411119Z",
     "shell.execute_reply": "2026-10-06T19:59:06.410422Z"
    }
   },
   "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>medyan_sapma</th>\n",
       "      <th>medyan_mutlak</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dilim</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>en ucuz %20</th>\n",
       "      <td>29.3</td>\n",
       "      <td>29.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.2</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.6</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en pahalı %20</th>\n",
       "      <td>-23.0</td>\n",
       "      <td>27.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               medyan_sapma  medyan_mutlak\n",
       "dilim                                     \n",
       "en ucuz %20            29.3           29.4\n",
       "2                       9.1           17.1\n",
       "3                       5.2           19.7\n",
       "4                      -5.6           18.1\n",
       "en pahalı %20         -23.0           27.8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = tahminler[\"agac\"]\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.scatter(np.exp(y), np.exp(p), s=4, alpha=0.25, color=ACCENT, linewidths=0)\n",
    "lim = [k[\"fiyat\"].min(), k[\"fiyat\"].max()]\n",
    "ax.plot(lim, lim, color=GRAY, linewidth=1)\n",
    "ax.set(xscale=\"log\", yscale=\"log\", xlabel=\"Gerçek fiyat (₺)\", ylabel=\"Tahmin (₺)\", title=\"Görülmemiş ev sahiplerinde tahmin\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# Hata hangi fiyat diliminde büyüyor?\n",
    "dilim = pd.qcut(k[\"fiyat\"], 5, labels=[\"en ucuz %20\", \"2\", \"3\", \"4\", \"en pahalı %20\"])\n",
    "hata = (np.exp(p - y) - 1)\n",
    "dl = pd.DataFrame({\"dilim\": dilim, \"hata\": hata}).groupby(\"dilim\", observed=True)[\"hata\"].agg(\n",
    "    medyan_sapma=lambda s: s.median() * 100, medyan_mutlak=lambda s: s.abs().median() * 100).round(1)\n",
    "S[\"dilim\"] = [{\"ad\": str(a), \"sapma\": float(r.medyan_sapma), \"mutlak\": float(r.medyan_mutlak)} for a, r in dl.iterrows()]\n",
    "dl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ecdae62",
   "metadata": {},
   "source": [
    "Model ortalamaya çekiyor: en ucuz beşte birlik dilimde fiyatı medyan %29 fazla, en pahalı dilimde\n",
    "%23 eksik tahmin ediyor. Ortadaki üç dilimde sapma küçük.\n",
    "\n",
    "### Fiyatı ne belirliyor?\n",
    "\n",
    "Değişkenleri anlam gruplarına ayırıp her grubu test verisinde birlikte karıştırıyorum\n",
    "(permütasyon önemi). Bir grup karıştırılınca R² ne kadar düşüyorsa model o gruba o kadar yaslanıyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d22f9003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:59:06.412630Z",
     "iopub.status.busy": "2026-10-06T19:59:06.412459Z",
     "iopub.status.idle": "2026-10-06T19:59:24.262355Z",
     "shell.execute_reply": "2026-10-06T19:59:24.261276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Büyüklük              0.568\n",
       "Konum                 0.162\n",
       "Olanaklar             0.150\n",
       "Yorumlar              0.081\n",
       "Konaklama tipi        0.029\n",
       "Ev sahibi             0.025\n",
       "Teklifin koşulları    0.023\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 770x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GRUP = {\n",
    "    \"Büyüklük\": [\"accommodates\", \"bedrooms\", \"beds\", \"banyo\"],\n",
    "    \"Konum\": [\"ilce\", \"latitude\", \"longitude\", \"taksim_km\"],\n",
    "    \"Konaklama tipi\": [\"room_type\", \"mulk\", \"ortak_banyo\"],\n",
    "    \"Olanaklar\": [\"olanak_sayisi\"] + [c for c in SAY if c.startswith(\"o_\")],\n",
    "    \"Ev sahibi\": [\"host_is_superhost\", \"host_listings_count\", \"hosts_time_as_host_years\", \"ev_sahibi_istanbulda\", \"izin\"],\n",
    "    \"Yorumlar\": [\"number_of_reviews\", \"reviews_per_month\", \"review_scores_rating\", \"yorum_var\"],\n",
    "    \"Teklifin koşulları\": [\"minimum_nights\", \"teklif_gece\", \"giris_gunu\", \"availability_365\"],\n",
    "}\n",
    "assert sorted(sum(GRUP.values(), [])) == sorted(KAT + SAY)\n",
    "rng = np.random.default_rng(SEED)\n",
    "dusus = {g: [] for g in GRUP}\n",
    "for tr, te in gk.split(X, y, grup):\n",
    "    agac.fit(X.iloc[tr], y.iloc[tr])\n",
    "    Xt, yt = X.iloc[te], y.iloc[te]\n",
    "    temel = r2_score(yt, agac.predict(Xt))\n",
    "    for g, kolonlar in GRUP.items():\n",
    "        for _ in range(3):\n",
    "            Xp = Xt.copy()\n",
    "            Xp[kolonlar] = Xt[kolonlar].to_numpy()[rng.permutation(len(Xt))]\n",
    "            dusus[g].append(temel - r2_score(yt, agac.predict(Xp)))\n",
    "onem = pd.Series({g: np.mean(v) for g, v in dusus.items()}).sort_values(ascending=False)\n",
