{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Water and Power Usage for the city of Los Angeles, California\n",
    "\n",
    "## An example project for PHY178/CSC171\n",
    "### M. J. Madsen\n",
    "\n",
    "I present here an example final project. This project is not intended to be a carbon-copy template for your project, but rather it should give you and idea of the general shape and style of work that I am looking for. Note that I am following the general outline and instructions for this sample project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Library Imports\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.covariance import EmpiricalCovariance\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA \n",
    "from time import process_time\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminary Data Exploration\n",
    "\n",
    "I am using data from the city of Los Angeles originally found from this website.\n",
    "\n",
    "> https://data.lacity.org/A-Livable-and-Sustainable-City/Water-and-Electric-Usage-from-2005-2013/asvq-scwp\n",
    "\n",
    "The dataset has four columns of interest:\n",
    "1. The month in which the data was recorded\n",
    "2. The zip code (geographical location) for the data\n",
    "3. The water use for that zip code (measured in HCF)\n",
    "4. The power use for that zip code (measure in kWh)\n",
    "\n",
    "The raw data has another column (Value Date) that we will not be using. I first examine the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text Date</th>\n",
       "      <th>Value Date</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mar_2008</td>\n",
       "      <td>Mar-08</td>\n",
       "      <td>90230\\n(33.99506171100046, -118.39500957899969)</td>\n",
       "      <td>16.70</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jul_2011</td>\n",
       "      <td>Jul-11</td>\n",
       "      <td>90272\\n(34.04886156900045, -118.53572692799969)</td>\n",
       "      <td>35.73</td>\n",
       "      <td>1013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Text Date Value Date                                         Zip Code  \\\n",
       "0  Mar_2008     Mar-08  90230\\n(33.99506171100046, -118.39500957899969)   \n",
       "1  Jul_2011     Jul-11  90272\\n(34.04886156900045, -118.53572692799969)   \n",
       "\n",
       "   Water Use  Power Use  \n",
       "0      16.70        396  \n",
       "1      35.73       1013  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv1 = pd.read_csv(\"Water_and_Electric_Usage_from_2005_-_2013.csv\")\n",
    "dfv1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving forward, there are some obvious data cleaning steps that need to be taken. I change the date from text to a datetime object and extract the 5-digit zip code from the Zip Code column. Finally, I drop the unneeded columns from the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.70</td>\n",
       "      <td>396</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>90230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.73</td>\n",
       "      <td>1013</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>90272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Water Use  Power Use       Date    Zip\n",
       "0      16.70        396 2008-03-01  90230\n",
       "1      35.73       1013 2011-07-01  90272"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv1['Date'] = pd.to_datetime(dfv1[\"Text Date\"], format=\"%b_%Y\")\n",
    "dfv1['Zip'] = dfv1['Zip Code'].str.extract(\"(.*)\\n\", expand=True).astype('int')\n",
    "dfv2 = dfv1.drop(['Text Date', 'Value Date','Zip Code'],axis=1)\n",
    "dfv2.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this project is to predict the water and power use. I plot the totals for each date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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qJVBT6n20HGkqrbqG/E8utDuHqLbegq56Hf7f7MGoqrnebqpMjiu4HHE0Cgig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\niIiISPXY0BAREZHq/Q9Heu8cBI1eUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7c1475c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfsum = dfv2.groupby('Date').sum()\n",
    "dfsum.reset_index(inplace=True)\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "ax1.plot(dfsum['Date'], dfsum['Water Use'], marker='o', linestyle='', ms=8)\n",
    "ax1.set_ylabel('Water Use (HCF)')\n",
    "\n",
    "ax2.plot(dfsum['Date'], dfsum['Power Use'], marker='o', linestyle='', ms=8)\n",
    "plt.xlabel('date')\n",
    "ax2.set_ylabel('Power Use (kWh)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "\n",
    "There is an extra point at the end that is separated from the rest of the dataset. I look specifically for times after Jan 1, 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>1349.45</td>\n",
       "      <td>54265</td>\n",
       "      <td>13136208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>1640.86</td>\n",
       "      <td>55289</td>\n",
       "      <td>13136208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>1788.75</td>\n",
       "      <td>52367</td>\n",
       "      <td>13136208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>2037.96</td>\n",
       "      <td>57791</td>\n",
       "      <td>13136208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>2112.38</td>\n",
       "      <td>62299</td>\n",
       "      <td>13136208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>1398.03</td>\n",
       "      <td>64430</td>\n",
       "      <td>13136208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Water Use  Power Use       Zip\n",
       "90 2013-02-01    1349.45      54265  13136208\n",
       "91 2013-03-01    1640.86      55289  13136208\n",
       "92 2013-04-01    1788.75      52367  13136208\n",
       "93 2013-05-01    2037.96      57791  13136208\n",
       "94 2013-06-01    2112.38      62299  13136208\n",
       "95 2013-12-01    1398.03      64430  13136208"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsum[dfsum['Date'] > pd.Timestamp('2013-01-01')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are missing data points between July 2013 and November 2013. I cut the data off at June 30, 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfv3 = dfv2[dfv2['Date']<pd.Timestamp('2013-07-01')]\n",
    "dfsum = dfv3.groupby('Date').sum()\n",
    "dfsum.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving forward, it looks like there was a major change in water usage around the mid-point of 2012. However, when I look at the data grouped by zip code, I see that the drop is due to a change in the number of reported zip codes that happened in the middle of 2012. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rr7+e9evXM2fOHCoqKrj//vux2+3o9Xqqqqq49957cTgcrF27ljlz5rBhwwZm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\nF9WjWNyoHishbw7msTtQTKd+gKrPTPs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mX+punZ0Oj4/SDiOpOg996jBR6fImxPMFQkqX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\ng8bFxcXFxcXlvOf/BxZgs82eyhJKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7cb78128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group data by zip code\n",
    "groups = dfv3.groupby('Zip')\n",
    "\n",
    "# Plot\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "\n",
    "ax1.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "# The next step is to cycle through the groups (based on our categories) and plot each one on the same axis.\n",
    "for name, group in groups:\n",
    "    ax1.plot(group['Date'], group['Water Use'], marker='o', linestyle='', ms=8, label=name)\n",
    "    #ax.set_aspect(1)\n",
    "    #break\n",
    "#ax.legend(bbox_to_anchor=(1,0.5))\n",
    "\n",
    "ax1.set_ylabel('Water Use')\n",
    "\n",
    "\n",
    "ax2.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "# The next step is to cycle through the groups (based on our categories) and plot each one on the same axis.\n",
    "for name, group in groups:\n",
    "    ax2.plot(group['Date'], group['Power Use'], marker='o', linestyle='', ms=8, label=name)\n",
    "    #ax.set_aspect(1)\n",
    "    #break\n",
    "#ax.legend(bbox_to_anchor=(1,0.5))\n",
    "plt.xlabel('date')\n",
    "ax2.set_ylabel('Power Use')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because my fits will be by zip code, this should not be an issue. However, there are a handfull of possible outliers in the water use data.\n",
    "\n",
    "# Outlier Detection\n",
    "\n",
    "I utilize the Mahalanobis distance to see how far from the average of the data the potential outliers are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3e7c6c1160>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1k2FZU1OS0NqqRHxUJSGCNmB+Qxj9TLQwQpSHWZt2r0dWOhTFOHGEWWvViKii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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7cb96c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Measure the mahalanobis distance.\n",
    "X=dfv3[['Water Use']].values\n",
    "emp_cov = EmpiricalCovariance().fit(X)\n",
    "mahal_dist = np.sqrt(emp_cov.mahalanobis(X))\n",
    "\n",
    "# Visualize the results\n",
    "plt.plot(mahal_dist,marker='o',linestyle='')\n",
    "plt.ylabel('Mahalanobis Distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are about 8 points that stand out as being very far from the rest of the 12,000 points (over 15 standard deviations). All of the points occur in two short time periods in the same zip code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/projects/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "      <th>M_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>2512.2</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>18.930970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10991</th>\n",
       "      <td>2407.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-04-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>18.121215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2694.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-05-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>20.336495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4406</th>\n",
       "      <td>3231.6</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>24.468400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10328</th>\n",
       "      <td>3235.6</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-08-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>24.499190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12452</th>\n",
       "      <td>3110.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-09-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>23.535489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7301</th>\n",
       "      <td>3202.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-10-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>24.243640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3227</th>\n",
       "      <td>3074.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-11-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>23.261465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Water Use  Power Use       Date    Zip     M_dist\n",
       "683       2512.2          0 2008-03-01  91350  18.930970\n",
       "10991     2407.0          0 2008-04-01  91350  18.121215\n",
       "8         2694.8          0 2008-05-01  91350  20.336495\n",
       "4406      3231.6          0 2009-07-01  91350  24.468400\n",
       "10328     3235.6          0 2009-08-01  91350  24.499190\n",
       "12452     3110.4          0 2009-09-01  91350  23.535489\n",
       "7301      3202.4          0 2009-10-01  91350  24.243640\n",
       "3227      3074.8          0 2009-11-01  91350  23.261465"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv3[\"M_dist\"] = mahal_dist\n",
    "dfv3[dfv3[\"M_dist\"]>15].sort_values('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There may have been something unusual happening during those time periods. I drop these points from the dataset as error outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.70</td>\n",
       "      <td>396</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>90230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.73</td>\n",
       "      <td>1013</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>90272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Water Use  Power Use       Date    Zip\n",
       "0      16.70        396 2008-03-01  90230\n",
       "1      35.73       1013 2011-07-01  90272"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv4 = dfv3[dfv3[\"M_dist\"]<15].drop('M_dist',axis=1).reset_index(drop=True)\n",
    "dfv4.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Enrichment\n",
    "\n",
