prepare-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic: Survival Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean, feature engineer, and split the classic Titanic survival dataset from OpenML, uploading the result to Hugging Face ready for ML applications.\n",
    "\n",
    "Based on https://github.com/fastai/course22/blob/master/clean/05-linear-model-and-neural-net-from-scratch.ipynb using the dataset from https://www.openml.org/search?type=data&sort=runs&id=40945&status=active."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plan\n",
    "\n",
    "- [x] Download dataset from OpenML\n",
    "- [x] Clean data\n",
    "- [x] Perform feature engineering\n",
    "- [x] Split into train/validation/test subsets\n",
    "- [x] Upload to Hugging Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Dataset from OpenML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openml\n",
    "\n",
    "dataset = openml.datasets.get_dataset(40945)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>female</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>328.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>male</td>\n",
       "      <td>26.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2656</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>304.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2670</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315082</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass survived                                             name  \\\n",
       "0          1        1                    Allen, Miss. Elisabeth Walton   \n",
       "1          1        1                   Allison, Master. Hudson Trevor   \n",
       "2          1        0                     Allison, Miss. Helen Loraine   \n",
       "3          1        0             Allison, Mr. Hudson Joshua Creighton   \n",
       "4          1        0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "...      ...      ...                                              ...   \n",
       "1304       3        0                             Zabour, Miss. Hileni   \n",
       "1305       3        0                            Zabour, Miss. Thamine   \n",
       "1306       3        0                        Zakarian, Mr. Mapriededer   \n",
       "1307       3        0                              Zakarian, Mr. Ortin   \n",
       "1308       3        0                               Zimmerman, Mr. Leo   \n",
       "\n",
       "         sex      age  sibsp  parch  ticket      fare    cabin embarked  boat  \\\n",
       "0     female  29.0000      0      0   24160  211.3375       B5        S     2   \n",
       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S    11   \n",
       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  None   \n",
       "...      ...      ...    ...    ...     ...       ...      ...      ...   ...   \n",
       "1304  female  14.5000      1      0    2665   14.4542     None        C  None   \n",
       "1305  female      NaN      1      0    2665   14.4542     None        C  None   \n",
       "1306    male  26.5000      0      0    2656    7.2250     None        C  None   \n",
       "1307    male  27.0000      0      0    2670    7.2250     None        C  None   \n",
       "1308    male  29.0000      0      0  315082    7.8750     None        S  None   \n",
       "\n",
       "       body                        home.dest  \n",
       "0       NaN                     St Louis, MO  \n",
       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
       "...     ...                              ...  \n",
       "1304  328.0                             None  \n",
       "1305    NaN                             None  \n",
       "1306  304.0                             None  \n",
       "1307    NaN                             None  \n",
       "1308    NaN                             None  \n",
       "\n",
       "[1309 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data, *_ = dataset.get_data()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['boat', 'body', 'home.dest'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass         0\n",
       "survived       0\n",
       "name           0\n",
       "sex            0\n",
       "age          263\n",
       "sibsp          0\n",
       "parch          0\n",
       "ticket         0\n",
       "fare           1\n",
       "cabin       1014\n",
       "embarked       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass                       3.0\n",
       "survived                       0\n",
       "name        Connolly, Miss. Kate\n",
       "sex                         male\n",
       "age                         24.0\n",
       "sibsp                        0.0\n",
       "parch                        0.0\n",
       "ticket                  CA. 2343\n",
       "fare                        8.05\n",
       "cabin                C23 C25 C27\n",
       "embarked                       S\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modes = data.mode().iloc[0]\n",
    "modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      0\n",
       "survived    0\n",
       "name        0\n",
       "sex         0\n",
       "age         0\n",
       "sibsp       0\n",
       "parch       0\n",
       "ticket      0\n",
       "fare        0\n",
       "cabin       0\n",
       "embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(modes, inplace=True)\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>28.699516</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.276193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>13.097103</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.743584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pclass          age        sibsp        parch         fare\n",