    "S[\"onem\"] = [{\"ad\": g, \"dusus\": round(float(v), 3)} for g, v in onem.items()]\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "onem.sort_values().plot.barh(ax=ax, color=ACCENT, width=0.6)\n",
    "ax.set(xlabel=\"Grup karıştırılınca R² düşüşü\", title=\"Model hangi bilgiye yaslanıyor?\")\n",
    "plt.tight_layout()\n",
    "onem.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c134dc",
   "metadata": {},
   "source": [
    "Büyüklük açık ara önde; konum ve olanaklar onu izliyor. Konaklama tipi ve ev sahibi bilgisi çok az\n",
    "şey ekliyor. Bir uyarı: değişkenler birbiriyle ilişkili (örneğin oda tipi büyüklükle), bu yüzden\n",
    "bu sıralama \"modelin neye yaslandığını\" gösterir, nedensel etkiyi değil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f32f1688",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:59:24.264265Z",
     "iopub.status.busy": "2026-10-06T19:59:24.264053Z",
     "iopub.status.idle": "2026-10-06T19:59:24.303737Z",
     "shell.execute_reply": "2026-10-06T19:59:24.302258Z"
    }
   },
   "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>ilan</th>\n",
       "      <th>medyan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taksim_km</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0–2</th>\n",
       "      <td>3391</td>\n",
       "      <td>3957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2–5</th>\n",
       "      <td>1646</td>\n",
       "      <td>3710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5–10</th>\n",
       "      <td>894</td>\n",
       "      <td>4424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10–20</th>\n",
       "      <td>677</td>\n",
       "      <td>3996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20+</th>\n",
       "      <td>552</td>\n",
       "      <td>3774.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ilan  medyan\n",
       "taksim_km              \n",
       "0–2        3391  3957.0\n",
       "2–5        1646  3710.0\n",
       "5–10        894  4424.0\n",
       "10–20       677  3996.0\n",
       "20+         552  3774.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Büyüklük sabitken konum: aynı kapasitedeki ilanlarda merkeze uzaklık\n",
    "iki = k[(k[\"room_type\"] == \"Entire home/apt\") & k[\"accommodates\"].between(2, 4)]\n",
    "uz = iki.groupby(pd.cut(iki[\"taksim_km\"], [0, 2, 5, 10, 20, 200], labels=[\"0–2\", \"2–5\", \"5–10\", \"10–20\", \"20+\"]), observed=True)[\"fiyat\"].agg(ilan=\"size\", medyan=\"median\")\n",
    "S[\"uzaklik\"] = [{\"ad\": str(a), \"ilan\": int(r.ilan), \"medyan\": float(round(r.medyan))} for a, r in uz.iterrows()]\n",
    "uz.round(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c90aedd",
   "metadata": {},
   "source": [
    "2–4 kişilik evlerde Taksim'e uzaklık tek başına fiyatı sıralamıyor: 5–10 km kuşağı merkezden pahalı.\n",
    "Konumun etkisi \"merkeze yakınlık\" kadar basit değil; Boğaz hattı ve sayfiye ilçeleri de pahalı."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "74865ccd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-06T19:59:24.305780Z",
     "iopub.status.busy": "2026-10-06T19:59:24.305533Z",
     "iopub.status.idle": "2026-10-06T19:59:24.383061Z",
     "shell.execute_reply": "2026-10-06T19:59:24.381909Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "özet ve temiz veri kaydedildi: 14774 ilan\n"
     ]
    }
   ],
   "source": [
    "S[\"veri\"] = {\"tarama\": \"2026-06-30\", \"kaynak\": \"Inside Airbnb\"}\n",
    "(OUT / \"summary.json\").write_text(json.dumps(S, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n",
    "k.drop(columns=[\"description\", \"host_about\", \"amenities\", \"price_quote_raw\", \"picture_url\", \"host_picture_url\"]).to_parquet(OUT / \"listings_clean.parquet\", index=False)\n",
    "print(\"özet ve temiz veri kaydedildi:\", len(k), \"ilan\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d7bf04e",
   "metadata": {},
   "source": [
    "## Sonuç\n",
    "\n",
    "- Ham verideki \"gecelik fiyat\" iki ayrı şeyi ölçüyordu; ayırmadan yapılan her analiz yanlış çıkardı.\n",
    "- Eksik değerlerin hiçbiri rastgele değildi: fiyat ve yatak sayısı tarama kaynağına, puanlar yorum\n",
    "  olup olmamasına bağlıydı.\n",
    "- Model, hiç görmediği ev sahiplerinin ilanlarında fiyatı medyan %22 sapmayla tahmin ediyor.\n",
    "  Rastgele bölmeyle ölçülseydi olduğundan iyi görünecekti (%16).\n",
    "- Modelin en çok yaslandığı bilgi büyüklük; konum ve olanaklar ikinci sırada. Ev sahibi bilgisi\n",
    "  çok az şey ekliyor.\n",
    "\n",
    "**Sınırlar:** Tek bir günün taraması; fiyatlar ilan fiyatı, gerçekleşen satış fiyatı değil. Teklifler\n",
    "farklı giriş günlerine ait. Uzun konaklama pazarı bu modelin dışında."
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "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": "3.13.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}