    "There is a definite month/year periodicity in the data. I add month and year data features to the model. I visualize the data to and see that the periodicity is clearly visible in both the water use and the power use data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R8XgKNCIiIuLxFGhERETE4ynQiIiIiMdToBERERGPp0AjIiIiHk+BRkRERDye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\nImJ4CjQiIiJieAo0IiIiYnj/D6vZgo9Qn7D3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7c173550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfv4['Month'] = dfv4['Date'].apply(lambda x: x.month)\n",
    "dfv4['Year'] = dfv4['Date'].apply(lambda x: x.year)\n",
    "\n",
    "dfsum = dfv4.groupby('Month').sum()\n",
    "dfsum.reset_index(inplace=True)\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "ax1.plot(dfsum['Month'], dfsum['Water Use'], marker='o', linestyle='', ms=8)\n",
    "ax1.set_ylabel('Water Use (HCF)')\n",
    "\n",
    "ax2.plot(dfsum['Month'], dfsum['Power Use'], marker='o', linestyle='', ms=8)\n",
    "plt.xlabel('month')\n",
    "ax2.set_ylabel('Power Use (kWh)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the water and the power usage go up in the summer months, which makes sense. There is also a power spike in January that may be related to heating on cold days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enriching Data with other Datasets\n",
    "\n",
    "I hypothesize that the water and power useage depends on the following additional factors:\n",
    "1) Population of each zip code\n",
    "2) Economic status of each zip code (weath distribution)\n",
    "3) Weather data\n",
    "\n",
    "I join the original dataset with three additonal datasets to add these features.\n",
    "\n",
    "\n",
    "### Adding Population Data\n",
    "\n",
    "The population data was retrieved from the US Census Bureau.\n",
    "> https://www.census.gov/geo/maps-data/data/zcta_rel_download.html\n",
    "\n",
    "The dataset includes information about the population, the land area, and the total area for each zip code. There are a number of other columns of data that I will not use. I convert the zip code column (ZCTA5) to an integer in order to match the datatype of my power/water usage dataframe zip code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZCTA5</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>POPPT</th>\n",
       "      <th>HUPT</th>\n",
       "      <th>AREAPT</th>\n",
       "      <th>AREALANDPT</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZHU</th>\n",
       "      <th>...</th>\n",
       "      <th>COAREA</th>\n",
       "      <th>COAREALAND</th>\n",
       "      <th>ZPOPPCT</th>\n",
       "      <th>ZHUPCT</th>\n",
       "      <th>ZAREAPCT</th>\n",
       "      <th>ZAREALANDPCT</th>\n",
       "      <th>COPOPPCT</th>\n",
       "      <th>COHUPCT</th>\n",
       "      <th>COAREAPCT</th>\n",
       "      <th>COAREALANDPCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>72001</td>\n",
       "      <td>18465</td>\n",
       "      <td>7695</td>\n",
       "      <td>165132671</td>\n",
       "      <td>164333375</td>\n",
       "      <td>18570</td>\n",
       "      <td>7744</td>\n",
       "      <td>...</td>\n",
       "      <td>173777444</td>\n",
       "      <td>172725651</td>\n",
       "      <td>99.43</td>\n",
       "      <td>99.37</td>\n",
       "      <td>98.61</td>\n",
       "      <td>98.6</td>\n",
       "      <td>94.77</td>\n",
       "      <td>94.71</td>\n",
       "      <td>95.03</td>\n",
       "      <td>95.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>601</td>\n",
       "      <td>72</td>\n",
       "      <td>141</td>\n",
       "      <td>72141</td>\n",
       "      <td>105</td>\n",
       "      <td>49</td>\n",
       "      <td>2326414</td>\n",
       "      <td>2326414</td>\n",
       "      <td>18570</td>\n",
       "      <td>7744</td>\n",
       "      <td>...</td>\n",
       "      <td>298027589</td>\n",
       "      <td>294039825</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ZCTA5  STATE  COUNTY  GEOID  POPPT  HUPT     AREAPT  AREALANDPT   ZPOP  \\\n",
       "0    601     72       1  72001  18465  7695  165132671   164333375  18570   \n",
       "1    601     72     141  72141    105    49    2326414     2326414  18570   \n",
       "\n",
       "    ZHU      ...           COAREA  COAREALAND  ZPOPPCT  ZHUPCT  ZAREAPCT  \\\n",
       "0  7744      ...        173777444   172725651    99.43   99.37     98.61   \n",
       "1  7744      ...        298027589   294039825     0.57    0.63      1.39   \n",
       "\n",
       "   ZAREALANDPCT  COPOPPCT  COHUPCT  COAREAPCT  COAREALANDPCT  \n",
       "0          98.6     94.77    94.71      95.03          95.14  \n",
       "1           1.4      0.32     0.35       0.78           0.79  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfzip = pd.read_csv(\"zcta_county_rel_10.txt\",dtype={'ZCTA5':'object'})\n",
    "dfzip['ZCTA5'] = dfzip['ZCTA5'].astype(int)\n",
    "dfzip.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the data in this set are intended to merge other datasets, there are multiple entries for each zip code. The \"Z\" columns are totals for each zip code, so I only need one of them - I use the `max` aggregation. I  cut the unneeded columns, keeping only the zip code, population, total area, and land area columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZCTA5</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZAREA</th>\n",
       "      <th>ZAREALAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601</td>\n",
       "      <td>18570</td>\n",
       "      <td>167459085</td>\n",
       "      <td>166659789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>602</td>\n",
       "      <td>41520</td>\n",
       "      <td>83734431</td>\n",
       "      <td>79288158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ZCTA5   ZPOP      ZAREA  ZAREALAND\n",
       "0    601  18570  167459085  166659789\n",
       "1    602  41520   83734431   79288158"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfzipgroups = (dfzip[['ZCTA5','ZPOP','ZAREA','ZAREALAND']].groupby(\"ZCTA5\").max())\n",
    "dfzipgroups.reset_index(inplace=True)\n",
    "dfzipgroups.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a sense of the dataset, I look at the relationship between the land area and the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e74bedcf8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AvlqX5c/8ajS0cWoIkC9E4HxMREQUaAwGvGSobfZ4/nqdyeMxERFRoDGB0Evupg/WGc0Q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\nASIiogjHYICIiCjC/X9aGLCSj+o9XgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7c145780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfzipgroups.plot.scatter(x='ZAREALAND',y='ZPOP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an interesting anti-correlation here. But perhaps that isn't too surprising, as larger population densities will have more zip codes associated with them, so most of the data would be in small land area, large populations.\n",
    "\n",
    "Now I merge this will the resource use database, keeping only the columns that I need after the join. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "950 rows lost in data merge.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZAREA</th>\n",
       "      <th>ZAREALAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.70</td>\n",
       "      <td>396</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>90230</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>31766</td>\n",
       "      <td>11785759</td>\n",
       "      <td>11672688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.59</td>\n",
       "      <td>407</td>\n",
       "      <td>2005-12-01</td>\n",
       "      <td>90230</td>\n",
       "      <td>12</td>\n",
       "      <td>2005</td>\n",
       "      <td>31766</td>\n",
       "      <td>11785759</td>\n",
       "      <td>11672688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Water Use  Power Use       Date    Zip  Month  Year   ZPOP     ZAREA  \\\n",
       "0      16.70        396 2008-03-01  90230      3  2008  31766  11785759   \n",
       "1      17.59        407 2005-12-01  90230     12  2005  31766  11785759   \n",
       "\n",
       "   ZAREALAND  \n",
       "0   11672688  \n",
       "1   11672688  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv5 = pd.merge(dfv4,dfzipgroups,left_on=\"Zip\",right_on=\"ZCTA5\")\n",
    "print(\"{} rows lost in data merge.\".format(len(dfv4.index)-len(dfv5.index)))\n",
    "dfv5.drop(['ZCTA5'],axis=1,inplace=True)\n",
    "dfv5.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were a number of rows lost in the merge- these were zip codes that were not in the US Census database.\n",
    "\n",
    "I graph the new inputs to see how the Water and Power use depend on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HhohIZSxNmHjlyhU88cQTmDp1KmbOnInCwkIEBASgoqIClZWVqKio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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e74651cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 3, sharey=True)\n",
    "ax1[0].plot(dfv5['ZPOP'], dfv5['Water Use'], marker='o', linestyle='', ms=8)\n",
    "ax1[0].set_ylabel('Water Use (HCF)')\n",
    "ax2[0].plot(dfv5['ZPOP'], dfv5['Power Use'], marker='o', linestyle='', ms=8)\n",
    "ax2[0].set_ylabel('Power Use (kWh)')\n",
    "ax2[0].set_xlabel('Population')\n",
    "\n",
    "ax1[1].plot(dfv5['ZAREA'], dfv5['Water Use'], marker='o', linestyle='', ms=8)\n",
    "ax2[1].plot(dfv5['ZAREA'], dfv5['Power Use'], marker='o', linestyle='', ms=8)\n",
    "ax2[1].set_xlabel('Area')\n",
    "\n",
    "ax1[2].plot(dfv5['ZAREALAND'], dfv5['Water Use'], marker='o', linestyle='', ms=8)\n",
    "ax2[2].plot(dfv5['ZAREALAND'], dfv5['Power Use'], marker='o', linestyle='', ms=8)\n",
    "ax2[2].set_xlabel('Land Area')\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the power use is more strongly correlation with the census and land area data.\n",
    "\n",
    "### Adding Economic Data\n",
    "\n",
    "I add the economic data which is based on IRS tax records:\n",
    "> https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-zip-code-data-soi\n",
    "\n",
    "This dataset has the following variables I am interested in:\n",
    "* Nreturns: number of filed tax returns\n",
    "* AGI: adjusted gross income (in thousands of \\$)\n",
    "* SW: Salary and Wages (in thousands of \\$)\n",
    "* EIC: total earned income tax credit (in thousands of \\$)\n",
    "\n",
    "However, the dataset is originally an Excel spreadsheet with multiple sheet names. I created a short function to retrieve the tax data I want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Nreturns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>SW</th>\n",
       "      <th>EIC</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90001</td>\n",
       "      <td>17313</td>\n",
       "      <td>406784</td>\n",
       "      <td>358687.0</td>\n",
       "      <td>16162.0</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90002</td>\n",
       "      <td>14712</td>\n",
       "      <td>331533</td>\n",
       "      <td>294916.0</td>\n",
       "      <td>16130.0</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Zip Code  Nreturns     AGI        SW      EIC  Year\n",
       "0     90001     17313  406784  358687.0  16162.0  2005\n",
       "1     90002     14712  331533  294916.0  16130.0  2005"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getTaxYear(year):\n",
    "    # This function reads in a single sheet from the Excel document, then returns it as a dataframe.\n",
    "    dftemp = pd.read_excel(\"allCAtaxdata2005-2013.xlsx\",sheetname='{}'.format(year),thousands=\",\",na_values=[\"*\",\"*              \",\".\",\"-\"])\n",
    "    dftemp['Zip Code'] = dftemp['Zip Code'].astype('int')\n",
    "    dftemp['Year'] = year\n",
    "    return dftemp\n",
    "\n",
    "dftaxes = pd.concat([getTaxYear(x) for x in range(2005,2014)])\n",
    "# Drop NaNs from data\n",
    "dftaxes.dropna(inplace=True)\n",
    "\n",
    "dftaxes.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I visualize the economic data in the following plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3e74ac0860>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiIjaAF7zISIiItJhQCIiIiLSYUAiIiIi0mFAIiIiItJhQCIiIiLSYUAiIiIi0mFAIiIi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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e74159ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfgroup = dftaxes.groupby('Year').sum()\n",
    "dfgroup.reset_index(inplace=True)\n",
    "\n",
    "plt.plot(dfgroup['Year'],dfgroup['AGI'],label='AGI')\n",
    "plt.plot(dfgroup['Year'],dfgroup['SW'],label='SW')\n",
    "plt.plot(dfgroup['Year'],dfgroup['EIC'],label='EIC')\n",
    "plt.legend(bbox_to_anchor=(1.2,0.5))\n",
    "plt.gca().set_yscale('log')\n",
    "plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.ylabel('IRS Data (1000$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I merge the IRS dataset with the population and usage dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190 rows lost in data merge.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZAREA</th>\n",
       "      <th>ZAREALAND</th>\n",
