Nairobi House Price Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89ca5d41",
   "metadata": {},
   "source": [
    "In this notebook, I will be tackling a project of creating a nairobi house price prediction model that will help determine the prices of houses in narobi with respect to the various features. This concept that we shall touch on today will be applicable to any house price prediction not just nairobi. I plan to use a very dirty dataset so tha we can get the chance to sharpen our logic senses, apply then concepts of mathematical calculus and statistics and also know how to tackle such a data in any other occurrence. I hope you enjoy. Lets do some coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e024203",
   "metadata": {},
   "outputs": [],
   "source": [
    "# below are the libraries we shall be using with their uses described.\n",
    "\"\"\"\n",
    "pandas for data manipulation and analysis.\n",
    "numpy for numerical operations.\n",
    "matplotlib.pyplot and seaborn for data visualization.\n",
    "train_test_split from sklearn.model_selection to split the data into training and test sets.\n",
    "LinearRegression from sklearn.linear_model for linear regression (you can use this for comparison with XGBoost).\n",
    "mean_squared_error and r2_score from sklearn.metrics to evaluate the model performance.\n",
    "xgboost for the XGBoost regression model.\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cbea6932",
   "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>title</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>selling price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6 Bed Villa with En Suite at Kiambu Road</td>\n",
       "      <td>Kiambu Road</td>\n",
       "      <td>6bdrm Mansion in Mushroom Estate, Kiambu / Kia...</td>\n",
       "      <td>KSh 130,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 Bed Townhouse with En Suite at Off Kiambu Road</td>\n",
       "      <td>Off Kiambu Road, Kiambu Road</td>\n",
       "      <td>4 Bedroom with DSQ for Sale</td>\n",
       "      <td>KSh 26,950,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4 Bed Villa with En Suite at Masai Lodge Road</td>\n",
       "      <td>masai lodge road, Ongata Rongai</td>\n",
       "      <td>New modern villa for sale.</td>\n",
       "      <td>KSh 16,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5 Bed House with En Suite in Ngong Road</td>\n",
       "      <td>Ngong Road</td>\n",
       "      <td>5 Bedroom Town House For Sale In Kilimani</td>\n",
       "      <td>KSh 45,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 Bed Townhouse with En Suite in Lavington</td>\n",
       "      <td>Lavington</td>\n",
       "      <td>Modern 5 Bedroom Townhouse For Sale</td>\n",
       "      <td>KSh 72,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>5 Bed House with En Suite at Nandi Road</td>\n",
       "      <td>Nandi road, Karen</td>\n",
       "      <td>5 BEDROOM HOUSE FOR SALE IN KAREN NAIROBI KENYA</td>\n",
       "      <td>KSh 199,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>4 Bed House with En Suite in Kitengela</td>\n",
       "      <td>Kitengela</td>\n",
       "      <td>4 Bedroom Maisonette for Sale in Kitengela EPZ...</td>\n",
       "      <td>KSh 16,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>4 Bed House with En Suite in Syokimau</td>\n",
       "      <td>Syokimau</td>\n",
       "      <td>4 BEDROOMS MAISONETTE IN SYOKIMAU.</td>\n",
       "      <td>KSh 13,800,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>4 Bed Townhouse with Garden in Lavington</td>\n",
       "      <td>Lavington</td>\n",
       "      <td>4 bedroom townhouse for sale in Lavington</td>\n",
       "      <td>KSh 79,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>3 Bed House with En Suite at Kimbo Matangi</td>\n",
       "      <td>Kimbo Matangi, Ruiru</td>\n",
       "      <td>READY AFFORDABLE BUNGALOWS ON OFFER</td>\n",
       "      <td>KSh 7,800,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1876 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "0             6 Bed Villa with En Suite at Kiambu Road   \n",
       "1     4 Bed Townhouse with En Suite at Off Kiambu Road   \n",
       "2        4 Bed Villa with En Suite at Masai Lodge Road   \n",
       "3              5 Bed House with En Suite in Ngong Road   \n",
       "4           5 Bed Townhouse with En Suite in Lavington   \n",
       "...                                                ...   \n",
       "1871           5 Bed House with En Suite at Nandi Road   \n",
       "1872            4 Bed House with En Suite in Kitengela   \n",
       "1873             4 Bed House with En Suite in Syokimau   \n",
       "1874          4 Bed Townhouse with Garden in Lavington   \n",
       "1875        3 Bed House with En Suite at Kimbo Matangi   \n",
       "\n",
       "                             location  \\\n",
       "0                         Kiambu Road   \n",
       "1        Off Kiambu Road, Kiambu Road   \n",
       "2     masai lodge road, Ongata Rongai   \n",
       "3                          Ngong Road   \n",
       "4                           Lavington   \n",
       "...                               ...   \n",
       "1871                Nandi road, Karen   \n",
       "1872                        Kitengela   \n",
       "1873                         Syokimau   \n",
       "1874                        Lavington   \n",
       "1875             Kimbo Matangi, Ruiru   \n",
       "\n",
       "                                                   size    selling price  \n",
       "0     6bdrm Mansion in Mushroom Estate, Kiambu / Kia...  KSh 130,000,000  \n",
       "1                           4 Bedroom with DSQ for Sale   KSh 26,950,000  \n",
       "2                            New modern villa for sale.   KSh 16,000,000  \n",
       "3             5 Bedroom Town House For Sale In Kilimani   KSh 45,000,000  \n",
       "4                   Modern 5 Bedroom Townhouse For Sale   KSh 72,000,000  \n",
       "...                                                 ...              ...  \n",
       "1871    5 BEDROOM HOUSE FOR SALE IN KAREN NAIROBI KENYA  KSh 199,000,000  \n",
       "1872  4 Bedroom Maisonette for Sale in Kitengela EPZ...   KSh 16,000,000  \n",
       "1873                 4 BEDROOMS MAISONETTE IN SYOKIMAU.   KSh 13,800,000  \n",
       "1874          4 bedroom townhouse for sale in Lavington   KSh 79,000,000  \n",
       "1875                READY AFFORDABLE BUNGALOWS ON OFFER    KSh 7,800,000  \n",
       "\n",
       "[1876 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"houses-for-sale.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0efe8c7",
   "metadata": {},
   "source": [
    "# Data overview\n",
    "   - Obcervations\n",
    "   - Sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75437c5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title            0\n",
       "location         0\n",
       "size             0\n",
       "selling price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we first confirm for null values and were off for a good start.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "372a527b",
   "metadata": {},
   "source": [
    "Just because there is no null values, this is not proof enough that our dataset is clean and we can start\n",
    "working with it. Use more prompts to check for unique values, duplicates, dtypes, and at times desriptive\n",
    "statustics. This tend to be very insightful from a mathematical and logical point of view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b59d7ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title            object\n",
       "location         object\n",
       "size             object\n",
       "selling price    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42bef2f4",
   "metadata": {},
   "source": [
    "Notice that all, the features contain dtype object. We will change this later on to maje sure we have everything as the appropriate dtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc161bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6 Bed Villa with En Suite at Kiambu Road',\n",
       "       '4 Bed Townhouse with En Suite at Off Kiambu Road',\n",
