tabular-playground-feb2021.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nimport os\\nimport sys\\nfrom tempfile import NamedTemporaryFile\\nfrom urllib.request import urlopen\\nfrom urllib.parse import unquote\\nfrom urllib.error import HTTPError\\nfrom zipfile import ZipFile\\n\\nCHUNK_SIZE = 40960 \\nDATASET_MAPPING = \\'tabular-playground-series-feb-2021:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-competitions-data%2Fkaggle-v2%2F25225%2F1923495%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20210208%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20210208T152806Z%26X-Goog-Expires%3D259199%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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\\'\\nKAGGLE_INPUT_PATH=\\'/home/kaggle/input\\'\\nKAGGLE_INPUT_SYMLINK=\\'/kaggle\\'\\n\\nos.makedirs(KAGGLE_INPUT_PATH, 777)\\nos.symlink(KAGGLE_INPUT_PATH, os.path.join(\\'..\\', \\'input\\'), target_is_directory=True)\\nos.makedirs(KAGGLE_INPUT_SYMLINK)\\nos.symlink(KAGGLE_INPUT_PATH, os.path.join(KAGGLE_INPUT_SYMLINK, \\'input\\'), target_is_directory=True)\\n\\nfor dataset_mapping in DATASET_MAPPING.split(\\',\\'):\\n    directory, download_url_encoded = dataset_mapping.split(\\':\\')\\n    download_url = unquote(download_url_encoded)\\n    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\\n    try:\\n        with urlopen(download_url) as zipfileres, NamedTemporaryFile() as tfile:\\n            total_length = zipfileres.headers[\\'content-length\\']\\n            print(f\\'Downloading {directory}, {total_length} bytes zipped\\')\\n            dl = 0\\n            data = zipfileres.read(CHUNK_SIZE)\\n            while len(data) > 0:\\n                dl += len(data)\\n                tfile.write(data)\\n                done = int(50 * dl / int(total_length))\\n                sys.stdout.write(f\"\\r[{\\'=\\' * done}{\\' \\' * (50-done)}] {dl} bytes downloaded\")\\n                sys.stdout.flush()\\n                data = zipfileres.read(CHUNK_SIZE)\\n            print(f\\'\\nUnzipping {directory}\\')\\n            with ZipFile(tfile) as zfile:\\n                zfile.extractall(destination_path)\\n    except HTTPError as e:\\n        print(f\\'Failed to load (likely expired) {download_url} to path {destination_path}\\')\\n        continue\\n    except OSError as e:\\n        print(f\\'Failed to load {download_url} to path {destination_path}\\')\\n        continue\\nprint(\\'Dataset import complete.\\')\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATASETS\n",
    "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
    "# THEN FEEL FREE TO DELETE CELL.\n",
    "'''\n",
    "import os\n",
    "import sys\n",
    "from tempfile import NamedTemporaryFile\n",
    "from urllib.request import urlopen\n",
    "from urllib.parse import unquote\n",
    "from urllib.error import HTTPError\n",
    "from zipfile import ZipFile\n",
    "\n",
    "CHUNK_SIZE = 40960 \n",
    "DATASET_MAPPING = 'tabular-playground-series-feb-2021:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-competitions-data%2Fkaggle-v2%2F25225%2F1923495%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20210208%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20210208T152806Z%26X-Goog-Expires%3D259199%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
    "KAGGLE_INPUT_PATH='/home/kaggle/input'\n",
    "KAGGLE_INPUT_SYMLINK='/kaggle'\n",
    "\n",
    "os.makedirs(KAGGLE_INPUT_PATH, 777)\n",
    "os.symlink(KAGGLE_INPUT_PATH, os.path.join('..', 'input'), target_is_directory=True)\n",
    "os.makedirs(KAGGLE_INPUT_SYMLINK)\n",
    "os.symlink(KAGGLE_INPUT_PATH, os.path.join(KAGGLE_INPUT_SYMLINK, 'input'), target_is_directory=True)\n",
    "\n",
    "for dataset_mapping in DATASET_MAPPING.split(','):\n",
    "    directory, download_url_encoded = dataset_mapping.split(':')\n",
    "    download_url = unquote(download_url_encoded)\n",
    "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
    "    try:\n",
    "        with urlopen(download_url) as zipfileres, NamedTemporaryFile() as tfile:\n",
    "            total_length = zipfileres.headers['content-length']\n",
    "            print(f'Downloading {directory}, {total_length} bytes zipped')\n",
    "            dl = 0\n",
    "            data = zipfileres.read(CHUNK_SIZE)\n",
    "            while len(data) > 0:\n",
    "                dl += len(data)\n",
    "                tfile.write(data)\n",
    "                done = int(50 * dl / int(total_length))\n",
    "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
