17-final-project/poi_id.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starter Code for the Lesson 17: Final Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pickle\n",
    "sys.path.append(\"../utils/\")\n",
    "\n",
    "from feature_format import feature_format, target_feature_split\n",
    "from tester import dump_classifier_and_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Select what features you'll use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# features_list is a list of strings, each of which is a feature name.\n",
    "# The first feature must be \"poi\".\n",
    "features_list = ['poi', 'salary']  # You will need to use more features\n",
    "\n",
    "# Load the dictionary containing the dataset\n",
    "with open(\"final_project_dataset.pkl\", \"rb\") as data_file:\n",
    "    data_dict = pickle.load(data_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Create new feature(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store to my_dataset for easy export below.\n",
    "my_dataset = data_dict\n",
    "\n",
    "# Extract features and labels from dataset for local testing\n",
    "data = feature_format(my_dataset, features_list, sort_keys=True)\n",
    "labels, features = target_feature_split(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: Try a varity of classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please name your classifier clf for easy export below.\n",
    "Note that if you want to do PCA or other multi-stage operations,\n",
    "you'll need to use Pipelines. For more info:\n",
    "http://scikit-learn.org/stable/modules/pipeline.html\n",
    "\n",
    "Provided to give you a starting point. Try a variety of classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "\n",
    "clf = GaussianNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: Tune your classifier to achieve better than .3 precision and recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using our testing script. Check the `tester.py` script in the final project\n",
    "folder for details on the evaluation method, especially the test_classifier\n",
    "function. Because of the small size of the dataset, the script uses\n",
    "stratified shuffle split cross validation. For more info:\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html\n",
    "\n",
    "Example starting point. Try investigating other evaluation techniques!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 6: Dump your classifier, dataset, and `features_list` so anyone can"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check your results. You do not need to change anything below, but make sure\n",
    "that the version of `poi_id.py` that you submit can be run on its own and\n",
    "generates the necessary `.pkl` files for validating your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_classifier_and_data(clf, my_dataset, features_list)"
   ]
  }
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