ch02-categorical-encoding/donwload-prepare-store-credit-approval-dataset.ipynb

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    "# Download, prepare and save the Credit Approval Dataset\n",
    "\n",
    "\n",
    "In this notebook, you will find guidelines to download, prepare, and store the Credit Approval Dataset from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml).\n",
    "\n",
    "\n",
    "## Download the data\n",
    "\n",
    "Follow these guidelines to download the data:\n",
    "\n",
    "- Visit [the UCI website](http://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/)\n",
    "- Click on **crx.data** to download the data. \n",
    "- Save crx.data in the same folder that contains this notebook.\n",
    "\n",
    "\n",
    "You can find more information about this particular dataset [here](https://archive.ics.uci.edu/ml/datasets/credit+approval)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
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       "      <th>0</th>\n",
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       "      <td>58.67</td>\n",
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       "      <td>q</td>\n",
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       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15  target\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  202.0    0       1\n",
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       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  280.0  824       1\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  100.0    3       1\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  120.0    0       1"
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   "source": [
    "# Load data\n",
    "data = pd.read_csv(\"crx.data\", header=None)\n",
    "\n",
    "# Create variable names according to UCI Machine Learning\n",
    "# Repository's information:\n",
    "varnames = [f\"A{s}\" for s in range(1, 17)]\n",
    "\n",
    "# Add column names to dataset:\n",
    "data.columns = varnames\n",
    "\n",
    "# Replace ? by np.nan:\n",
    "data = data.replace(\"?\", np.nan)\n",
    "\n",
    "# Cast variables to correct data types:\n",
    "data[\"A2\"] = data[\"A2\"].astype(\"float\")\n",
    "data[\"A14\"] = data[\"A14\"].astype(\"float\")\n",
    "\n",
    "# Encode target to binary notation:\n",
    "data[\"A16\"] = data[\"A16\"].map({\"+\": 1, \"-\": 0})\n",
    "\n",
    "# Rename target:\n",
    "data.rename(columns={\"A16\": \"target\"}, inplace=True)\n",
    "\n",
    "# Display first 5 rows of data:\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find categorical variables\n",
    "cat_cols = [c for c in data.columns if data[c].dtypes == \"O\"]\n",
    "\n",
    "# find numerical variables\n",
    "num_cols = [c for c in data.columns if data[c].dtypes != \"O\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in missing values\n",
    "\n",
    "data[num_cols] = data[num_cols].fillna(0)\n",
    "data[cat_cols] = data[cat_cols].fillna(\"Missing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save dataset\n",
    "\n",
    "data.to_csv(\"credit_approval_uci.csv\", index=False)"
   ]
  }
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