    "                sys.stdout.flush()\n",
    "                data = zipfileres.read(CHUNK_SIZE)\n",
    "            print(f'\\nUnzipping {directory}')\n",
    "            with ZipFile(tfile) as zfile:\n",
    "                zfile.extractall(destination_path)\n",
    "    except HTTPError as e:\n",
    "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
    "        continue\n",
    "    except OSError as e:\n",
    "        print(f'Failed to load {download_url} to path {destination_path}')\n",
    "        continue\n",
    "print('Dataset import complete.')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../input/tabular-playground-series-feb-2021/train.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-2b60ee97b540>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../input/tabular-playground-series-feb-2021/train.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../input/tabular-playground-series-feb-2021/test.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    944\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 946\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1176\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1179\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2008\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2009\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/tabular-playground-series-feb-2021/train.csv'"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv('../input/tabular-playground-series-feb-2021/train.csv')\n",
    "test_df = pd.read_csv('../input/tabular-playground-series-feb-2021/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>0.881122</td>\n",
       "      <td>0.421650</td>\n",
       "      <td>0.741413</td>\n",
       "      <td>0.895799</td>\n",
       "      <td>0.802461</td>\n",
       "      <td>0.724417</td>\n",
       "      <td>0.701915</td>\n",
       "      <td>0.877618</td>\n",
       "      <td>0.719903</td>\n",
       "      <td>6.994023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.440011</td>\n",
       "      <td>0.346230</td>\n",
       "      <td>0.278495</td>\n",
       "      <td>0.593413</td>\n",
       "      <td>0.546056</td>\n",
       "      <td>0.613252</td>\n",
       "      <td>0.741289</td>\n",
       "      <td>0.326679</td>\n",
       "      <td>0.808464</td>\n",
       "      <td>8.071256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>0.914155</td>\n",
       "      <td>0.369602</td>\n",
       "      <td>0.832564</td>\n",
       "      <td>0.865620</td>\n",
       "      <td>0.825251</td>\n",
       "      <td>0.264104</td>\n",
       "      <td>0.695561</td>\n",
       "      <td>0.869133</td>\n",
       "      <td>0.828352</td>\n",
       "      <td>5.760456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934138</td>\n",
       "      <td>0.578930</td>\n",
       "      <td>0.407313</td>\n",
       "      <td>0.868099</td>\n",
       "      <td>0.794402</td>\n",
       "      <td>0.494269</td>\n",
       "      <td>0.698125</td>\n",
       "      <td>0.809799</td>\n",
       "      <td>0.614766</td>\n",
       "      <td>7.806457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382600</td>\n",
       "      <td>0.705940</td>\n",
       "      <td>0.325193</td>\n",
       "      <td>0.440967</td>\n",
       "      <td>0.462146</td>\n",
       "      <td>0.724447</td>\n",
       "      <td>0.683073</td>\n",
       "      <td>0.343457</td>\n",
       "      <td>0.297743</td>\n",
       "      <td>6.868974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id cat0 cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8  ...     cont5     cont6  \\\n",
       "0   1    A    B    A    A    B    D    A    E    C  ...  0.881122  0.421650   \n",
       "1   2    B    A    A    A    B    B    A    E    A  ...  0.440011  0.346230   \n",
       "2   3    A    A    A    C    B    D    A    B    C  ...  0.914155  0.369602   \n",
       "3   4    A    A    A    C    B    D    A    E    G  ...  0.934138  0.578930   \n",
       "4   6    A    B    A    A    B    B    A    E    C  ...  0.382600  0.705940   \n",
       "\n",
       "      cont7     cont8     cont9    cont10    cont11    cont12    cont13  \\\n",
       "0  0.741413  0.895799  0.802461  0.724417  0.701915  0.877618  0.719903   \n",
       "1  0.278495  0.593413  0.546056  0.613252  0.741289  0.326679  0.808464   \n",
       "2  0.832564  0.865620  0.825251  0.264104  0.695561  0.869133  0.828352   \n",
       "3  0.407313  0.868099  0.794402  0.494269  0.698125  0.809799  0.614766   \n",