       "      <th>Nreturns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>SW</th>\n",
       "      <th>EIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.70</td>\n",
       "      <td>396</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>90230</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>31766</td>\n",
       "      <td>11785759</td>\n",
       "      <td>11672688</td>\n",
       "      <td>15572</td>\n",
       "      <td>1008925</td>\n",
       "      <td>765127.0</td>\n",
       "      <td>3537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.95</td>\n",
       "      <td>368</td>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>90230</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>31766</td>\n",
       "      <td>11785759</td>\n",
       "      <td>11672688</td>\n",
       "      <td>15572</td>\n",
       "      <td>1008925</td>\n",
       "      <td>765127.0</td>\n",
       "      <td>3537.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Water Use  Power Use       Date    Zip  Month  Year   ZPOP     ZAREA  \\\n",
       "0      16.70        396 2008-03-01  90230      3  2008  31766  11785759   \n",
       "1      20.95        368 2008-07-01  90230      7  2008  31766  11785759   \n",
       "\n",
       "   ZAREALAND  Nreturns      AGI        SW     EIC  \n",
       "0   11672688     15572  1008925  765127.0  3537.0  \n",
       "1   11672688     15572  1008925  765127.0  3537.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv6 = pd.merge(dfv5,dftaxes,how=\"inner\",left_on=[\"Zip\",\"Year\"],right_on=[\"Zip Code\",\"Year\"])\n",
    "print(\"{} rows lost in data merge.\".format(len(dfv5.index)-len(dfv6.index)))\n",
    "dfv6.drop('Zip Code',axis=1,inplace=True)\n",
    "dfv6.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an additional data loss in this merge. However, I still have most of my original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining data (from original dataset): 90.7%\n"
     ]
    }
   ],
   "source": [
    "print(\"Remaining data (from original dataset): {0:.1f}%\".format(float(len(dfv6.index))/(len(dfv1.index))*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I visualize the combined dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 46370.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A48ePxz333IPRo0fj8OHD2L17N1588UWPDiaXy6HTWYd8VVdXIzk5GSkpKVCrrwbDmpoa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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e76496390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot usage vs Adjusted Gross Income\n",
    "f, (ax1, ax2) = plt.subplots(2, 2, sharey=True)\n",
    "\n",
    "ax1[0].scatter(dfv6['AGI'],dfv6['Water Use'])\n",
    "ax1[0].set_ylabel(\"Water Use\")\n",
    "\n",
    "ax2[0].scatter(dfv6['AGI'],dfv6['Power Use'])\n",
    "ax2[0].set_ylabel(\"Power Use\")\n",
    "ax2[0].set_xlabel(\"Adjusted Gross Income\")\n",
    "ax2[0].set_xlim(0,max(dfv6['AGI']))\n",
    "\n",
    "ax1[1].scatter(dfv6['EIC'],dfv6['Water Use'])\n",
    "ax2[1].scatter(dfv6['EIC'],dfv6['Power Use'])\n",
    "ax2[1].set_xlabel(\"Earned Income Credit\")\n",
    "ax2[1].set_xlim(0,max(dfv6['EIC']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there aren't strong correlations here, but the correlations that exist may improve the model. I am also interested in how closely this IRS dataset tracks the US Census dataset. The number of tax returns in a zip code should be close to the number of people living in that area. I plot the number of returns versus the census population to check this correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3e76862780>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z0J48SO/qHrVWQYD328jnPhMvCvju9kCBXxTw3e0M/BQLdKf3hwwZgrKyMhw5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\nhBBCmIn/A+yuYlbF3mSEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7cb6bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We'll use a different tool to plot the data now that we know how to group the data by a category. This will help us make better combined plots later on.\n",
    "groups = dfv6.groupby('Year')\n",
    "\n",
    "# Plot\n",
    "trainfig, ax = plt.subplots()\n",
    "#ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "# The next step is to cycle through the groups (based on our categories) and plot each one on the same axis.\n",
    "colors = iter(plt.cm.rainbow(np.linspace(0, 1, len(groups))))\n",
    "for name, group in groups:\n",
    "    ax.plot(group['ZPOP'],group['Nreturns'],'o',label=name,color=next(colors))\n",
    "ax.legend(bbox_to_anchor=(1,0.5))\n",
    "ax.set_ylabel('N Returns')\n",
    "ax.set_xlabel('2010 Cenus Population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation is as expected. Howver, there is also a trend based on the year which probably corresponds to the growth and decline in populations.\n",
    "\n",
    "### Adding Weather Data\n",
    "\n",
    "I add historical weather data to my input features. This dataset comes from:\n",
    "\n",
    "> https://www.ncdc.noaa.gov/cdo-web/search?datasetid=GSOM\n",
    "\n",
    "The dataset has the following variables:\n",
    "\n",
    "* AWND: average wind speed (m/s)\n",
    "* CLDD: cooling degree days\n",
    "* HTDD: heating degree days\n",
    "* PRCP: preciptiation (cm)\n",
    "* TSUN: daily total sunshine\n",
    "* TAVG: Average daily temperature (celsius) [Not Used]\n",
    "\n",
    "The weather data is essentially the same for all of the zip codes in this dataset- they are all in the city of Los Angeles. Any small variation in the rain or temperature will be negligible because I only have the average over the full month of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AWND</th>\n",
       "      <th>CLDD</th>\n",
       "      <th>HTDD</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TSUN</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>135.2</td>\n",
       "      <td>174.5</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.5</td>\n",
       "      <td>176.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005-02-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AWND  CLDD   HTDD   PRCP  TAVG  TSUN       Date\n",
       "0   3.2   4.8  135.2  174.5  14.1   0.0 2005-01-01\n",
       "1   3.5   0.0  106.5  176.6  14.5   0.0 2005-02-01"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weatherdf = pd.read_csv(\"LAX_weather.csv\",parse_dates=[2])\n",
    "weatherdf.fillna(0.0, inplace=True)\n",
    "weatherdf['Date']=pd.to_datetime(weatherdf['DATE'])\n",
    "weatherdf.drop(['STATION', 'NAME','DATE'],axis=1, inplace=True)\n",
    "weatherdf.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qKhIhGBfCHDipIiDYUI6wdnbgEsA8kzlANogkqA9KFWVzGpIJxWEve8HH5Quj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\nnTDXZJEgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7660feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot weather data\n",
    "f, (ax1, ax2) = plt.subplots(2, 3, sharex=True)\n",
    "\n",
    "ax1[0].plot(weatherdf['Date'],weatherdf['AWND'])\n",
    "ax1[0].set_ylabel(\"AWND\")\n",
    "ax2[0].plot(weatherdf['Date'],weatherdf['CLDD'])\n",
    "ax2[0].set_ylabel(\"CLDD\")\n",
    "\n",
    "ax1[1].plot(weatherdf['Date'],weatherdf['HTDD'])\n",
    "ax1[1].set_ylabel(\"HTDD\")\n",
    "ax2[1].plot(weatherdf['Date'],weatherdf['PRCP'])\n",
    "ax2[1].set_ylabel(\"PRCP\")\n",
    "ax1[2].plot(weatherdf['Date'],weatherdf['TAVG'])\n",
    "ax1[2].set_ylabel(\"TAVG\")\n",
    "ax2[2].plot(weatherdf['Date'],weatherdf['TSUN'])\n",
    "ax2[2].set_ylabel(\"TSUN\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.gcf().autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I merge this last dataset with my other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 rows lost in data merge.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZAREA</th>\n",
       "      <th>ZAREALAND</th>\n",
       "      <th>Nreturns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>SW</th>\n",
       "      <th>EIC</th>\n",
       "      <th>AWND</th>\n",
       "      <th>CLDD</th>\n",
       "      <th>HTDD</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.70</td>\n",
       "      <td>396</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>90230</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>31766</td>\n",
       "      <td>11785759</td>\n",
       "      <td>11672688</td>\n",
       "      <td>15572</td>\n",
       "      <td>1008925</td>\n",
       "      <td>765127.0</td>\n",
       "      <td>3537.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.18</td>\n",
       "      <td>970</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>90272</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>22986</td>\n",
       "      <td>60557886</td>\n",
       "      <td>59133992</td>\n",
       "      <td>11165</td>\n",
       "      <td>3569670</td>\n",
       "      <td>1750980.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Water Use  Power Use       Date    Zip  Month  Year   ZPOP     ZAREA  \\\n",
       "0      16.70        396 2008-03-01  90230      3  2008  31766  11785759   \n",
       "1      30.18        970 2008-03-01  90272      3  2008  22986  60557886   \n",
       "\n",
       "   ZAREALAND  Nreturns      AGI         SW     EIC  AWND  CLDD  HTDD  PRCP  \\\n",
       "0   11672688     15572  1008925   765127.0  3537.0   3.2   6.7  91.5   0.8   \n",
       "1   59133992     11165  3569670  1750980.0   355.0   3.2   6.7  91.5   0.8   \n",
       "\n",
       "   TAVG  \n",
       "0  15.6  \n",
       "1  15.6  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv7 = pd.merge(dfv6, weatherdf, on='Date')\n",
    "print(\"{} rows lost in data merge.\".format(len(dfv6.index)-len(dfv7.index)))\n",
    "dfv7.drop(['TSUN'],axis=1,inplace=True)\n",
    "dfv7.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were weather data for all of the existing rows, so there wasn't any data lost in this step. I visualize the weather data and the water/power use data together to look for correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 224.19999999999999)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BbNFLh4ldUR6sqsNNYwchJz0e8ao4zvLGYphRPMTnmEoRhyyNlDNGpL6pk9Nd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\nCQQCgUBwQ/4/zBRqRwtDZvAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e764b9e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot usage vs Adjusted Gross Income\n",
    "f, (ax1, ax2) = plt.subplots(2, 2, sharey=True)\n",
    "\n",
    "ax1[0].scatter(dfv7['TAVG'],dfv7['Water Use'])\n",
    "ax1[0].set_ylabel(\"Water Use\")\n",
    "\n",
    "ax2[0].scatter(dfv7['TAVG'],dfv7['Power Use'])\n",
    "ax2[0].set_ylabel(\"Power Use\")\n",
    "ax2[0].set_xlabel(\"Average Temp\")\n",
    "\n",
    "\n",
    "ax1[1].scatter(dfv7['PRCP'],dfv7['Water Use'])\n",
    "ax1[1].set_xlim(0,max(dfv7['PRCP']))\n",
    "ax2[1].scatter(dfv7['PRCP'],dfv7['Power Use'])\n",
    "ax2[1].set_xlabel(\"Precipitation\")\n",
    "ax2[1].set_xlim(0,max(dfv7['PRCP']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It certainly looks like there are correlations here - at least between the Water Use/Precepitation and the Power Use/Average Temp datasets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformations\n",
    "\n",
    "## Categorical Data Transformation\n",
    "\n",
    "I have three columns that are categorical in nature: Zip, Month, and Year. The other columns are continuous variables. I re-define these three variables as dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZAREA</th>\n",
       "      <th>ZAREALAND</th>\n",
       "      <th>Nreturns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>SW</th>\n",
       "      <th>EIC</th>\n",
       "      <th>AWND</th>\n",
       "      <th>...</th>\n",
       "      <th>M_12</th>\n",
       "      <th>Y_2005</th>\n",
       "      <th>Y_2006</th>\n",
       "      <th>Y_2007</th>\n",
       "      <th>Y_2008</th>\n",
       "      <th>Y_2009</th>\n",
       "      <th>Y_2010</th>\n",
       "      <th>Y_2011</th>\n",
       "      <th>Y_2012</th>\n",
       "      <th>Y_2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.70</td>\n",
       "      <td>396</td>\n",
       "      <td>31766</td>\n",
       "      <td>11785759</td>\n",
       "      <td>11672688</td>\n",
       "      <td>15572</td>\n",
       "      <td>1008925</td>\n",
       "      <td>765127.0</td>\n",
       "      <td>3537.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.18</td>\n",
       "      <td>970</td>\n",
       "      <td>22986</td>\n",
       "      <td>60557886</td>\n",
       "      <td>59133992</td>\n",
       "      <td>11165</td>\n",
       "      <td>3569670</td>\n",
       "      <td>1750980.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Water Use  Power Use   ZPOP     ZAREA  ZAREALAND  Nreturns      AGI  \\\n",
       "0      16.70        396  31766  11785759   11672688     15572  1008925   \n",
       "1      30.18        970  22986  60557886   59133992     11165  3569670   \n",
       "\n",