       "       '4 Bed Villa with En Suite at Masai Lodge Road', ...,\n",
       "       '3 Bed House with En Suite at Kona Ya Musa',\n",
       "       '3 Bed House with En Suite in Kileleshwa',\n",
       "       '5 Bed House with En Suite at Nandi Road'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d750913",
   "metadata": {},
   "source": [
    " You can notice how wranglled the tittle column is. we can can come up with almost more that two new features that can be very important in our analysis. In this case, I shall apply a regex function that willl beak the title column to bera new columns namels; number_of_bedrooms, house_type, location, and amenities. This will become very crucial during model training since they all determine the price of a house hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70eb5f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['KSh 130,000,000', 'KSh 26,950,000', 'KSh 16,000,000',\n",
       "       'KSh 45,000,000', 'KSh 72,000,000', 'KSh 46,000,000',\n",
       "       'KSh 85,000,000', 'KSh 50,000,000', 'KSh 75,000,000',\n",
       "       'KSh 500,000,000', 'KSh 140,000,000', 'KSh 15,000,000',\n",
       "       'KSh 60,000,000', 'KSh 55,000,000', 'Price not communicated',\n",
       "       'KSh 427,000,000', 'KSh 65,000,000', 'KSh 48,000,000',\n",
       "       'KSh 88,000,000', 'KSh 27,000,000', 'KSh 150,000,000',\n",
       "       'KSh 120,000,000', 'KSh 110,000,000', 'KSh 90,000,000',\n",
       "       'KSh 35,000,000', 'KSh 59,000,000', 'KSh 95,000,000',\n",
       "       'KSh 24,000,000', 'KSh 20,000,000', 'KSh 23,000,000',\n",
       "       'KSh 115,000,000', 'KSh 38,000,000', 'KSh 16,500,000',\n",
       "       'KSh 32,250,000', 'KSh 39,000,000', 'KSh 9,500,000',\n",
       "       'KSh 28,000,000', 'KSh 8,950,000', 'KSh 50,600,000',\n",
       "       'KSh 70,250,000', 'KSh 19,000,000', 'KSh 155,000,000',\n",
       "       'KSh 180,000,000', 'KSh 38,500,000', 'KSh 5,300,000',\n",
       "       'KSh 8,500,000', 'KSh 29,999,000', 'KSh 25,000,000',\n",
       "       'KSh 72,499,999', 'KSh 7,200,000', 'KSh 6,500,000',\n",
       "       'KSh 26,500,000', 'KSh 17,000,000', 'KSh 64,990,000',\n",
       "       'KSh 70,000,000', 'KSh 30,500,000', 'KSh 100,000,000',\n",
       "       'KSh 29,500,000', 'KSh 15,500,000', 'KSh 14,500,000',\n",
       "       'KSh 7,500,000', 'KSh 31,500,000', 'KSh 16,200,000',\n",
       "       'KSh 6,800,000', 'KSh 11,500,000', 'KSh 295,000,000',\n",
       "       'KSh 10,300,000', 'KSh 14,000,000', 'KSh 22,500,000',\n",
       "       'KSh 121,000,000', 'KSh 8,850,000', 'KSh 170,000,000',\n",
       "       'KSh 99,000,000', 'KSh 7,600,000', 'KSh 83,000,000',\n",
       "       'KSh 12,500,000', 'KSh 31,000,000', 'KSh 7,700,000',\n",
       "       'KSh 40,000,000', 'KSh 18,500,000', 'KSh 19,500,000',\n",
       "       'KSh 74,000,000', 'KSh 69,000,000', 'KSh 160,000,000',\n",
       "       'KSh 145,000,000', 'KSh 34,000,000', 'KSh 13,000,000',\n",
       "       'KSh 10,500,000', 'KSh 21,000,000', 'KSh 17,500,000',\n",
       "       'KSh 30,000,000', 'KSh 12,600,000', 'KSh 13,500,000',\n",
       "       'KSh 64,000,000', 'KSh 16,499,000', 'KSh 56,000,000',\n",
       "       'KSh 350,000,000', 'KSh 11,950,000', 'KSh 6,100,000',\n",
       "       'KSh 12,100,000', 'KSh 26,000,000', 'KSh 11,000,000',\n",
       "       'KSh 300,000,000', 'KSh 125,000,000', 'KSh 68,000,000',\n",
       "       'KSh 14,200,000', 'KSh 6,750,000', 'KSh 63,000,000',\n",
       "       'KSh 22,000,000', 'KSh 66,000,000', 'KSh 6,000,000',\n",
       "       'KSh 650,000,000', 'KSh 29,000,000', 'KSh 105,000,000',\n",
       "       'KSh 135,000,000', 'KSh 200,000,000', 'KSh 12,000,000',\n",
       "       'KSh 250,000,000', 'KSh 58,000,000', 'KSh 4,500,000',\n",
       "       'KSh 80,000,000', 'KSh 5,700,000', 'KSh 33,000,000',\n",
       "       'KSh 67,000,000', 'KSh 27,500,000', 'KSh 67,500,000',\n",
       "       'KSh 7,800,000', 'KSh 25,945,000', 'KSh 37,000,000',\n",
       "       'KSh 43,000,000', 'KSh 61,950,000', 'KSh 24,500,000',\n",
       "       'KSh 87,000,000', 'KSh 128,000,000', 'KSh 37,500,000',\n",
       "       'KSh 6,300,000', 'KSh 7,000,000', 'KSh 8,000,000', 'KSh 5,800,000',\n",
       "       'KSh 78,000,000', 'KSh 13,800,000', 'KSh 220,000,000',\n",
       "       'KSh 19,250,000', 'KSh 8,150,000', 'KSh 165,000,000',\n",
       "       'KSh 12,300,000', 'KSh 14,750,000', 'KSh 29,990,000',\n",
       "       'KSh 41,000,000', 'KSh 40,750,000', 'KSh 36,000,000',\n",
       "       'KSh 10,250,000', 'KSh 32,000,000', 'KSh 7,250,000',\n",
       "       'KSh 10,000,000', 'KSh 28,500,000', 'KSh 32,500,000',\n",
       "       'KSh 190,000,000', 'KSh 5,750,000', 'KSh 97,000,000',\n",
       "       'KSh 18,000,000', 'KSh 230,000,000', 'KSh 6,700,000',\n",
       "       'KSh 9,000,000', 'KSh 20,500,000', 'KSh 6,200,000',\n",
       "       'KSh 6,400,000', 'KSh 14,800,000', 'KSh 7,400,000',\n",
       "       'KSh 11,300,000', 'KSh 52,000,000', 'KSh 39,500,000',\n",
       "       'KSh 42,000,000', 'KSh 185,000,000', 'KSh 50,950,000',\n",
       "       'KSh 7,900,000', 'KSh 5,850,000', 'KSh 400,000,000',\n",
       "       'KSh 235,000,000', 'KSh 6,950,000', 'KSh 46,750,000',\n",
       "       'KSh 108,500,000', 'KSh 225,000,000', 'KSh 280,000,000',\n",
       "       'KSh 240,000,000', 'KSh 9,980,000', 'KSh 12,800,000',\n",
       "       'KSh 6,250,000', 'KSh 86,000,000', 'KSh 40,500,000',\n",
       "       'KSh 35,800,000', 'KSh 35,500,000', 'KSh 29,900,000',\n",
       "       'KSh 19,950,000', 'KSh 47,000,000', 'KSh 111,000,000',\n",
       "       'KSh 2,700,000', 'KSh 92,000,000', 'KSh 3,700,000',\n",
       "       'KSh 5,500,000', 'KSh 82,000,000', 'KSh 8,800,000',\n",
       "       'KSh 59,500,000', 'KSh 11,800,000', 'KSh 87,500,000',\n",
       "       'KSh 210,000,000', 'KSh 11,250,000', 'KSh 158,145,000',\n",
       "       'KSh 97,500,000', 'KSh 36,500,000', 'KSh 290,000,000',\n",
       "       'KSh 49,000,000', 'KSh 107,000,000', 'KSh 16,850,000',\n",
       "       'KSh 62,000,000', 'KSh 7,950,000', 'KSh 21,817,000',\n",
       "       'KSh 270,000,000', 'KSh 25,500,000', 'KSh 123,000,000',\n",
       "       'KSh 20,950,000', 'KSh 600,000,000', 'KSh 245,403,755',\n",
       "       'KSh 48,450,000', 'KSh 69,500,000', 'KSh 81,420,000',\n",
       "       'KSh 15,800,000', 'KSh 29,980,000', 'KSh 9,950,000',\n",
       "       'KSh 61,000,000', 'KSh 13,100,000', 'KSh 118,500,000',\n",
       "       'KSh 10,950,000', 'KSh 4,600,000', 'KSh 63,950,000',\n",
       "       'KSh 550,000,000', 'KSh 19,700,000', 'KSh 49,500,000',\n",
       "       'KSh 299,700,000', 'KSh 28,250,000', 'KSh 260,000,000',\n",
       "       'KSh 7,300,000', 'KSh 43,580,000', 'KSh 34,450,000',\n",
       "       'KSh 47,500,000', 'KSh 68,500,000', 'KSh 9,250,000',\n",
       "       'KSh 340,000,000', 'KSh 480,000,000', 'KSh 8,600,000',\n",
       "       'KSh 14,150,000', 'KSh 684,000,000', 'KSh 34,400,000',\n",
       "       'KSh 15,900,000', 'KSh 275,000,000', 'KSh 29,700,000',\n",
       "       'KSh 171,375,000', 'KSh 133,000,000', 'KSh 21,950,000',\n",
       "       'KSh 118,000,000', 'KSh 57,000,000', 'KSh 11,700,000',\n",
       "       'KSh 98,000,000', 'KSh 13,600,000', 'KSh 12,700,000',\n",
       "       'KSh 53,000,000', 'KSh 220,863,379', 'KSh 10,900,000',\n",
       "       'KSh 5,990,000', 'KSh 17,800,000', 'KSh 4,800,000',\n",
       "       'KSh 45,800,000', 'KSh 5,250,000', 'KSh 4,450,000',\n",
       "       'KSh 8,700,000', 'KSh 648,000,000', 'KSh 26,800,000',\n",
       "       'KSh 20,295,000', 'KSh 11,980,000', 'KSh 29,400,000',\n",
       "       'KSh 138,000,000', 'KSh 12,980,000', 'KSh 132,000,000',\n",
       "       'KSh 211,840,000', 'KSh 42,500,000', 'KSh 54,000,000',\n",
       "       'KSh 21,500,000', 'KSh 23,500,000', 'KSh 29,850,000',\n",
       "       'KSh 10,050,000', 'KSh 7,499,000', 'KSh 199,000,000',\n",
       "       'KSh 79,000,000'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['selling price'].unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4802b34",
   "metadata": {},
   "source": [