       "4  0.325193  0.440967  0.462146  0.724447  0.683073  0.343457  0.297743   \n",
       "\n",
       "     target  \n",
       "0  6.994023  \n",
       "1  8.071256  \n",
       "2  5.760456  \n",
       "3  7.806457  \n",
       "4  6.868974  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>cont0</th>\n",
       "      <th>cont1</th>\n",
       "      <th>cont2</th>\n",
       "      <th>cont3</th>\n",
       "      <th>cont4</th>\n",
       "      <th>cont5</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>250018.576947</td>\n",
       "      <td>0.524634</td>\n",
       "      <td>0.506649</td>\n",
       "      <td>0.444115</td>\n",
       "      <td>0.446214</td>\n",
       "      <td>0.455471</td>\n",
       "      <td>0.508337</td>\n",
       "      <td>0.478345</td>\n",
       "      <td>0.455904</td>\n",
       "      <td>0.459321</td>\n",
       "      <td>0.526899</td>\n",
       "      <td>0.504943</td>\n",
       "      <td>0.529938</td>\n",
       "      <td>0.524549</td>\n",
       "      <td>0.503349</td>\n",
       "      <td>7.456260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144450.150010</td>\n",
       "      <td>0.204875</td>\n",
       "      <td>0.235269</td>\n",
       "      <td>0.200089</td>\n",
       "      <td>0.238669</td>\n",
       "      <td>0.200695</td>\n",
       "      <td>0.231612</td>\n",
       "      <td>0.192432</td>\n",
       "      <td>0.204493</td>\n",
       "      <td>0.220642</td>\n",
       "      <td>0.204025</td>\n",
       "      <td>0.201549</td>\n",
       "      <td>0.230860</td>\n",
       "      <td>0.220892</td>\n",
       "      <td>0.225218</td>\n",
       "      <td>0.887295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.093505</td>\n",
       "      <td>-0.055105</td>\n",
       "      <td>-0.060274</td>\n",
       "      <td>0.134760</td>\n",
       "      <td>0.189216</td>\n",
       "      <td>-0.087247</td>\n",
       "      <td>0.043953</td>\n",
       "      <td>0.208703</td>\n",
       "      <td>0.004041</td>\n",
       "      <td>0.073040</td>\n",
       "      <td>0.059644</td>\n",
       "      <td>0.064161</td>\n",
       "      <td>-0.005600</td>\n",
       "      <td>0.158121</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>124772.500000</td>\n",
       "      <td>0.370451</td>\n",
       "      <td>0.352307</td>\n",
       "      <td>0.314121</td>\n",
       "      <td>0.214572</td>\n",
       "      <td>0.279853</td>\n",
       "      <td>0.338747</td>\n",
       "      <td>0.339896</td>\n",
       "      <td>0.278041</td>\n",
       "      <td>0.308655</td>\n",
       "      <td>0.361957</td>\n",
       "      <td>0.338898</td>\n",
       "      <td>0.316662</td>\n",
       "      <td>0.332143</td>\n",
       "      <td>0.291289</td>\n",
       "      <td>6.798341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250002.500000</td>\n",
       "      <td>0.492208</td>\n",
       "      <td>0.615156</td>\n",
       "      <td>0.457271</td>\n",
       "      <td>0.377823</td>\n",
       "      <td>0.411351</td>\n",
       "      <td>0.441384</td>\n",
       "      <td>0.410090</td>\n",
       "      <td>0.360736</td>\n",
       "      <td>0.425801</td>\n",
       "      <td>0.488867</td>\n",
       "      <td>0.519855</td>\n",
       "      <td>0.558827</td>\n",
       "      <td>0.407365</td>\n",
       "      <td>0.433909</td>\n",
       "      <td>7.496503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>375226.500000</td>\n",
       "      <td>0.654793</td>\n",
       "      <td>0.688150</td>\n",
       "      <td>0.554835</td>\n",
       "      <td>0.719758</td>\n",
       "      <td>0.621808</td>\n",
       "      <td>0.709515</td>\n",
       "      <td>0.604246</td>\n",
       "      <td>0.639388</td>\n",
       "      <td>0.541525</td>\n",
       "      <td>0.752765</td>\n",
       "      <td>0.672809</td>\n",
       "      <td>0.720381</td>\n",
       "      <td>0.732431</td>\n",
       "      <td>0.730870</td>\n",
       "      <td>8.161166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>499999.000000</td>\n",
       "      <td>1.052666</td>\n",
       "      <td>0.851746</td>\n",
       "      <td>1.017689</td>\n",
       "      <td>1.006469</td>\n",
       "      <td>0.994050</td>\n",
       "      <td>1.044433</td>\n",
       "      <td>1.093312</td>\n",
       "      <td>1.036541</td>\n",
       "      <td>1.014156</td>\n",
       "      <td>0.972091</td>\n",
       "      <td>1.029773</td>\n",
       "      <td>1.038049</td>\n",
       "      <td>0.961370</td>\n",
