Real_Estate_data_Web_scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec443ed7",
   "metadata": {},
   "source": [
    "# Kenya Real Estate Prices Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96159f50",
   "metadata": {},
   "source": [
    "## Task: Web Scraping Real Estate Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4781f204",
   "metadata": {},
   "source": [
    "### Objectives\n",
    "\n",
    "Scrape Real Estate data from [buyrentkenya.com](https://www.buyrentkenya.com):\n",
    "\n",
    "* Collect house data\n",
    "* Collect apartment data\n",
    "* Collect land data\n",
    "\n",
    "Save the data in csv files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c840716c",
   "metadata": {},
   "source": [
    "Import useful libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae42f968",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from selenium import webdriver\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17a3242",
   "metadata": {},
   "source": [
    "Create a function that will go throgh all the pages on the website after extracting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dbbef414",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape(webpage):\n",
    "    # Create lists where we will store all the scraped data\n",
    "    links = []\n",
    "    bedroom_data = []\n",
    "    bathroom_data = []\n",
    "    prices = []\n",
    "    size = []\n",
    "    title = []\n",
    "    locations = []\n",
    "    \n",
    "    # page number\n",
    "    page = 1\n",
    "\n",
    "    # Get the webpage where we will begin to scrape the data.\n",
    "    driver = webdriver.Chrome(executable_path='/Selenium Drivers/chromedriver.exe')\n",
    "    driver.get(webpage)\n",
    "    time.sleep(20)\n",
    "\n",
    "    while True:\n",
    "        # Get all the properties on the current page\n",
    "        properties = driver.find_elements_by_css_selector(\"div[class='mb-3 w-full']\")\n",
    "\n",
    "        # Iterate through the properties to get all the information\n",
    "        for house in properties:\n",
    "            # link to property\n",
    "            url = house.find_element_by_css_selector(\"a[class='text-black no-underline block']\").get_attribute('href')\n",
    "            links.append(url)\n",
    "\n",
    "            # property description\n",
    "            description  = house.find_element_by_css_selector(\"a[class='no-underline text-black']\").text\n",
    "            title.append(description)\n",
    "\n",
    "            # number of bedrooms\n",
    "            try:\n",
    "                bedrooms = house.find_element_by_css_selector(\"span[data-cy='card-beds']\").text\n",
    "                bedroom_data.append(bedrooms)\n",
    "            except:\n",
    "                bedroom_data.append(np.nan)\n",
    "\n",
    "            # number of bathrooms\n",
    "            try:\n",
    "                bathrooms = house.find_element_by_css_selector(\"div[data-cy='card-baths']\")\n",
    "                bathroom_data.append(bathrooms.text)\n",
    "\n",
    "            except:\n",
    "                bathroom_data.append(np.nan)\n",
    "\n",
    "            # location\n",
    "            location = house.find_element_by_css_selector(\"p[class='text-md md:text-sm font-normal text-grey-darker mt-1 md:mt-0']\").text\n",
    "            locations.append(location)\n",
    "\n",
    "            # price\n",
    "            price = house.find_element_by_css_selector(\"a[class='no-underline']\").text\n",
    "            prices.append(price)\n",
    "\n",
    "            # Get size of property in as Area per metre squared\n",
    "            try:\n",
    "                area = house.find_element_by_css_selector(\"span[data-cy='card-area']\")\n",
    "                size.append(area.text)\n",
    "\n",
    "            except:\n",
    "                size.append(np.nan)\n",
    "\n",
    "        print(f'Page {page} Done!')\n",
    "\n",
    "        try:\n",
    "            # go to next page\n",
    "            next_page = driver.find_element_by_css_selector(\"li[class='page-item pagination-next-nav ']\")\n",
    "            next_page.click()\n",
    "            page += 1\n",
    "            time.sleep(25)\n",
    "\n",
    "        except Exception as E:\n",
    "            print(E)\n",
    "            driver.quit()\n",
    "            break\n",
    "    \n",
    "    # create dictionary that will be transformed into pandas data frame\n",
    "    data_dict = {'Title': title, 'URL': links, 'Bedrooms':bedroom_data, 'Bathrooms': bathroom_data, 'Price': prices,\n",
    "                     'Size': size, 'Location':locations}\n",
    "    # create dataframe\n",
    "    df = pd.DataFrame(data_dict)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991b272a",
   "metadata": {},
   "source": [
    "## Scrape house data:\n",
    "\n",
    "Begin by inserting the webpage with data on houses for sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f85ef0dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "webpage = 'https://www.buyrentkenya.com/houses-for-sale'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47e78cae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "  (Session info: chrome=99.0.4844.82)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "webpage = 'https://www.buyrentkenya.com/houses-for-sale'\n",
    "df_houses = scrape(webpage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e40c00ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_houses.to_csv('kenya_house_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8fa6cd3",
   "metadata": {},
   "source": [
    "## Scrape apartment data:\n",
    "\n",
    "Next, we will insert the web page with data on apartments for sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c2c20d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "  (Session info: chrome=99.0.4844.82)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_apartments = scrape('https://www.buyrentkenya.com/flats-apartments-for-sale')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981d1805",
   "metadata": {},
   "source": [
    "Save dataframe to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65ada39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_apartments.to_csv('Apartment_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463a922f",
   "metadata": {},
   "source": [
    "## Scrape land data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c124f5cd",
   "metadata": {},
   "source": [
    "Finally, we will scrape data on land that is for sale and save it in a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9da61f56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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      "Message: no such element: Unable to locate element: {\"method\":\"css selector\",\"selector\":\"li[class='page-item pagination-next-nav ']\"}\n",
      "  (Session info: chrome=100.0.4896.75)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_land = scrape('https://www.buyrentkenya.com/land-for-sale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd44c49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_land.to_csv('Land_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85bf49f",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/molomunyansanga/\">Molo Munyansanga</a> is a Data Science enthusiast with certificates in Statistics, Data Science and Machine Learning. He is also enrolled in the Deep Learning Specialization by DeepLearning.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a465e3e3",
   "metadata": {},
   "source": [
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2022-04-14        | 1.6       | Molo. M       | Created Notebook and Completed Tasks         |"
   ]
  }
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