From e3db9302f36d142934aa0e1fb4d6767830296fc0 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Tue, 23 Jun 2015 18:45:42 -0400
Subject: [PATCH 01/13] First commit

---
 How Much is Your Car Worth.ipynb | 343 ++++++++++++++++++++++++++++++-
 requirements.txt                 |   2 +-
 2 files changed, 340 insertions(+), 5 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index bfc2fbe..d1ccc66 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,9 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -14,6 +14,17 @@
     "from sklearn import linear_model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -59,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -67,6 +78,330 @@
    "source": [
     "df = pd.read_csv(\"car_data.csv\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Make</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Trim</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17314.103129</td>\n",
+       "      <td>8221</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17542.036083</td>\n",
+       "      <td>9135</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16218.847862</td>\n",
+       "      <td>13196</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16336.913140</td>\n",
+       "      <td>16342</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16339.170324</td>\n",
+       "      <td>19832</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Price  Mileage   Make    Model      Trim   Type  Cylinder  Liter  \\\n",
+       "0  17314.103129     8221  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "1  17542.036083     9135  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "2  16218.847862    13196  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "3  16336.913140    16342  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "4  16339.170324    19832  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "\n",
+       "   Doors  Cruise  Sound  Leather  \n",
+       "0      4       1      1        1  \n",
+       "1      4       1      1        0  \n",
+       "2      4       1      1        0  \n",
+       "3      4       1      0        0  \n",
+       "4      4       1      0        1  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17314.103129</td>\n",
+       "      <td>8221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17542.036083</td>\n",
+       "      <td>9135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16218.847862</td>\n",
+       "      <td>13196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16336.913140</td>\n",
+       "      <td>16342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16339.170324</td>\n",
+       "      <td>19832</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Price  Mileage\n",
+       "0  17314.103129     8221\n",
+       "1  17542.036083     9135\n",
+       "2  16218.847862    13196\n",
+       "3  16336.913140    16342\n",
+       "4  16339.170324    19832"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mileage_p = df[['Price', 'Mileage']]\n",
+    "mileage_p.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pv0tY5bmhpM6CNoVZ1FzR84mWMm7VN1b3CepRppwNqyJiWJdQ5A2KTOP6XqFi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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10925cb70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(mileage_p['Price'], mileage_p['Mileage'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = mileage_p.loc[:, ['Price', 'Mileage']]\n",
+    "df.dropna(inplace=True)\n",
+    "price = df[['Price']]\n",
+    "mile = df[['Mileage']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coeficient: [[-0.1725205]]\n",
+      "0.0204634473235\n"
+     ]
+    }
+   ],
+   "source": [
+    "regrp = linear_model.LinearRegression()\n",
+    "regrp.fit(mile, price)\n",
+    "print(\"Coeficient: {}\".format(regrp.coef_))\n",
+    "print(regrp.score(mile, price))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "equ1 = lambda x: 0 + m * x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def linear_least_squares(df, fn):\n",
+    "    values = df.index.map(fn)\n",
+    "    diffs = df.mean_minutes - values\n",
+    "    diffs_squared = diffs ** 2\n",
+    "    return diffs_squared.sum() / (2 * len(diffs)) "
+   ]
   }
  ],
  "metadata": {
@@ -85,7 +420,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.4.2"
   }
  },
  "nbformat": 4,
diff --git a/requirements.txt b/requirements.txt
index 9c0bc7e..abdd30c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 gnureadline==6.3.3
-ipython==3.1.0
+ipython[notebook]
 Jinja2==2.7.3
 jsonschema==2.4.0
 MarkupSafe==0.23

From a933ce2e5920ab6e0b074299d885ef69246adf14 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Tue, 23 Jun 2015 20:34:10 -0400
Subject: [PATCH 02/13] Coming along

---
 How Much is Your Car Worth.ipynb | 479 ++++++++++++++++++++++++++++---
 1 file changed, 433 insertions(+), 46 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index d1ccc66..f6365cf 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -11,12 +11,13 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from sklearn import linear_model"