    "This column selling price is also a bit mixed. We have some of the readings as \"Price not communicated\" thus we need to come up with a strategy to handle this. Also later on, we shall remove the prefix \"Ksh\" which states the currency type to be left with only integers. Buckle up, this shall be a train ride."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c12c426",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "   - Missing value treatment (if needed)\n",
    "   - Feature engineering\n",
    "   - Outlier detection and treatment (if needed)\n",
    "   - Preparing data for modeling\n",
    "   - Any other preprocessing steps (if needed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed31bdc",
   "metadata": {},
   "source": [
    "Creating a copy of the dataset and working on the copy is a trend that not most people use. I find this trend very useful especially if we are working on a very crucial dataset. We might make a mistake, and this might cost us alot if we are working on the original piece. \n",
    "Work on the duplicate and once everything looks ood, you can choose to push the changes to the original dataset. This is just my view though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31426646",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create copy of dataset\n",
    "df1= df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef4df104",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# Define a function to extract property type, number of bedrooms, and amenities from the title\n",
    "def extract_info(title):\n",
    "    # Extract the number of bedrooms\n",
    "    bedrooms = re.search('(\\d+) Bed', title)\n",
    "    if bedrooms:\n",
    "        bedrooms = bedrooms.group(1)\n",
    "    else:\n",
    "        bedrooms = 'Unknown'\n",
    "    \n",
    "    # Extract property type\n",
    "    property_type = re.search('([a-zA-Z]+) with', title)\n",
    "    if property_type:\n",
    "        property_type = property_type.group(1)\n",
    "    else:\n",
    "        property_type = 'Unknown'\n",
    "    \n",
    "    # Extract amenities\n",
    "    amenities = []\n",
    "    for amenity in ['En Suite', 'Swimming Pool', 'Gym', 'Garden', 'Backup Generator', 'DSQ']:\n",
    "        if amenity.lower() in title.lower():\n",
    "            amenities.append(amenity)\n",
    "    amenities_str = ', '.join(amenities) if amenities else 'Unknown'\n",
    "    \n",
    "    return bedrooms, property_type, amenities_str\n",
    "\n",
    "# Apply the function to the 'title' column\n",
    "df1[['bedrooms', 'property_type', 'amenities']] = df1['title'].apply(lambda x: pd.Series(extract_info(x)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3f36ae5",
   "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>location</th>\n",
       "      <th>selling price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>property_type</th>\n",
       "      <th>amenities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kiambu Road</td>\n",
       "      <td>KSh 130,000,000</td>\n",
       "      <td>6</td>\n",
       "      <td>Villa</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Off Kiambu Road, Kiambu Road</td>\n",
       "      <td>KSh 26,950,000</td>\n",
       "      <td>4</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>masai lodge road, Ongata Rongai</td>\n",
       "      <td>KSh 16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>Villa</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ngong Road</td>\n",
       "      <td>KSh 45,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lavington</td>\n",
       "      <td>KSh 72,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>Nandi road, Karen</td>\n",
       "      <td>KSh 199,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>Kitengela</td>\n",
       "      <td>KSh 16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>Syokimau</td>\n",
       "      <td>KSh 13,800,000</td>\n",
       "      <td>4</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>Lavington</td>\n",
       "      <td>KSh 79,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>Garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Kimbo Matangi, Ruiru</td>\n",
       "      <td>KSh 7,800,000</td>\n",
       "      <td>3</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1876 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             location    selling price bedrooms property_type  \\\n",
       "0                         Kiambu Road  KSh 130,000,000        6         Villa   \n",
       "1        Off Kiambu Road, Kiambu Road   KSh 26,950,000        4     Townhouse   \n",
       "2     masai lodge road, Ongata Rongai   KSh 16,000,000        4         Villa   \n",
       "3                          Ngong Road   KSh 45,000,000        5         House   \n",
       "4                           Lavington   KSh 72,000,000        5     Townhouse   \n",
       "...                               ...              ...      ...           ...   \n",
       "1871                Nandi road, Karen  KSh 199,000,000        5         House   \n",
       "1872                        Kitengela   KSh 16,000,000        4         House   \n",
       "1873                         Syokimau   KSh 13,800,000        4         House   \n",
       "1874                        Lavington   KSh 79,000,000        4     Townhouse   \n",
       "1875             Kimbo Matangi, Ruiru    KSh 7,800,000        3         House   \n",
       "\n",
       "     amenities  \n",
       "0     En Suite  \n",
       "1     En Suite  \n",
       "2     En Suite  \n",
       "3     En Suite  \n",
       "4     En Suite  \n",
       "...        ...  \n",
       "1871  En Suite  \n",
       "1872  En Suite  \n",
       "1873  En Suite  \n",
       "1874    Garden  \n",
       "1875  En Suite  \n",
       "\n",
       "[1876 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first few rows of the DataFrame\n",
    "df1 = df1.drop(['title', 'size'], axis = 1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f443fe9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>location</th>\n",
       "      <th>selling price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>property_type</th>\n",
       "      <th>amenities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kiambu Road</td>\n",
       "      <td>130,000,000</td>\n",
       "      <td>6</td>\n",
       "      <td>Villa</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Off Kiambu Road, Kiambu Road</td>\n",
       "      <td>26,950,000</td>\n",
       "      <td>4</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>masai lodge road, Ongata Rongai</td>\n",
       "      <td>16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>Villa</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ngong Road</td>\n",
       "      <td>45,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lavington</td>\n",
       "      <td>72,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>Nandi road, Karen</td>\n",
       "      <td>199,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>Kitengela</td>\n",
       "      <td>16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>Syokimau</td>\n",
       "      <td>13,800,000</td>\n",
       "      <td>4</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>Lavington</td>\n",
       "      <td>79,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>Garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Kimbo Matangi, Ruiru</td>\n",
       "      <td>7,800,000</td>\n",
       "      <td>3</td>\n",
       "      <td>House</td>\n",
       "      <td>En Suite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1876 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             location selling price bedrooms property_type  \\\n",
       "0                         Kiambu Road   130,000,000        6         Villa   \n",
       "1        Off Kiambu Road, Kiambu Road    26,950,000        4     Townhouse   \n",
       "2     masai lodge road, Ongata Rongai    16,000,000        4         Villa   \n",
       "3                          Ngong Road    45,000,000        5         House   \n",
       "4                           Lavington    72,000,000        5     Townhouse   \n",
       "...                               ...           ...      ...           ...   \n",
       "1871                Nandi road, Karen   199,000,000        5         House   \n",