       "          SW     EIC  AWND   ...    M_12  Y_2005  Y_2006  Y_2007  Y_2008  \\\n",
       "0   765127.0  3537.0   3.2   ...       0       0       0       0       1   \n",
       "1  1750980.0   355.0   3.2   ...       0       0       0       0       1   \n",
       "\n",
       "   Y_2009  Y_2010  Y_2011  Y_2012  Y_2013  \n",
       "0       0       0       0       0       0  \n",
       "1       0       0       0       0       0  \n",
       "\n",
       "[2 rows x 169 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipdummy = pd.get_dummies(dfv7['Zip'],prefix='Z')\n",
    "monthdummy = pd.get_dummies(dfv7['Month'],prefix='M')\n",
    "yeardummy = pd.get_dummies(dfv7['Year'],prefix='Y')\n",
    "\n",
    "dfv8 = dfv7.join(zipdummy).join(monthdummy).join(yeardummy)\n",
    "dfv8.drop(['Date','Zip','Month','Year'],inplace=True,axis=1)\n",
    "dfv8.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data test/training split\n",
    "I split my data into 80% training and 20% testing data sets. I do this before doing any further data transformations in order to avoid \"data snooping\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train, test = train_test_split(dfv8, test_size=0.2, random_state=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Scaling\n",
    "\n",
    "Almost all of my data columns are on different scales. I regularize the non-categorical data before fitting. Because only some of the columns need to be regularized, I split the data, regularize part of it, then re-join the training dataset. I do the same transformation to the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Fit the scaler\n",
    "std_scaler = StandardScaler().fit(train.ix[:,2:14])\n",
    "\n",
    "# Apply the transformation to the train and test datasets\n",
    "train_std = pd.DataFrame(std_scaler.transform(train.ix[:,2:14]),columns=train.columns[2:14])\n",
    "test_std = pd.DataFrame(std_scaler.transform(test.ix[:,2:14]),columns=test.columns[2:14])\n",
    "\n",
    "# Recombine the scaled datasets\n",
    "trainToMerge=train.reset_index(drop=True)\n",
    "train_scaled=trainToMerge.ix[:,0:2].merge(train_std.merge(trainToMerge.ix[:,15:],left_index=True,right_index=True),left_index=True,right_index=True)\n",
    "\n",
    "testToMerge=test.reset_index(drop=True)\n",
    "test_scaled=testToMerge.ix[:,0:2].merge(test_std.merge(testToMerge.ix[:,15:],left_index=True,right_index=True),left_index=True,right_index=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I now separate out the features and the targets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_features = train_scaled.ix[:,2:].values\n",
    "test_features = test_scaled.ix[:,2:].values\n",
    "\n",
    "water_target = train_scaled['Water Use'].values\n",
    "water_actual = test_scaled['Water Use'].values\n",
    "\n",
    "power_target = train_scaled['Power Use'].values\n",
    "power_actual = test_scaled['Power Use'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA\n",
    "\n",
    "I tested reducing the dimensionality of the features using the PCA. However, this did not improve either the model performance or the time it takes to fit the models. So I will not be using PCA for this analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My first model is to simply use the \"prior year\" data in order to predict the usage. I am using the Root-Mean-Square (RMS) as my metric for model performance. The RMS value gives me an estimate of the prediction error in the same units as the Water/Power use data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1748 rows lost in data merge.\n"
     ]
    }
   ],
   "source": [
    "dfoffset = dfv4\n",
    "dfoffset['Prior Year'] = dfoffset['Year'].apply(lambda year: year - 1)\n",
    "\n",
    "df_new = dfv4.merge(dfoffset, left_on=['Prior Year', 'Month','Zip'], right_on=['Year','Month','Zip'],suffixes=['','_Prior'])\n",
    "df_new.drop(['Year_Prior','Prior Year_Prior','Prior Year'],inplace=True,axis=1)\n",
    "print(\"{} rows lost in data merge.\".format(len(dfv4.index)-len(df_new.index)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Water RMS Error: 62.052 for 'Last-year' Model\n",
      "Power RMS Error: 139.855 for 'Last-year' Model\n"
     ]
    }
   ],
   "source": [
    "print(\"Water RMS Error: {0:.3f} for 'Last-year' Model\".format( np.sqrt(np.mean( (df_new['Water Use'].values - df_new['Water Use_Prior'].values)**2)) ))\n",
    "print(\"Power RMS Error: {0:.3f} for 'Last-year' Model\".format( np.sqrt(np.mean( (df_new['Power Use'].values - df_new['Power Use_Prior'].values)**2)) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "I next create a baseline predictive model using a linear regression using the same metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Water RMS Error: 44.844 for LinearRegression\n",
      "Power RMS Error: 85.875 for LinearRegression\n",
      "Fit Time: 1.3261661869999983 seconds\n",
      "Predict Time: 0.01653146099999958 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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7w9vb26jLd+RqZgVGPf4y+tf1go2dIwCgUdWAW8mfIyoqCi8sGAJnB1u4CFuP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+HGI0eO9Pky8vHjx5GSktJv0dDb+zIDGWwvGPaSISKiP2PAgsbPzw85OTloaGiAJEmwtbU1RVxD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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ec2a9b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fitAndPlot(train_features, test_features, model, **extraArgs):\n",
    "\n",
    "    # Create linear regression objects for water and power\n",
    "    water_model= model(**extraArgs)\n",
    "    power_model= model(**extraArgs)\n",
    "    \n",
    "    # Timing\n",
    "    start = process_time()\n",
    "    # Fit the data\n",
    "    water_model.fit(train_features,water_target)\n",
    "    power_model.fit(train_features,power_target)\n",
    "    fit_time = process_time() - start\n",
    "    \n",
    "    start = process_time()\n",
    "    # Get the predictions\n",
    "    wpredictions = water_model.predict(test_features)\n",
    "    ppredictions = power_model.predict(test_features)\n",
    "    predict_time = process_time() - start\n",
    "\n",
    "    # Plot the actuals and the predictions\n",
    "    f, (ax1, ax2) = plt.subplots(2, 1)\n",
    "\n",
    "    ax1.scatter(water_actual, wpredictions, color='black')\n",
    "    ax1.set_ylabel('Water Model Predictions')\n",
    "    ax1.set_xlabel('Actuals')\n",
    "    ax1.set_xlim(0,max(water_actual))\n",
    "    #Plot the slope=1 line for reference\n",
    "    X=np.linspace(ax1.get_xlim()[0], ax1.get_xlim()[1], 100)\n",
    "    ax1.plot(X,X,linestyle='--')\n",
    "\n",
    "    ax2.scatter(power_actual, ppredictions, color='black')\n",
    "    ax2.set_ylabel('Power Model Predictions')\n",
    "    ax2.set_xlabel('Actuals')\n",
    "    ax2.set_xlim(0,max(power_actual))\n",
    "    #Plot the slope=1 line for reference\n",
    "    X=np.linspace(ax2.get_xlim()[0], ax2.get_xlim()[1], 100)\n",
    "    ax2.plot(X,X,linestyle='--')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Get the RMS values\n",
    "    print(\"Water RMS Error: {0:.3f} for {1}\".format( np.sqrt(np.mean((wpredictions - water_actual) ** 2)),model.__name__))\n",
    "    print(\"Power RMS Error: {0:.3f} for {1}\".format( np.sqrt(np.mean((ppredictions - power_actual) ** 2)),model.__name__))\n",
    "    print(\"Fit Time: {} seconds\".format(fit_time))\n",
    "    print(\"Predict Time: {} seconds\".format(predict_time))\n",
    "    \n",
    "    \n",
    "    return (water_model, power_model)\n",
    "\n",
    "water_model,power_model = fitAndPlot(train_features, test_features,LinearRegression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear regression model is already performing better than the 'last year' model. I follow this with further regression models.\n",
    "\n",
    "\n",
    "## Random Forest Regression\n",
    "I next apply the random forest regression using the same train/test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Water RMS Error: 32.742 for RandomForestRegressor\n",
      "Power RMS Error: 66.054 for RandomForestRegressor\n",
      "Fit Time: 82.216455096 seconds\n",
      "Predict Time: 0.30887336899999696 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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PP/ywOcMlhNyjK00eWFQqhZO9FayFfPyQmInP/3tNvc7f0x6RISLI5UrARoCH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6feb3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "water_model,power_model = fitAndPlot(train_features,test_features, RandomForestRegressor, n_estimators=300, min_samples_leaf=10,random_state=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest has better performance than the linear model, though it took significantly longer.\n",
    "\n",
    "## Adaboost Regression\n",
    "\n",
    "I look at two other models. First, the Adaboost ensemble method using the Decision Tree Regressor as the base model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Water RMS Error: 31.656 for AdaBoostRegressor\n",
      "Power RMS Error: 57.677 for AdaBoostRegressor\n",
      "Fit Time: 74.75545528799921 seconds\n",
      "Predict Time: 0.4766953270009253 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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gNjYWqampmDhxoimbSwixUKUVUrg62cCWb4WvLuTgs+9uavYFejshJkwIhUIF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\nRERExPJU0IiIiIjlqaARERERy/s/FJ2SZbFOmqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e766c1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "water_model2,power_model2 = fitAndPlot(train_features, test_features, AdaBoostRegressor, base_estimator=DecisionTreeRegressor(min_samples_leaf=10), n_estimators=300,random_state=32,loss='square')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance and timing are both slightly better than the Random forest model. One final model:\n",
    "\n",
    "## Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Iter       Train Loss   Remaining Time \n",
      "         1        9026.1864            5.67m\n",
      "         2        7532.4162            5.65m\n",
      "         3        6313.0380            5.65m\n",
      "         4        5318.0338            5.74m\n",
      "         5        4502.7267            5.72m\n",
      "         6        3838.5703            5.78m\n",
      "         7        3295.1466            5.66m\n",
      "         8        2843.7231            5.65m\n",
      "         9        2476.8513            5.72m\n",
      "        10        2171.9484            5.71m\n",
      "        20         879.0091            5.94m\n",
      "        30         584.7577            6.20m\n",
      "        40         440.3867            6.37m\n",
      "        50         333.8454            6.53m\n",
      "        60         262.5081            6.76m\n",
      "        70         208.6896            6.76m\n",
      "        80         167.7968            6.71m\n",
      "        90         138.7181            6.69m\n",
      "       100         116.2387            6.59m\n",
      "       200          22.5917            5.50m\n",
      "       300           5.2009            3.83m\n",
      "       400           1.5141            1.97m\n",
      "       500           0.5305            0.00s\n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1       72742.7815            3.30m\n",
      "         2       59565.0999            3.37m\n",
      "         3       48890.1988            3.48m\n",
      "         4       40206.0674            3.48m\n",
      "         5       33137.9319            3.52m\n",
      "         6       27406.6225            3.55m\n",
      "         7       22723.0439            3.53m\n",
      "         8       18899.7597            3.52m\n",
      "         9       15794.2527            3.56m\n",
      "        10       13260.4569            3.69m\n",
      "        20        3323.1769            3.83m\n",
      "        30        1711.0881            4.23m\n",
      "        40        1234.3053            4.42m\n",
      "        50         984.0259            4.96m\n",
      "        60         797.3781            5.32m\n",
      "        70         651.6153            5.83m\n",
      "        80         538.6646            6.00m\n",
      "        90         451.1444            6.03m\n",
      "       100         384.7462            6.10m\n",
      "       200         108.3785            5.43m\n",
      "       300          38.9381            3.91m\n",
      "       400          17.0833            1.99m\n",
      "       500           9.3090            0.00s\n",
      "Water RMS Error: 34.926 for GradientBoostingRegressor\n",
      "Power RMS Error: 47.818 for GradientBoostingRegressor\n",
      "Fit Time: 1173.0020852920002 seconds\n",