+    "from sklearn import linear_model\n",
+    "import itertools\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 43,
    "metadata": {
     "collapsed": true
    },
@@ -70,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -81,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -204,7 +205,7 @@
        "4      4       1      0        1  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -215,7 +216,171 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mileage_p = df[['Price', 'Mileage']]\n",
+    "func_one = lambda x: 0 + .5 * x\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SuDI6fw1wKs7EF5vv3gXcFJ2z2B/XNd/hFqiEtKTo/uhcv7fffZYWNS/G3rSz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\nNgHz/PFhJOa7K4Ero/PWAKf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GYRhGaTChZBiGYZQGE0qGYRhGaTChZBiGYZQG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\nDMMwSoMJJcMwDKM0/P/SLjHrjQG0QwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106428128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(mileage_p['Mileage'], mileage_p['Price'])\n",
+    "plt.ylabel('Price')\n",
+    "plt.xlabel('Mileage')\n",
+    "plt.plot(mileage_p.Mileage, func_one(mileage_p.Mileage), linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dfa = mileage_p.loc[:, ['Price', 'Mileage']]\n",
+    "dfa.dropna(inplace=True)\n",
+    "price = dfa[['Price']]\n",
+    "mile = dfa['Mileage']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coeficient: [-0.11861458]\n",
+      "0.0204634473235\n"
+     ]
+    }
+   ],
+   "source": [
+    "regrp = linear_model.LinearRegression()\n",
+    "regrp.fit(price, mile)\n",
+    "print(\"Coeficient: {}\".format(regrp.coef_))\n",
+    "print(regrp.score(price, mile))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###This equation is not a good fit for the data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = regrp.coef_[0]\n",
+    "func_two = lambda x: 0 + a * x\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QV8KSSyiHyoW/9Qwb1fMmn1ZOMi9+1g2asFCntGVgSqnV7YnmUm4dFf9zSt42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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10955e400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(mileage_p['Price'], mileage_p['Mileage'])\n",
+    "plt.xlabel('Price')\n",
+    "plt.ylabel('Mileage')\n",
+    "plt.plot(mileage_p.Price, func_two(mileage_p.Price), linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yz/fvJ3Pbhdkbku+dpvGi1+i8MLlrm8b7J4WLaKMkrps0DqiYn3Hvij2e1AnW\n1Im9LqFUOT7R4t/zfLvpQsALsJ1p2plrq70fOo5UCtesxbTpfY+FZPZi3OxnGm6dWur7KRnRO/uz\nFxRP7i08kppm0f0ZzyWvebMen1Kbqj4kIpG5T0XkaB3XGWNAh0KQZ/bDd7tdWPUi3Hqf43D+n1VU\n+1Dm4kK3byP2p3wU5/NZj/MZ3RW8FwU2/J4/nubbuAXnpzkRuBzY4INbvMWIe3AO+Ggd0ADOF6XA\n3b5uYdD+lb5NcLn1uoFf9W1EYds/GIRXhvxJWeHNlb6f5BqoFcCrP9NEgEJ8TVo+vus+4II3AP50\n0PVnHi7cfk9rdbDA/lvhB78Bq1rjup7Dbv1S5xo3iOcFz58ng89ATxsVQRADwF/NSA/Vn7zYeq9x\nQh3S8JvA6cSa0ruBbxYtpceDxG9Av5ZVZjjo1Nh3kpkWSJ3m0KZxSHJoSos0mdBklhXSvCTQmKJf\n30Oa2SGXTWKmxmHoSQ2kbSfQF+/JFPW1V+Ekf5+Z4TVHGLHvJ20cOqvCr2tnfgjNjfVpFwzl2xvy\nWfVV9nf4bBQZn3dmSHfW+1T5mcJs6RVpnia1+c42f2z0eKK5tFvHjU/D+ZRexcW+fpeMbcXHaymj\nUCJzs7pIqCxICIGKSVDjfHXhpNTpr1+ilX6jUCi9Kzj35opJPp7w2vudsEzbBykUgLO9MGsLkshu\nGuaaKD9dtn/HtVlLKEV+nHCNV9TezVq9BcfJ/nk3abymaKQBClnrl0Kz3vBmo+HOq6edSuHV6z+/\nyo0eJ2MxodTo8URzaXcEHWgHji96IMbT4I6hPzWSjIYLYi/TOLN3Uhgkne2ztTppaxRxFwVRvEYr\nAxyGBFR/Zf9qaR2pWciDrdNPGEYo1hRKA7GGEgm2ZMRZ6AtbotB6JBaMHYNuvC5T53uKAi2isTgz\nEkIjjlhL72+kuc7YG++y2xyNpV4hOJnKcJqolRGPp+bSbo0b9gZlZVB6gZVFD8h4GNzR9yfL/DJH\n48k43MQvaTY7LiFsOhSm73VhzMmJ8zV+cp7tJ+WozSgCLAoECCe5SPPJ0lIibS0UlG07q8PD53gB\nEQmQcDM+llVqLFGfoj6EJsjzfP+Hsicknj2ZMHaOr0v2ffaRrEmqPg2mYsJTp4FlarN1ReRZafT/\nlgnrBo6l5tFurUCH43Fe6ySSUW80nCig4CZcctN7gL/w7y0CPoVbOPsccCPQilukOh/nrP8CcBQX\naLC522Xir9b1AAAgAElEQVR9SDKA+zhv869XABcAn8MFRUwD2j8AU94La6e5c3oUlgN/QmXmh+UK\n08X9bnk7cT44gNY5bnVBGGSQDFKIgwJUdbNI+49dVot5/tn3ALoUjr4Kdymsc9E3rAJ+E2dlfmIA\nrp4SLzDejlvImkwY+4mUsdDtEC5gdcEVdTrIz3VbhVynbsyXiRvXSxL3vdPXvaEbHvtano52t9li\n10r/LGtVdU0e9xlP6KgyhRhNpWhpW4ZCiTQl0E644r8mfnUPOjPURq9ZROa7SFvJ2kuoyx8v9NrI\nmVq58LVDXXh2muO/zb+3xP/iT54z12sb5/n3Zw5SscleqK2coE5LS/YzO6iAodDq0P8Uttmq0DkY\nb4/h1vRU+puie6Td5+yEFtNxiKpghY5DcTBDun/L97Wv2iw6M0Mbi4JHRhYEMYrvdF+1Bh2vebJi\nZawlr3kzU1MSkdWqeouI/GW6LNO8Y10nHSLMB56Nc8dFXCk4lQWX/foXuOzaryfeujzUWFbi0gTN\nxGkK/wl81r+3AmeNO+xfL6SaGbiAy5dwmk0P1XnkTgF2AR/BaSFnS3Wo9SqcdvQ3wIMtcMcgrGqJ\n339CcZp3Cl29sLY13oL9O1RnLv/wz+H5h9zxS5FW0w/L74f1frwe89clM6jPAw4PwopHXRqfKAVR\nRe64Vliflbon2Kq9a2WcDzBiPXCA6hyCkdaXvSXHcNSXCTytTytX4nJHGUZpqWW+e8z/7U95b8zm\nOxE5Cfg88Frf3p2quk5EunBbZZwMPA38oaq+5K+5AZfgbADoUdUtvv5cYCNuAc8Dqnqtr2/191gE\n7AMuV9Vnxtr3HDk4/Cmn+r+hMNmfOCeaiJ4Bfgi8H/iV4P31OIHSjkug2uvrzqNyH6XrcEJhnT8n\nuudqYnPajThBkbYm5nR/n6i/Az+GQ6+F62a49TUHNkDPjcRrbAbdWp+QaF3UXKoF6PSjUSLVSqYO\nOgEJbquNe/yYrAeexH3tO4Gpj6r+7NzoKpHulLx484APAVcEdauBq1pgwzDJTncfdabGa38KU57y\nz1ZzS47hsLU2xoSnQNVvLnCOP56JW3l5Fs5R8jFfvxr4pD9eADyK0xhOwc124t/bBrzFHz8AXOyP\nlwO3++PLgS83Uw0d2/gs+Oc4UEAVXlQ4HLwebRlQ+GmN938QHH9M42wPC4Pjiki/Y9lBGZGJ6jSF\n9oGEeW/Ab6OxwZnehoIUgrU3YVhzm1ab8qrNUdVRcFG74bqs+ZpM4eO/B32VfQnD1aNsF2HU4JCp\nMcV815YaNMEYHe1uzNL7Uf0sZr6zkl/Ja96sdcP7cT9/708p9+XwgF8H3