       "1872                        Kitengela    16,000,000        4         House   \n",
       "1873                         Syokimau    13,800,000        4         House   \n",
       "1874                        Lavington    79,000,000        4     Townhouse   \n",
       "1875             Kimbo Matangi, Ruiru     7,800,000        3         House   \n",
       "\n",
       "     amenities  \n",
       "0     En Suite  \n",
       "1     En Suite  \n",
       "2     En Suite  \n",
       "3     En Suite  \n",
       "4     En Suite  \n",
       "...        ...  \n",
       "1871  En Suite  \n",
       "1872  En Suite  \n",
       "1873  En Suite  \n",
       "1874    Garden  \n",
       "1875  En Suite  \n",
       "\n",
       "[1876 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove 'KSh' prefix from 'selling_price'\n",
    "df1['selling price'] = df1['selling price'].str.replace('KSh ', '')\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a67d595",
   "metadata": {},
   "source": [
    "We shall do stanerdization of capitalization to our feilds location, property_type and amenities.\n",
    "\n",
    "Consistency: It helps maintain consistency in the dataset or document, making it easier to read and analyze.\n",
    "\n",
    "Analysis: Standardization can make it easier to perform text analysis, such as searching for specific terms or conducting natural language processing tasks.\n",
    "\n",
    "Comparison: It enables easier comparison between texts, as differences in capitalization won't affect the comparison.\n",
    "\n",
    "Readability: Consistent capitalization can improve the overall readability of the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "adfdfd36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>location</th>\n",
       "      <th>selling price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>property_type</th>\n",
       "      <th>amenities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kiambu road</td>\n",
       "      <td>130,000,000</td>\n",
       "      <td>6</td>\n",
       "      <td>villa</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>off kiambu road, kiambu road</td>\n",
       "      <td>26,950,000</td>\n",
       "      <td>4</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>masai lodge road, ongata rongai</td>\n",
       "      <td>16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>villa</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ngong road</td>\n",
       "      <td>45,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lavington</td>\n",
       "      <td>72,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>nandi road, karen</td>\n",
       "      <td>199,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>kitengela</td>\n",
       "      <td>16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>syokimau</td>\n",
       "      <td>13,800,000</td>\n",
       "      <td>4</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>lavington</td>\n",
       "      <td>79,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>kimbo matangi, ruiru</td>\n",
       "      <td>7,800,000</td>\n",
       "      <td>3</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1876 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             location selling price bedrooms property_type  \\\n",
       "0                         kiambu road   130,000,000        6         villa   \n",
       "1        off kiambu road, kiambu road    26,950,000        4     townhouse   \n",
       "2     masai lodge road, ongata rongai    16,000,000        4         villa   \n",
       "3                          ngong road    45,000,000        5         house   \n",
       "4                           lavington    72,000,000        5     townhouse   \n",
       "...                               ...           ...      ...           ...   \n",
       "1871                nandi road, karen   199,000,000        5         house   \n",
       "1872                        kitengela    16,000,000        4         house   \n",
       "1873                         syokimau    13,800,000        4         house   \n",
       "1874                        lavington    79,000,000        4     townhouse   \n",
       "1875             kimbo matangi, ruiru     7,800,000        3         house   \n",
       "\n",
       "     amenities  \n",
       "0     en suite  \n",
       "1     en suite  \n",
       "2     en suite  \n",
       "3     en suite  \n",
       "4     en suite  \n",
       "...        ...  \n",
       "1871  en suite  \n",
       "1872  en suite  \n",
       "1873  en suite  \n",
       "1874    garden  \n",
       "1875  en suite  \n",
       "\n",
       "[1876 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the 'location' column to lowercase\n",
    "df1['location'] = df1['location'].str.lower()\n",
    "df1['property_type'] = df1['property_type'].str.lower()\n",
    "df1['amenities'] = df1['amenities'].str.lower()\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d219ba38",
   "metadata": {},
   "source": [
    "Remember when we were checking through our data for anomalies, we did notice that the selling price column had records where the prices were not mentioned and noted as \"Price not communicated\". There was\n",
    "several ways to deal with this where one bwas to delete the records and work with the rest. Since i did not want to deleta any records from my data set, I had to amputate these records with the average values af houses that are located in the same location, and have a chance of having almost the same no. of rooms. This was quite hectic process since I had ri group the data into location and no.of  bedrooms then find the average, then create a new df where i could fix the mising records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c128a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the original DataFrame to work with\n",
    "df2 = df1.copy()\n",
    "\n",
    "# Remove commas from 'selling price' and convert to numeric, ignoring errors to handle 'Price not communicated'\n",
    "df2['selling price'] = pd.to_numeric(df2['selling price'].str.replace(',', ''), errors='coerce')\n",
    "\n",
    "# Group by 'location' and 'bedrooms', calculate the mean selling price\n",
    "location_bedrooms_means = df2.groupby(['location', 'bedrooms'])['selling price'].mean()\n",
    "\n",
    "# Fill missing 'selling_price' values based on the calculated means\n",
    "for index, row in df2.iterrows():\n",
    "    if pd.isnull(row['selling price']):  # Check if the selling price is missing\n",
    "        location = row['location']\n",
    "        bedrooms = row['bedrooms']\n",
    "        # Use the mean for the location and closest number of bedrooms\n",
    "        try:\n",
    "            df2.at[index, 'selling price'] = location_bedrooms_means.loc[location, bedrooms]\n",
    "        except KeyError:\n",
    "            # If the exact number of bedrooms is not found, try the closest one\n",
    "            bedrooms = min(location_bedrooms_means.loc[location].index, key=lambda x: abs(x - bedrooms))\n",
    "            df2.at[index, 'selling price'] = location_bedrooms_means.loc[location, bedrooms]\n",
    "\n",
    "# Convert 'selling_price' back to string format\n",
    "df2['selling price'] = df2['selling price'].apply(lambda x: f\"KSh {x:,.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2535c14c",
   "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>location</th>\n",
       "      <th>selling price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>property_type</th>\n",
       "      <th>amenities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kiambu road</td>\n",
       "      <td>KSh 130,000,000.00</td>\n",
       "      <td>6</td>\n",
       "      <td>villa</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>off kiambu road, kiambu road</td>\n",
       "      <td>KSh 26,950,000.00</td>\n",
       "      <td>4</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>masai lodge road, ongata rongai</td>\n",
       "      <td>KSh 16,000,000.00</td>\n",
       "      <td>4</td>\n",
       "      <td>villa</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ngong road</td>\n",
       "      <td>KSh 45,000,000.00</td>\n",
       "      <td>5</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lavington</td>\n",
       "      <td>KSh 72,000,000.00</td>\n",
       "      <td>5</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>nandi road, karen</td>\n",
       "      <td>KSh 199,000,000.00</td>\n",
       "      <td>5</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>kitengela</td>\n",
       "      <td>KSh 16,000,000.00</td>\n",
       "      <td>4</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>syokimau</td>\n",
       "      <td>KSh 13,800,000.00</td>\n",
       "      <td>4</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>lavington</td>\n",
       "      <td>KSh 79,000,000.00</td>\n",
       "      <td>4</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>kimbo matangi, ruiru</td>\n",
       "      <td>KSh 7,800,000.00</td>\n",
       "      <td>3</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1876 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             location       selling price bedrooms  \\\n",
       "0                         kiambu road  KSh 130,000,000.00        6   \n",
       "1        off kiambu road, kiambu road   KSh 26,950,000.00        4   \n",
       "2     masai lodge road, ongata rongai   KSh 16,000,000.00        4   \n",
       "3                          ngong road   KSh 45,000,000.00        5   \n",
       "4                           lavington   KSh 72,000,000.00        5   \n",
       "...                               ...                 ...      ...   \n",
       "1871                nandi road, karen  KSh 199,000,000.00        5   \n",
       "1872                        kitengela   KSh 16,000,000.00        4   \n",
       "1873                         syokimau   KSh 13,800,000.00        4   \n",
       "1874                        lavington   KSh 79,000,000.00        4   \n",
       "1875             kimbo matangi, ruiru    KSh 7,800,000.00        3   \n",
       "\n",
       "     property_type amenities  \n",
       "0            villa  en suite  \n",
       "1        townhouse  en suite  \n",
       "2            villa  en suite  \n",
       "3            house  en suite  \n",
       "4        townhouse  en suite  \n",
       "...            ...       ...  \n",
       "1871         house  en suite  \n",
       "1872         house  en suite  \n",
       "1873         house  en suite  \n",
       "1874     townhouse    garden  \n",
       "1875         house  en suite  \n",
       "\n",
       "[1876 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the df\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "10133d83",
   "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>location</th>\n",
       "      <th>selling price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>property_type</th>\n",
       "      <th>amenities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [location, selling price, bedrooms, property_type, amenities]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['selling price'] == 'Price not communicated']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "22dad1cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130000000', '26950000', '16000000', '45000000', '72000000',\n",
       "       '46000000', '85000000', '50000000', '75000000', '500000000',\n",
       "       '140000000', '15000000', '60000000', '55000000',\n",
       "       'Pricenotcommunicated', '427000000', '65000000', '48000000',\n",
       "       '88000000', '27000000', '150000000', '120000000', '110000000',\n",
       "       '90000000', '35000000', '59000000', '95000000', '24000000',\n",
       "       '20000000', '23000000', '115000000', '38000000', '16500000',\n",
       "       '32250000', '39000000', '9500000', '28000000', '8950000',\n",
       "       '50600000', '70250000', '19000000', '155000000', '180000000',\n",
       "       '38500000', '5300000', '8500000', '29999000', '25000000',\n",
       "       '72499999', '7200000', '6500000', '26500000', '17000000',\n",
       "       '64990000', '70000000', '30500000', '100000000', '29500000',\n",
       "       '15500000', '14500000', '7500000', '31500000', '16200000',\n",
       "       '6800000', '11500000', '295000000', '10300000', '14000000',\n",
       "       '22500000', '121000000', '8850000', '170000000', '99000000',\n",
       "       '7600000', '83000000', '12500000', '31000000', '7700000',\n",
       "       '40000000', '18500000', '19500000', '74000000', '69000000',\n",
       "       '160000000', '145000000', '34000000', '13000000', '10500000',\n",
       "       '21000000', '17500000', '30000000', '12600000', '13500000',\n",
       "       '64000000', '16499000', '56000000', '350000000', '11950000',\n",
       "       '6100000', '12100000', '26000000', '11000000', '300000000',\n",
       "       '125000000', '68000000', '14200000', '6750000', '63000000',\n",
       "       '22000000', '66000000', '6000000', '650000000', '29000000',\n",
       "       '105000000', '135000000', '200000000', '12000000', '250000000',\n",
       "       '58000000', '4500000', '80000000', '5700000', '33000000',\n",
       "       '67000000', '27500000', '67500000', '7800000', '25945000',\n",
       "       '37000000', '43000000', '61950000', '24500000', '87000000',\n",
       "       '128000000', '37500000', '6300000', '7000000', '8000000',\n",
       "       '5800000', '78000000', '13800000', '220000000', '19250000',\n",
       "       '8150000', '165000000', '12300000', '14750000', '29990000',\n",
       "       '41000000', '40750000', '36000000', '10250000', '32000000',\n",
       "       '7250000', '10000000', '28500000', '32500000', '190000000',\n",
       "       '5750000', '97000000', '18000000', '230000000', '6700000',\n",
       "       '9000000', '20500000', '6200000', '6400000', '14800000', '7400000',\n",
       "       '11300000', '52000000', '39500000', '42000000', '185000000',\n",
       "       '50950000', '7900000', '5850000', '400000000', '235000000',\n",
       "       '6950000', '46750000', '108500000', '225000000', '280000000',\n",
       "       '240000000', '9980000', '12800000', '6250000', '86000000',\n",
       "       '40500000', '35800000', '35500000', '29900000', '19950000',\n",
       "       '47000000', '111000000', '2700000', '92000000', '3700000',\n",
       "       '5500000', '82000000', '8800000', '59500000', '11800000',\n",
       "       '87500000', '210000000', '11250000', '158145000', '97500000',\n",
       "       '36500000', '290000000', '49000000', '107000000', '16850000',\n",
       "       '62000000', '7950000', '21817000', '270000000', '25500000',\n",
       "       '123000000', '20950000', '600000000', '245403755', '48450000',\n",
       "       '69500000', '81420000', '15800000', '29980000', '9950000',\n",
       "       '61000000', '13100000', '118500000', '10950000', '4600000',\n",
       "       '63950000', '550000000', '19700000', '49500000', '299700000',\n",
       "       '28250000', '260000000', '7300000', '43580000', '34450000',\n",
       "       '47500000', '68500000', '9250000', '340000000', '480000000',\n",
       "       '8600000', '14150000', '684000000', '34400000', '15900000',\n",
       "       '275000000', '29700000', '171375000', '133000000', '21950000',\n",
       "       '118000000', '57000000', '11700000', '98000000', '13600000',\n",
       "       '12700000', '53000000', '220863379', '10900000', '5990000',\n",
       "       '17800000', '4800000', '45800000', '5250000', '4450000', '8700000',\n",
       "       '648000000', '26800000', '20295000', '11980000', '29400000',\n",
       "       '138000000', '12980000', '132000000', '211840000', '42500000',\n",
       "       '54000000', '21500000', '23500000', '29850000', '10050000',\n",
       "       '7499000', '199000000', '79000000'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['selling price'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e7b1c9de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2[df2['bedrooms'] != 'Unknown']\n",
    "df2 = df2[df2['selling price'] != 'Pricenotcommunicated']\n",
    "df2 = df2[df2['selling price'] != '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1ff6f671",
   "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>location</th>\n",
