{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting the tree to employee data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the classification algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Initialize it and call model by specifying the random_state parameter\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# Apply a decision tree model to fit features to the target\n",
    "model.fit(features_train, target_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the accuracy of prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n",
      "97.22666666666666\n"
     ]
    }
   ],
   "source": [
    "# Check the accuracy score of the prediction for the training set\n",
    "print(model.score(features_train,target_train)*100)\n",
    "\n",
    "# Check the accuracy score of the prediction for the test set\n",
    "print(model.score(features_test, target_test)*100)"
   ]
  }
 ],
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