gHsAOb4urnADn98A7A6\nOH8TsASXcuDxoP4KYH1wzmJ/PBV4sZmDO8bxyNj3J5zYo2So8xR++yh8dBD+/xoC51ADhJoqvKzw\nnMJtx+CaPfA2hXv8RD5bnb8mmQD2bE33ryQX1ib9SlEqoUjwRT60tp3p45YUSlVJSgMBUxXtl+HH\n26jVW7VXLWztcwlgZx/xi4YbHtlFjYjEjPP74nx8JpCsNLYUIZReBB7BhXOd78tSX85v8MOdgrM1\nHQ/8LKiX6DVuT4T3Bu99Dpd751zgwaD+bcD9/ng7MC94bxfQ1azBbcC4bEgmDoWZR+MQ6pv9hJ8V\nDBDlw4syQSxRF+jwCYU/UXi2QUKqVnlB4VGFzyn8a0L4nKbp2b3DfG9Zi4hrTcRJIdSyF2YfhM6j\n1RpNu1+Qm75Ytzojd3HhxLXWbhX9XbUy+Upe82Ytn9KJuFji9/jyj8CXVPWHNa4ZMSIyE/gqcK2q\n/jzKsRc9sYhoI+9Xox83BS+3qurWZtw3CxfO2/EB+Iyv6fmAiOyEzldh/vHOZ7IBt/V3MsAgCnSY\nSrzp3WpcQMIenN/oCaAN59uIggeuxbnr/hiXuKMH99GD25PpRVzgQucInuS1vvxKov7KRJ/BBVac\nOgj//AsRPgg3tMFr/ye8f0YiYKCGLybcCBGcf2zDw6r7LhLp3gu3dSfG6uTsvrfsi7cj7+p1QQVp\nPqzhyXGb8kcb2JZhZCIiS3FKSb7UKRFbgQ8Ae4EPN1DSTsM5ilcEdTuAuf74RGLz3fXA9cF5m3DZ\ny+dSab57D3BHcM4SfzzOzHezD6Ys7jwI9DkNqEPjbA5p4c5dKfXRYtdwu/NN/vwoC0RkFrpZq9MB\nRSHOJ2n1wtkvKPyOwnZ//HzeWtjPQP8F9C9BrwZ9C2hbrVQy1eHikTYU7stUkXUhESLeNuBMdCNL\n2UOdOetG207R31Urk7PkNW8Od9PjcCayvwP+Dfez+3UNeiDBRcZ9JlH/KbzvyAuiZKDDdFwI2o+I\nAx0e8gJKqA50iATUFYyrQIfZR9IyDvj++k3mom0s0oRH1tqlDnVrjMKAhbRzK4IC1GWJiHLhRX6j\nqgk+EHDJ907zbZ6t8Pngvf9Q2JazADv6Cuj/C/9xH6w4Bv+o8Dfq/UY7E76iKABjWaWAS+bs6xjj\nlhTJBLd1bwKYssmfZSiw0vzSdKGESwfwMHAzsDCHB3orMOgFzSO+XAx0Ad/Cxe5uATqDa/pwfqEd\nVDqZz8X5j3YB64L6VuBeYCfwPTISyZZTKLWm7PvTujN+P5roosWrszVOxBqmIAqvn6kuOCIUYL0a\np+DRhAALI+iizf6GNunTyv69JnidFlww5OcaTAgBrd5/qWuv/1yWQfcr8A2F7yj85VF4/vugP8lP\ngB1ReOwl0P8Nf/E4PODr01Iq1V74SkWgRvUzjrS99PZNc2rO/6MJ/5Qx0VzarXHDQeDnGeVg0QMy\nHgZ3jH1a5qK/5qsrrRUhxvGEFIUuR1pNNMlHddFur9FWFFXal38v1AJmeaGzQCuTpm5UOD+aQLUy\nTDqZvSDqT5e6XXJPUqehzTzAUOaFzgF3TlLTa++vfM76JgPQaaBvBH0P6F+APgD6XH4C7LDCjgPw\n+DdBPwJ6Pujsys8nTTCn7Rg7GqFkWa+b979owj9lXDSXdot+sDKUMgol3686tzFo73eTfHs/tB6N\nJ/oOrcwYnpVAVTXOJn5aIMxC7SgSTtFOtJdp5dqkNIF3vm9rrsZaXHJS3uTbz96ptfo5R7vGJzmJ\n/4PChxRuVRcZuD8n4aXq1of94Bg8sBu+tR5u/RicOqaJzoRSs/4PbZzTxwXNpd2iH6wMpaxCaYTP\n4EOn07axyPSLeOGiCSEVrX+JhFjkP4q0ry5/7aZAmM08UL2VeLvGARdRAEWYJTvq181e2M0+QvWC\n1oRGGPp+6BuZJpX8xdulce6/UGuM/DXRuX+gcI4/53MK9yvszlGAqYI+CnoPaC/ohaBzhn8e+wWf\nz/+WCaX0cUFzabfoBytDGe9CqXpyCif/M7Xat3S+xn6n5JYP0SaCYRvROaGwCxOdRtFkyWwDWQEU\nGxVm7cwQNolFqbX2bpo1UGtBa/ZYdW2BWS/H9w3b7FTnz+va6wTUuxReq9U+ofmpkxRoC+gvwYY/\ng784Ak8rHMhZgD31c3jwJ/AvnwP9LdB5oFL093KiFBP+meOiubRb9IOVoYwHoVRLI8jeYC4KSogy\nIUQCJ3leNBlfps6fdLxW7kfUNhCb2qIIuhPUaTbtQ+HR6Sl+0vrl/Eaxdpf9K7S2UJqbUlfv1uPt\n/elh82drpZAONb80DTR7kqr9mUXZGf5O4VsKaxQeUfjPl3PWwB4D/RLoDaC/A3qSCbCx/f9N1pLX\nvFlr8axREkaXhPMJhTeK20toIW4Pn3/CLbiNEpuuwmXwjvY9ug/4f4Av4nLirh+EYz+GD5/uFtZO\nI15ouwr4o2lw11nxPfdvhZ4L49ePAR8N+nQtcDZu8erdqOpmke6HcYu0M9h/K/S8Fa5OLKD9KPBq\n9mUJEotXt0LXyS6ByLXBWauB9wFP+bKOyoW2N+EWH/cABzfCyte5+oOpi2G15t49Xb2wtgXe7V8/\nh9uf6snvpi3QFUFw2WF/xZc3+bKg9pNXcZYvYYZZJD1X+5PAD4Ly78AzqugI7znuqf1ZGg2laGlb\nhkLJNaXhbNpUmRci30+062q0yDYK5T5JKwMgojbP04QpbdD5ii5Tl18vyxQXbmEeReSdr7GmFkYA\nVkbYkZE4lMpfpn3xjrezBmJNrSrsPTXlTuX4JDWgtsT4RBpRmmY2+8hY8shVPlNa9F2nXyM1tl/j\noAJ6Iugy0I+CfgH033PWwH4E+g+gHwe9FPQ00Jai/3es5FfymjdNU5oAqNtH6FJY+UW3o2mk/SwE\nVu6DQz+Dx38JaHH13x6EV/bAXSfCwmD31rk4TWhIOxD4dIfbu6neH+S/g7v/LbilY3cD/BwuON5p\nGZ/w93nKn5+WFmj9ZTDlxlAzhP2X+ufsA/0ELGhxz3cPbr+l54GBjJQ74T5Jl1GtAX34IOzY5Za/\nHXkj7Gl167PDLcN6XoWDo94iIkXbPQzLD+PW0uF28z08AP97UfTMo92SQhUFfuLLsNeL8Fqc1hVq\nYG8CWmpdl+CXfLk00XYaT1Otgf1IlYER3K8U5Jg+avJStLQtQ6H0mlK2o5WKX99paXSiSLLOfpci\npy3IMt12yNf3x4lek9dHKYmSW1MMaWOJvkTBC1EG8EjrqdpF9lDcryqNJCXFUqiNpfUn2/lcqWnW\nXgRLqoY2dj/C8Alfx75uaezfseGfFfQE0LeDXgt6N+i/gR7JUQN7FvT/gP456OWgZ4FOLfp/crj/\ny8lQ8po3C3+wMpSyCyXfx6pJo/qfomp7hQFn8mo7VClMQod9mBcuaQ6brZWLYqOIujnq9keqzgEX\nC5quvZVBEFkCs70/3t49SoPUWYdQCvvTtbf2RFrLfNehcNzLI81nN/LPbzgTbD5hx/UIm7wmV9DZ\noL+BW1j8OdCHQF/NUYDtBv0m6C2g7wU9G3RaUZ/pRC8mlMbh4Obc54x1SdEC0zC5aFIQhSHbbkL3\nE9Ohah9QchKf5dtrzdjPKCv5aJamEEbBbVR33LqztmY4/ASanIwTrzfHC3bD7Bf157Mb3eeV3e+M\n98ekqdU/VsVPrqCzQM8D/RPQO0C/C/qLHAXYHtDNoP8T9P2gvwI6fWR9Ln7ciiwmlMbh4Da4j+GG\nbRuy1yV1bXFrgNKCEqLjE7VyfVJoxhtytG+IJ+7LvLAIM4lnBRWk/6PGQi/U2NqOuPVCacIqEiIV\nfUoxWVYnJR3e3Jm2seD5uU8qw2kt1abD+jOLp2vS9U2a421yBZ0JugT0j0H/CvRfQQ/kKMBeBP02\n6GdAPwi6CPQ4M9+hubRb9IOVoZRdKHkhpJUmp7RMDNGv66wEqxsV2hIb3SUTqXYOxIIgrY3wtYtE\nqxQItbaOSNvKO82PFe48W/ufPv2cdFOhO79rS/r6qW6ttYtrzp9vmpBNWb/VnppZfGTaaerW6RN2\ncgVtA/010KtAPwv6z6D7cxRg+/09Puvv+WugbUWPQ07fW82l3aIfrAylzELJTRhpW0FUZRTYG09E\nSd9QlGB19sF0zSQyASbDq9MW2iZfJxONpv/Cd8+SNkmel7zvMOdXTqoZ52QuyI3HJ1z8GqVV6hxs\n9mScIhAOOc01TXCmP9cw2mmi7fSQ83p8TxO9OO1HF3lt6DNeO3oxRwF2AKfl/RXoh0AXg7YXPQ4j\n+O5qHu1aSHjp6ep1O8QmeRkXDg3QMwgH16pbjNrrQqU/ggu/fhk4DKwHOB6uy7jPnbgw7iujihZY\nMQgLfVhwj8KxY3DPNPd6tb//Hlw496cBZsDKpbD/Uljpw2TdwlIXOtve7foahRpHi3fBLUx9ch8c\nfK+OOaz28DPQ0+b6A5U71e6/Fe56K8yb4cZkXvAc7j9tNIw+NDgMVweg1fXrJirD1ntehZZngO56\n+6TxUoFeGOyGY2+E21NDztUWh6LKIdx2PQ8Pd64Irbhtn6Pw+WhR82tHcMsO3BY+b020neRXVekf\nQbvjm6KlbRkKpdaUurY4U13VAtPNztQ2lBInMuX0xQtCw+CBKNAhra12Tf9lHoYsh2alZHBF6K8a\nzjwUmQhnJaIC6zXNVflVRuR7iduNov6S2tLIzXdZ/Rz+mqy9lpKZ2yuCUeo231V/j8aP32giFdyW\nKmfjIgJvwUUI7h6BRnVH0c+Q/lxoLu0W/WBlKOUWStGEc5n6TNoKbMg22XRtqdzuPHrvHC88lihM\nORrvHnum+iSkGvuiIlPPcNkRIqFWYb5LMwulCLLQBzVcuPKwQQyjilJL93GNRiiNbMIfZgwPpQnr\neBzSt+8YbixNKI2PAjoVtxbrctzarFtAu4ruV3pf0VzaLfrBylDKLJR8/+qOrHIlLXigIoGoOl9O\nt8aa1ix1W0hEgivcErzmBJgpEKon38oowVrPlz0WjZtcR6PhNKJP2X6wkUcTNvtZrViJigmlcTi4\nOfc5Ocn4vGn0uYWtYfaEKAedZgipcP8k9cczD7g2q9ofgSaSNvmeoC5/ncsfN9LJMntCH90EOxKB\nmH19cl3YaJ6hs39k549m6/TqRc1WrIy2mFAah4PbhH4nJsTIXzN9N7QNxlkS2lPW5aRF0oVbNmRt\nO1H/L+z0yXRoD6ZDsUAYLsKulg+pOt1Rc8c/aYZz273XcV1yzVbmwt2xCiXTkpr9nZgcUYwmlMbh\n4Dan71m54KIFsdFC2GRwQ1Jz6lQfLrw3Dl5IToRRXeWv+tpmvqQASfqVhku/k+lDSvVTFTP2Yd/r\nE9zp/qzKwJJhxmCMGqv5kxr/fZhcwj+vedNCwscpQQjyIpeN+z4SId2tsHKf6r6LRLq3VGbivhCX\nvTvaV+la4NAu1UPnunO5ED5EZUhyGALOOSKyTIdCvav2eroZupa6EPBju+G6k6F1mmtvWdDmYDe8\n1OeygKeFb0NKyLQPO+dhuObCyvZGNX40NrvzPOCaGT4kvkabrfvgGuLnugfgHLfHEoQh21oR2g1Z\n+zcZRZP6XR3me2BUUbS0LUNhnGlKpJqNzk7RbMJkq8n3ZqoLiDi7wnRU2XYUADFbXfTfEnXBEZcF\nbSd/hfdqvPA29Gulham3eRPeSHfVTV0YOgKzIn0Jf1kygq+uaL7qPqQHctT5GaYsVm6MNjOWsbIy\nknGeXBppXvNm4Q9WhjL+hFLal78qMCHIjNDeXykg5qiLtKvMsB0Lh6F8c34NzQKtDBcPN+lL9iXy\nGaWZ/zq8IDy/avLOEky1JtRawqzGZ52R+y56vqos4sMELaQGOtQVFFLZ/7aUfIWNm9BGM1ZWRjPG\nk0f4T0ihhNur+wVge1DXhdtV7klgC9AZvHcDsBO3G9tFQf25OBvWTuCzQX0rbl/vncD3gJObObj5\njVum9uB9LfHuqAytE+r1wiDK8ZY2wSf/oaL8d+enCJgoaWp7f6UwnDWQLZTS1k8Nr/U0ckJ1bWQF\ncWhGv4cXDrFwavc74y7RWsEL1dem7zdV9HfNyki/X5NH+E9UofQ24M0JofQp4GP+eDXwSX+8AHgU\nmAacAuwCxL+3DXiLP34AuNgfLwdu98eXA19u5uDmOG4pOc1ad6abpKJN96IJL0y6Gk78aYKubac7\nN23dU2eQHDRqs81H/UURcWnaWcXEO2zi0Fr/5Mn36pkQ3HtpuQEj09nohJJrO6mRxhpl7evCYJV4\nnVjR3zMrVmqVCSmU/IOdkhBKO4A5/ngusMMf3wCsDs7bBCwBTgQeD+qvANYH5yz2x1OBF5s5uDmP\nW2BqqyeB583qM0IcISVkudrv1KvQMZht0krLIh6tfYom1zPVZSEPtbO2gWRGghH6jby/p72/Mqy6\naoPDGvsshbvjdilcGQipVPNdnT6m1ISpe4f/LCeXL8LKxCiTSSj9LDiW6DXwl8B7g/c+B1yGM909\nGNS/DbjfH28H5gXv7QKqUnaMR6EU9z2a0FJ/4e+NhUTtrcOrf+V3abWQCrMOpE2kaRpVFLadrQFk\nCJ+Ue0RBFFG7ab6siudPndirBXqVBpkIeqjPT5AeUJK9KDbx/KFANdOdldKXvObNUoeEq6qKiDbj\nXiJyU/Byq6pubcZ9G0cyhLvnVTi4FnpuhAUzEuHiKaGqrfvc+/cB+4ApifYXAgM/U33pIgARIRHK\nPQgXtjiL61AfFI4dgT2tcAmw/DBM6xCZfRCOKUzdBS/1aUbIs8t4HvJdYF1L3M/RoUFGbBcaHt33\npaFQaxHpdyG+XSvrD/N9qQ96voHzZQI9h+FgX329OobP5A4ca4H2Ne75GxmubhijR0SWAktzv1EJ\npO0pVJvv5vrjE4nNd9cD1wfnbQIW40x8ofnuPcAdwTlL/PGEMt8FfU/JwB37ixgKdEjTokITWuRs\njxK0Jn1CJ6hz4ofnRxF6kXYRmsU6B1xdmEi040hle7W3IKdKgwqj5jYl+lef+W7kY1q/BhaM9wgj\nAmtmvpjQEVxWxm/Ja94sw4MlhdKn8L4jL4iSgQ7TgVOBHxEHOjzkBZRQHegQCagrmCCBDin9HyZD\nNH3pGR2iyTzaCr3tSOUkHG2dcJoGPpgtsQCLUhONdr1RZHKse5JPmNIqslbUFehQ33iGfU0Kv8YL\nieyx0WHHx4qVosqEFErAl4DngSPAs7i0A13At0gPCe/D+YV2hBMDcUj4LmBdUN8K3EscEn5KMwe3\nLMVN3FHC1UjAaMqEe4K67OFpv9pna7TfkIvKG1mU2WiFUspn1cDw8Ky1UWm+rErNssH/B8OmY8rj\n+a1YGUuZkEKpLGXiC6XQfBcGRKQFR5yvlUERs9WZ9M5Ub2rb4Oqq8s7VjDIjNQlpbfNdzp/5MIty\ns02i+fUnGXxRq19m2psIZTz/yDChNA4Ht8DnSazfCSPrwpDnrEWkver8Smf740gwTd1N1W6tUWaG\n2QfrS8vT2Q+dB10Ginwn+tpjVE8i2OTC4OYIgbSJiiHfYLFJaK00+nMevz8y8po3Sx19Z6STSCa6\nFbqW+mOfyLQyQSocvBn0jbC+FV4CULjuF/DqFFjZFre8EjhyEJ58CF49FWaeDk/hrKwPAnfPg9uo\njPK7CXgMuPp4WHhhmEi0sq+D3dAOtOyD/X1a8ogyHYr+W9tSdILN6qS3V+ITuBrjGkvgmoYJpXFG\nSlbuC50rbiFOALU8DrelZNXe/07YvgamnOPCqjnehXG/nTi8+oPAhodcZnHpgx2fAFq8QBqEM1uq\ne7QLuBr4dHi/XiAlg/gqXL/uqhBcxbD/1trZyYshLes6DDwO62ak/BgoRZ8No6EUrQKWoTCOzHfp\nCzQrIrXSwr8zMnqHYdbhotW0DNZtO6vT83SqSys0kvuNPLghx899uKjFRPaHTnXpnIYzUY5lJ9us\nnXXT6ooyfY5fP0hZCgWahxv4DJpHu6YpjSPcr+jOc2qfdfgZ6GkjVQMYOLX6fP0xrHzKHQ8tWt2S\nMCu0wIqDcNercPUMt8hzB/CL52H6a2DVtLi9nsPj5de7Botos953e0PddTOsE1e76nSY8g0Reacm\nNL2MvaUaoBEOpn2m7x17uyMnv2ecPMRjuG6GCxpeMQg8CgdLb9ZuCkVL2zIUxommlJ5MNFxzFGo6\naY7y9oHqUO7pe6sziw+XhbzzoEu+OvuIu3d6GiFSQ52L2bp8bGOeFgxSrek1Iodd9Zhlf6blGY/i\ntd7xVCbKGOY1b5qmNO5YiHNy34lb4nVsF2yo0HT8iYlfXF298IYWOI/Yh3Q+8GA3rPWve/7cpQ8i\nzd+yFTpuDPxDxztN6m5gNvBV36+V+6I7akX6oMFuOAps2BdpUiKz+6HlZKfdvWy/EkmOGdT+TGPy\n20nXMJpM0dK2DIVxoymNZbfVNC0ryuqtoY/pYHyvMKy8pn9oRBoQqWuW6tt7aPh2G6tNjKSvY/l8\nivpejHQMi3rGiVQmyhjmNW8W/mBlKONFKPm+jnK31badsakv2rJhVppQ0mS7ZObPi4TSEvVbYowx\n19voTBjE658GkqbMRoxx3H7X3uF2lM1DMI5uPEc+lvVOlkU840QrE2EMTSiNw8HNsb91f6ErJ5pe\nL4xOUrcYdtqh6sWwvRUTWnx9WoLW3uCa2ls0VPY5LYJw5EKJ1AimcEFvfhNzmUrjhNLE8HVYaU7J\na940n9I4Y+TRT8kFegtxPqVLgE+2ut3o1wPzcD6hPYl7dX0R3jADLsSVm4AnXoEjx8F3W+B9wF01\nt2io7vPyw9BzFLeLMG790iuH4ZW6o/biNhfMgGuoXMNzp3++0TAeFzSWc82VYYwGE0rjjvRJ0wUo\n1Ovofh7oweWr/SOcMLoGJ5B6FAa63eLZMLDhyuC8ld+Fl2+FJ3td3tyDw9yvqs+t8KcPwwriQIdX\nRhjoELWZtq/S84QT80QPAtDawREjwISbUQKKVgHLUBhH5ruMBbD91Ytdne+D1IWwMw+4vHZRO9EW\nFVHAQrioNrzPklGZs/IwC8VtVu2qOxD6fVKev2b/qQpsaEzCWEruQ4j7196f3K7eipW0kte8WfiD\nlaGML6GUNsm2Z/ho0te4uJIldKLXqZvbjSqLwEgFw8jbzM7kPVKB6Nqt3iuq8Z9ZeSb8svfPSjmL\nCaVxOLg59nck4dqpEzBuw7wgQCDaL0m99nGmJjYGHEH4eWaW6xzCtWu3OXKhlKdW17g2G/t9Knf/\nrJSz5DVvmk9pHKKJ9DjOnxT6AlaTDFpIaWONiPTDijXAOXBBS5x5+i7cRr9TgT8dhGl1p0AZJhCj\nob6c+tqc+H6Sie4zMyYZRUvbMhTGmaaU8Qx1r9VhKJS6a683efXF/oSZL490V9nKtsv3q5tRh9Dn\nYWpsrHmsEW0P18ZIxs/K5Cl5zZuFP1gZykQQSsGz1LEiv+1QpeCJnflZGanrvd/ofDjDT3jNnBjz\nuFde/W/swtm0reFHLvRMiE2OYkJpHA5uGYvThuZr1kSWvrA1fWFsyoTlt7io3s47baKqd8LLU9MY\n7yVvzXR0PzLss5oMxYTSOBzcshU/YQxkRNZtCc6pKyQ6XYDNV2g74s2DQbRfWubr+ia8MpoEq8e1\nGJRrukAAAAslSURBVM0gbyFQhkARK+Usec2bFugwqejy23vPpTIDwtCC2WXqFmK+c7iFmNl7O80H\nDk2DV1Hdd5E7t2p/phlx++ObovcX0oYtnM1i4geKGCWjaGlbhsKk0ZRCzSZaMBuFglev9aGGBsBQ\n1vHXBL6pirxzeyvPrf71zAQw300GzYChfbTiPbdqnFvaz8pKw78Xmku7RT9YGcrkEUrt/ZUBDp0a\nb9JXkRXhFTcR1YrIioRSpzfZLfHtaNR2jc3+4rZqCb7EZ5SMGCzFRDfRhdJohEy9n6mVesa+vONo\nQmlsg3cxbv/uncDqZg1uTs8y6i9qLEje5cuZWrnQVsOJNS0KL5E9PMoKUSXUqvxQY/0HK+sv8LL2\nq3HPN7GFblnLePhemVAa/cBNAXYBp+CyUj8KnNWMwc3hWcb0Ra2+vu2QEyBZKYVqT0bp5sDRpSIa\nvu/lnRzL/ot2oo77RC7jYdzzmjcnQ6DDW4Bdqvo0gIh8GXgn8HiRnRodY9tWQauc4tFWEdvXQM85\nQIt73fMqHFwLPTdS08H9Uh/0fC0+57FX4eB7dZJlFNAcslWUBwt0MJrLZBBKrwOeDV4/BywuqC+F\nkzGBbnZRZJURXC4NUXZUV7WQa3TkV4hNjkXQ3M/YiJm833fxatiERUQuAy5W1av96/cBi1X1I8E5\nCvxZcNlWVd3a1I7WQRx+vC78ojYt/LhoLMebMZko2/ddRJYCS4Oqj6uqNPw+k0AoLQFuUtWL/esb\ngEFVvSU4R/MY3Dwo2xfVMIzJSV7z5mQQSlOBJ4ALcFuSbgPeo6qPB+eMG6FkGIZRBvKaNye8T0lV\nj4nIh3F+lCnA3aFAMgzDMMrDhNeU6sE0JcMwjJGR17zZ0ugGDcMwDGO0mFAyDMMwSoMJJcMwDKM0\nmFAyDMMwSoMJJcMwDKM0mFAyDMMwSoMJJcMwDKM0mFAyDMMwSoMJJcMwDKM0mFAyDMMwSoMJJcMw\nDKM0mFAyDMMwSoMJJcMwDKM0mFAyDMMwSoMJJcMwDKM0mFAyDMMwSoMJJcMwDKM0mFAyDMMwSoMJ\nJcMwDKM0mFAyDMMwSoMJJcMwDKM0mFAyDMMwSkMhQklE/kBEfigiAyKyKPHeDSKyU0R2iMhFQf25\nIrLdv/fZoL5VRL7i678nIicH710pIk/68l+b83SGYRjGaClKU9oOXAp8J6wUkQXA5cAC4GLgdhER\n//YdwFWqegZwhohc7OuvAvb5+s8At/i2uoD/DrzFl4+LSGeuT5UjIrK06D7Ug/WzsVg/G4v1s/wU\nIpRUdYeqPpny1juBL6nqUVV9GtgFLBaRE4HjVXWbP+/zwO/740uAe/zxV4EL/PEyYIuqvqSqLwEP\n4gTdeGVp0R2ok6VFd6BOlhbdgTpZWnQH6mRp0R2ok6VFd6BOlhbdgaIom09pHvBc8Po54HUp9bt9\nPf7vswCqegw4ICLdNdoyDMMwSsrUvBoWkQeBuSlv9anq/Xnd1zAMwxi/5CaUVPXCUVy2GzgpeD0f\np+Hs9sfJ+uia1wPPi8hUYJaq7hOR3VSqwCcB/5R1YxHRUfS3qYjIx4vuQz1YPxuL9bOxWD/LTW5C\naQRIcHwf8LcishZnajsD2KaqKiIHRWQxsA14P7AuuOZK4HvAu4Fv+/otwBof3CDAhcDqtA6oqqTV\nG4ZhGM2lEKEkIpfihMoJwD+KyCOq+luq+piI3As8BhwDlqtqpMEsBzYCM4AHVHWTr78b+IKI7AT2\nAVcAqOp+EfkE8G/+vD/zAQ+GYRhGSZF4zjcMwzCMYilb9F1TEZGL/SLdnSKSatpr8P3+WkReEJHt\nQV2XiDzoF/huCddSNXIh8Qj7eZKI/LNf4PwfItJTxr6KyHEi8pCIPCoij4nIX5Sxn0FbU0TkERG5\nv6z9FJGnReQHvp/bStzPThH5exF53H/2i8vUTxH5ZT+GUTkgIj1l6mPivj/09/hb325x/VTVSVmA\nKbh1UKcA04BHgbNyvufbgDcD24O6TwEf88ergU/64wW+T9N8H3cRa7bbgLf44weAi/3xcuB2f3w5\n8OVR9nMucI4/ngk8AZxV0r62+b9TcX7Ft5axn/76lcAXgftK/Nk/BXQl6srYz3uAPwo++1ll7Ke/\nvgX4CS7YqlR99Pf6MdDqX38F56MvrJ+5TcBlL8CvA5uC19cD1zfhvqdQKZR2AHP88Vxghz++AVgd\nnLcJWAKcCDwe1F8BrA/OWeyPpwIvNqjPXwfeUea+Am04/+Eby9hPXMTot4C3A/eX9bPHCaXuRF2p\n+okTQD9OqS9VP4N2LwL+tYx9BLpwPzpn+zbuxwWFFdbPyWy+G1p06ylqce0cVX3BH78AzPHHjVpI\n3DWWzonIKTjt7qEy9lVEWkTkUd+ff1bVH5axn7gUWB8FBoO6MvZTgW+JyPdF5OqS9vNU4EUR2SAi\nD4vIXSLSXsJ+RlwBfMkfl6qPqrofuBX4T+B54CVVfbDIfk5moaRFdyCJup8SpemXiMzEpW66VlV/\nHr5Xlr6q6qCqnoPTRH5DRN6eeL/wforI7wI/VdVHqFwCMUQZ+uk5T1XfDPwW8Kci8rbwzZL0cyqw\nCGcSWgS8jLN0DFGSfiIi04HfA/4u+V4Z+igipwErcBacecBMEXlfeE6z+zmZhVJyoe5JVEr6ZvGC\niMwFEJfj76e+fiwLiZF4IfH+0XRKRKbhBNIXVPXrZe4rgKoeAP4ROLeE/fwvwCUi8hTuF/NvisgX\nSthPVPUn/u+LwNdwyYzL1s/ngOdUNVru8fc4IbWnZP0EJ9z7/XhC+cbyV4H/T1X3eS3mH3CujcLG\ncjILpe/jso2f4n/NXI5biNtsosW/+L9fD+qvEJHpInIq8ULiPcBBH20kuIXE30hpK1xIPCJ8u3cD\nj6nqbWXtq4icEEUFicgMnC38kbL1U1X7VPUkVT0VZ8r5J1V9f9n6KSJtInK8P27H+UK2l62fvv1n\nReQNvuodwA9x/pDS9NPzHmLTXbLdMvRxB7BERGb49t+BWyda3FiO1nk3EQruV8wTuAiSG5pwvy/h\n7LZHcDbWD+Icjd8CnsRloegMzu/zfdsBLAvqz8VNFruAdUF9K3AvsBMXiXbKKPv5Vpzv41HcJP8I\nLsN6qfoKLAQe9v38AfBRX1+qfib6fD5x9F2p+onz1Tzqy39E/xNl66dv51dwgS3/jvt1P6ts/QTa\ngb24HQ6iulL10bfzMZxQ346LapxWZD9t8axhGIZRGiaz+c4wDMMoGSaUDMMwjNJgQskwDMMoDSaU\nDMMwjNJgQskwDMMoDSaUDMMwjNJgQskwmoyIDIjbzmC7iNzrF/6mnffdZvfNMIrGhJJhNJ9XVPXN\nqroQt5D6mvBNn4oFVT2viM4ZRpGYUDKMYvlX4HQROV9E/lVEvoHLpoCI/CI6SURWi9t871GJNzM8\nTUS+6TN6f0dEfrmYRzCMxjG16A4YxmTFa0S/jdsQDdwWIW9U1Wf8a/Xn/RZwCW4DtUMS7wJ6J/DH\nqrpLRBYDtwMXNO0BDCMHTCgZRvOZISKP+OPvAH8NnIdLbPlMyvnvAP5aVQ8BqOpLfluRXwf+zuW/\nBGB6vt02jPwxoWQYzedVdXsWDeEFy8sZ5yvV+zC14DZke3PK+YYxbjGfkmGUnweBD0ZReiIyW1UP\nAk+JyLt9nYjIm4rspGE0AhNKhtF80lLzp+3uqQCquhm3J833vdmv17//XuAqcdvB/wfO72QY4xrb\nusIwDMMoDaYpGYZhGKXBhJJhGIZRGkwoGYZhGKXBhJJhGIZRGkwoGYZhGKXBhJJhGIZRGkwoGYZh\nGKXBhJJhGIZRGv4v47RO3mstTn0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1094f6748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(price, mile)\n",
+    "plt.xlabel('Price')\n",
+    "plt.ylabel('Mileage')\n",
+    "plt.plot(price, regrp.predict(price), linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Start of Part 2:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use mileage, cylinders, liters, doors, cruise, sound, and leather to find the linear regression equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -230,6 +395,16 @@
        "      <th></th>\n",
        "      <th>Price</th>\n",
        "      <th>Mileage</th>\n",
+       "      <th>Make</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Trim</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -237,90 +412,227 @@
        "      <th>0</th>\n",
        "      <td>17314.103129</td>\n",
        "      <td>8221</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>17542.036083</td>\n",
        "      <td>9135</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>16218.847862</td>\n",
        "      <td>13196</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>16336.913140</td>\n",
        "      <td>16342</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>16339.170324</td>\n",
        "      <td>19832</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          Price  Mileage\n",
-       "0  17314.103129     8221\n",
-       "1  17542.036083     9135\n",
-       "2  16218.847862    13196\n",
-       "3  16336.913140    16342\n",
-       "4  16339.170324    19832"
+       "          Price  Mileage   Make    Model      Trim   Type  Cylinder  Liter  \\\n",