       "      <th>selling price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>property_type</th>\n",
       "      <th>amenities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kiambu road</td>\n",
       "      <td>130,000,000</td>\n",
       "      <td>6</td>\n",
       "      <td>villa</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>off kiambu road, kiambu road</td>\n",
       "      <td>26,950,000</td>\n",
       "      <td>4</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>masai lodge road, ongata rongai</td>\n",
       "      <td>16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>villa</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ngong road</td>\n",
       "      <td>45,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lavington</td>\n",
       "      <td>72,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>nandi road, karen</td>\n",
       "      <td>199,000,000</td>\n",
       "      <td>5</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>kitengela</td>\n",
       "      <td>16,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>syokimau</td>\n",
       "      <td>13,800,000</td>\n",
       "      <td>4</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>lavington</td>\n",
       "      <td>79,000,000</td>\n",
       "      <td>4</td>\n",
       "      <td>townhouse</td>\n",
       "      <td>garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>kimbo matangi, ruiru</td>\n",
       "      <td>7,800,000</td>\n",
       "      <td>3</td>\n",
       "      <td>house</td>\n",
       "      <td>en suite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1847 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             location selling price bedrooms property_type  \\\n",
       "0                         kiambu road   130,000,000        6         villa   \n",
       "1        off kiambu road, kiambu road    26,950,000        4     townhouse   \n",
       "2     masai lodge road, ongata rongai    16,000,000        4         villa   \n",
       "3                          ngong road    45,000,000        5         house   \n",
       "4                           lavington    72,000,000        5     townhouse   \n",
       "...                               ...           ...      ...           ...   \n",
       "1871                nandi road, karen   199,000,000        5         house   \n",
       "1872                        kitengela    16,000,000        4         house   \n",
       "1873                         syokimau    13,800,000        4         house   \n",
       "1874                        lavington    79,000,000        4     townhouse   \n",
       "1875             kimbo matangi, ruiru     7,800,000        3         house   \n",
       "\n",
       "     amenities  \n",
       "0     en suite  \n",
       "1     en suite  \n",
       "2     en suite  \n",
       "3     en suite  \n",
       "4     en suite  \n",
       "...        ...  \n",
       "1871  en suite  \n",
       "1872  en suite  \n",
       "1873  en suite  \n",
       "1874    garden  \n",
       "1875  en suite  \n",
       "\n",
       "[1847 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['selling price'] = df1['selling price'].str.replace('KSh ', '')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4cc034c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6', '4', '5', '3', '8', '2', '7', '10', '1', '9'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['bedrooms'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "03ca4161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130,000,000', '26,950,000', '16,000,000', '45,000,000',\n",
       "       '72,000,000', '46,000,000', '85,000,000', '50,000,000',\n",
       "       '75,000,000', '500,000,000', '140,000,000', '15,000,000',\n",
       "       '60,000,000', '55,000,000', '427,000,000', '65,000,000',\n",
       "       '48,000,000', '88,000,000', '27,000,000', '150,000,000',\n",
       "       '120,000,000', '110,000,000', '90,000,000', '35,000,000',\n",
       "       '59,000,000', '95,000,000', '24,000,000', '20,000,000',\n",
       "       '23,000,000', '115,000,000', '38,000,000', '16,500,000',\n",
       "       '32,250,000', '39,000,000', '9,500,000', '28,000,000', '8,950,000',\n",
       "       '50,600,000', '70,250,000', '19,000,000', '155,000,000',\n",
       "       '180,000,000', '38,500,000', '5,300,000', '8,500,000',\n",
       "       '29,999,000', '25,000,000', '72,499,999', '7,200,000', '6,500,000',\n",
       "       '26,500,000', '17,000,000', '64,990,000', '70,000,000',\n",
       "       '30,500,000', '100,000,000', '29,500,000', '15,500,000',\n",
       "       '14,500,000', '7,500,000', '31,500,000', '16,200,000', '6,800,000',\n",
       "       '11,500,000', '295,000,000', '10,300,000', '14,000,000',\n",
       "       '22,500,000', '121,000,000', '8,850,000', '170,000,000',\n",
       "       '99,000,000', '7,600,000', '83,000,000', '12,500,000',\n",
       "       '31,000,000', '7,700,000', '40,000,000', '18,500,000',\n",
       "       '19,500,000', '74,000,000', '69,000,000', '160,000,000',\n",
       "       '145,000,000', '34,000,000', '13,000,000', '10,500,000',\n",
       "       '21,000,000', '17,500,000', '30,000,000', '12,600,000',\n",
       "       '13,500,000', '64,000,000', '16,499,000', '56,000,000',\n",
       "       '350,000,000', '11,950,000', '6,100,000', '12,100,000',\n",
       "       '26,000,000', '11,000,000', '300,000,000', '125,000,000',\n",
       "       '68,000,000', '14,200,000', '6,750,000', '63,000,000',\n",
       "       '22,000,000', '66,000,000', '6,000,000', '650,000,000',\n",
       "       '29,000,000', '105,000,000', '135,000,000', '200,000,000',\n",
       "       '12,000,000', '250,000,000', '58,000,000', '4,500,000',\n",
       "       '80,000,000', '5,700,000', '33,000,000', '67,000,000',\n",
       "       '27,500,000', '67,500,000', '7,800,000', '25,945,000',\n",
       "       '37,000,000', '43,000,000', '61,950,000', '24,500,000',\n",
       "       '87,000,000', '128,000,000', '37,500,000', '6,300,000',\n",
       "       '7,000,000', '8,000,000', '5,800,000', '78,000,000', '13,800,000',\n",
       "       '220,000,000', '19,250,000', '8,150,000', '165,000,000',\n",
       "       '12,300,000', '14,750,000', '29,990,000', '41,000,000',\n",
       "       '40,750,000', '36,000,000', '10,250,000', '32,000,000',\n",
       "       '7,250,000', '10,000,000', '28,500,000', '32,500,000',\n",
       "       '190,000,000', '5,750,000', '97,000,000', '18,000,000',\n",
       "       '230,000,000', '6,700,000', '9,000,000', '20,500,000', '6,200,000',\n",
       "       '6,400,000', '14,800,000', '7,400,000', '11,300,000', '52,000,000',\n",
       "       '39,500,000', '42,000,000', '185,000,000', '50,950,000',\n",
       "       '7,900,000', '5,850,000', '400,000,000', '235,000,000',\n",
       "       '6,950,000', '46,750,000', '108,500,000', '225,000,000',\n",
       "       '280,000,000', '240,000,000', '9,980,000', '12,800,000',\n",
       "       '6,250,000', '86,000,000', '40,500,000', '35,800,000',\n",
       "       '35,500,000', '29,900,000', '19,950,000', '47,000,000',\n",
       "       '111,000,000', '2,700,000', '92,000,000', '3,700,000', '5,500,000',\n",
       "       '82,000,000', '8,800,000', '59,500,000', '11,800,000',\n",
       "       '87,500,000', '210,000,000', '11,250,000', '158,145,000',\n",
       "       '97,500,000', '36,500,000', '290,000,000', '49,000,000',\n",
       "       '107,000,000', '16,850,000', '62,000,000', '7,950,000',\n",
       "       '21,817,000', '270,000,000', '25,500,000', '123,000,000',\n",
       "       '20,950,000', '600,000,000', '245,403,755', '48,450,000',\n",
       "       '69,500,000', '81,420,000', '15,800,000', '29,980,000',\n",
       "       '9,950,000', '61,000,000', '13,100,000', '118,500,000',\n",
       "       '10,950,000', '4,600,000', '63,950,000', '550,000,000',\n",
       "       '19,700,000', '49,500,000', '299,700,000', '28,250,000',\n",
       "       '260,000,000', '7,300,000', '43,580,000', '34,450,000',\n",
       "       '47,500,000', '68,500,000', '9,250,000', '340,000,000',\n",
       "       '480,000,000', '8,600,000', '14,150,000', '684,000,000',\n",
       "       '34,400,000', '15,900,000', '275,000,000', '29,700,000',\n",