       "count  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000\n",
       "mean      2.294882    28.699516     0.498854     0.385027    33.276193\n",
       "std       0.837836    13.097103     1.041658     0.865560    51.743584\n",
       "min       1.000000     0.166700     0.000000     0.000000     0.000000\n",
       "25%       2.000000    22.000000     0.000000     0.000000     7.895800\n",
       "50%       3.000000    24.000000     0.000000     0.000000    14.454200\n",
       "75%       3.000000    35.000000     1.000000     0.000000    31.275000\n",
       "max       3.000000    80.000000     8.000000     9.000000   512.329200"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data.describe(include=np.number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the dataset we can see that the `fare` column has a very skewed distribution. We can try to transform it into a more normal distribution by taking the log:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['fare'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['log_fare'] = np.log(data['fare']+1)\n",
    "data['log_fare'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also from the dataset we can see that the `pclass` column consists of of integers, but is actually a categorical variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[np.uint8(1), np.uint8(2), np.uint8(3)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(data.pclass.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same is true of some other non-numeric columns (`survived`, `sex`, `embarked`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>ticket</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>1307</td>\n",
       "      <td>2</td>\n",
       "      <td>929</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>809</td>\n",
       "      <td>2</td>\n",
       "      <td>843</td>\n",
       "      <td>11</td>\n",
       "      <td>1020</td>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       survived              name   sex    ticket        cabin embarked\n",
       "count      1309              1309  1309      1309         1309     1309\n",
       "unique        2              1307     2       929          186        3\n",
       "top           0  Kelly, Mr. James  male  CA. 2343  C23 C25 C27        S\n",
       "freq        809                 2   843        11         1020      916"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(exclude=np.number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We convert the categorical variables to dummy variables using one-hot encoding. Note that `survived` is already in the desired form, the column type just needs to be set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'name', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin',\n",
       "       'log_fare', 'pclass_1', 'pclass_2', 'pclass_3', 'sex_female',\n",
       "       'sex_male', 'embarked_C', 'embarked_Q', 'embarked_S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['survived'] = data['survived'].astype('int').astype('bool')\n",
    "data = pd.get_dummies(data, columns=['pclass', 'sex', 'embarked'])\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "added_cols = ['pclass_1', 'pclass_2', 'pclass_3', 'sex_female', 'sex_male', 'embarked_C', 'embarked_Q', 'embarked_S']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>log_fare</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>5.358177</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>5.027492</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>5.027492</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>5.027492</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>5.027492</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>False</td>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>2.737881</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>False</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>2.737881</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>False</td>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>26.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2656</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>2.107178</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>False</td>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2670</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>2.107178</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>False</td>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315082</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>2.183238</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      survived                                             name      age  \\\n",
       "0         True                    Allen, Miss. Elisabeth Walton  29.0000   \n",
       "1         True                   Allison, Master. Hudson Trevor   0.9167   \n",
       "2        False                     Allison, Miss. Helen Loraine   2.0000   \n",
       "3        False             Allison, Mr. Hudson Joshua Creighton  30.0000   \n",
       "4        False  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  25.0000   \n",
       "...        ...                                              ...      ...   \n",
       "1304     False                             Zabour, Miss. Hileni  14.5000   \n",
       "1305     False                            Zabour, Miss. Thamine  24.0000   \n",
       "1306     False                        Zakarian, Mr. Mapriededer  26.5000   \n",
       "1307     False                              Zakarian, Mr. Ortin  27.0000   \n",
       "1308     False                               Zimmerman, Mr. Leo  29.0000   \n",
       "\n",
       "      sibsp  parch  ticket      fare        cabin  log_fare  pclass_1  \\\n",
       "0         0      0   24160  211.3375           B5  5.358177      True   \n",
