{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86f2dc65-3b0f-4ac1-87ad-93375d1a9fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</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>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv(\"Housing.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d73e427d-cc36-40da-8eb8-1280439f6a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                int64\n",
       "area                 int64\n",
       "bedrooms             int64\n",
       "bathrooms            int64\n",
       "stories              int64\n",
       "mainroad            object\n",
       "guestroom           object\n",
       "basement            object\n",
       "hotwaterheating     object\n",
       "airconditioning     object\n",
       "parking              int64\n",
       "prefarea            object\n",
       "furnishingstatus    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91f01ad4-b8d3-458c-a0d9-df5f4ff26b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fd59dc0-5415-4c28-9a2a-2aeb1a08300f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               0\n",
       "area                0\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    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "920c73ec-51e4-451e-8b20-d278d431535a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "missing_values = df.isnull().sum()\n",
    "print(missing_values[missing_values > 0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e252700b-413e-42d6-8320-cf7ed7b1cb42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price               0\n",
      "area                0\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    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def replace_outliers_with_NaN(column):\n",
    "    Q1 = column.quantail(0.25)\n",
    "    Q3 = column.quantail(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n",
    "    numeric_columns = df.select_dtypes(include=[\"number\"]).columns \n",
    "    df_outlier = df.copy()\n",
    "    for col in numeric_columns: df_outlier[col]=replace_outlier_with_NaN(df[col])\n",
    "\n",
    "    for col in numeric_columns: median_value = df_outlier[col].median\n",
    "    df_outlier[col] = df_outlier[col].fillna(median_value)\n",
    "print(df.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "47c84c77-375c-4bc5-8291-e65dea0714c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\"Housing.csv\")\n",
    "df_outlier = df.copy()\n",
    "# Distribution of pric\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.histplot(df_outlier[\"price\"], bins=30, kde=True)\n",
    "plt.title(\"Distribution of House Prices\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7a0a62bf-1940-4040-8c37-9083b7d47d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.boxplot(x=df_outlier[\"price\"])\n",
    "plt.title(\"Boxplot of House Price\")\n",
    "plt.xlabel(\"Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "17dccd88-674d-4e9d-bdb9-9372b05ef7c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(x=df_outlier[\"area\"], y=df_outlier[\"price\"])\n",
    "plt.title(\"scatter Plot: Area vs Price\")\n",
    "plt.xlabel(\"Area\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "799a7169-c617-4995-bc93-07bd77c0a9c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}