       "      <td>0.873579</td>\n",
       "      <td>10.309208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id          cont0          cont1          cont2  \\\n",
       "count  300000.000000  300000.000000  300000.000000  300000.000000   \n",
       "mean   250018.576947       0.524634       0.506649       0.444115   \n",
       "std    144450.150010       0.204875       0.235269       0.200089   \n",
       "min         1.000000      -0.093505      -0.055105      -0.060274   \n",
       "25%    124772.500000       0.370451       0.352307       0.314121   \n",
       "50%    250002.500000       0.492208       0.615156       0.457271   \n",
       "75%    375226.500000       0.654793       0.688150       0.554835   \n",
       "max    499999.000000       1.052666       0.851746       1.017689   \n",
       "\n",
       "               cont3          cont4          cont5          cont6  \\\n",
       "count  300000.000000  300000.000000  300000.000000  300000.000000   \n",
       "mean        0.446214       0.455471       0.508337       0.478345   \n",
       "std         0.238669       0.200695       0.231612       0.192432   \n",
       "min         0.134760       0.189216      -0.087247       0.043953   \n",
       "25%         0.214572       0.279853       0.338747       0.339896   \n",
       "50%         0.377823       0.411351       0.441384       0.410090   \n",
       "75%         0.719758       0.621808       0.709515       0.604246   \n",
       "max         1.006469       0.994050       1.044433       1.093312   \n",
       "\n",
       "               cont7          cont8          cont9         cont10  \\\n",
       "count  300000.000000  300000.000000  300000.000000  300000.000000   \n",
       "mean        0.455904       0.459321       0.526899       0.504943   \n",
       "std         0.204493       0.220642       0.204025       0.201549   \n",
       "min         0.208703       0.004041       0.073040       0.059644   \n",
       "25%         0.278041       0.308655       0.361957       0.338898   \n",
       "50%         0.360736       0.425801       0.488867       0.519855   \n",
       "75%         0.639388       0.541525       0.752765       0.672809   \n",
       "max         1.036541       1.014156       0.972091       1.029773   \n",
       "\n",
       "              cont11         cont12         cont13         target  \n",
       "count  300000.000000  300000.000000  300000.000000  300000.000000  \n",
       "mean        0.529938       0.524549       0.503349       7.456260  \n",
       "std         0.230860       0.220892       0.225218       0.887295  \n",
       "min         0.064161      -0.005600       0.158121       0.000000  \n",
       "25%         0.316662       0.332143       0.291289       6.798341  \n",
       "50%         0.558827       0.407365       0.433909       7.496503  \n",
       "75%         0.720381       0.732431       0.730870       8.161166  \n",
       "max         1.038049       0.961370       0.873579      10.309208  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        0\n",
       "cat0      0\n",
       "cat1      0\n",
       "cat2      0\n",
       "cat3      0\n",
       "cat4      0\n",
       "cat5      0\n",
       "cat6      0\n",
       "cat7      0\n",
       "cat8      0\n",
       "cat9      0\n",
       "cont0     0\n",
       "cont1     0\n",
       "cont2     0\n",
       "cont3     0\n",
       "cont4     0\n",
       "cont5     0\n",
       "cont6     0\n",
       "cont7     0\n",
       "cont8     0\n",
       "cont9     0\n",
       "cont10    0\n",
       "cont11    0\n",
       "cont12    0\n",
       "cont13    0\n",
       "target    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unique values for Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unique_val(df, col):\n",
    "    return df[col].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of cat0: \n",
      "['A' 'B']\n",
      "Unique values of cat1: \n",
      "['B' 'A']\n",
      "Unique values of cat2: \n",
      "['A' 'B']\n",
      "Unique values of cat3: \n",
      "['A' 'C' 'D' 'B']\n",
      "Unique values of cat4: \n",
      "['B' 'C' 'A' 'D']\n",
      "Unique values of cat5: \n",
      "['D' 'B' 'A' 'C']\n",
      "Unique values of cat6: \n",
      "['A' 'B' 'D' 'C' 'E' 'I' 'G' 'H']\n",
      "Unique values of cat7: \n",
      "['E' 'B' 'D' 'G' 'F' 'A' 'C' 'I']\n",
      "Unique values of cat8: \n",
      "['C' 'A' 'G' 'E' 'D' 'F' 'B']\n",
      "Unique values of cat9: \n",
      "['I' 'F' 'N' 'K' 'B' 'L' 'G' 'H' 'O' 'A' 'J' 'M' 'C' 'D' 'E']\n"
