{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ca0f098",
   "metadata": {},
   "source": [
    "# 01 · Exploring the data (EDA)\n",
    "\n",
    "**Goal:** understand the data *before* building any model. By the end we should be able to answer:\n",
    "1. How big is the data, and what does each column mean?\n",
    "2. Is anything broken or missing?\n",
    "3. What is the target and how is it distributed?\n",
    "4. Which columns seem to drive the price tier? (our *hypotheses*)\n",
    "\n",
    "No models in this notebook, just looking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c4e61f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:21.174284Z",
     "iopub.status.busy": "2026-10-23T06:11:21.174284Z",
     "iopub.status.idle": "2026-10-23T06:11:26.137846Z",
     "shell.execute_reply": "2026-10-23T06:11:26.137846Z"
    }
   },
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "      <th>price_tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7420.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "      <td>premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8960.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "      <td>premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9960.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "      <td>premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "      <td>premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>unfurnished</td>\n",
       "      <td>premium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    area  bedrooms  bathrooms  stories mainroad guestroom basement hotwaterheating airconditioning  parking prefarea  \\\n",
       "0   0  7420.0         4          2        3      yes        no       no              no             yes        2      yes   \n",
       "1   1  8960.0         4          4        4      yes        no       no              no             yes        3       no   \n",
       "2   2  9960.0         3          2        2      yes        no      yes              no              no        2      yes   \n",
       "3   5  7500.0         3          3        1      yes        no      yes              no             yes        2      yes   \n",
       "4   9  5750.0         3          2        4      yes       yes       no              no             yes        1      yes   \n",
       "\n",
       "  furnishingstatus price_tier  \n",
       "0        furnished    premium  \n",
       "1        furnished    premium  \n",
       "2   semi-furnished    premium  \n",
       "3   semi-furnished    premium  \n",
       "4      unfurnished    premium  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from utils import load_data, TIERS, TIER_TO_NUM, YES_NO\n",
    "\n",
    "pd.set_option(\"display.width\", 140)\n",
    "train, test = load_data()\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08bcbdba",
   "metadata": {},
   "source": [
    "Each row is **one house for sale**.\n",
    "\n",
    "| column | meaning |\n",
    "|---|---|\n",
    "| `area` | size of the house/plot in square feet |\n",
    "| `bedrooms`, `bathrooms`, `stories` | counts |\n",
    "| `mainroad` | is it on a main road? (yes/no) |\n",
    "| `guestroom`, `basement` | has one? (yes/no) |\n",
    "| `hotwaterheating`, `airconditioning` | has it? (yes/no) |\n",
    "| `parking` | number of parking spots |\n",
    "| `prefarea` | in a preferred (desirable) neighbourhood? (yes/no) |\n",
    "| `furnishingstatus` | furnished / semi-furnished / unfurnished |\n",
    "| **`price_tier`** | **the target**: budget / mid / premium |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efc60ca2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:26.140297Z",
     "iopub.status.busy": "2026-10-23T06:11:26.140297Z",
     "iopub.status.idle": "2026-10-23T06:11:26.197581Z",
     "shell.execute_reply": "2026-10-23T06:11:26.196369Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: (408, 14)  test: (137, 13)\n",
      "columns only in train: {'price_tier'}\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 408 entries, 0 to 407\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   id                408 non-null    int64  \n",
      " 1   area              398 non-null    float64\n",
      " 2   bedrooms          408 non-null    int64  \n",
      " 3   bathrooms         408 non-null    int64  \n",
      " 4   stories           408 non-null    int64  \n",
      " 5   mainroad          408 non-null    str    \n",
      " 6   guestroom         408 non-null    str    \n",
      " 7   basement          408 non-null    str    \n",
      " 8   hotwaterheating   408 non-null    str    \n",
      " 9   airconditioning   408 non-null    str    \n",
      " 10  parking           408 non-null    int64  \n",
      " 11  prefarea          408 non-null    str    \n",
      " 12  furnishingstatus  393 non-null    str    \n",
      " 13  price_tier        408 non-null    str    \n",
      "dtypes: float64(1), int64(5), str(8)\n",
      "memory usage: 44.8 KB\n"
     ]
    }
   ],
   "source": [
    "print(\"train:\", train.shape, \" test:\", test.shape)\n",
    "print(\"columns only in train:\", set(train.columns) - set(test.columns))\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5a1313",
