titanic_starting_kit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Paris Saclay Center for Data Science](http://www.datascience-paris-saclay.fr)\n",
    "\n",
    "## [Titanic RAMP](http://www.ramp.studio/problems/titanic): survival prediction of Titanic passengers\n",
    "\n",
    "## Introduction\n",
    "This is an initiation project to introduce RAMP and get you to know how it works.\n",
    "\n",
    "The goal is to develop prediction models able to **identify people who survived from the sinking of the Titanic, based on gender, age, and ticketing information**. \n",
    "\n",
    "The data we will manipulate is from the [Titanic kaggle challenge](https://www.kaggle.com/c/titanic-gettingStarted)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use some utilities from RAMP to load the public train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = problem.get_train_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`X_train` contains information about each person which has a ticket to go on the boat while `y_train` is the information if a passenger survived or did not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Pclass</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>Palsson, Mrs. Nils (Alma Cornelia Berglund)</td>\n      <td>female</td>\n      <td>29.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>349909</td>\n      <td>21.075</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Beane, Mr. Edward</td>\n      <td>male</td>\n      <td>32.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2908</td>\n      <td>26.000</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Palsson, Miss. Stina Viola</td>\n      <td>female</td>\n      <td>3.0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>349909</td>\n      <td>21.075</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Torber, Mr. Ernst William</td>\n      <td>male</td>\n      <td>44.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>364511</td>\n      <td>8.050</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>Bystrom, Mrs. (Karolina)</td>\n      <td>female</td>\n      <td>42.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>236852</td>\n      <td>13.000</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   Pclass                                         Name     Sex   Age  SibSp  \\\n0       3  Palsson, Mrs. Nils (Alma Cornelia Berglund)  female  29.0      0   \n1       2                            Beane, Mr. Edward    male  32.0      1   \n2       3                   Palsson, Miss. Stina Viola  female   3.0      3   \n3       3                    Torber, Mr. Ernst William    male  44.0      0   \n4       2                     Bystrom, Mrs. (Karolina)  female  42.0      0   \n\n   Parch  Ticket    Fare Cabin Embarked  \n0      4  349909  21.075   NaN        S  \n1      0    2908  26.000   NaN        S  \n2      1  349909  21.075   NaN        S  \n3      0  364511   8.050   NaN        S  \n4      0  236852  13.000   NaN        S  "
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 1, 0, 0, 1])"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can first look at some descriptive statistics regarding our data."
   ]
  },
  {
   "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>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>356.000000</td>\n      <td>290.000000</td>\n      <td>356.000000</td>\n      <td>356.000000</td>\n      <td>356.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.300562</td>\n      <td>29.123862</td>\n      <td>0.550562</td>\n      <td>0.412921</td>\n      <td>31.657970</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.833861</td>\n      <td>14.103122</td>\n      <td>1.120978</td>\n      <td>0.798415</td>\n      <td>43.474154</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.920000</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>19.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.925000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>15.245800</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.000000</td>\n      <td>37.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>31.275000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>71.000000</td>\n      <td>8.000000</td>\n      <td>6.000000</td>\n      <td>263.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "           Pclass         Age       SibSp       Parch        Fare\ncount  356.000000  290.000000  356.000000  356.000000  356.000000\nmean     2.300562   29.123862    0.550562    0.412921   31.657970\nstd      0.833861   14.103122    1.120978    0.798415   43.474154\nmin      1.000000    0.920000    0.000000    0.000000    0.000000\n25%      2.000000   19.000000    0.000000    0.000000    7.925000\n50%      3.000000   28.000000    0.000000    0.000000   15.245800\n75%      3.000000   37.000000    1.000000    1.000000   31.275000\nmax      3.000000   71.000000    8.000000    6.000000  263.000000"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, this is good to have an overlook regarding the type of data, we are dealing with and if there is any missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 356 entries, 0 to 355\nData columns (total 10 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   Pclass    356 non-null    int64  \n 1   Name      356 non-null    object \n 2   Sex       356 non-null    object \n 3   Age       290 non-null    float64\n 4   SibSp     356 non-null    int64  \n 5   Parch     356 non-null    int64  \n 6   Ticket    356 non-null    object \n 7   Fare      356 non-null    float64\n 8   Cabin     83 non-null     object \n 9   Embarked  356 non-null    object \ndtypes: float64(2), int64(3), object(5)\nmemory usage: 27.9+ KB\n"
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original training data frame has 891 rows. In the starting kit, we give you a subset of 356 rows. Some passengers have missing information: in particular `Age` and `Cabin` info can be missing. The meaning of the columns is explained on the [challenge website](https://www.kaggle.com/c/titanic-gettingStarted/data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting survival\n",
    "\n",
    "The goal is to predict whether a passenger has survived from other known attributes. Let us group the data according to the `Survived` columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {
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   ],
   "source": [
    "_ = pd.Series(y_train).value_counts().rename({0: \"deceased\", 1: \"survived\"}).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About two thirds of the passengers perished in the event. A dummy classifier that systematically returns \"0\" would have an accuracy of 62%, higher than that of a random model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Features densities and co-evolution\n",
    "A scatterplot matrix allows us to visualize:\n",
    "* on the diagonal, the density estimation for each feature\n",
    "* on each of the off-diagonal plots, a scatterplot between two features. Each dot represents an instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = X_train.copy()\n",