      "Predict Time: 0.5419542839999849 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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zrDto+os7JydHbYXkxr/AFy5caNBgQFajQB2rn98l5ZYYK3cm/ZWB4YAgX0fI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Wi1WiQmJsLT0xMA4OXlhfj4eCQlJWFsbAwuLi7IysqCtbU1qqurUVRUBAsLC0RERGD79u0SV29+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e74159fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "water_model3,power_model3 = fitAndPlot(train_features, test_features, GradientBoostingRegressor,min_samples_leaf=10, n_estimators=500,random_state=32,max_depth=200,verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Optimization\n",
    "\n",
    "The Adaboost model has the best performance for the Water Use data, but the Gradient Boosting regressor has the best performance for the Power Use model. Because they are two separate datasets, I use the best performing model for each one and tune the hyperparameters separately.\n",
    "\n",
    "### Water Use Model Hyperparameter Tuning\n",
    "\n",
    "I tune the adaboost model hyperparameters, focusing on the minimum number of samples per leaf for the underlying decision tree regressor. I increased the number of estimators to reduce the noise in the trend data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3e7409a208>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K6u8hjj/++ANPP/10/esHmVRAoVBg3rx5KCwsRGVlJWbOnAkfHx/861//Qk1N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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e740cce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_leaf_grid = range(1,41,4)\n",
    "rms_scores = []\n",
    "for  min_leaf in min_leaf_grid:\n",
    "    # Create linear regression objects for water and power\n",
    "    print(\"Working min_leaf {}\".format(min_leaf))\n",
    "    water_model= AdaBoostRegressor(base_estimator=DecisionTreeRegressor(min_samples_leaf=min_leaf), n_estimators=1200,random_state=32,loss='square')\n",
    "\n",
    "    # Timing\n",
    "    start = process_time()\n",
    "    # Fit the data\n",
    "    water_model.fit(train_features,water_target)\n",
    "    fit_time = process_time() - start\n",
    "\n",
    "    start = process_time()\n",
    "    # Get the predictions\n",
    "    wpredictions = water_model.predict(test_features)\n",
    "    predict_time = process_time() - start\n",
    "    rms_score = np.sqrt(np.mean((wpredictions - water_actual) ** 2))\n",
    "    rms_scores.append(rms_score)\n",
    "\n",
    "plt.plot(min_leaf_grid, rms_scores,marker='o')\n",
    "plt.xlabel(\"Minimum Number of Leaves\")\n",
    "plt.ylabel(\"RMS Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model performance comes around a minimum leaf size of 20. The model is overfitting with a smaller minimum leaf size and is underfitting with a larger minimum leaf size. Therefore, I run one final model for the water use data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Water RMS Error: 31.016 for Adaboost Regressor\n",
      "Fit Time: 90.67291085299985 seconds\n",
      "Predict Time: 0.774193978000767 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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h+++LSiuwZc8/+PnPTADAyH5BCG/fxqzxElma3gQvFouxcuVK9O7dGyKRCH//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\nJogBT0REZIIY8ERERCaIAU9ERGSC/g/iz+yRltq43AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e764ac198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_water_model= AdaBoostRegressor(base_estimator=DecisionTreeRegressor(min_samples_leaf=20), n_estimators=1200,random_state=32,loss='square')\n",
    "# Timing\n",
    "start = process_time()\n",
    "# Fit the data\n",
    "final_water_model.fit(train_features,water_target)\n",
    "fit_time = process_time() - start\n",
    "\n",
    "start = process_time()\n",
    "# Get the predictions\n",
    "wpredictions = final_water_model.predict(test_features)\n",
    "predict_time = process_time() - start\n",
    "rms_score = np.sqrt(np.mean((wpredictions - water_actual) ** 2))\n",
    "\n",
    "plt.scatter(water_actual, wpredictions, color='black')\n",
    "ax1 = plt.gca()\n",
    "ax1.set_ylabel('Water Model Predictions')\n",
    "ax1.set_xlabel('Actuals')\n",
    "ax1.set_xlim(0,max(water_actual))\n",
    "#Plot the slope=1 line for reference\n",
    "X=np.linspace(ax1.get_xlim()[0], ax1.get_xlim()[1], 100)\n",
    "ax1.plot(X,X,linestyle='--')\n",
    "\n",
    "# Get the RMS values\n",
    "print(\"Water RMS Error: {0:.3f} for Adaboost Regressor\".format( np.sqrt(np.mean((wpredictions - water_actual) ** 2))))\n",
    "print(\"Fit Time: {} seconds\".format(fit_time))\n",
    "print(\"Predict Time: {} seconds\".format(predict_time))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Power Use Model Optimization\n",
    "\n",
    "I change to the Gradient Booting regression to optimize a model for predicting the power use. I again focus on tuning the minimum number of leaves hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3e6ff86128>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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66adx8OBBVFdXm7vbvb29UVBQ0OT4wsJCeHl5mbe1Wm2zY6hlMZyEhojI7kl2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\nOVEUcSG3HPuP5+DAz5dRWFo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atGn49NNPMW/ePCxfvhweHh7o3bs3fvjhBwD189H36dMH\njo6OLbahrKwMGzduxJYtW7Bp0yY88sgjTdaUIJILr9SJ7kB8fDzGjh2L119/HV999RVmzJiBjRs3\ntnhsnz59AABBQUGorKxssdv7gQceAFC/2Efv3r0BAAEBAa2GcmONj+/cubN52UdPT09UVFQ0WQay\ngZeXF5599lksX74cCxcuvI0WAz169IBGo0FAQABMJlOTmisqKgAA7u7u5nWkY2NjsWHDBpw9exYF\nBQX485//DAAwGo1NVuuKjY1t9lnp6eno3r27eT2GPn36YPPmzbdV581SU1Nx7do1rFq1CkD90sRF\nRUXw9fXF0qVLsXz5ctTV1ZkX4Xjsscewe/duDB8+HLt27cLjjz/eahs8PDwwaNAgTJo0CQ8//DDi\n4+MREBBwV3UStSeGOtEd8PLyQvfu3bF9+3YUFBTgvvvua/VYtbrp/14tzcisUqla/L0ldXV1rb7/\n7XxWg4SEBIwbNw5paWnmfTcvP1xbW9tqXY0/q+Fzbu6tEAQBGo0GgYGB2LBhQ4t13M7c3/eyNLJG\no8HKlSuh1Wqb7H/uuecwevRoPPXUUzhz5gwmT54MoP6WQ8NV+MmTJ7Fs2TKcP3++1TasWLEC586d\nw/79+zFp0iSsXLkSOp3urmolai/sfie6Q2PHjsXy5csxevRoyT/Lzc0NV65cAQAcOnSoXd5TpVJh\n7ty5eOutt5p8Tl5eHgDg7Nmzd9Q9DwBlZWU4deoUAOD48eOIjo5GeHg4SkpKcObMGQDAkSNH8OWX\nX97yfXr06IFTp06hsrISAPDTTz+ZxyzcqQceeADffvstAKC4uBhvv/02gPrbFlFRUQCAXbt2mb/A\nODs7o2/fvli+fDmGDh0KjUbTahuys7Oxfv16REZG4o9//CMefvhhnD59+q7qJGpPvFInukNxcXF4\n88038fjjj0v+WZMmTcKCBQvw9ddft+syjg8++CCCg4Nx9epVAMCjjz6K7du3Y8KECejRowe6dOly\nR+8XHByMnTt3YunSpaitrcWKFSvg7OyMZcuW4S9/+QucnJwAAH/7299u+T4BAQGYNm0annvuOXOX\n/2uvvdbm5y9ZsgQeHh7m7ZUrV+Ivf/kL3nzzTXzzzTeora3FlClTAAB//OMfMXPmTAQHB+PZZ5/F\nnj17sGTJEsyePRtjxozBiy++iM8//xwAWm2Dv78/0tPT8dRTT6FDhw7w8PDAyy+/fEf/zoikwFXa\niIiIFILd70RERArBUCciIlIIhjoREZFCMNSJiIgUgqFORESkEAx1IiIihWCoExERKQRDnYiISCH+\nP692Te2yW9ztAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6fd909b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "min_leaf_grid = range(1,81,10)\n",
    "rms_scores = []\n",
    "for  min_leaf in min_leaf_grid:\n",
    "    # Create linear regression objects for water and power\n",
    "    print(\"Working min_leaf {}\".format(min_leaf))\n",
    "    power_model= GradientBoostingRegressor(min_samples_leaf=min_leaf, n_estimators=500,random_state=32,max_depth=200)\n",
    "\n",
    "    # Timing\n",
    "    start = process_time()\n",
    "    # Fit the data\n",
    "    power_model.fit(train_features,power_target)\n",
    "    fit_time = process_time() - start\n",
    "\n",
    "    start = process_time()\n",
    "    # Get the predictions\n",
    "    ppredictions = power_model.predict(test_features)\n",
    "    predict_time = process_time() - start\n",
    "    rms_score = np.sqrt(np.mean((ppredictions - power_actual) ** 2))\n",
    "    rms_scores.append(rms_score)\n",
    "\n",
    "plt.plot(min_leaf_grid, rms_scores,marker='o')\n",
    "plt.xlabel(\"Minimum Number of Leaves\")\n",
    "plt.ylabel(\"RMS Error\")\n",
    "\n",
    "plt.plot(min_leaf_grid, rms_scores,marker='o')\n",
    "plt.xlabel(\"Minimum Number of Leaves\")\n",
    "plt.ylabel(\"RMS Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there is evidence that, for a minimum leaf size of less than 30, the model is overfitting. There is a soft minimum around 40 before the model underfits the data, so I use that parameter to train a final Power Use model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power RMS Error: 45.757 for Gradient Boosting Regressor\n",
      "Fit Time: 256.22809825200056 seconds\n",
      "Predict Time: 0.16546982399995613 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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jxjF8+HASEhLw9/dn4cKF7Nq1i4ULF8qeyEKIcmwtOzMVkklJSWHdunX4+fmZ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\nguMucwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6fd837f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_power_model= GradientBoostingRegressor(min_samples_leaf=40, n_estimators=500,random_state=32,max_depth=200)\n",
    "\n",
    "# Timing\n",
    "start = process_time()\n",
    "# Fit the data\n",
    "final_power_model.fit(train_features,power_target)\n",
    "fit_time = process_time() - start\n",
    "\n",
    "start = process_time()\n",
    "# Get the predictions\n",
    "ppredictions = final_power_model.predict(test_features)\n",
    "predict_time = process_time() - start\n",
    "\n",
    "plt.scatter(power_actual, ppredictions, color='black')\n",
    "ax1 = plt.gca()\n",
    "ax1.set_ylabel('Power Model Predictions')\n",
    "ax1.set_xlabel('Actuals')\n",
    "ax1.set_xlim(0,max(power_actual))\n",
    "#Plot the slope=1 line for reference\n",
    "X=np.linspace(ax1.get_xlim()[0], ax1.get_xlim()[1], 100)\n",
    "ax1.plot(X,X,linestyle='--')\n",
    "\n",
    "# Get the RMS values\n",
    "print(\"Power RMS Error: {0:.3f} for Gradient Boosting Regressor\".format( np.sqrt(np.mean((ppredictions - power_actual) ** 2))))\n",
    "print(\"Fit Time: {} seconds\".format(fit_time))\n",
    "print(\"Predict Time: {} seconds\".format(predict_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Limitations\n",
    "I investigate the limitations of each model - where do the models disagree from the actual data? I plot the residuals for the Water Use model first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3e6e724e10>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ioKAA8+fPb8bXISIialkku9Z//PFHjB07FgkJCQCAzZs349ChQy5/0LRp0/DRRx8hJSUF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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6e711f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "water_residuals = np.abs(wpredictions - water_actual)\n",
    "plt.plot(water_residuals,marker='o',linestyle='')\n",
    "plt.xlabel(\"Point number\")\n",
    "plt.ylabel(\"Water Model Residuals\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I investigate all points with a residual greater than 150."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZAREA</th>\n",
       "      <th>ZAREALAND</th>\n",
       "      <th>Nreturns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>SW</th>\n",
       "      <th>EIC</th>\n",
       "      <th>AWND</th>\n",
       "      <th>CLDD</th>\n",
       "      <th>HTDD</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>Model Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9691</th>\n",
       "      <td>945.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>7</td>\n",
       "      <td>2011</td>\n",
       "      <td>33348</td>\n",
       "      <td>43948937</td>\n",
       "      <td>43886336</td>\n",
       "      <td>15077</td>\n",
       "      <td>1093595</td>\n",
       "      <td>908039.0</td>\n",
       "      <td>2387.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>574.852632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>908.40</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>33348</td>\n",
       "      <td>43948937</td>\n",
       "      <td>43886336</td>\n",
       "      <td>15400</td>\n",
       "      <td>1158979</td>\n",
       "      <td>958999.0</td>\n",
       "      <td>2508.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>102.1</td>\n",
       "      <td>30.2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>394.325714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>415.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>91201</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>22781</td>\n",
       "      <td>6143646</td>\n",
       "      <td>6116160</td>\n",
       "      <td>10115</td>\n",
       "      <td>494601</td>\n",
       "      <td>369000.0</td>\n",
       "      <td>3797.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>15.6</td>\n",
       "      <td>583.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>277.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2005-10-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>10</td>\n",
       "      <td>2005</td>\n",
       "      <td>33348</td>\n",
       "      <td>43948937</td>\n",
       "      <td>43886336</td>\n",
       "      <td>13548</td>\n",
       "      <td>877019</td>\n",
       "      <td>743564.0</td>\n",
       "      <td>1370.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>29.4</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.3</td>\n",
       "      <td>434.602727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>574.48</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>90071</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>15</td>\n",
