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   "source": [
    "#!/usr/bin/python\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "    Starter code for the validation mini-project.\n",
    "    The first step toward building your POI identifier!\n",
    "\n",
    "    Start by loading/formatting the data\n",
    "\n",
    "    After that, it's not our code anymore--it's yours!\n",
    "\"\"\"\n",
    "\n",
    "import pickle\n",
    "import sys\n",
    "sys.path.append(\"../tools/\")\n",
    "from feature_format import featureFormat, targetFeatureSplit\n",
    "\n",
    "data_dict = pickle.load(open(\"../final_project/final_project_dataset.pkl\", \"rb\") )\n",
    "\n",
    "### first element is our labels, any added elements are predictor\n",
    "### features. Keep this the same for the mini-project, but you'll\n",
    "### have a different feature list when you do the final project.\n",
    "features_list = [\"poi\", \"salary\"]\n",
    "\n",
    "data = featureFormat(data_dict, features_list, sort_keys = '../tools/python2_lesson13_keys.pkl')\n",
    "labels, features = targetFeatureSplit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "end_time": "2019-09-01T03:47:44.785291Z",
     "start_time": "2019-09-01T03:47:44.777944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-01T03:58:48.227230Z",
     "start_time": "2019-09-01T03:58:48.210049Z"
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   },
   "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,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### it's all yours from here forward! \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size = 0.3, random_state = 42)\n",
    "\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(features_train, labels_train)"
   ]
  },
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   "execution_count": 13,
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     "end_time": "2019-09-01T03:58:50.429390Z",
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "clf.score(features_test, labels_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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