   "metadata": {},
   "source": [
    "- `train` has the answer column `price_tier`; `test` doesn't. That's what we must predict.\n",
    "- It's a **small** dataset: about 400 training rows. Keep that in mind; it will matter a lot in 03.\n",
    "- `area` and `furnishingstatus` have fewer non-null values than the rest → **missing values**.\n",
    "- Many columns are **text** (`yes`/`no`, furnishing). Models need numbers, so we'll convert them in 02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbfc3964",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:26.199353Z",
     "iopub.status.busy": "2026-10-23T06:11:26.199353Z",
     "iopub.status.idle": "2026-10-23T06:11:26.206943Z",
     "shell.execute_reply": "2026-10-23T06:11:26.206943Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "area                10\n",
       "bedrooms             0\n",
       "bathrooms            0\n",
       "stories              0\n",
       "mainroad             0\n",
       "guestroom            0\n",
       "basement             0\n",
       "hotwaterheating      0\n",
       "airconditioning      0\n",
       "parking              0\n",
       "prefarea             0\n",
       "furnishingstatus    15\n",
       "price_tier           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49436ea1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:26.206943Z",
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     "shell.execute_reply": "2026-10-23T06:11:26.228436Z"
    }
   },
   "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</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>408.0</td>\n",
       "      <td>272.63</td>\n",
       "      <td>155.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.75</td>\n",
       "      <td>272.5</td>\n",
       "      <td>408.25</td>\n",
       "      <td>544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>398.0</td>\n",
       "      <td>5109.51</td>\n",
       "      <td>2141.57</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>3520.00</td>\n",
       "      <td>4505.0</td>\n",
       "      <td>6405.00</td>\n",
       "      <td>15600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>408.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>408.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stories</th>\n",
       "      <td>408.0</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parking</th>\n",
       "      <td>408.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count     mean      std     min      25%     50%      75%      max\n",
       "id         408.0   272.63   155.88     0.0   141.75   272.5   408.25    544.0\n",
       "area       398.0  5109.51  2141.57  1650.0  3520.00  4505.0  6405.00  15600.0\n",
       "bedrooms   408.0     2.95     0.71     1.0     2.00     3.0     3.00      5.0\n",
       "bathrooms  408.0     1.28     0.49     1.0     1.00     1.0     2.00      4.0\n",
       "stories    408.0     1.81     0.89     1.0     1.00     2.0     2.00      4.0\n",
       "parking    408.0     0.69     0.87     0.0     0.00     0.0     1.00      3.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().T.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9561c87",
   "metadata": {},
   "source": [
    "`area` goes from ~1,650 to 16,200 sq ft, and most houses have 2–4 bedrooms, 1–2 bathrooms and 1–2 stories. A few big houses stretch the range, but nothing looks broken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abb9f4ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:26.231202Z",
     "iopub.status.busy": "2026-10-23T06:11:26.231202Z",
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     "shell.execute_reply": "2026-10-23T06:11:26.451728Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price_tier\n",
      "budget     139\n",
      "premium    136\n",
      "mid        133\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train.price_tier.value_counts())\n",
    "train.price_tier.value_counts().reindex(TIERS).plot.bar(title=\"Target distribution\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9536ab6",
   "metadata": {},
   "source": [
    "The 3 tiers are **balanced** (~⅓ each). So:\n",
    "- guessing randomly gets ~33% accuracy,\n",
    "- accuracy is a fair metric here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a516754",
   "metadata": {},
   "source": [
    "### Numeric columns vs price tier\n",
    "Boxplots: one box per tier. If the boxes move up as the tier rises, that column is useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bcfc809",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:26.454539Z",
     "iopub.status.busy": "2026-10-23T06:11:26.454539Z",
     "iopub.status.idle": "2026-10-23T06:11:27.222229Z",
     "shell.execute_reply": "2026-10-23T06:11:27.222229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(18, 4))\n",
    "for ax, col in zip(axes, [\"area\", \"bedrooms\", \"bathrooms\", \"stories\", \"parking\"]):\n",
    "    sns.boxplot(data=train, x=\"price_tier\", y=col, order=TIERS, ax=ax)\n",
    "    ax.set_title(col)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b421bee",
   "metadata": {},
   "source": [
    "### Yes/no columns vs price tier\n",