     ]
    }
   ],
   "source": [
    "# Training data\n",
    "for i in range(0,10):\n",
    "    print('Unique values of cat'+ str(i) + ': ')\n",
    "    print(get_unique_val(train_df, 'cat'+ str(i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of cat0: \n",
      "['A' 'B']\n",
      "Unique values of cat1: \n",
      "['B' 'A']\n",
      "Unique values of cat2: \n",
      "['A' 'B']\n",
      "Unique values of cat3: \n",
      "['C' 'A' 'D' 'B']\n",
      "Unique values of cat4: \n",
      "['B' 'A' 'C' 'D']\n",
      "Unique values of cat5: \n",
      "['D' 'B' 'A' 'C']\n",
      "Unique values of cat6: \n",
      "['A' 'B' 'C' 'D' 'E' 'I' 'H']\n",
      "Unique values of cat7: \n",
      "['E' 'D' 'B' 'G' 'F' 'I' 'A' 'C']\n",
      "Unique values of cat8: \n",
      "['E' 'C' 'D' 'G' 'A' 'F' 'B']\n",
      "Unique values of cat9: \n",
      "['G' 'L' 'F' 'I' 'A' 'K' 'M' 'O' 'N' 'H' 'B' 'J' 'C' 'E' 'D']\n"
     ]
    }
   ],
   "source": [
    "# Test Data\n",
    "for i in range(0,10):\n",
    "    print('Unique values of cat'+ str(i) + ': ')\n",
    "    print(get_unique_val(test_df, 'cat'+ str(i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "{'G'}\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n",
      "-----Train - Test-----\n",
      "set()\n",
      "-----Test - Train-----\n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    train_minus_test = set(get_unique_val(train_df, 'cat'+ str(i))).difference(set(get_unique_val(test_df, 'cat'+ str(i))))\n",
    "    test_minus_train = set(get_unique_val(test_df, 'cat'+ str(i))).difference(set(get_unique_val(train_df, 'cat'+ str(i))))\n",
    "    print('-----Train - Test-----')\n",
    "    print(train_minus_test)\n",
    "    print('-----Test - Train-----')\n",
    "    print(test_minus_train)\n",
    "    if len(train_minus_test) > 0:\n",
    "        for item in train_minus_test:\n",
    "            train_df = train_df[train_df['cat'+ str(i)] != item]\n",
    "    \n",
    "    if len(test_minus_train) > 0:\n",
    "        for item in test_minus_train:\n",
    "            test_df = test_df[test_df['cat'+ str(i)] != item]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of cat0: \n",
      "['A' 'B']\n",
      "Unique values of cat1: \n",
      "['B' 'A']\n",
      "Unique values of cat2: \n",
      "['A' 'B']\n",
      "Unique values of cat3: \n",
      "['A' 'C' 'D' 'B']\n",
      "Unique values of cat4: \n",
      "['B' 'C' 'A' 'D']\n",
      "Unique values of cat5: \n",
      "['D' 'B' 'A' 'C']\n",
      "Unique values of cat6: \n",
      "['A' 'B' 'D' 'C' 'E' 'I' 'H']\n",
      "Unique values of cat7: \n",
      "['E' 'B' 'D' 'G' 'F' 'A' 'C' 'I']\n",
      "Unique values of cat8: \n",
      "['C' 'A' 'G' 'E' 'D' 'F' 'B']\n",
      "Unique values of cat9: \n",
      "['I' 'F' 'N' 'K' 'B' 'L' 'G' 'H' 'O' 'A' 'J' 'M' 'C' 'D' 'E']\n"
     ]
    }
   ],
   "source": [
    "# Training data after removal of extra records that were present in either of training data and testing data but absent in another one\n",
    "for i in range(0,10):\n",
    "    print('Unique values of cat'+ str(i) + ': ')\n",
    "    print(get_unique_val(train_df, 'cat'+ str(i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of cat0: \n",
      "['A' 'B']\n",
      "Unique values of cat1: \n",
      "['B' 'A']\n",
      "Unique values of cat2: \n",
      "['A' 'B']\n",
      "Unique values of cat3: \n",
      "['C' 'A' 'D' 'B']\n",
      "Unique values of cat4: \n",
      "['B' 'A' 'C' 'D']\n",
      "Unique values of cat5: \n",
      "['D' 'B' 'A' 'C']\n",
      "Unique values of cat6: \n",
      "['A' 'B' 'C' 'D' 'E' 'I' 'H']\n",
      "Unique values of cat7: \n",
      "['E' 'D' 'B' 'G' 'F' 'I' 'A' 'C']\n",
      "Unique values of cat8: \n",
      "['E' 'C' 'D' 'G' 'A' 'F' 'B']\n",
      "Unique values of cat9: \n",
      "['G' 'L' 'F' 'I' 'A' 'K' 'M' 'O' 'N' 'H' 'B' 'J' 'C' 'E' 'D']\n"
     ]
    }
   ],
   "source": [
    "# Test Data after removal of extra records that were present in either of training data and testing data but absent in another one\n",
    "for i in range(0,10):\n",
    "    print('Unique values of cat'+ str(i) + ': ')\n",
    "    print(get_unique_val(test_df, 'cat'+ str(i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        299997\n",
       "cat0      299997\n",
       "cat1      299997\n",
       "cat2      299997\n",
       "cat3      299997\n",
       "cat4      299997\n",
       "cat5      299997\n",
       "cat6      299997\n",
       "cat7      299997\n",
