09-10-k-means/k_means_cluster.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starter Code for k-means Clustering and Feature Scaling Mini-Projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.append('../utils/')\n",
    "\n",
    "from feature_format import feature_format, target_feature_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(pred, features, poi, mark_poi=False, name='image.png', f1_name='feature 1', f2_name='feature 2'):\n",
    "    \"\"\"Some plotting code designed to help you visualize your clusters \"\"\"\n",
    "\n",
    "    # Plot each cluster with a different color--add more colors for\n",
    "    # drawing more than five clusters\n",
    "    colors = [\"b\", \"c\", \"k\", \"m\", \"g\"]\n",
    "    for ii, pp in enumerate(pred):\n",
    "        plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]])\n",
    "\n",
    "    # If you like, place red stars over points that are POIs (just for funsies)\n",
    "    if mark_poi:\n",
    "        for ii, pp in enumerate(pred):\n",
    "            if poi[ii]:\n",
    "                plt.scatter(features[ii][0], features[ii][1], color=\"r\", marker=\"*\")\n",
    "    plt.xlabel(f1_name)\n",
    "    plt.ylabel(f2_name)\n",
    "    plt.savefig(name)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load in the dict of dicts containing all the data on each person in the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../17-final-project/final_project_dataset.pkl', 'rb') as f:\n",
    "    data_dict = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There's an outlier--remove it!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict.pop('TOTAL', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input features we want to use can be any key in the person-level dictionary (`salary`, `director_fees`, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_1 = 'salary'\n",
    "feature_2 = 'exercised_stock_options'\n",
    "poi = 'poi'\n",
    "\n",
    "features_list = [poi, feature_1, feature_2]\n",
    "data = feature_format(data_dict, features_list)\n",
    "poi, finance_features = target_feature_split(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the \"clustering with 3 features\" part of the mini-project, you'll want to change this line to for \n",
    "```python\n",
    "f1, f2, _ in finance_features:\n",
    "```\n",
    "as it's currently written, the line below assumes 2 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f1, f2 in finance_features:\n",
    "    plt.scatter(f1, f2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ↓↓↓ Your Code Goes Here ↓↓↓"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster here; create predictions of the cluster labels for the data and store them to a list called pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the `name` parameter when you change the number of features so that the figure gets saved to a different file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    draw(pred, finance_features, poi, mark_poi=False, name=\"clusters.pdf\", f1_name=feature_1, f2_name=feature_2)\n",
    "except NameError:\n",
    "    print('No predictions object named pred found, no clusters to plot')"
   ]
  }
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