+       "0  17314.103129     8221  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "1  17542.036083     9135  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "2  16218.847862    13196  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "3  16336.913140    16342  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "4  16339.170324    19832  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "\n",
+       "   Doors  Cruise  Sound  Leather  \n",
+       "0      4       1      1        1  \n",
+       "1      4       1      1        0  \n",
+       "2      4       1      1        0  \n",
+       "3      4       1      0        0  \n",
+       "4      4       1      0        1  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mileage_p = df[['Price', 'Mileage']]\n",
-    "mileage_p.head()\n"
+    "set_one = df\n",
+    "set_one.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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pv0tY5bmhpM6CNoVZ1FzR84mWMm7VN1b3CepRppwNqyJiWJdQ5A2KTOP6XqFi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\n",
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17314.103129</td>\n",
+       "      <td>8221</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17542.036083</td>\n",
+       "      <td>9135</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16218.847862</td>\n",
+       "      <td>13196</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16336.913140</td>\n",
+       "      <td>16342</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16339.170324</td>\n",
+       "      <td>19832</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10925cb70>"
+       "          Price  Mileage  Cylinder  Liter  Doors  Cruise  Sound  Leather\n",
+       "0  17314.103129     8221         6    3.1      4       1      1        1\n",
+       "1  17542.036083     9135         6    3.1      4       1      1        0\n",
+       "2  16218.847862    13196         6    3.1      4       1      1        0\n",
+       "3  16336.913140    16342         6    3.1      4       1      0        0\n",
+       "4  16339.170324    19832         6    3.1      4       1      0        1"
       ]
      },
+     "execution_count": 17,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.scatter(mileage_p['Price'], mileage_p['Mileage'])\n",
-    "plt.show()"
+    "set_one = set_one[['Price', 'Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather']]\n",
+    "set_one.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "df = mileage_p.loc[:, ['Price', 'Mileage']]\n",
-    "df.dropna(inplace=True)\n",
-    "price = df[['Price']]\n",
-    "mile = df[['Mileage']]"
+    "input_data = set_one[['Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather']]\n",
+    "predict_value = set_one['Price']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -329,16 +641,81 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Coeficient: [[-0.1725205]]\n",
-      "0.0204634473235\n"
+      "Coeficient: [ -1.69747832e-01   3.79237893e+03  -7.87220732e+02  -1.54274585e+03\n",
+      "   6.28899715e+03  -1.99379528e+03   3.34936162e+03]\n",
+      "0.446264353673\n"
      ]
     }
    ],
    "source": [
-    "regrp = linear_model.LinearRegression()\n",
-    "regrp.fit(mile, price)\n",
-    "print(\"Coeficient: {}\".format(regrp.coef_))\n",
-    "print(regrp.score(mile, price))\n"
+    "regrt = linear_model.LinearRegression()\n",
+    "regrt.fit(input_data, predict_value)\n",
+    "print('Coeficient: {}'.format(regrt.coef_))\n",
+    "print(regrt.score(input_data, predict_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###This data set only has a 44% accuracy at predicting car price.  Which sucks, but much better than just mileage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coeficient: [ -1.79922895e-01  -1.74267951e+03  -2.91228419e+03]\n",
+      "0.0592505439204\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_input = input_data[['Mileage', 'Doors', 'Sound']]\n",
+    "\n",
+    "newreg = linear_model.LinearRegression()\n",
+    "newreg.fit(new_input, predict_value)\n",
+    "print('Coeficient: {}'.format(newreg.coef_))\n",
+    "print(newreg.score(new_input, predict_value))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coeficient: [ 4054.20250438]\n",
+      "0.323859037595\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_i = input_data[['Cylinder']]\n",
+    "\n",
+    "newr = linear_model.LinearRegression()\n",
+    "newr.fit(new_i, predict_value)\n",
+    "print('Coeficient: {}'.format(newr.coef_))\n",
+    "print(newr.score(new_i, predict_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The number of cylinders is the most import feature of the car to predict price, although it is still only accurate 32% of the time by its self."
    ]
   },
   {
@@ -384,9 +761,7 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": [
-    "equ1 = lambda x: 0 + m * x"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -395,13 +770,25 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": [
-    "def linear_least_squares(df, fn):\n",
-    "    values = df.index.map(fn)\n",
