train_employee_turnover.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "import os"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Dataset, Run\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "run = Run.get_context()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_col = ['City', 'EmailDomain', 'HiredthroughSMTP', 'ManagerRatingOfLikelihoodToLeave', \n",
    "         'MarkedForPHTProgram', 'MostRecentPerformanceEvaluation', 'SocialMediaActivity',\n",
    "         'Survey_AttitudeTowardWorkType', 'Survey_AttitudeTowardWorkload', 'Survey_RelativePeerAverageAttitudeTowardManager']\n",
    "y_col = ['EmployeeLeft']\n",
    "categories = ['City', 'EmailDomain', 'HiredthroughSMTP', 'ManagerRatingOfLikelihoodToLeave', 'MarkedForPHTProgram', 'SocialMediaActivity',\n",
    "         'Survey_AttitudeTowardWorkType', 'Survey_AttitudeTowardWorkload', 'Survey_RelativePeerAverageAttitudeTowardManager']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[categories] = df[categories].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"City\"] = df[\"City\"].cat.codes\n",
    "df[\"EmailDomain\"] = df[\"EmailDomain\"].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_df = df.loc[:, x_col]\n",
    "y_df = df.loc[:, y_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing X,y into train and test data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'train': {'X': x_train, 'y': y_train},\n",
    "\n",
    "        'test': {'X': x_test, 'y': y_test}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier().fit(data['train']['X'], data['train']['y'])\n",
    "model_file_name = 'decision_tree.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clf.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('./outputs', exist_ok=True)\n",
    "with open(model_file_name, 'wb') as file:\n",
    "    joblib.dump(value=clf, filename='outputs/' + model_file_name)"
   ]
  }
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