       "      <td>319155</td>\n",
       "      <td>319155</td>\n",
       "      <td>639</td>\n",
       "      <td>364405</td>\n",
       "      <td>75762.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.7</td>\n",
       "      <td>55.2</td>\n",
       "      <td>13.8</td>\n",
       "      <td>730.676563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2942</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>90211</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>8434</td>\n",
       "      <td>1813980</td>\n",
       "      <td>1813980</td>\n",
       "      <td>5240</td>\n",
       "      <td>977691</td>\n",
       "      <td>477587.0</td>\n",
       "      <td>947.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>13.4</td>\n",
       "      <td>52.1</td>\n",
       "      <td>33.4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>501.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-04-01</td>\n",
       "      <td>91201</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>22781</td>\n",
       "      <td>6143646</td>\n",
       "      <td>6116160</td>\n",
       "      <td>10115</td>\n",
       "      <td>494601</td>\n",
       "      <td>369000.0</td>\n",
       "      <td>3797.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>40.9</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17.1</td>\n",
       "      <td>742.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8095</th>\n",
       "      <td>634.50</td>\n",
       "      <td>0</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>33348</td>\n",
       "      <td>43948937</td>\n",
       "      <td>43886336</td>\n",
       "      <td>15114</td>\n",
       "      <td>1073120</td>\n",
       "      <td>870460.0</td>\n",
       "      <td>1539.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>184.2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>256.880656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>622.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>90265</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>18116</td>\n",
       "      <td>298041072</td>\n",
       "      <td>279184812</td>\n",
       "      <td>7969</td>\n",
       "      <td>2185356</td>\n",
       "      <td>748069.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>122.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>14.2</td>\n",
       "      <td>853.484516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8893</th>\n",
       "      <td>88.60</td>\n",
       "      <td>0</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>33348</td>\n",
       "      <td>43948937</td>\n",
       "      <td>43886336</td>\n",
       "      <td>15114</td>\n",
       "      <td>1073120</td>\n",
       "      <td>870460.0</td>\n",
       "      <td>1539.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>72.2</td>\n",
       "      <td>12.7</td>\n",
       "      <td>16.4</td>\n",
       "      <td>278.685714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10655</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>91201</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>22781</td>\n",
       "      <td>6143646</td>\n",
       "      <td>6116160</td>\n",
       "      <td>10399</td>\n",
       "      <td>525338</td>\n",
       "      <td>394901.0</td>\n",
       "      <td>5178.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>102.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>156.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10522</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>91201</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>22781</td>\n",
       "      <td>6143646</td>\n",
       "      <td>6116160</td>\n",
       "      <td>10399</td>\n",
       "      <td>525338</td>\n",
       "      <td>394901.0</td>\n",
       "      <td>5178.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>172.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>156.883721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6233</th>\n",
       "      <td>652.80</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-08-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>33348</td>\n",
       "      <td>43948937</td>\n",
       "      <td>43886336</td>\n",
       "      <td>14655</td>\n",
       "      <td>1054878</td>\n",
       "      <td>876876.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>460.494444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>115.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-06-01</td>\n",
       "      <td>91350</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>33348</td>\n",
       "      <td>43948937</td>\n",
       "      <td>43886336</td>\n",
       "      <td>15077</td>\n",
       "      <td>1093595</td>\n",
       "      <td>908039.0</td>\n",
       "      <td>2387.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>797.615227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>825.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>90211</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>8434</td>\n",
       "      <td>1813980</td>\n",
       "      <td>1813980</td>\n",
       "      <td>5438</td>\n",
       "      <td>894757</td>\n",
       "      <td>471144.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.7</td>\n",
       "      <td>63.8</td>\n",
       "      <td>12.7</td>\n",
       "      <td>625.862069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>679.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>90265</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>18116</td>\n",
       "      <td>298041072</td>\n",
       "      <td>279184812</td>\n",
       "      <td>7837</td>\n",
       "      <td>1771931</td>\n",
       "      <td>682034.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>158.0</td>\n",
       "      <td>118.5</td>\n",
       "      <td>13.3</td>\n",
       "      <td>380.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>854.67</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>90265</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>18116</td>\n",
       "      <td>298041072</td>\n",
       "      <td>279184812</td>\n",
       "      <td>7837</td>\n",
       "      <td>1771931</td>\n",
       "      <td>682034.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1060.796800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Water Use  Power Use       Date    Zip  Month  Year   ZPOP      ZAREA  \\\n",
       "9691      945.00          0 2011-07-01  91350      7  2011  33348   43948937   \n",
       "3839      908.40          0 2012-01-01  91350      1  2012  33348   43948937   \n",
       "18        415.00          0 2008-03-01  91201      3  2008  22781    6143646   \n",
       "2110      277.00          0 2005-10-01  91350     10  2005  33348   43948937   \n",
       "508       574.48          0 2008-02-01  90071      2  2008     15     319155   \n",
       "2942        0.00          0 2012-11-01  90211     11  2012   8434    1813980   \n",
       "682       501.00          0 2008-04-01  91201      4  2008  22781    6143646   \n",
       "8095      634.50          0 2007-01-01  91350      1  2007  33348   43948937   \n",
       "8928      622.00          0 2007-02-01  90265      2  2007  18116  298041072   \n",
       "8893       88.60          0 2007-11-01  91350     11  2007  33348   43948937   \n",
       "10655       0.00          0 2011-11-01  91201     11  2011  22781    6143646   \n",
       "10522       0.00          0 2011-12-01  91201     12  2011  22781    6143646   \n",
       "6233      652.80          0 2010-08-01  91350      8  2010  33348   43948937   \n",
       "10356     115.00          0 2011-06-01  91350      6  2011  33348   43948937   \n",
       "284       825.00          0 2008-12-01  90211     12  2008   8434    1813980   \n",
       "551       679.00          0 2008-01-01  90265      1  2008  18116  298041072   \n",
       "152       854.67          0 2008-07-01  90265      7  2008  18116  298041072   \n",
       "\n",
       "       ZAREALAND  Nreturns      AGI        SW     EIC  AWND  CLDD   HTDD  \\\n",
       "9691    43886336     15077  1093595  908039.0  2387.0   3.6  57.9    0.8   \n",
       "3839    43886336     15400  1158979  958999.0  2508.0   2.2   5.3  102.1   \n",
       "18       6116160     10115   494601  369000.0  3797.0   3.2   6.7   91.5   \n",
       "2110    43886336     13548   877019  743564.0  1370.0   2.7  29.1   29.4   \n",
       "508       319155       639   364405   75762.0    33.0   3.0   2.0  134.7   \n",
       "2942     1813980      5240   977691  477587.0   947.0   2.8  13.4   52.1   \n",
       "682      6116160     10115   494601  369000.0  3797.0   3.4  40.9   79.6   \n",
       "8095    43886336     15114  1073120  870460.0  1539.0   2.5   3.0  184.2   \n",
       "8928   279184812      7969  2185356  748069.0   480.0   3.4   5.9  122.7   \n",
       "8893    43886336     15114  1073120  870460.0  1539.0   2.4  13.7   72.2   \n",
       "10655    6116160     10399   525338  394901.0  5178.0   2.5   4.5  102.2   \n",
       "10522    6116160     10399   525338  394901.0  5178.0   2.0   0.0  172.2   \n",
       "6233    43886336     14655  1054878  876876.0  2352.0   3.3  25.6   11.6   \n",
       "10356   43886336     15077  1093595  908039.0  2387.0   3.4   6.2   26.7   \n",
       "284      1813980      5438   894757  471144.0   780.0   2.6   0.0  174.7   \n",
       "551    279184812      7837  1771931  682034.0   533.0   3.2   0.8  158.0   \n",
       "152    279184812      7837  1771931  682034.0   533.0   3.3  83.0    0.0   \n",
       "\n",
       "        PRCP  TAVG  Model Predictions  \n",
       "9691     0.0  20.2         574.852632  \n",
       "3839    30.2  15.2         394.325714  \n",
       "18       0.8  15.6         583.333333  \n",
       "2110    25.7  18.3         434.602727  \n",
       "508     55.2  13.8         730.676563  \n",
       "2942    33.4  17.0         198.000000  \n",
       "682      0.8  17.1         742.842105  \n",
       "8095     9.9  12.5         256.880656  \n",
       "8928    20.9  14.2         853.484516  \n",
       "8893    12.7  16.4         278.685714  \n",
       "10655   43.0  15.1         156.121212  \n",
       "10522   17.0  12.8         156.883721  \n",
       "6233     0.0  18.8         460.494444  \n",
       "10356    0.5  17.6         797.615227  \n",
       "284     63.8  12.7         625.862069  \n",
       "551    118.5  13.3         380.111111  \n",
       "152      0.0  21.0        1060.796800  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water_poor_fit=dfv7.ix[test.index[water_residuals > 150]]\n",
    "water_poor_fit['Model Predictions']=wpredictions[water_residuals > 150]\n",
    "water_poor_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Most of these points where the model does a poor job fitting the data come from only a handful of zip codes: 91350, 91201, 91265, and 90211. There may be other events or other unique problems with these locations that caused the model predictions to not match the actuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3e6fb42b38>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LNuyBEWMgjwK8AYwVKQEm3CuAY4f2wrypw5HRIxE2qwXdkuLRLSkeL6+vwKzi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\nQB4AUY1NqSab0SNRcUKJnmM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h0KBByh8KgF6bwRAREXnSe5OwsGiRf/XVVzh69Cg2bNiA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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6fc37dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "power_residuals = np.abs(ppredictions - power_actual)\n",
    "plt.plot(power_residuals,marker='o',linestyle='')\n",
    "plt.xlabel(\"Point number\")\n",
    "plt.ylabel(\"Power Model Residuals\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the power model case, I investigate points where the residuals are greater than 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Water Use</th>\n",
       "      <th>Power Use</th>\n",
       "      <th>Date</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>ZPOP</th>\n",
       "      <th>ZAREA</th>\n",
       "      <th>ZAREALAND</th>\n",
       "      <th>Nreturns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>SW</th>\n",
       "      <th>EIC</th>\n",
       "      <th>AWND</th>\n",
       "      <th>CLDD</th>\n",
       "      <th>HTDD</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>Model Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>30.68</td>\n",
       "      <td>343</td>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>90731</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>59662</td>\n",
       "      <td>40481090</td>\n",
       "      <td>23803092</td>\n",
       "      <td>23793</td>\n",
       "      <td>1149101</td>\n",
       "      <td>940785.0</td>\n",
       "      <td>9937.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.7</td>\n",
       "      <td>55.2</td>\n",
       "      <td>13.8</td>\n",
