notebooks/eda-previous_application.ipynb

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   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from sklearn.externals import joblib\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "DIR = '/mnt/ml-team/minerva/open-solutions/home-credit'\n",
    "description = pd.read_csv(os.path.join(DIR,'data/HomeCredit_columns_description.csv'),encoding = 'latin1')\n",
    "application = pd.read_csv(os.path.join(DIR, 'files/unzipped_data/application_train.csv'))\n",
    "previous_application = pd.read_csv(os.path.join(DIR, 'files/unzipped_data/previous_application.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "previous_application.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "## Solution 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "## Solution 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PREVIOUS_APPLICATION_AGGREGATION_RECIPIES = []\n",
    "for agg in ['mean', 'min', 'max', 'sum', 'var']:\n",
    "    for select in ['AMT_ANNUITY',\n",
    "                   'AMT_APPLICATION',\n",
    "                   'AMT_CREDIT',\n",
    "                   'AMT_DOWN_PAYMENT',\n",
    "                   'AMT_GOODS_PRICE',\n",
    "                   'CNT_PAYMENT',\n",
    "                   'DAYS_DECISION',\n",
    "                   'HOUR_APPR_PROCESS_START',\n",
    "                   'RATE_DOWN_PAYMENT'\n",
    "                   ]:\n",
    "        PREVIOUS_APPLICATION_AGGREGATION_RECIPIES.append((select, agg))\n",
    "PREVIOUS_APPLICATION_AGGREGATION_RECIPIES = [(['SK_ID_CURR'], PREVIOUS_APPLICATION_AGGREGATION_RECIPIES)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_aggregate_names = []\n",
    "for groupby_cols, specs in tqdm(PREVIOUS_APPLICATION_AGGREGATION_RECIPIES):\n",
    "    group_object = previous_application.groupby(groupby_cols)\n",
    "    for select, agg in tqdm(specs):\n",
    "        groupby_aggregate_name = '{}_{}_{}'.format('_'.join(groupby_cols), agg, select)\n",
    "        application = application.merge(group_object[select]\n",
    "                              .agg(agg)\n",
    "                              .reset_index()\n",
    "                              .rename(index=str,\n",
    "                                      columns={select: groupby_aggregate_name})\n",
    "                              [groupby_cols + [groupby_aggregate_name]],\n",
    "                              on=groupby_cols,\n",
    "                              how='left')\n",
    "        groupby_aggregate_names.append(groupby_aggregate_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "application.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "application_agg = application[groupby_aggregate_names + ['TARGET']]\n",
    "application_agg_corr = abs(application_agg.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "application_agg_corr.sort_values('TARGET', ascending=False)['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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