-    "    diffs = df.mean_minutes - values\n",
-    "    diffs_squared = diffs ** 2\n",
-    "    return diffs_squared.sum() / (2 * len(diffs)) "
-   ]
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 508a72196bcbc32880a2ef76c90261d8fbbe14b1 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Tue, 23 Jun 2015 21:40:00 -0400
Subject: [PATCH 03/13] building sorting function

---
 How Much is Your Car Worth.ipynb | 102 +++++++++++++++++++++++++------
 1 file changed, 83 insertions(+), 19 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index f6365cf..3c81f2a 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -12,7 +12,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn import linear_model\n",
-    "import itertools\n"
+    "\n"
    ]
   },
   {
@@ -715,44 +715,108 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The number of cylinders is the most import feature of the car to predict price, although it is still only accurate 32% of the time by its self."
+    "####The number of cylinders is the most import feature of the car to predict price, although it is still only accurate 32% of the time by its self. "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Now time to try automating important variables\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "import itertools\n",
+    "\n",
+    "dependant_variables = list(set_one.columns)\n",
+    "dependant_variables.remove('Price')\n",
+    "\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[('Mileage', 'Cylinder'), ('Mileage', 'Liter'), ('Mileage', 'Doors'), ('Mileage', 'Cruise'), ('Mileage', 'Sound'), ('Mileage', 'Leather'), ('Cylinder', 'Liter'), ('Cylinder', 'Doors'), ('Cylinder', 'Cruise'), ('Cylinder', 'Sound'), ('Cylinder', 'Leather'), ('Liter', 'Doors'), ('Liter', 'Cruise'), ('Liter', 'Sound'), ('Liter', 'Leather'), ('Doors', 'Cruise'), ('Doors', 'Sound'), ('Doors', 'Leather'), ('Cruise', 'Sound'), ('Cruise', 'Leather'), ('Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter'), ('Mileage', 'Cylinder', 'Doors'), ('Mileage', 'Cylinder', 'Cruise'), ('Mileage', 'Cylinder', 'Sound'), ('Mileage', 'Cylinder', 'Leather'), ('Mileage', 'Liter', 'Doors'), ('Mileage', 'Liter', 'Cruise'), ('Mileage', 'Liter', 'Sound'), ('Mileage', 'Liter', 'Leather'), ('Mileage', 'Doors', 'Cruise'), ('Mileage', 'Doors', 'Sound'), ('Mileage', 'Doors', 'Leather'), ('Mileage', 'Cruise', 'Sound'), ('Mileage', 'Cruise', 'Leather'), ('Mileage', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors'), ('Cylinder', 'Liter', 'Cruise'), ('Cylinder', 'Liter', 'Sound'), ('Cylinder', 'Liter', 'Leather'), ('Cylinder', 'Doors', 'Cruise'), ('Cylinder', 'Doors', 'Sound'), ('Cylinder', 'Doors', 'Leather'), ('Cylinder', 'Cruise', 'Sound'), ('Cylinder', 'Cruise', 'Leather'), ('Cylinder', 'Sound', 'Leather'), ('Liter', 'Doors', 'Cruise'), ('Liter', 'Doors', 'Sound'), ('Liter', 'Doors', 'Leather'), ('Liter', 'Cruise', 'Sound'), ('Liter', 'Cruise', 'Leather'), ('Liter', 'Sound', 'Leather'), ('Doors', 'Cruise', 'Sound'), ('Doors', 'Cruise', 'Leather'), ('Doors', 'Sound', 'Leather'), ('Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors'), ('Mileage', 'Cylinder', 'Liter', 'Cruise'), ('Mileage', 'Cylinder', 'Liter', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Cruise'), ('Mileage', 'Cylinder', 'Doors', 'Sound'), ('Mileage', 'Cylinder', 'Doors', 'Leather'), ('Mileage', 'Cylinder', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Cruise'), ('Mileage', 'Liter', 'Doors', 'Sound'), ('Mileage', 'Liter', 'Doors', 'Leather'), ('Mileage', 'Liter', 'Cruise', 'Sound'), ('Mileage', 'Liter', 'Cruise', 'Leather'), ('Mileage', 'Liter', 'Sound', 'Leather'), ('Mileage', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Cruise'), ('Cylinder', 'Liter', 'Doors', 'Sound'), ('Cylinder', 'Liter', 'Doors', 'Leather'), ('Cylinder', 'Liter', 'Cruise', 'Sound'), ('Cylinder', 'Liter', 'Cruise', 'Leather'), ('Cylinder', 'Liter', 'Sound', 'Leather'), ('Cylinder', 'Doors', 'Cruise', 'Sound'), ('Cylinder', 'Doors', 'Cruise', 'Leather'), ('Cylinder', 'Doors', 'Sound', 'Leather'), ('Cylinder', 'Cruise', 'Sound', 'Leather'), ('Liter', 'Doors', 'Cruise', 'Sound'), ('Liter', 'Doors', 'Cruise', 'Leather'), ('Liter', 'Doors', 'Sound', 'Leather'), ('Liter', 'Cruise', 'Sound', 'Leather'), ('Doors', 'Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Liter', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound'), ('Cylinder', 'Liter', 'Doors', 'Cruise', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Liter', 'Doors', 'Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather')]]\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Found array with 0 sample(s) (shape=(0, 21)) while a minimum of 1 is required.