    "For each yes/no column: what share of each tier has a \"yes\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c928fe5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:27.224753Z",
     "iopub.status.busy": "2026-10-23T06:11:27.224753Z",
     "iopub.status.idle": "2026-10-23T06:11:27.239149Z",
     "shell.execute_reply": "2026-10-23T06:11:27.239149Z"
    }
   },
   "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>price_tier</th>\n",
       "      <th>budget</th>\n",
       "      <th>mid</th>\n",
       "      <th>premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mainroad</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guestroom</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basement</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hotwaterheating</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airconditioning</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prefarea</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "price_tier       budget   mid  premium\n",
       "mainroad           0.75  0.86     0.97\n",
       "guestroom          0.04  0.14     0.33\n",
       "basement           0.19  0.41     0.43\n",
       "hotwaterheating    0.04  0.03     0.07\n",
       "airconditioning    0.10  0.29     0.57\n",
       "prefarea           0.08  0.20     0.45"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yes_share = pd.DataFrame({col: train.groupby(\"price_tier\")[col].apply(lambda s: (s == \"yes\").mean()) for col in YES_NO}).T[TIERS]\n",
    "yes_share.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67aa2d2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:27.240900Z",
     "iopub.status.busy": "2026-10-23T06:11:27.240900Z",
     "iopub.status.idle": "2026-10-23T06:11:27.400638Z",
     "shell.execute_reply": "2026-10-23T06:11:27.400638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yes_share.plot.bar(figsize=(10, 4), title='Share of \"yes\" in each tier');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f92e1f82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:27.400638Z",
     "iopub.status.busy": "2026-10-23T06:11:27.400638Z",
     "iopub.status.idle": "2026-10-23T06:11:27.421930Z",
     "shell.execute_reply": "2026-10-23T06:11:27.421930Z"
    }
   },
   "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>price_tier</th>\n",
       "      <th>budget</th>\n",
       "      <th>mid</th>\n",
       "      <th>premium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>furnishingstatus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>furnished</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semi-furnished</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unfurnished</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "price_tier        budget   mid  premium\n",
       "furnishingstatus                       \n",
       "furnished           0.22  0.29     0.49\n",
       "semi-furnished      0.21  0.46     0.33\n",
       "unfurnished         0.62  0.15     0.23"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train.furnishingstatus, train.price_tier, normalize=\"index\")[TIERS].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d1d2fd",
   "metadata": {},
   "source": [
    "Correlation with the tier (budget=0, mid=1, premium=2), after turning yes/no into 1/0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92129b96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T06:11:27.421930Z",
     "iopub.status.busy": "2026-10-23T06:11:27.421930Z",
     "iopub.status.idle": "2026-10-23T06:11:27.435130Z",
     "shell.execute_reply": "2026-10-23T06:11:27.435130Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotwaterheating    0.057344\n",
       "basement           0.213995\n",
       "mainroad           0.263354\n",
       "parking            0.300412\n",
       "guestroom          0.322620\n",
       "prefarea           0.354464\n",
       "stories            0.412867\n",
       "airconditioning    0.415406\n",
       "bedrooms           0.430120\n",
       "bathrooms          0.492073\n",
       "area               0.551234\n",
       "Name: tier_num, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = train.drop(columns=[\"id\", \"furnishingstatus\"]).copy()\n",
    "for col in YES_NO:\n",
    "    num[col] = (num[col] == \"yes\").astype(int)\n",
    "num[\"tier_num\"] = num.pop(\"price_tier\").map(TIER_TO_NUM)\n",
    "num.corr()[\"tier_num\"].drop(\"tier_num\").sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9f8786",
   "metadata": {},
   "source": [
    "## What we learned → our hypotheses\n",
    "\n",
    "1. **`area` is the strongest signal**: bigger houses sit in higher tiers.\n",
    "2. **Bathrooms, stories, parking and air conditioning** all climb with the tier.\n",
    "3. **`prefarea`** (good neighbourhood) and **`mainroad`** matter. Premium houses are almost always on a main road.\n",
    "4. **Furnished** houses lean premium, and unfurnished ones lean budget.\n",
    "5. Rare features (`hotwaterheating`, only a few % say yes) can't help much on their own.\n",
    "6. Data issues to handle: missing `area` / `furnishingstatus`, and text columns.\n",
    "\n",
    "Next: **02_baseline**: get a first submission on the leaderboard."
   ]
  }
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