challenges/02 - Real Estate Regression Challenge.ipynb

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    "# Regression Challenge\n",
    "\n",
    "Predicting the selling price of a residential property depends on a number of factors, including the property age, availability of local amenities, and location.\n",
    "\n",
    "In this challenge, you will use a dataset of real estate sales transactions to predict the price-per-unit of a property based on its features. The price-per-unit in this data is based on a unit measurement of 3.3 square meters.\n",
    "\n",
    "> **Citation**: The data used in this exercise originates from the following study:\n",
    ">\n",
    "> *Yeh, I. C., & Hsu, T. K. (2018). Building real estate valuation models with comparative approach through case-based reasoning. Applied Soft Computing, 65, 260-271.*\n",
    ">\n",
    "> It was obtained from the UCI dataset repository (Dua, D. and Graff, C. (2019). [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science).\n",
    "\n",
    "## Review the data\n",
    "\n",
    "Run the following cell to load the data and view the first few rows."
   ]
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   "source": [
    "import pandas as pd\n",
    "\n",
    "# load the training dataset\n",
    "data = pd.read_csv('data/real_estate.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of the following variables:\n",
    "\n",
    "- **transaction_date** - the transaction date (for example, 2013.250=2013 March, 2013.500=2013 June, etc.)\n",
    "- **house_age** - the house age (in years)\n",
    "- **transit_distance** - the distance to the nearest light rail station (in meters)\n",
    "- **local_convenience_stores** - the number of convenience stores within walking distance\n",
    "- **latitude** - the geographic coordinate, latitude\n",
    "- **longitude** - the geographic coordinate, longitude\n",
    "- **price_per_unit** house price of unit area (3.3 square meters)\n",
    "\n",
    "## Train a Regression Model\n",
    "\n",
    "Your challenge is to explore and prepare the data, identify predictive features that will help predict the **price_per_unit** label, and train a regression model that achieves the lowest Root Mean Square Error (RMSE) you can achieve (which must be less than **7**) when evaluated against a test subset of data.\n",
    "\n",
    "Add markdown and code cells as required to create your solution.\n",
    "\n",
    "> **Note**: There is no single \"correct\" solution. A sample solution is provided in [02 - Real Estate Regression Solution.ipynb](02%20-%20Real%20Estate%20Regression%20Solution.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code to explore data and train a regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the Trained Model\n",
    "\n",
    "Save your trained model, and then use it to predict the price-per-unit for the following real estate transactions:\n",
    "\n",
    "| transaction_date | house_age | transit_distance | local_convenience_stores | latitude | longitude |\n",
    "| ---------------- | --------- | ---------------- | ------------------------ | -------- | --------- |\n",
    "|2013.167|16.2|289.3248|5|24.98203|121.54348|\n",
    "|2013.000|13.6|4082.015|0|24.94155|121.50381|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code to use the trained model"
   ]
  }
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