04-decision-tree/dt_author_id.ipynb

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   "source": [
    "# This is the code to accompany the Lesson 3 (decision tree) mini-project\n",
    "\n",
    "### Use a Decision Tree to identify emails from the Enron corpus by author:\n",
    "\n",
    "- Sara has label `0`\n",
    "- Chris has label `1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from time import time\n",
    "sys.path.append('../utils/')\n",
    "\n",
    "from email_preprocess import preprocess"
   ]
  },
  {
   "cell_type": "markdown",
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    "- `features_train` and `features_test` are the features for the training and testing datasets, respectively\n",
    "- `labels_train` and `labels_test` are the corresponding item labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "no. of Chris training emails: 7936\n",
      "no. of Sara training emails: 7884\n"
     ]
    }
   ],
   "source": [
    "features_train, features_test, labels_train, labels_test = preprocess()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ↓↓↓ Your Code Goes Here ↓↓↓"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "execution_count": 5,
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   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "clf = DecisionTreeClassifier(min_samples_split=40)\n",
    "clf.fit(features_train, labels_train)\n",
    "\n",
    "pred = clf.predict(features_test)\n",
    "\n",
    "accuracy_score(labels_test, pred)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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       "379"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(features_train[0])"
   ]
  }
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