       "      <td>577.653810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2899</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>91355</td>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>32605</td>\n",
       "      <td>40487220</td>\n",
       "      <td>40079320</td>\n",
       "      <td>15080</td>\n",
       "      <td>1280204</td>\n",
       "      <td>950721.0</td>\n",
       "      <td>2222.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>124.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>482.980702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9089</th>\n",
       "      <td>31.76</td>\n",
       "      <td>1107</td>\n",
       "      <td>2007-06-01</td>\n",
       "      <td>91307</td>\n",
       "      <td>6</td>\n",
       "      <td>2007</td>\n",
       "      <td>24474</td>\n",
       "      <td>20860392</td>\n",
       "      <td>20836532</td>\n",
       "      <td>11961</td>\n",
       "      <td>1019116</td>\n",
       "      <td>688451.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>903.294856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10879</th>\n",
       "      <td>16.83</td>\n",
       "      <td>626</td>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>91355</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>32605</td>\n",
       "      <td>40487220</td>\n",
       "      <td>40079320</td>\n",
       "      <td>15155</td>\n",
       "      <td>1363894</td>\n",
       "      <td>941778.0</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>151.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>158.648852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7196</th>\n",
       "      <td>31.10</td>\n",
       "      <td>2002</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>90210</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>21741</td>\n",
       "      <td>26383345</td>\n",
       "      <td>26226245</td>\n",
       "      <td>9770</td>\n",
       "      <td>4939447</td>\n",
       "      <td>1504798.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>149.2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>1764.628705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3089</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1081</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>91205</td>\n",
       "      <td>9</td>\n",
       "      <td>2012</td>\n",
       "      <td>37810</td>\n",
       "      <td>4898344</td>\n",
       "      <td>4897491</td>\n",
       "      <td>15790</td>\n",
       "      <td>594462</td>\n",
       "      <td>488257.0</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>120.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>848.008836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2762</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>91210</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>328</td>\n",
       "      <td>113997</td>\n",
       "      <td>113997</td>\n",
       "      <td>200</td>\n",
       "      <td>31641</td>\n",
       "      <td>19477.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>139.3</td>\n",
       "      <td>45.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>268.884486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9594</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>90245</td>\n",
       "      <td>7</td>\n",
       "      <td>2011</td>\n",
       "      <td>16654</td>\n",
       "      <td>15142094</td>\n",
       "      <td>14148053</td>\n",
       "      <td>9338</td>\n",
       "      <td>913373</td>\n",
       "      <td>669719.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>291.448270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11008</th>\n",
       "      <td>30.00</td>\n",
       "      <td>1060</td>\n",
       "      <td>2011-05-01</td>\n",
       "      <td>91210</td>\n",
       "      <td>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>328</td>\n",
       "      <td>113997</td>\n",
       "      <td>113997</td>\n",
       "      <td>215</td>\n",
       "      <td>35619</td>\n",
       "      <td>20750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>60.7</td>\n",
       "      <td>13.5</td>\n",
       "      <td>16.9</td>\n",
       "      <td>538.781944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>68.58</td>\n",
       "      <td>885</td>\n",
       "      <td>2008-10-01</td>\n",
       "      <td>91302</td>\n",
       "      <td>10</td>\n",
       "      <td>2008</td>\n",
       "      <td>25709</td>\n",
       "      <td>69273356</td>\n",
       "      <td>69143126</td>\n",
       "      <td>12150</td>\n",
       "      <td>2315019</td>\n",
       "      <td>1324983.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>79.6</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1097.152344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10172</th>\n",
       "      <td>5.00</td>\n",
       "      <td>254</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>90275</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>41804</td>\n",
       "      <td>43402847</td>\n",
       "      <td>35004660</td>\n",
       "      <td>19040</td>\n",
       "      <td>2716592</td>\n",
       "      <td>1498530.0</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>103.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>513.265376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3186</th>\n",
       "      <td>39.22</td>\n",
       "      <td>1477</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>91436</td>\n",
       "      <td>9</td>\n",
       "      <td>2012</td>\n",
       "      <td>14372</td>\n",
       "      <td>14891780</td>\n",
       "      <td>14751608</td>\n",
       "      <td>9180</td>\n",
       "      <td>3782435</td>\n",
       "      <td>1814704.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>120.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>1706.908344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>47.71</td>\n",
       "      <td>1025</td>\n",
       "      <td>2005-07-01</td>\n",
       "      <td>91324</td>\n",
       "      <td>7</td>\n",
       "      <td>2005</td>\n",
       "      <td>27669</td>\n",
       "      <td>11437330</td>\n",
       "      <td>11373551</td>\n",
       "      <td>11136</td>\n",
       "      <td>715075</td>\n",
       "      <td>438407.0</td>\n",
       "      <td>2909.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>65.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.4</td>\n",
       "      <td>744.429354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8838</th>\n",
       "      <td>0.00</td>\n",
       "      <td>765</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>90403</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>24525</td>\n",
       "      <td>3944526</td>\n",
       "      <td>3697303</td>\n",
       "      <td>15272</td>\n",
       "      <td>1707337</td>\n",
       "      <td>956655.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>72.2</td>\n",
       "      <td>12.7</td>\n",
       "      <td>16.4</td>\n",
       "      <td>477.825001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10343</th>\n",
       "      <td>34.50</td>\n",
       "      <td>1224</td>\n",
       "      <td>2011-06-01</td>\n",
       "      <td>91210</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>328</td>\n",
       "      <td>113997</td>\n",
       "      <td>113997</td>\n",
       "      <td>215</td>\n",
       "      <td>35619</td>\n",
       "      <td>20750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>604.413090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5954</th>\n",
       "      <td>32.00</td>\n",
       "      <td>1494</td>\n",
       "      <td>2010-10-01</td>\n",
       "      <td>91210</td>\n",
       "      <td>10</td>\n",
       "      <td>2010</td>\n",
       "      <td>328</td>\n",
       "      <td>113997</td>\n",
       "      <td>113997</td>\n",
       "      <td>187</td>\n",
       "      <td>23612</td>\n",
       "      <td>15682.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>27.6</td>\n",
       "      <td>17.7</td>\n",
       "      <td>39.5</td>\n",
       "      <td>18.6</td>\n",
       "      <td>1163.261399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3871</th>\n",
       "      <td>68.34</td>\n",
       "      <td>2481</td>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>90210</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>21741</td>\n",
       "      <td>26383345</td>\n",
       "      <td>26226245</td>\n",
       "      <td>10497</td>\n",
       "      <td>5238507</td>\n",
       "      <td>1638406.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>159.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2273.446871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>49.21</td>\n",
       "      <td>1055</td>\n",
       "      <td>2005-08-01</td>\n",
       "      <td>91324</td>\n",
       "      <td>8</td>\n",
       "      <td>2005</td>\n",
       "      <td>27669</td>\n",
       "      <td>11437330</td>\n",
       "      <td>11373551</td>\n",
       "      <td>11136</td>\n",
       "      <td>715075</td>\n",
       "      <td>438407.0</td>\n",
       "      <td>2909.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>79.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>837.734290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>22.42</td>\n",
       "      <td>310</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>90731</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>59662</td>\n",
       "      <td>40481090</td>\n",
       "      <td>23803092</td>\n",
       "      <td>25280</td>\n",
       "      <td>1240957</td>\n",
       "      <td>981814.0</td>\n",
       "      <td>14301.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>592.611504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9815</th>\n",
       "      <td>12.00</td>\n",
       "      <td>648</td>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>91355</td>\n",
       "      <td>4</td>\n",
       "      <td>2011</td>\n",
       "      <td>32605</td>\n",
       "      <td>40487220</td>\n",
       "      <td>40079320</td>\n",
       "      <td>15155</td>\n",
       "      <td>1363894</td>\n",
       "      <td>941778.0</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>63.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>323.366802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3896</th>\n",
       "      <td>50.47</td>\n",
       "      <td>1147</td>\n",
       "      <td>2006-07-01</td>\n",
       "      <td>91324</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>27669</td>\n",
       "      <td>11437330</td>\n",
       "      <td>11373551</td>\n",
       "      <td>11373</td>\n",
       "      <td>705898</td>\n",
       "      <td>457689.0</td>\n",
       "      <td>2932.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>159.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>890.208032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Water Use  Power Use       Date    Zip  Month  Year   ZPOP     ZAREA  \\\n",
       "520        30.68        343 2008-02-01  90731      2  2008  59662  40481090   \n",
       "2899        0.00          0 2012-08-01  91355      8  2012  32605  40487220   \n",
       "9089       31.76       1107 2007-06-01  91307      6  2007  24474  20860392   \n",
       "10879      16.83        626 2011-02-01  91355      2  2011  32605  40487220   \n",
       "7196       31.10       2002 2013-01-01  90210      1  2013  21741  26383345   \n",
       "3089        0.00       1081 2012-09-01  91205      9  2012  37810   4898344   \n",
       "2762        0.00          0 2012-03-01  91210      3  2012    328    113997   \n",
       "9594        0.00          0 2011-07-01  90245      7  2011  16654  15142094   \n",
       "11008      30.00       1060 2011-05-01  91210      5  2011    328    113997   \n",
       "842        68.58        885 2008-10-01  91302     10  2008  25709  69273356   \n",
       "10172       5.00        254 2011-01-01  90275      1  2011  41804  43402847   \n",
       "3186       39.22       1477 2012-09-01  91436      9  2012  14372  14891780   \n",
       "2167       47.71       1025 2005-07-01  91324      7  2005  27669  11437330   \n",
       "8838        0.00        765 2007-11-01  90403     11  2007  24525   3944526   \n",
       "10343      34.50       1224 2011-06-01  91210      6  2011    328    113997   \n",
       "5954       32.00       1494 2010-10-01  91210     10  2010    328    113997   \n",
       "3871       68.34       2481 2006-07-01  90210      7  2006  21741  26383345   \n",
       "1768       49.21       1055 2005-08-01  91324      8  2005  27669  11437330   \n",
       "2513       22.42        310 2012-05-01  90731      5  2012  59662  40481090   \n",
       "9815       12.00        648 2011-04-01  91355      4  2011  32605  40487220   \n",
       "3896       50.47       1147 2006-07-01  91324      7  2006  27669  11437330   \n",
       "\n",
       "       ZAREALAND  Nreturns      AGI         SW      EIC  AWND   CLDD   HTDD  \\\n",
