A2 Making Submissions in the Kaggle Competition.ipynb

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    "<img align=\"center\" src=\"http://sydney.edu.au/images/content/about/logo-mono.jpg\">\n",
    "<h1 align=\"center\" style=\"margin-top:10px\">Statistical Learning with Python</h1>\n",
    "<h2 align=\"center\" style=\"margin-top:20px\">Appendix 2: Making Submissions in the Kaggle Competition</h2>\n",
    "<br>\n",
    "\n",
    "In this notebook I'll show you have you to prepare the submission for the Kaggle competition. Before this, please go over the sample submission so that you can first see the format that we are trying to achieve.\n",
    "\n",
    "This notebook relies on the following imports and settings."
   ]
  },
  {
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   "source": [
    "### 1. Loading the Data\n",
    "\n"
   ]
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       "               ZIP_CODE  REC  FRE     MON  CC_CARD    AVRG  PC_CALC20  \\\n",
       "HHKEY                                                                   \n",
       "9955628074270     48328   76    1  125.96        0  125.96         11   \n",
       "9955630420476     53572  167    5  316.99        0   63.39         11   \n",
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       "\n",
       "               PSWEATERS  PKNIT_TOPS  PKNIT_DRES  ...  VALPHON  WEB  MAILED  \\\n",
       "HHKEY                                             ...                         \n",
       "9955628074270       0.00        0.00         0.0  ...        Y    0       0   \n",
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       "\n",
       "               RESPONDED  RESPONSERATE     HI  LTFREDAY  CLUSTYPE  PERCRET  \\\n",
       "HHKEY                                                                        \n",
       "9955628074270          0           0.0  50.61     76.00         4     0.00   \n",
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       "\n",
       "               RESP  \n",
       "HHKEY                \n",
       "9955628074270     0  \n",
       "9955630420476     0  \n",
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     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "train = pd.read_csv('data\\CS_data.csv', index_col='HHKEY')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
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       "               ZIP_CODE  REC  FRE      MON  CC_CARD   AVRG  PC_CALC20  \\\n",
       "HHKEY                                                                   \n",
       "9955621115332     37373   31    1    93.00        0  93.00         11   \n",
       "9955649946385     93722  139    3   239.37        0  79.79         16   \n",
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       "9955648461892     91201   15   50  2929.75        1  58.59         11   \n",
       "9955604008806     10562  306    1    88.00        0  88.00         11   \n",
       "\n",
       "               PSWEATERS  PKNIT_TOPS  PKNIT_DRES  ...  STORELOY  VALPHON  WEB  \\\n",
       "HHKEY                                             ...                           \n",
       "9955621115332       1.00    0.000000        0.00  ...      4510        Y    0   \n",
       "9955649946385       0.36    0.050000        0.00  ...      2620        N    0   \n",
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       "9955604008806       0.00    0.000000        0.00  ...       685        N    0   \n",
       "\n",
       "               MAILED  RESPONDED  RESPONSERATE      HI  LTFREDAY  CLUSTYPE  \\\n",
       "HHKEY                                                                        \n",
       "9955621115332       0          0           0.0  100.00     31.00        38   \n",
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       "9955604008806       2          0           0.0  100.00    306.00        15   \n",
       "\n",
       "               PERCRET  \n",
       "HHKEY                   \n",
       "9955621115332      0.0  \n",
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       "\n",
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('data\\CS_kaggle.csv', index_col='HHKEY')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Fitting a Simple Model\n",
    "\n",
    "Note the difference between the report and the Kaggle competition. For the report, you need to split the main data so that you can do model evaluation. In the Kaggle competition, the whole initial dataset becomes the training data. That's because the purpose of the Kaggle competition is for you to deploy your machine learning system after careful model building, selection and evaluation, as would be the case in actual business setting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "response ='RESP'\n",
    "predictors = ['FRE', 'RESPONSERATE']\n",
    "\n",
    "y_train = train[response].to_numpy()\n",
