{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baa36839-fad6-4b9c-b349-a6649d017c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2fca7b50-17e0-4681-ab0b-a2fa3ed34a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\3423626311.py:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\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>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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the  dataset into a DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "34b02aa4-109b-43f9-b9e0-14c4120aedfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\2162125405.py:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n"
     ]
    },
    {
     "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting features type\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be3c1d06-856e-4c56-ab89-682171dfe126",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\1629810612.py:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()\n",
    "df.dtypes\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d030dea8-732a-4e1b-8acf-358f73a3ae67",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\552475588.py:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.sum of      price   area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0    False  False     False      False    False     False      False   \n",
       "1    False  False     False      False    False     False      False   \n",
       "2    False  False     False      False    False     False      False   \n",
       "3    False  False     False      False    False     False      False   \n",
       "4    False  False     False      False    False     False      False   \n",
       "..     ...    ...       ...        ...      ...       ...        ...   \n",
       "540  False  False     False      False    False     False      False   \n",
       "541  False  False     False      False    False     False      False   \n",
       "542  False  False     False      False    False     False      False   \n",
       "543  False  False     False      False    False     False      False   \n",
       "544  False  False     False      False    False     False      False   \n",
       "\n",
       "     basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0       False            False            False    False     False   \n",
       "1       False            False            False    False     False   \n",
       "2       False            False            False    False     False   \n",
       "3       False            False            False    False     False   \n",
       "4       False            False            False    False     False   \n",
       "..        ...              ...              ...      ...       ...   \n",
       "540     False            False            False    False     False   \n",
       "541     False            False            False    False     False   \n",
       "542     False            False            False    False     False   \n",
       "543     False            False            False    False     False   \n",
       "544     False            False            False    False     False   \n",
       "\n",
       "     furnishingstatus  \n",
       "0               False  \n",
       "1               False  \n",
       "2               False  \n",
       "3               False  \n",
       "4               False  \n",
       "..                ...  \n",
       "540             False  \n",
       "541             False  \n",
       "542             False  \n",
       "543             False  \n",
       "544             False  \n",
       "\n",
       "[545 rows x 13 columns]>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the count of missing values\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()\n",
    "df.dtypes\n",
    "df.describe()\n",
    "df.isnull().sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fd88dd55-5e8b-4ffd-be80-2f9def9b7e99",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns dropped: []\n",
      "Remaining columns: Index(['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'mainroad',\n",
      "       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',\n",
      "       'parking', 'prefarea', 'furnishingstatus'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\2997253621.py:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n"
     ]
    }
   ],
   "source": [
    "#Deleting columns that have 30% missing values\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()\n",
    "df.dtypes\n",
    "df.describe()\n",
    "df.isnull().sum\n",
    "missing_percentage = df.isnull().mean() * 100\n",
    "threshold = 30\n",
    "# Filter columns with more than the threshold percentage of missing values\n",
    "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n",
    "# Drop the columns\n",
    "df = df.drop(columns=columns_to_drop)\n",
    "# Display the resulting dataframe after removing columns with too many missing valu\n",
    "print(f\"Columns dropped: {list(columns_to_drop)}\")\n",
    "print(f\"Remaining columns: {df.columns}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6adcaa6f-cb4c-407c-a1f3-59cdadcb7536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns dropped: []\n",
      "Remaining columns: Index(['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'mainroad',\n",
      "       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',\n",
      "       'parking', 'prefarea', 'furnishingstatus'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\1235802284.py:4: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0  13300000  7420         4          2        3         1          0   \n",
       "1  12250000  8960         4          4        4         1          0   \n",
       "2  12250000  9960         3          2        2         1          0   \n",
       "3  12215000  7500         4          2        2         1          0   \n",
       "4  11410000  7420         4          1        2         1          1   \n",
       "\n",
       "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0         0                0                1        2         1   \n",
       "1         0                0                1        3         0   \n",
       "2         1                0                0        2         1   \n",
       "3         1                0                1        3         1   \n",
       "4         1                0                1        2         0   \n",
       "\n",
       "   furnishingstatus  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pre-processing on dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()\n",
    "df.dtypes\n",
    "df.describe()\n",
    "df.isnull().sum\n",
    "missing_percentage = df.isnull().mean() * 100\n",
    "threshold = 30\n",
    "# Filter columns with more than the threshold percentage of missing values\n",
    "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n",
    "# Drop the columns\n",
    "df = df.drop(columns=columns_to_drop)\n",
    "# Display the resulting dataframe after removing columns with too many missing valu\n",
    "print(f\"Columns dropped: {list(columns_to_drop)}\")\n",
    "print(f\"Remaining columns: {df.columns}\")\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    " df[column] = label_encoder.fit_transform(df[column])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "81f64623-523d-4232-b23a-c5827f95edbe",
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filling missing values based on feature type\n",
    "# Fill missing values in columns with mode\n",
    "for column in df.select_dtypes(include=['float64']).columns:\n",
    " mean_value = df[column].mean() # Get the mode (most frequent value)\n",
    " df[column] = df[column].fillna(mean_value)\n",
