EDA-housing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e89dd3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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_lac</th>\n",
       "      <th>area_sqft</th>\n",
       "      <th>bhk</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>balconies</th>\n",
       "      <th>floors</th>\n",
       "      <th>furnishing_type</th>\n",
       "      <th>location</th>\n",
       "      <th>property_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Brahmavar</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Doddanagudde</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Doddanagudde</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Semi furnished</td>\n",
       "      <td>Indrali</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Semi furnished</td>\n",
       "      <td>Indrali</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Indrali</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Indrali</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>Indrali</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Semi furnished</td>\n",
       "      <td>Indrali</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Semi furnished</td>\n",
       "      <td>Indrali</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price_lac  area_sqft  bhk  bathrooms  balconies  floors furnishing_type  \\\n",
       "0       20.0      560.0    1        2.0          1       6     Unfurnished   \n",
       "1       20.0      600.0    1        1.0          1       4     Unfurnished   \n",
       "2       20.0      600.0    1        1.0          1       4     Unfurnished   \n",
       "3       12.0      605.0    2        2.0          1       4  Semi furnished   \n",
       "4       15.0      605.0    2        2.0          1       4  Semi furnished   \n",
       "5       19.0      605.0    2        2.0          0       1     Unfurnished   \n",
       "6       17.0      612.0    2        NaN          0       3             NaN   \n",
       "7       16.0      620.0    2        2.0          0       1       Furnished   \n",
       "8       12.0      650.0    2        2.0          1       4  Semi furnished   \n",
       "9       20.0      650.0    2        2.0          1       4  Semi furnished   \n",
       "\n",
       "       location property_type  \n",
       "0     Brahmavar     Apartment  \n",
       "1  Doddanagudde     Apartment  \n",
       "2  Doddanagudde     Apartment  \n",
       "3       Indrali     Apartment  \n",
       "4       Indrali     Apartment  \n",
       "5       Indrali     Apartment  \n",
       "6       Indrali     Apartment  \n",
       "7       Indrali     Apartment  \n",
       "8       Indrali     Apartment  \n",
       "9       Indrali     Apartment  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#Loading our dataset using read_csv function of pandas\n",
    "df = pd.read_csv('Housing_Data.csv')\n",
    "#Inspecting the first 10 rows of the dataset\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0c82d480",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price_lac          0\n",
      "area_sqft          2\n",
      "bhk                0\n",
      "bathrooms          3\n",
      "balconies          0\n",
      "floors             0\n",
      "furnishing_type    2\n",
      "location           0\n",
      "property_type      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fc7766d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We replace missing values of bathrooms column with mode as it is a categorical variable\n",
    "df['bathrooms'].fillna(df.bathrooms.mode()[0],inplace=True)\n",
    "#We replace missing values of area_sqft column with mode as it is a numerical variable\n",
    "df['area_sqft'].fillna(df.area_sqft.mean(),inplace=True)\n",
    "#We replace missing values of furnishing_type column with mode as it is a categorical variable\n",
    "df['furnishing_type'].fillna(df.furnishing_type.mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7b81609d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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_lac</th>\n",
       "      <th>area_sqft</th>\n",
       "      <th>bhk</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>balconies</th>\n",
       "      <th>floors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.486277</td>\n",
       "      <td>1908.065217</td>\n",
       "      <td>2.446809</td>\n",
       "      <td>2.244681</td>\n",
       "      <td>1.127660</td>\n",
       "      <td>3.882979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.841617</td>\n",
       "      <td>1467.322738</td>\n",
       "      <td>0.862816</td>\n",
       "      <td>0.799007</td>\n",
       "      <td>0.870472</td>\n",
       "      <td>2.816451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>2344.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>8720.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price_lac    area_sqft        bhk  bathrooms  balconies     floors\n",
       "count   94.000000    94.000000  94.000000  94.000000  94.000000  94.000000\n",
       "mean    56.486277  1908.065217   2.446809   2.244681   1.127660   3.882979\n",
       "std     38.841617  1467.322738   0.862816   0.799007   0.870472   2.816451\n",
       "min     12.000000   560.000000   1.000000   1.000000   0.000000   1.000000\n",
       "25%     31.000000  1000.000000   2.000000   2.000000   0.000000   2.000000\n",
       "50%     48.000000  1460.000000   2.000000   2.000000   1.000000   3.000000\n",