       "cat8      299997\n",
       "cat9      299997\n",
       "cont0     299997\n",
       "cont1     299997\n",
       "cont2     299997\n",
       "cont3     299997\n",
       "cont4     299997\n",
       "cont5     299997\n",
       "cont6     299997\n",
       "cont7     299997\n",
       "cont8     299997\n",
       "cont9     299997\n",
       "cont10    299997\n",
       "cont11    299997\n",
       "cont12    299997\n",
       "cont13    299997\n",
       "target    299997\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        200000\n",
       "cat0      200000\n",
       "cat1      200000\n",
       "cat2      200000\n",
       "cat3      200000\n",
       "cat4      200000\n",
       "cat5      200000\n",
       "cat6      200000\n",
       "cat7      200000\n",
       "cat8      200000\n",
       "cat9      200000\n",
       "cont0     200000\n",
       "cont1     200000\n",
       "cont2     200000\n",
       "cont3     200000\n",
       "cont4     200000\n",
       "cont5     200000\n",
       "cont6     200000\n",
       "cont7     200000\n",
       "cont8     200000\n",
       "cont9     200000\n",
       "cont10    200000\n",
       "cont11    200000\n",
       "cont12    200000\n",
       "cont13    200000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a dataframe of categorical columns only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>cat0</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat4</th>\n",
       "      <th>cat5</th>\n",
       "      <th>cat6</th>\n",
       "      <th>cat7</th>\n",
       "      <th>cat8</th>\n",
       "      <th>cat9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cat0 cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9\n",
       "0    A    B    A    A    B    D    A    E    C    I\n",
       "1    B    A    A    A    B    B    A    E    A    F\n",
       "2    A    A    A    C    B    D    A    B    C    N\n",
       "3    A    A    A    C    B    D    A    E    G    K\n",
       "4    A    B    A    A    B    B    A    E    C    F"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_cols = train_df.select_dtypes(include=['object'])\n",
    "object_cols.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One - hot encoding\n",
    "One - hot encoding does not work well if any categorical column has a lot of categorical values because it will increase size of the data. Hence, we will consider columns with upto 10 categorical values, rest categorical values will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_cardinality_cols = [col for col in object_cols if train_df[col].nunique()<10]\n",
    "low_cardinality_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cat9']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_cardinality_cols = list(set(object_cols) - set(low_cardinality_cols))\n",
    "high_cardinality_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "one_hot_encoder = OneHotEncoder(handle_unknown = 'ignore', sparse = False)\n",
    "one_hot_encoded_cols = pd.DataFrame(one_hot_encoder.fit_transform(train_df[low_cardinality_cols]))\n",
    "one_hot_encoded_cols.index = train_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>30</th>\n",
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       "      <th>33</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "one_hot_encoded_cols.head(5)"
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoded_test_cols = pd.DataFrame(one_hot_encoder.fit_transform(test_df[low_cardinality_cols]))\n",
    "one_hot_encoded_test_cols.index = test_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_train_df = train_df.drop(object_cols, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_test_df = test_df.drop(object_cols, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "      <td>0.530677</td>\n",
       "      <td>0.618126</td>\n",
       "      <td>0.760865</td>\n",
       "      <td>0.199872</td>\n",
       "      <td>0.279508</td>\n",
       "      <td>0.676395</td>\n",
       "      <td>0.695284</td>\n",
       "      <td>0.253316</td>\n",
       "      <td>0.586934</td>\n",
       "      <td>0.548555</td>\n",
       "      <td>0.836193</td>\n",
       "      <td>0.759788</td>\n",
       "      <td>0.333572</td>\n",
       "      <td>0.273905</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
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       "      <td>0.487403</td>\n",
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       "3  16  0.439653  0.487403  0.715786  0.192808  0.479503  0.759875  0.240049   \n",