    "df[\"Survived\"] = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 411.875x360 with 6 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['Fare', 'Age', 'Survived']\n",
    "_ = sns.pairplot(\n",
    "    df[features], hue=\"Survived\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": "<Figure size 502.5x432 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=df,\n",
    "    height=6, kind=\"bar\", palette=\"muted\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Non-linearly transformed data\n",
    "\n",
    "The `Fare` variable has a very heavy tail. We can log-transform it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 411.875x360 with 6 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"LogFare\"] = np.log(df[\"Fare\"] + 10)\n",
    "features = ['LogFare', 'Age', 'Survived']\n",
    "_ = sns.pairplot(\n",
    "    df[features], hue=\"Survived\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the bivariate distributions and marginals of two variables \n",
    "\n",
    "Another way of visualizing relationships between variables is to plot their bivariate distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 504x504 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.jointplot(\n",
    "    df.Age[df.Survived == 1],\n",
    "    df.LogFare[df.Survived == 1],\n",
    "    kind=\"kde\", height=7, space=0, color=\"b\"\n",
    ")\n",
    "\n",
    "_ = sns.jointplot(\n",
    "    df.Age[df.Survived == 0],\n",
    "    df.LogFare[df.Survived == 0],\n",
    "    kind=\"kde\", height=7, space=0, color=\"y\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions\n",
    "\n",
    "A basic prediction workflow, using scikit-learn, will be presented below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will perform some simple preprocessing of our data:\n",
    "\n",
    "* [one-hot encode](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) the categorical features: `Sex`, `Pclass`, `Embarked`\n",
    "* for the numerical columns `Age`, `SibSp`, `Parch`, `Fare`, fill in missing values with a default value (`-1`)\n",
    "* all remaining columns will be dropped\n",
    "\n",
    "This can be done succintly with [`make_column_transformer`](https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html) which performs specific transformations on specific features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "categorical_cols = ['Sex', 'Pclass', 'Embarked']\n",
    "categorical_pipeline = make_pipeline(OneHotEncoder(handle_unknown='ignore'))\n",
    "numerical_cols = ['Age', 'SibSp', 'Parch', 'Fare']\n",
    "numerical_pipeline = make_pipeline(\n",
    "    StandardScaler(), SimpleImputer(strategy='constant', fill_value=-1)\n",
    ")\n",
    "\n",
    "preprocessor = make_column_transformer(\n",
    "    (categorical_pipeline, categorical_cols),\n",
    "    (numerical_pipeline, numerical_cols),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `preprocessor` object created with `make_column_transformer` can be used in a scikit-learn [`pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). A `pipeline` assembles several steps together and can be used to cross validate an entire workflow. Generally, transformation steps are combined with a final estimator.\n",
    "\n",
    "We will create a pipeline consisting of the `preprocessor` created above and a final estimator, `LogisticRegression`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('transformer', preprocessor),\n",
    "    ('classifier', LogisticRegression()),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can cross-validate our `pipeline` using `cross_val_score`. Below we will have specified `cv=8` meaning KFold cross-valdiation splitting will be used, with 8 folds. The Area Under the Receiver Operating Characteristic Curve (ROC AUC) score is calculated for each split. The output `score` will be an array of 8 scores from each KFold. The score mean and standard deviation of the 8 scores is printed at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "mean: 8.438462e-01 (+/- 4.992179e-02)\n"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(pipeline, X_train, y_train, cv=8, scoring='roc_auc')\n",
    "\n",
    "print(\"mean: %e (+/- %e)\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing\n",
    "\n",
    "Once you have created a model with cross-valdiation scores you are happy with, you can test how well your model performs on the independent test data.\n",
    "\n",
    "First we will read in our test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Pclass</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Bissette, Miss. Amelia</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>PC 17760</td>\n      <td>135.6333</td>\n      <td>C99</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>McGowan, Miss. Anna \"Annie\"</td>\n      <td>female</td>\n      <td>15.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>330923</td>\n      <td>8.0292</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Lam, Mr. Len</td>\n      <td>male</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1601</td>\n      <td>56.4958</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Olsson, Miss. Elina</td>\n      <td>female</td>\n      <td>31.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>350407</td>\n      <td>7.8542</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>Peter, Miss. Anna</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2668</td>\n      <td>22.3583</td>\n      <td>F E69</td>\n      <td>C</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   Pclass                         Name     Sex   Age  SibSp  Parch    Ticket  \\\n0       1       Bissette, Miss. Amelia  female  35.0      0      0  PC 17760   \n1       3  McGowan, Miss. Anna \"Annie\"  female  15.0      0      0    330923   \n2       3                 Lam, Mr. Len    male   NaN      0      0      1601   \n3       3          Olsson, Miss. Elina  female  31.0      0      0    350407   \n4       3            Peter, Miss. Anna  female   NaN      1      1      2668   \n\n       Fare  Cabin Embarked  \n0  135.6333    C99        S  \n1    8.0292    NaN        Q  \n2   56.4958    NaN        S  \n3    7.8542    NaN        S  \n4   22.3583  F E69        C  "
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test, y_test = problem.get_test_data()\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to fit our pipeline on our training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can predict on our test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can calculate how well our model performed on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.8390374331550802"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "score = roc_auc_score(y_test, y_pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission\n",
    "\n",
    "To submit your code, you can refer to the [online documentation](https://paris-saclay-cds.github.io/ramp-docs/ramp-workflow/stable/using_kits.html)."
   ]
  }
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