    "X_train = train[predictors].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=inf, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logit = LogisticRegression(C=np.inf, solver='lbfgs')\n",
    "logit.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Predictions and Submission File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test[predictors].to_numpy()\n",
    "y_prob = logit.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>RESP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HHKEY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9955621115332</th>\n",
       "      <td>0.075225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9955649946385</th>\n",
       "      <td>0.106690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9955640398666</th>\n",
       "      <td>0.075225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9955648461892</th>\n",
       "      <td>0.990724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9955604008806</th>\n",
       "      <td>0.075225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RESP\n",
       "HHKEY                  \n",
       "9955621115332  0.075225\n",
       "9955649946385  0.106690\n",
       "9955640398666  0.075225\n",
       "9955648461892  0.990724\n",
       "9955604008806  0.075225"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame(y_prob, index=test.index, columns=[response])\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A subdirectory or file Kaggle already exists.\n"
     ]
    }
   ],
   "source": [
    "!mkdir Kaggle\n",
    "submission = submission.to_csv('Kaggle\\submission00.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Code\n",
    "\n",
    "The two cells below format the notebook for display online. Please omit them from your work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "@import url('https://fonts.googleapis.com/css?family=Source+Sans+Pro|Open+Sans:800&display=swap');\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "@import url('https://fonts.googleapis.com/css?family=Source+Sans+Pro|Open+Sans:800&display=swap');\n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.CodeMirror pre {font-family: monospace; font-size: 12pt; line-height: 22.4px;}\n",
       "\n",
       "pre {font-family: monospace; font-size: 12pt; line-height: 22.4px;}\n",
       "\n",
       ".output_area pre {font-family: monospace; font-size: 12pt; line-height: 22.4px;}\n",
       "\n",
       ".rendered_html table {font-size: 10.5pt;}\n",
       "\n",
       "h1 {\n",
       "  font-family: 'Open Sans' !important;\n",
       "  color: #222;\n",
       "  font-size: 2.5em !important;\n",
       "  padding-bottom: 0px;  \n",
       "  margin-top: 14px;\n",
       "  margin-bottom: 6px;\n",
       "  font-weight: 800 !important;\n",
       "}\n",
       "\n",
       "\n",
       "h2 {\n",
       "  font-family: 'Open Sans' !important;\n",
       "  font-size: 1.6em !important;\n",
       "  font-weight: 800 !important;\n",
       "  color: #222;\n",
       "}\n",
       "\n",
       "\n",
       "h3 {\n",
       "  font-family: 'Open Sans' !important;\n",
       "  font-size: 25px !important;\n",
       "  color: #222 !important;\n",
       "  margin-bottom: 30px !important;\n",
       "  margin-top: 25px !important;\n",
       "}\n",
       "\n",
       "\n",
       "p {\n",
       "  font-family: 'Source Sans Pro', sans-serif;\n",
       "  color: #222;\n",
       "  font-size: 19px;\n",
       "  margin-bottom: 10px;\n",
       "  margin-top:0;\n",
       "  line-height: 1.4;\n",
       "}\n",
       "\n",
       "li {\n",
       "  font-family: 'Source Sans Pro', sans-serif;\n",
       "  color: #222;\n",
       "  font-size: 19px;\n",
       "  line-height: 1.4;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/*code {\n",
       "     padding: 2px 4px !important; \n",
       "     font-size: 90% !important;\n",
       "     color: #c7254e !important; \n",
       "     background-color: #f9f2f4 !important; \n",
       "     border-radius: 2px !important;\n",
       "     text-decoration: none !important;\n",
       "      font-size: 90% !important;\n",
       "}\n",
       "*/\n",
       "\n",
       "\n",
       "code {\n",
       "      font-size: 90% !important;\n",
       "/*      border-style: solid !important;\n",
       "      border-width: 1px !important;\n",
       "      border-color :#cccccc !important;*/\n",
       "}\n",
       "\n",
       "TT {\n",
       "      font-family: monospace;\n",
       "      font-size: 95% !important;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "a.neutral {\n",
       "  text-decoration: none !important;\n",
       "  border-style: dotted !important;\n",
       "  border-width: 1px !important;\n",
       "  border-color :#cccccc !important;\n",
       "\n",
       "}\n",
       "\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "style = open('jstyle.css', \"r\").read()\n",
    "HTML('<style>'+ style +'</style>')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}