    "# Fill missing values in categorical columns with mode\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    " mode_value = df[column].mode()[0] # Get the mode (most frequent value)\n",
    " df[column] = df[column].fillna(mode_value)\n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae2c155b-dfe9-4136-b2e6-96ac1542e2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <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",
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       "      <th>mainroad</th>\n",
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       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
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       "      <td>1750000</td>\n",
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       "      <td>3</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0    13300000  7420         4          2        3         1          0   \n",
       "1    12250000  8960         4          4        4         1          0   \n",
       "2    12250000  9960         3          2        2         1          0   \n",
       "3    12215000  7500         4          2        2         1          0   \n",
       "4    11410000  7420         4          1        2         1          1   \n",
       "..        ...   ...       ...        ...      ...       ...        ...   \n",
       "540   1820000  3000         2          1        1         1          0   \n",
       "541   1767150  2400         3          1        1         0          0   \n",
       "542   1750000  3620         2          1        1         1          0   \n",
       "543   1750000  2910         3          1        1         0          0   \n",
       "544   1750000  3850         3          1        2         1          0   \n",
       "\n",
       "     basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0           0                0                1        2         1   \n",
       "1           0                0                1        3         0   \n",
       "2           1                0                0        2         1   \n",
       "3           1                0                1        3         1   \n",
       "4           1                0                1        2         0   \n",
       "..        ...              ...              ...      ...       ...   \n",
       "540         1                0                0        2         0   \n",
       "541         0                0                0        0         0   \n",
       "542         0                0                0        0         0   \n",
       "543         0                0                0        0         0   \n",
       "544         0                0                0        0         0   \n",
       "\n",
       "     furnishingstatus  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   1  \n",
       "3                   0  \n",
       "4                   0  \n",
       "..                ...  \n",
       "540                 2  \n",
       "541                 1  \n",
       "542                 2  \n",
       "543                 0  \n",
       "544                 2  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8e7721d4-b6b2-4efd-8426-1ce073e072e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of null values in each column:\n",
      "price                7\n",
      "area                 9\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             12\n",
      "prefarea             0\n",
      "furnishingstatus     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Detecting Outliers \n",
    "import numpy as np\n",
    "\n",
    "# Define function to replace outliers with NaN\n",
    "def replace_outliers_with_nan(column):\n",
    "    Q1 = column.quantile(0.25)\n",
    "    Q3 = column.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    # Replace outliers with NaN\n",
    "    return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n",
    "\n",
    "# Replace outliers with NaN for numeric features only\n",
    "for col in df.select_dtypes(include=[np.number]).columns:\n",
    "    df[col] = replace_outliers_with_nan(df[col])\n",
    "\n",
    "# Count of null values after replacing outliers\n",
    "null_counts = df.isnull().sum()\n",
    "print(\"Sum of null values in each column:\")\n",
    "print(null_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2913fbb-6d29-4183-8101-96d57c5f3506",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a85e408e-5d8f-44f2-8b49-95c3dd827476",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:7: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:7: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\852615607.py:7: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Visulization\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "\n",
    "# Plot the histogram and KDE\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(df['price'], bins=15, kde=True, color='blue')\n",
    "plt.title('Price Distribution')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "205781a1-2041-471d-a2f7-1924c2ee597c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\1697839882.py:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\1697839882.py:10: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(x='furnishingstatus', data=df, palette='Set2')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "\n",
    "# Plot the countplot for the 'furnishingstatus' column\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='furnishingstatus', data=df, palette='Set2')\n",
    "plt.title('Furnishing Status Distribution')\n",
    "plt.xlabel('Furnishing Status')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "81557685-a204-4760-bc98-c739fd78c661",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\597547197.py:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "\n",
    "# Plot the scatterplot for the relationship between 'area' and 'bedrooms'\n",
    "# The hue will classify the data based on 'furnishingstatus'\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='area', y='bedrooms', data=df, hue='furnishingstatus', palette='Set1')\n",
    "plt.title('Area vs Bedrooms by Furnishing Status')\n",
    "plt.xlabel('Area')\n",
    "plt.ylabel('Bedrooms')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "504b53d7-0251-473e-8edf-cca4f1aca86b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\1196817829.py:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "\n",
    "# Scatter plot for 'Area' vs 'Price' and classify by 'furnishingstatus'\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='area', y='price', data=df, hue='furnishingstatus', palette='Set2')\n",
    "plt.title('Area vs Price by Furnishing Status')\n",
    "plt.xlabel('Area')\n",
    "plt.ylabel('Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "089a08b9-be57-4435-ab60-5ba7999d3455",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "<>:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\1046563554.py:6: SyntaxWarning: invalid escape sequence '\\A'\n",
      "  df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
      "C:\\Users\\mohamed\\AppData\\Local\\Temp\\ipykernel_23692\\1046563554.py:10: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x='furnishingstatus', y='price', data=df, palette='Set1')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# Load the dataset\n",
    "df = pd.read_csv('C:\\Assignment\\Housing.csv')\n",
    "\n",
    "# Boxplot for 'Price' by 'Furnishing Status'\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x='furnishingstatus', y='price', data=df, palette='Set1')\n",
    "plt.title('Price Distribution by Furnishing Status')\n",
    "plt.xlabel('Furnishing Status')\n",
    "plt.ylabel('Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "847805dd-dc5f-41bc-97ea-51b934ea8fd3",
   "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
}