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       "3  0.298074  0.442475  0.596746  0.414131  0.255382  0.589080  0.311625  \n",
       "4  0.616663  0.170475  0.263235  0.710961  0.224045  0.285860  0.794931  "
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     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_test_df.head(5)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining one hot encoded categorical and numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_train_df = pd.concat([one_hot_encoded_cols, numeric_train_df], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test_df = pd.concat([one_hot_encoded_test_cols, numeric_test_df], axis = 1)"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
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       "      cont6     cont7     cont8     cont9    cont10    cont11    cont12  \\\n",
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       "1  0.397436  0.476742  0.857073  0.516393  0.562065  0.730542  0.318492   \n",
       "2  0.695284  0.253316  0.586934  0.548555  0.836193  0.759788  0.333572   \n",
       "3  0.240049  0.298074  0.442475  0.596746  0.414131  0.255382  0.589080   \n",
       "4  0.329851  0.616663  0.170475  0.263235  0.710961  0.224045  0.285860   \n",
       "\n",
       "     cont13  \n",
       "0  0.302448  \n",
       "1  0.736251  \n",
       "2  0.273905  \n",
       "3  0.311625  \n",
       "4  0.794931  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([       0,        1,        2,        3,        4,        5,        6,\n",
       "              7,        8,        9,       10,       11,       12,       13,\n",
       "             14,       15,       16,       17,       18,       19,       20,\n",
       "             21,       22,       23,       24,       25,       26,       27,\n",
       "             28,       29,       30,       31,       32,       33,       34,\n",
       "             35,       36,       37,       38,       39,     'id',  'cont0',\n",
       "        'cont1',  'cont2',  'cont3',  'cont4',  'cont5',  'cont6',  'cont7',\n",
       "        'cont8',  'cont9', 'cont10', 'cont11', 'cont12', 'cont13', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([       0,        1,        2,        3,        4,        5,        6,\n",
       "              7,        8,        9,       10,       11,       12,       13,\n",
       "             14,       15,       16,       17,       18,       19,       20,\n",
       "             21,       22,       23,       24,       25,       26,       27,\n",
       "             28,       29,       30,       31,       32,       33,       34,\n",
       "             35,       36,       37,       38,       39,     'id',  'cont0',\n",
       "        'cont1',  'cont2',  'cont3',  'cont4',  'cont5',  'cont6',  'cont7',\n",
       "        'cont8',  'cont9', 'cont10', 'cont11', 'cont12', 'cont13'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting of training data into training and validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.994023\n",
       "1    8.071256\n",
       "2    5.760456\n",
       "3    7.806457\n",
       "4    6.868974\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = final_train_df.pop('target')\n",
    "y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     0    1    2    3    4    5    6    7    8    9  ...     cont4     cont5  \\\n",
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       "\n",
       "      cont6     cont7     cont8     cont9    cont10    cont11    cont12  \\\n",
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       "4  0.705940  0.325193  0.440967  0.462146  0.724447  0.683073  0.343457   \n",
       "\n",
       "     cont13  \n",
       "0  0.719903  \n",
       "1  0.808464  \n",
       "2  0.828352  \n",
       "3  0.614766  \n",
       "4  0.297743  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final_train_df\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_val, y_train, y_val = train_test_split(X, y, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "          cont5     cont6     cont7     cont8     cont9    cont10    cont11  \\\n",
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       "87267  0.296080  0.346007  0.294980  0.286144  0.565461  0.752770  0.309088   \n",
       "19167  0.183948  0.371749  0.453102  0.139595  0.303255  0.297499  0.250610   \n",
       "31322  0.233538  0.336393  0.596583  0.301505  0.322828  0.353156  0.376640   \n",
       "\n",
       "         cont12    cont13  \n",
       "306    0.325750  0.353437  \n",