       "       '171,375,000', '133,000,000', '21,950,000', '118,000,000',\n",
       "       '57,000,000', '11,700,000', '98,000,000', '13,600,000',\n",
       "       '12,700,000', '53,000,000', '220,863,379', '10,900,000',\n",
       "       '5,990,000', '17,800,000', '4,800,000', '45,800,000', '5,250,000',\n",
       "       '4,450,000', '8,700,000', '648,000,000', '26,800,000',\n",
       "       '20,295,000', '11,980,000', '29,400,000', '138,000,000',\n",
       "       '12,980,000', '132,000,000', '211,840,000', '42,500,000',\n",
       "       '54,000,000', '21,500,000', '23,500,000', '29,850,000',\n",
       "       '10,050,000', '7,499,000', '199,000,000', '79,000,000'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['selling price'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "552229c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove white spaces and commas from values in the 'selling price' column\n",
    "df2['selling price'] = df2['selling price'].astype(str).str.replace(' ', '').str.replace(',', '')\n",
    "\n",
    "# Convert the 'selling price' column to integer\n",
    "df2['selling price'] = df2['selling price'].astype(int)\n",
    "\n",
    "# Convert the 'bedrooms' column to integer\n",
    "df2['bedrooms'] = df2['bedrooms'].astype(int)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ba84dd",
   "metadata": {},
   "source": [
    "# Model Bulding - LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cd6594ed",
   "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>selling price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location_01, ongata rongai</th>\n",
       "      <th>location_01, runda, runda, westlands</th>\n",
       "      <th>location_01, runda, westlands</th>\n",
       "      <th>location_012, kimlat, kitengela</th>\n",
       "      <th>location_0208, ngong road, ngong</th>\n",
       "      <th>location_04, gateway mall, syokimau</th>\n",
       "      <th>location_046, kindaruma road, kilimani</th>\n",
       "      <th>location_0706771810, bamburi mwisho, bamburi</th>\n",
       "      <th>...</th>\n",
       "      <th>location_yukos, kitengela</th>\n",
       "      <th>property_type_townhouse</th>\n",
       "      <th>property_type_unknown</th>\n",
       "      <th>property_type_villa</th>\n",
       "      <th>amenities_en suite</th>\n",
       "      <th>amenities_en suite, garden</th>\n",
       "      <th>amenities_garden</th>\n",
       "      <th>amenities_gym</th>\n",
       "      <th>amenities_swimming pool</th>\n",
       "      <th>amenities_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130000000</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26950000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>199000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>16000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>13800000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>79000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>7800000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1847 rows × 686 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      selling price  bedrooms  location_01, ongata rongai  \\\n",
       "0         130000000         6                           0   \n",
       "1          26950000         4                           0   \n",
       "2          16000000         4                           0   \n",
       "3          45000000         5                           0   \n",
       "4          72000000         5                           0   \n",
       "...             ...       ...                         ...   \n",
       "1871      199000000         5                           0   \n",
       "1872       16000000         4                           0   \n",
       "1873       13800000         4                           0   \n",
       "1874       79000000         4                           0   \n",
       "1875        7800000         3                           0   \n",
       "\n",
       "      location_01, runda, runda, westlands  location_01, runda, westlands  \\\n",
       "0                                        0                              0   \n",
       "1                                        0                              0   \n",
       "2                                        0                              0   \n",
       "3                                        0                              0   \n",
       "4                                        0                              0   \n",
       "...                                    ...                            ...   \n",
       "1871                                     0                              0   \n",
       "1872                                     0                              0   \n",
       "1873                                     0                              0   \n",
       "1874                                     0                              0   \n",
       "1875                                     0                              0   \n",
       "\n",
       "      location_012, kimlat, kitengela  location_0208, ngong road, ngong  \\\n",
       "0                                   0                                 0   \n",
       "1                                   0                                 0   \n",
       "2                                   0                                 0   \n",
       "3                                   0                                 0   \n",
       "4                                   0                                 0   \n",
       "...                               ...                               ...   \n",
       "1871                                0                                 0   \n",
       "1872                                0                                 0   \n",
       "1873                                0                                 0   \n",
       "1874                                0                                 0   \n",
       "1875                                0                                 0   \n",
       "\n",
       "      location_04, gateway mall, syokimau  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "...                                   ...   \n",
       "1871                                    0   \n",
       "1872                                    0   \n",
       "1873                                    0   \n",
       "1874                                    0   \n",
       "1875                                    0   \n",
       "\n",
       "      location_046, kindaruma road, kilimani  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "...                                      ...   \n",
       "1871                                       0   \n",
       "1872                                       0   \n",
       "1873                                       0   \n",
       "1874                                       0   \n",
       "1875                                       0   \n",
       "\n",
       "      location_0706771810, bamburi mwisho, bamburi  ...  \\\n",
       "0                                                0  ...   \n",
       "1                                                0  ...   \n",
       "2                                                0  ...   \n",
       "3                                                0  ...   \n",
       "4                                                0  ...   \n",
       "...                                            ...  ...   \n",
       "1871                                             0  ...   \n",
       "1872                                             0  ...   \n",
       "1873                                             0  ...   \n",
       "1874                                             0  ...   \n",
       "1875                                             0  ...   \n",
       "\n",
       "      location_yukos, kitengela  property_type_townhouse  \\\n",
       "0                             0                        0   \n",
       "1                             0                        1   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        1   \n",
       "...                         ...                      ...   \n",