       "75%     63.000000  2344.500000   3.000000   2.000000   2.000000   5.000000\n",
       "max    180.000000  8720.000000   5.000000   5.000000   3.000000  12.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Function to describe the dataset with basic summary statistics\n",
    "df.describe()\n",
    "#To include categorical variables in the output \n",
    "#df.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "17db44cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.486276595744684\n"
     ]
    }
   ],
   "source": [
    "#MEAN - The average value of the variable, calculated as the sum of all values divided by the number of values\n",
    "mean_price = df.price_lac.mean()\n",
    "print(mean_price)\n",
    "#Output = 56.48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "68f80a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.0\n"
     ]
    }
   ],
   "source": [
    "#MODE - The middle value of the variable when sorted\n",
    "mode_price = df.price_lac.mode()[0]\n",
    "print(mode_price)\n",
    "#Output = 55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "70d82a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48.0\n"
     ]
    }
   ],
   "source": [
    "#MEDIAN - The most frequent value of the variable.\n",
    "median_price = df.price_lac.median()\n",
    "print(median_price)\n",
    "#Output = 48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "52fe2987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "168.0\n"
     ]
    }
   ],
   "source": [
    "#RANGE - The difference between the maximum and minimum values of a variable\n",
    "range_price = df.price_lac.max() - df.price_lac.min()\n",
    "print(range_price)\n",
    "#Output = 81000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b20353e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.0\n"
     ]
    }
   ],
   "source": [
    "#InterQuartile Range(IQR) - The difference between the 75th and 25th percentile values.\n",
    "#We import iqr function from scipy stats to calculate iqr value for the variable\n",
    "from scipy.stats import iqr\n",
    "iqr_price = iqr(df.price_lac)\n",
    "print(iqr_price)\n",
    "#Output = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7afed792",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1508.6712343742852\n"
     ]
    }
   ],
   "source": [
    "#Variance - The average of the squared distance from each data point to the mean\n",
    "var_price_lac = df.price_lac.var()\n",
    "print(var_price_lac)\n",
    "#Output = 1508.67"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "15b6c402",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.84161729864354\n"
     ]
    }
   ],
   "source": [
    "#Standard deviation - Square root of variance\n",
    "stddev_price_lac = df.price_lac.std()\n",
    "print(stddev_price_lac)\n",
    "#Output = 38.84"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8974c87e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.448225441376202\n"
     ]
    }
   ],
   "source": [
    "#Mean absolute deviation (MAD) - The mean absolute value of the distance between each data point and the mean.\n",
    "mad_price_lac = df.price_lac.mad()\n",
    "print(mad_price_lac)\n",
    "#Output = 27.44"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5ef80d55",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Histogram\n",
    "from matplotlib.pyplot import figure\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#Setting figure dimensions\n",
    "figure(figsize=(6, 4), dpi=400)\n",
    "#Plotting histogram\n",
    "sns.histplot(x = 'price_lac', data = df, color = 'Blue')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c07cbcf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Boxplot\n",
    "from matplotlib.pyplot import figure\n",
    "import seaborn as sns\n",
    "#Setting color palette\n",
    "sns.set_palette(\"BuPu\")\n",
    "#Setting figure dimensions\n",
    "figure(figsize=(6, 4), dpi=200)\n",
    "#Plotting boxplot\n",
    "sns.boxplot(x = 'price_lac', data = df)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5ebe7c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Apartment'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.property_type.mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5045a437",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apartment            55\n",
      "Independent house    37\n",
      "Builder floor         2\n",
      "Name: property_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "count_propty_type = df.property_type.value_counts()\n",
    "print(count_propty_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7c9a1474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apartment            0.585106\n",
      "Independent house    0.393617\n",
      "Builder floor        0.021277\n",
      "Name: property_type, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "prop_propty_type = df.property_type.value_counts(normalize = True)\n",
    "print(prop_propty_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "eb48b1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "import seaborn as sns\n",
    "figure(figsize=(6, 4), dpi=200)\n",
    "#Setting the color sche,e for the histplot\n",
    "sns.set_palette(\"BuPu_r\")\n",
    "#Calling countplot function from seaborn to visualize frequency of race\n",
    "sns.countplot(data = df, x = 'property_type')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0f68ad8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}