       "520     23803092     23793  1149101   940785.0   9937.0   3.0    2.0  134.7   \n",
       "2899    40079320     15080  1280204   950721.0   2222.0   3.2  124.7    0.0   \n",
       "9089    20836532     11961  1019116   688451.0   1508.0   3.0   13.1   16.9   \n",
       "10879   40079320     15155  1363894   941778.0   2212.0   2.9    0.3  151.0   \n",
       "7196    26226245      9770  4939447  1504798.0    955.0   2.5    2.5  149.2   \n",
       "3089     4897491     15790   594462   488257.0   9300.0   2.9  120.5    0.0   \n",
       "2762      113997       200    31641    19477.0      0.0   3.4    4.0  139.3   \n",
       "9594    14148053      9338   913373   669719.0   1191.0   3.6   57.9    0.8   \n",
       "11008     113997       215    35619    20750.0      0.0   3.9   16.1   60.7   \n",
       "842     69143126     12150  2315019  1324983.0    890.0   2.6   79.6   12.8   \n",
       "10172   35004660     19040  2716592  1498530.0   1706.0   2.2    3.4  103.6   \n",
       "3186    14751608      9180  3782435  1814704.0    499.0   2.9  120.5    0.0   \n",
       "2167    11373551     11136   715075   438407.0   2909.0   3.5   65.1    2.5   \n",
       "8838     3697303     15272  1707337   956655.0    836.0   2.4   13.7   72.2   \n",
       "10343     113997       215    35619    20750.0      0.0   3.4    6.2   26.7   \n",
       "5954      113997       187    23612    15682.0      0.0   2.9   27.6   17.7   \n",
       "3871    26226245     10497  5238507  1638406.0    433.0   3.6  159.3    0.0   \n",
       "1768    11373551     11136   715075   438407.0   2909.0   3.5   79.2    0.0   \n",
       "2513    23803092     25280  1240957   981814.0  14301.0   3.4    2.8   36.5   \n",
       "9815    40079320     15155  1363894   941778.0   2212.0   3.7    9.5   63.5   \n",
       "3896    11373551     11373   705898   457689.0   2932.0   3.6  159.3    0.0   \n",
       "\n",
       "       PRCP  TAVG  Model Predictions  \n",
       "520    55.2  13.8         577.653810  \n",
       "2899    0.0  22.4         482.980702  \n",
       "9089    0.0  18.2         903.294856  \n",
       "10879  37.4  12.9         158.648852  \n",
       "7196   33.0  13.6        1764.628705  \n",
       "3089    0.0  22.4         848.008836  \n",
       "2762   45.3  14.0         268.884486  \n",
       "9594    0.0  20.2         291.448270  \n",
       "11008  13.5  16.9         538.781944  \n",
       "842     0.0  20.5        1097.152344  \n",
       "10172  20.6  15.1         513.265376  \n",
       "3186    0.0  22.4        1706.908344  \n",
       "2167    0.0  20.4         744.429354  \n",
       "8838   12.7  16.4         477.825001  \n",
       "10343   0.5  17.6         604.413090  \n",
       "5954   39.5  18.6        1163.261399  \n",
       "3871    2.5  23.5        2273.446871  \n",
       "1768    0.0  20.9         837.734290  \n",
       "2513    0.3  17.3         592.611504  \n",
       "9815    0.0  16.5         323.366802  \n",
       "3896    2.5  23.5         890.208032  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power_poor_fit=dfv7.ix[test.index[power_residuals > 200]]\n",
    "power_poor_fit['Model Predictions']=ppredictions[power_residuals > 200]\n",
    "power_poor_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The points where the power model fails are not as clearly grouped into a few locations, sizes, or any other obvious grouping. There may be a pattern where the HTDD or the CTDD are larger than typical, but that is not definitive.\n",
    "\n",
    "## Model Feature Importances\n",
    "\n",
    "Finally, I look at the feature importances for both the water and power use models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Water Use Model Feature ranking:\n",
      "1. Nreturns(3): 11.77(8.20)% \n",
      "2. EIC(6): 8.21(8.56)% \n",
      "3. SW(5): 7.51(6.84)% \n",
      "4. AGI(4): 6.47(5.93)% \n",
      "5. Z_91350(123): 5.84(5.94)% \n",
      "6. ZAREALAND(2): 5.70(8.04)% \n",
      "7. PRCP(10): 5.63(4.76)% \n",
      "8. ZAREA(1): 5.42(7.53)% \n",
      "9. CLDD(8): 5.08(3.83)% \n",
      "10. Z_90265(82): 4.66(8.24)% \n",
      "11. AWND(7): 3.92(3.22)% \n",
      "12. M_10(154): 3.49(5.20)% \n",
      "13. Z_91201(102): 3.45(3.08)% \n",
      "14. TAVG(11): 3.37(4.16)% \n",
      "15. HTDD(9): 3.32(5.07)% \n",
      "16. ZPOP(0): 2.97(3.98)% \n",
      "17. Z_90071(71): 2.48(2.81)% \n",
      "18. Y_2011(163): 1.63(3.84)% \n",
      "19. Y_2007(159): 1.44(3.80)% \n",
      "20. Y_2012(164): 1.39(3.38)% \n",
      "21. Y_2013(165): 1.06(2.60)% \n"
     ]
    },
    {
     "data": {
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wBgDAMoQzAACWIZwBALAM4QwAgGUIZwAALEM4AwBgGcIZAADLEM4AAFiGcAYA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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6e3243c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = final_water_model.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in final_water_model.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Water Use Model Feature ranking:\")\n",
    "\n",
    "ntop = 0\n",
    "for f in range(train_features.shape[1]):\n",
    "    if importances[indices[f]] > 0.01:\n",
    "        ntop += 1\n",
    "        print(\"{0:d}. {1:s}({4:d}): {2:.2f}({3:.2f})% \".format(f + 1, train_scaled.ix[:,2:].columns[indices[f]], importances[indices[f]]*100,std[indices[f]]*100,indices[f]))\n",
    "plt.title(\"Water Use Model Feature importances\")\n",
    "plt.bar(range(train_features.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xlim([-1, 36])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important features for determining water use are: the number of people, the level of low-income houses, overall economic status, the size of the zip code (more importantly the land area), and the amount of precipitation. Those all agree with my original hypothesis that population, economics, and weather all play important roles in determining water use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power Use Model Feature ranking:\n",
      "1. Nreturns(3): 16.03(13.83)% \n",
      "2. AGI(4): 13.29(9.80)% \n",
      "3. SW(5): 12.65(11.62)% \n",
      "4. EIC(6): 12.56(9.35)% \n",
      "5. ZAREA(1): 7.06(5.69)% \n",
      "6. ZAREALAND(2): 5.69(5.03)% \n",
      "7. ZPOP(0): 5.36(6.18)% \n",
      "8. HTDD(9): 3.57(5.41)% \n",
      "9. Z_90731(96): 3.25(3.84)% \n",
      "10. CLDD(8): 3.01(2.68)% \n",
      "11. TAVG(11): 2.40(2.82)% \n",
      "12. AWND(7): 2.11(2.02)% \n",
      "13. PRCP(10): 2.07(2.23)% \n",
      "14. Z_90046(54): 1.95(2.26)% \n",
      "15. Z_91355(125): 1.56(2.20)% \n"
     ]
    },
    {
     "data": {
      "image/png": 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62gAAWMLqnvahQBeArqE1vXR66AA6kNWhDXRqXXhInqFjoGMQ2kBnxLPZATSC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\neTWQ1LQJugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6e52acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = final_power_model.feature_importances_\n",
    "std = np.std([tree[0].feature_importances_ for tree in final_power_model.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Power Use Model Feature ranking:\")\n",
    "\n",
    "ntop = 0\n",
    "for f in range(train_features.shape[1]):\n",
    "    if importances[indices[f]] > 0.01:\n",
    "        ntop += 1\n",
    "        print(\"{0:d}. {1:s}({4:d}): {2:.2f}({3:.2f})% \".format(f + 1, train_scaled.ix[:,2:].columns[indices[f]], importances[indices[f]]*100,std[indices[f]]*100,indices[f]))\n",
    "plt.title(\"Power Use Model Feature importances\")\n",
    "plt.bar(range(train_features.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xlim([-1, 30])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important features for the power use model are population, economics, land area, then heating degree days, cooling degree dyas, and average temperature. Again, these agree with my hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model First Date: 2005-07-01 00:00:00\n",
      "Model Last Date: 2005-07-01 00:00:00\n",
      "Total Zip Codes: 145\n",
      "Total Population: 4729404 people\n"
     ]
    }
   ],
   "source": [
    "# Miscellaneous Model Properties\n",
    "print( \"Model First Date: {}\".format(dfv4['Date'].min()))\n",
    "print( \"Model Last Date: {}\".format(dfv4['Date'].min()))\n",
    "print(\"Total Zip Codes: {}\".format(len(dfv4['Zip'].unique())))\n",
    "print(\"Total Population: {} people\".format(dfzipgroups[dfzipgroups['ZCTA5'].isin(dfv4['Zip'].unique())]['ZPOP'].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I calculated the accuracy of both the water use and the power use models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U1DRp4ykpKcrNzZVhGEpKSlJ4eLhj3bfffquFCxfqxIkTSk9PlyRlZ2crISFBd911lyTp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\nHwKkgKHEqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6d57ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68% of the power use predictions have better than a 95% accuracy\n"
     ]
    }
   ],
   "source": [
    "nonzero = power_actual>0\n",
    "power_accuracy = (ppredictions[nonzero] - power_actual[nonzero])/power_actual[nonzero]\n",
    "\n",
    "x,bins,p= plt.hist(np.abs(power_accuracy),bins=100,normed=1)\n",
    "plt.xlabel('Prediction Fractional Error: $|$Prediction - Actual$|$/Actual')\n",
    "plt.ylabel('Fraction of Total Predictions')\n",
    "for item in p:\n",
    "    item.set_height(item.get_height()/sum(x))\n",
    "plt.ylim(0,.3)\n",
    "plt.xlim(0,1)\n",
    "plt.show()\n",
    "\n",
    "print(\"{0:.0f}% of the power use predictions have better than a 95% accuracy\".format(float(len(np.extract(np.abs(power_accuracy) < 0.05,power_accuracy)))/len(power_accuracy)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7BQAAruM28GfPnq2qqir5+fmpf//+OnfunIYPH+52w7m5uSoqKlJGRoYKCwuVkpKijIwM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\nWalTp04pNDRUu3b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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e6e047c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72% of the water use predictions have better than a 60% accuracy\n"
     ]
    }
   ],
   "source": [
    "nonzero = water_actual>0\n",
    "water_accuracy = (wpredictions[nonzero] - water_actual[nonzero])/water_actual[nonzero]\n",
    "\n",
    "x,bins,p= plt.hist(np.abs(water_accuracy),bins=200,normed=1)\n",
    "plt.xlabel('Prediction Fractional Error: $|$Prediction - Actual$|$/Actual')\n",
    "plt.ylabel('Fraction of Total Predictions')\n",
    "for item in p:\n",
    "    item.set_height(item.get_height()/sum(x))\n",
    "plt.ylim(0,.3)\n",
    "plt.xlim(0,1)\n",
    "plt.show()\n",
    "\n",
    "print(\"{0:.0f}% of the water use predictions have better than a 60% accuracy\".format(float(len(np.extract(np.abs(water_accuracy) < 0.4,water_accuracy)))/len(water_accuracy)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "I built machine learning models to predict the water use (in HCF) and power use (in kWh) for the city of Los Angeles. The models were based on data from July 2005 to June 2013. They cover the water and power usage for 145 different zip codes housing around 4.7 million people.\n",
    "\n",
    "I compared a very simple initial model (that used the data from the previous year to predict the current month's usage) to machine learning models that utilize:\n",
    "* The zip code\n",
    "* The month and year\n",
    "* US Census population and land area data\n",
    "* IRS income tax return data (AGI, Salaries and Wages, and EIC)\n",
    "* NOAA weather data (Heating- and cooling-degree days, average temperature, wind, and precipitation).\n",
    "\n",
    "I measured the model performace using the root-mean-squared of the differece between the model's predictions and the actual usage for approximately 20% of the dataset, reserved as a set of test data.\n",
    "\n",
    "The initial  model had the following RMS errors:\n",
    "\n",
    "> Water Use prediction RMS Error: 62.052 for the 'Last-year' Model\n",
    "\n",
    "> Power Use prediction RMS Error: 139.855 for the 'Last-year' Model\n",
    "\n",
    "The best machine learning models had significantly improved meaures:\n",
    "\n",
    ">Water Use prediction RMS Error: 31.016 using an Adaboost Regressor\n",
    "\n",
    ">Power Use prediction RMS Error: 45.757 using a Gradient Boosting Regressor\n",
    "\n",
    "Finally, I measured the relative error of the predictions of the machine learning models. This is calculated as\n",
    "\n",
    "> $\\frac{|\\textrm{Predicted Usage} - \\textrm{Actual Usage}|}{\\textrm{Actual Usage}}$.\n",
    "\n",
    "The power use model performed very well with 68% of the power use predictions scoring better than a 95% accuracy. The water use model did not perform quite as well; however, 72% of the water use predictions had better than a 60% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Anaconda)",
   "language": "python",
   "name": "anaconda3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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