       "1871                          0                        0   \n",
       "1872                          0                        0   \n",
       "1873                          0                        0   \n",
       "1874                          0                        1   \n",
       "1875                          0                        0   \n",
       "\n",
       "      property_type_unknown  property_type_villa  amenities_en suite  \\\n",
       "0                         0                    1                   1   \n",
       "1                         0                    0                   1   \n",
       "2                         0                    1                   1   \n",
       "3                         0                    0                   1   \n",
       "4                         0                    0                   1   \n",
       "...                     ...                  ...                 ...   \n",
       "1871                      0                    0                   1   \n",
       "1872                      0                    0                   1   \n",
       "1873                      0                    0                   1   \n",
       "1874                      0                    0                   0   \n",
       "1875                      0                    0                   1   \n",
       "\n",
       "      amenities_en suite, garden  amenities_garden  amenities_gym  \\\n",
       "0                              0                 0              0   \n",
       "1                              0                 0              0   \n",
       "2                              0                 0              0   \n",
       "3                              0                 0              0   \n",
       "4                              0                 0              0   \n",
       "...                          ...               ...            ...   \n",
       "1871                           0                 0              0   \n",
       "1872                           0                 0              0   \n",
       "1873                           0                 0              0   \n",
       "1874                           0                 1              0   \n",
       "1875                           0                 0              0   \n",
       "\n",
       "      amenities_swimming pool  amenities_unknown  \n",
       "0                           0                  0  \n",
       "1                           0                  0  \n",
       "2                           0                  0  \n",
       "3                           0                  0  \n",
       "4                           0                  0  \n",
       "...                       ...                ...  \n",
       "1871                        0                  0  \n",
       "1872                        0                  0  \n",
       "1873                        0                  0  \n",
       "1874                        0                  0  \n",
       "1875                        0                  0  \n",
       "\n",
       "[1847 rows x 686 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # LABEL ENCODING\n",
    "# df2 = pd.get_dummies(df2, columns=['location', 'property_type', 'amenities'], drop_first=True, dtype='int8')\n",
    "# df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "87a3606f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df2['selling price']\n",
    "X = df2.drop('selling price', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7b5a972c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature engineering\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "X_poly = poly.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0486b237",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scalling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f973764a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f8a8a255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gradient boosting\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "gb = GradientBoostingRegressor()\n",
    "gb.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "05e41165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rifge regression\n",
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge(alpha=1.0)\n",
    "ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1ec6db5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model selection\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ec464f42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in train data = 1477\n",
      "Number of rows in test data = 370\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows in train data =\", X_train.shape[0])\n",
    "print(\"Number of rows in test data =\", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "76e92fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "52c9ccbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': [10, 20, 30],\n",
       "                         'n_estimators': [100, 200, 300]})"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hyperparameter Tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "params = {'n_estimators': [100, 200, 300], 'max_depth': [10, 20, 30]}\n",
    "grid_search = GridSearchCV(rf, param_grid=params, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7c45fa04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "65024253",
   "metadata": {},
   "outputs": [],
   "source": [
    "#crosss validation (k-fold)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(rf, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc4461cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the intercept, or y-cut, of our linear model\n",
    "a = float(lm.intercept_)\n",
    "# Extract the coefficient, or gradient, of our linear model\n",
    "b = lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8814f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Slope:\\t\\t\", b)\n",
    "print(\"Intercept:\\t\", float(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e383213",
   "metadata": {},
   "source": [
    "# Model performance check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a6bef45a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE: 1099726801155672.2\n",
      "Training R-squared: 0.7820668706257982\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions on the training set\n",
    "y_train_pred = lm.predict(X_train)\n",
    "\n",
    "# Calculate the Mean Squared Error (MSE)\n",
    "train_mse = metrics.mean_squared_error(y_train, y_train_pred)\n",
    "\n",
    "# Calculate the R-squared\n",
    "train_r2 = metrics.r2_score(y_train, y_train_pred)\n",
    "\n",
    "# Print the training MSE and R-squared score\n",
    "print(\"Training MSE:\", train_mse)\n",
    "print(\"Training R-squared:\", train_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2987de69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing MSE: 1.4153579376362048e+40\n",
      "Testing R-squared: -2.8837743267611474e+24\n"
     ]
    }
   ],
   "source": [
    "# Your solution here...\n",
    "# Generate predictions on the testing set\n",
    "y_test_pred = lm.predict(X_test)\n",
    "\n",
    "# Calculate the Mean Squared Error (MSE)\n",
    "test_mse = metrics.mean_squared_error(y_test, y_test_pred)\n",
    "\n",
    "# Calculate the R-squared\n",
    "test_r2 = metrics.r2_score(y_test, y_test_pred)\n",
    "\n",
    "# Print the testing MSE and R-squared score\n",
    "print(\"Testing MSE:\", test_mse)\n",
    "print(\"Testing R-squared:\", test_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b8569404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1.4153579376362048e+40\n",
      "R^2 Score: -2.8837743267611474e+24\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test data\n",
    "y_pred = lm.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdaee31b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9373ca5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38847c49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f929b82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ef99eb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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