       "1         1      2  113781  151.5500      C22 C26  5.027492      True   \n",
       "2         1      2  113781  151.5500      C22 C26  5.027492      True   \n",
       "3         1      2  113781  151.5500      C22 C26  5.027492      True   \n",
       "4         1      2  113781  151.5500      C22 C26  5.027492      True   \n",
       "...     ...    ...     ...       ...          ...       ...       ...   \n",
       "1304      1      0    2665   14.4542  C23 C25 C27  2.737881     False   \n",
       "1305      1      0    2665   14.4542  C23 C25 C27  2.737881     False   \n",
       "1306      0      0    2656    7.2250  C23 C25 C27  2.107178     False   \n",
       "1307      0      0    2670    7.2250  C23 C25 C27  2.107178     False   \n",
       "1308      0      0  315082    7.8750  C23 C25 C27  2.183238     False   \n",
       "\n",
       "      pclass_2  pclass_3  sex_female  sex_male  embarked_C  embarked_Q  \\\n",
       "0        False     False        True     False       False       False   \n",
       "1        False     False       False      True       False       False   \n",
       "2        False     False        True     False       False       False   \n",
       "3        False     False       False      True       False       False   \n",
       "4        False     False        True     False       False       False   \n",
       "...        ...       ...         ...       ...         ...         ...   \n",
       "1304     False      True        True     False        True       False   \n",
       "1305     False      True        True     False        True       False   \n",
       "1306     False      True       False      True        True       False   \n",
       "1307     False      True       False      True        True       False   \n",
       "1308     False      True       False      True       False       False   \n",
       "\n",
       "      embarked_S  \n",
       "0           True  \n",
       "1           True  \n",
       "2           True  \n",
       "3           True  \n",
       "4           True  \n",
       "...          ...  \n",
       "1304       False  \n",
       "1305       False  \n",
       "1306       False  \n",
       "1307       False  \n",
       "1308        True  \n",
       "\n",
       "[1309 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Into Train/Validation/Test Subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import *\n",
    "\n",
    "dataset = Dataset.from_pandas(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset, validation_dataset = dataset.train_test_split(0.2, seed=42).values()\n",
    "validation_dataset, test_dataset = validation_dataset.train_test_split(0.5, seed=42).values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['survived', 'name', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'log_fare', 'pclass_1', 'pclass_2', 'pclass_3', 'sex_female', 'sex_male', 'embarked_C', 'embarked_Q', 'embarked_S'],\n",
       "        num_rows: 1047\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['survived', 'name', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'log_fare', 'pclass_1', 'pclass_2', 'pclass_3', 'sex_female', 'sex_male', 'embarked_C', 'embarked_Q', 'embarked_S'],\n",
       "        num_rows: 131\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['survived', 'name', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'log_fare', 'pclass_1', 'pclass_2', 'pclass_3', 'sex_female', 'sex_male', 'embarked_C', 'embarked_Q', 'embarked_S'],\n",
       "        num_rows: 131\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_dict = DatasetDict({'train': train_dataset,\n",
    "                            'validation': validation_dataset,\n",
    "                            'test': test_dataset})\n",
    "\n",
    "dataset_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload to Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2410182136'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import *\n",
    "\n",
    "timestamp = datetime.now().strftime('%y%m%d%H%M')\n",
    "timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef4f3d23ce1248f5821b0ccb4de99175",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25413a88702942c782138cf96e3ce804",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "635c729e256e4f82bb8dd64007fd44b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60afbc5c622f4728aef0e39569a121fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "908140c9bb42445abf2bacc997fc8ce4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b27cde37699e4f728b1dfcdab50b7f71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d93c80a161d24526b1715665996ef036",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/1.51k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/datasets/jamieoliver/titanic-2410/commit/aad6dc0690e2053fb1ee680ba748954dd30f58d9', commit_message='Dataset timestamp 2410182136', commit_description='', oid='aad6dc0690e2053fb1ee680ba748954dd30f58d9', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/jamieoliver/titanic-2410', endpoint='https://huggingface.co', repo_type='dataset', repo_id='jamieoliver/titanic-2410'), pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from huggingface_hub import *\n",
    "\n",
    "repo = 'jamieoliver/titanic-2410'\n",
    "branch = f'timestamp-{timestamp}'\n",
    "\n",
    "create_branch(repo,\n",
    "              branch=branch,\n",
    "              repo_type='dataset')\n",
    "\n",
    "dataset_dict.push_to_hub(repo,\n",
    "                         revision=branch,\n",
    "                         commit_message=f'Dataset timestamp {timestamp}')"
   ]
  }
 ],
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  "kernelspec": {
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
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