       "48612  0.707692  0.194632  \n",
       "87267  0.725224  0.374624  \n",
       "19167  0.375246  0.725660  \n",
       "31322  0.442699  0.833210  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.634051</td>\n",
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       "          0    1    2    3    4    5    6    7    8    9  ...     cont4  \\\n",
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       "\n",
       "           cont5     cont6     cont7     cont8     cont9    cont10    cont11  \\\n",
       "95629   0.584195  0.537794  0.321365  0.421584  0.645232  0.695090  0.816376   \n",
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       "\n",
       "          cont12    cont13  \n",
       "95629   0.639613  0.779314  \n",
       "9088    0.823816  0.230092  \n",
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       "20801   0.821053  0.357360  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_val.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8629479507070059"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "model = RandomForestRegressor(n_estimators = 100, random_state = 0)\n",
    "model.fit(x_train, y_train)\n",
    "pred = model.predict(x_val)\n",
    "rmse = mean_squared_error(y_val, pred, squared = False)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.170475</td>\n",
       "      <td>0.263235</td>\n",
       "      <td>0.710961</td>\n",
       "      <td>0.224045</td>\n",
       "      <td>0.285860</td>\n",
       "      <td>0.794931</td>\n",
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       "     0    1    2    3    4    5    6    7    8    9  ...     cont4     cont5  \\\n",
       "0  1.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0  0.0  ...  0.701679  0.595507   \n",
       "1  1.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0  0.0  ...  0.277480  0.479552   \n",
       "2  1.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0  0.0  ...  0.279508  0.676395   \n",
       "3  1.0  0.0  1.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  ...  0.479503  0.759875   \n",
       "4  1.0  0.0  0.0  1.0  1.0  0.0  1.0  0.0  0.0  0.0  ...  0.757845  0.210232   \n",
       "\n",
       "      cont6     cont7     cont8     cont9    cont10    cont11    cont12  \\\n",
       "0  0.286912  0.279884  0.202234  0.242654  0.285147  0.264308  0.653654   \n",
       "1  0.397436  0.476742  0.857073  0.516393  0.562065  0.730542  0.318492   \n",
       "2  0.695284  0.253316  0.586934  0.548555  0.836193  0.759788  0.333572   \n",
       "3  0.240049  0.298074  0.442475  0.596746  0.414131  0.255382  0.589080   \n",
       "4  0.329851  0.616663  0.170475  0.263235  0.710961  0.224045  0.285860   \n",
       "\n",
       "     cont13  \n",
       "0  0.302448  \n",
       "1  0.736251  \n",
       "2  0.273905  \n",
       "3  0.311625  \n",
       "4  0.794931  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.67545224, 7.70351859, 7.57978512, ..., 7.37587732, 7.79308431,\n",
       "       7.55770055])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(final_test_df)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>7.703519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>7.579785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>7.224458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>7.432672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    target\n",
       "0   0  7.675452\n",
       "1   5  7.703519\n",
       "2  15  7.579785\n",
       "3  16  7.224458\n",
       "4  17  7.432672"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame()\n",
    "result['id'] = final_test_df['id']\n",
    "result['target'] = predictions\n",
    "result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('../output/submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/bin/kaggle\", line 5, in <module>\n",
      "    from kaggle.cli import main\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/kaggle/__init__.py\", line 23, in <module>\n",
      "    api.authenticate()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/kaggle/api/kaggle_api_extended.py\", line 166, in authenticate\n",
      "    self.config_file, self.config_dir))\n",
      "OSError: Could not find kaggle.json. Make sure it's located in /root/.kaggle. Or use the environment method.\n"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c tabular-playground-series-feb-2021 -f submission.csv -m \"Message\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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