{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a01e8382-dcb0-4208-a05c-91222cc7d0d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /opt/anaconda3/lib/python3.12/site-packages (2.2.2)\n",
      "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.12/site-packages (1.26.4)\n",
      "Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.12/site-packages (3.9.2)\n",
      "Requirement already satisfied: seaborn in /opt/anaconda3/lib/python3.12/site-packages (0.13.2)\n",
      "Requirement already satisfied: scikit-learn in /opt/anaconda3/lib/python3.12/site-packages (1.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.12/site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/lib/python3.12/site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/anaconda3/lib/python3.12/site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.12/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/anaconda3/lib/python3.12/site-packages (from matplotlib) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/anaconda3/lib/python3.12/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/lib/python3.12/site-packages (from matplotlib) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in /opt/anaconda3/lib/python3.12/site-packages (from matplotlib) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/anaconda3/lib/python3.12/site-packages (from matplotlib) (3.1.2)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /opt/anaconda3/lib/python3.12/site-packages (from scikit-learn) (1.13.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /opt/anaconda3/lib/python3.12/site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/anaconda3/lib/python3.12/site-packages (from scikit-learn) (3.5.0)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas numpy matplotlib seaborn scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6d173d3-3734-4c81-a81a-4c4e23283f6e",
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# تحميل البيانات\n",
    "df = pd.read_csv('Housing.csv')\n",
    "\n",
    "# عرض أول 5 صفوف\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6d57099-b919-4d9a-a43e-915bb838776b",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# التحقق من القيم المفقودة\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31f22fda-c892-480f-afc7-653f7c331d30",
   "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": [
    "from sklearn.impute import KNNImputer\n",
    "import numpy as np\n",
    "\n",
    "# إنشاء كائن KNN Imputer\n",
    "imputer = KNNImputer(n_neighbors=2)\n",
    "\n",
    "# تطبيق التعويض على البيانات الرقمية فقط\n",
    "df[df.select_dtypes(include=[np.number]).columns] = imputer.fit_transform(df.select_dtypes(include=[np.number]))\n",
    "\n",
    "# التحقق من القيم المفقودة بعد التعويض\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "049591c9-5d13-4c5d-95ed-6fba497f5214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "عدد الصفوف بعد التنظيف: 530\n"
     ]
    }
   ],
   "source": [
    "# حساب نطاق القيم الطبيعية باستخدام IQR\n",
    "Q1 = df['price'].quantile(0.25)\n",
    "Q3 = df['price'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# تحديد الحدود الدنيا والعليا\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# إزالة القيم الشاذة\n",
    "df_cleaned = df[(df['price'] >= lower_bound) & (df['price'] <= upper_bound)]\n",
    "\n",
    "# التحقق من عدد الصفوف بعد إزالة القيم الشاذة\n",
    "print(f\"عدد الصفوف بعد التنظيف: {df_cleaned.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bf30f87-35cf-4a52-99be-b3df618b91bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "عدد الصفوف بعد التنظيف: 539\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# حساب القيم الشاذة باستخدام Z-score\n",
    "z_scores = np.abs(stats.zscore(df['price']))\n",
    "df_cleaned = df[z_scores < 3]  # الاحتفاظ بالقيم التي Z-score لها أقل من 3\n",
    "\n",
    "# عرض عدد القيم بعد الإزالة\n",
    "print(f\"عدد الصفوف بعد التنظيف: {df_cleaned.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bce4d85-f81d-4692-899d-ade2639aeae3",
   "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 seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.boxplot(df_cleaned['price'])\n",
    "plt.title('Boxplot for Price')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6bca5c4-86d2-4f78-9b7a-2483bf92eeb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(df_cleaned['area'], df_cleaned['price'], alpha=0.7, color='green')\n",
    "plt.title('Price vs Area')\n",
    "plt.xlabel('Area')\n",
    "plt.ylabel('Price')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48a7e971-a067-4fd3-8fa5-9e6d04ea0215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.hist(df_cleaned['price'], bins=30, color='skyblue', edgecolor='black')\n",
    "plt.title('Histogram for Price')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b1c8e9-6197-405e-a3ea-f4b59b845cfc",
   "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": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}