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-91-399d309eba09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcombo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcombos\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregression_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-91-399d309eba09>\u001b[0m in \u001b[0;36mregression_for\u001b[0;34m(combo)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mregr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, n_jobs)\u001b[0m\n\u001b[1;32m    374\u001b[0m             \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 376\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         X, y, X_mean, y_mean, X_std = self._center_data(\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric)\u001b[0m\n\u001b[1;32m    442\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    443\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m                     ensure_min_features)\n\u001b[0m\u001b[1;32m    445\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features)\u001b[0m\n\u001b[1;32m    358\u001b[0m             raise ValueError(\"Found array with %d sample(s) (shape=%s) while a\"\n\u001b[1;32m    359\u001b[0m                              \u001b[0;34m\" minimum of %d is required.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m                              % (n_samples, shape_repr, ensure_min_samples))\n\u001b[0m\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_features\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0, 21)) while a minimum of 1 is required."
+     ]
+    }
+   ],
+   "source": [
+    "choices = []\n",
+    "\n",
+    "def combos(list_of_series):\n",
+    "    combos = []\n",
+    "    x = len(list_of_series) + 1\n",
+    "    for num in range(2,x):\n",
+    "        combos.append(list(itertools.combinations(list_of_series, num)))\n",
+    "        x -= 1\n",
+    "    return combos\n",
+    "\n",
+    "combos = combos(dependant_variables)\n",
+    "print(combos)\n",
+    "\n",
+    "def regression_for(combo):\n",
+    "    combo = list(combo)\n",
+    "    df = set_one.loc[:, combo + ['Price']]\n",
+    "    df.dropna(inplace=True)\n",
+    "    input_data = df[combo]\n",
+    "    price = df['Price']\n",
+    "    regr = linear_model.LinearRegression()\n",
+    "    regr.fit(input_data, price)\n",
+    "    return regr, regr.score(input_data, price)\n",
+    "\n",
+    "\n",
+    "for combo in combos:\n",
+    "    regr, score = regression_for(combo)\n",
+    "    choices.append((combo, score))\n",
+    "                   \n",
+    "best = sorted(choices, key=lambda x: x[1])[-1]\n",
+    "print(best)\n",
+    "regr, score = regression_for(best[0])\n",
+    "print(regr.coef_, regr.intercept_)\n",
+    "print(choices)\n",
+    "combos(dependant_variables)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "\n"
+   ]
   },
   {
    "cell_type": "code",

From a7645121f4a6c3a17519ebf6d5af48942acb39cc Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Tue, 23 Jun 2015 23:21:16 -0400
Subject: [PATCH 04/13] still trying to figure out what the best combo is

---
 How Much is Your Car Worth.ipynb | 452 +++++++++++++++++++++++++++++--
 1 file changed, 432 insertions(+), 20 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index 3c81f2a..a81e4aa 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -742,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 118,
    "metadata": {
     "collapsed": false
    },
@@ -751,26 +751,332 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[('Mileage', 'Cylinder'), ('Mileage', 'Liter'), ('Mileage', 'Doors'), ('Mileage', 'Cruise'), ('Mileage', 'Sound'), ('Mileage', 'Leather'), ('Cylinder', 'Liter'), ('Cylinder', 'Doors'), ('Cylinder', 'Cruise'), ('Cylinder', 'Sound'), ('Cylinder', 'Leather'), ('Liter', 'Doors'), ('Liter', 'Cruise'), ('Liter', 'Sound'), ('Liter', 'Leather'), ('Doors', 'Cruise'), ('Doors', 'Sound'), ('Doors', 'Leather'), ('Cruise', 'Sound'), ('Cruise', 'Leather'), ('Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter'), ('Mileage', 'Cylinder', 'Doors'), ('Mileage', 'Cylinder', 'Cruise'), ('Mileage', 'Cylinder', 'Sound'), ('Mileage', 'Cylinder', 'Leather'), ('Mileage', 'Liter', 'Doors'), ('Mileage', 'Liter', 'Cruise'), ('Mileage', 'Liter', 'Sound'), ('Mileage', 'Liter', 'Leather'), ('Mileage', 'Doors', 'Cruise'), ('Mileage', 'Doors', 'Sound'), ('Mileage', 'Doors', 'Leather'), ('Mileage', 'Cruise', 'Sound'), ('Mileage', 'Cruise', 'Leather'), ('Mileage', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors'), ('Cylinder', 'Liter', 'Cruise'), ('Cylinder', 'Liter', 'Sound'), ('Cylinder', 'Liter', 'Leather'), ('Cylinder', 'Doors', 'Cruise'), ('Cylinder', 'Doors', 'Sound'), ('Cylinder', 'Doors', 'Leather'), ('Cylinder', 'Cruise', 'Sound'), ('Cylinder', 'Cruise', 'Leather'), ('Cylinder', 'Sound', 'Leather'), ('Liter', 'Doors', 'Cruise'), ('Liter', 'Doors', 'Sound'), ('Liter', 'Doors', 'Leather'), ('Liter', 'Cruise', 'Sound'), ('Liter', 'Cruise', 'Leather'), ('Liter', 'Sound', 'Leather'), ('Doors', 'Cruise', 'Sound'), ('Doors', 'Cruise', 'Leather'), ('Doors', 'Sound', 'Leather'), ('Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors'), ('Mileage', 'Cylinder', 'Liter', 'Cruise'), ('Mileage', 'Cylinder', 'Liter', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Cruise'), ('Mileage', 'Cylinder', 'Doors', 'Sound'), ('Mileage', 'Cylinder', 'Doors', 'Leather'), ('Mileage', 'Cylinder', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Cruise'), ('Mileage', 'Liter', 'Doors', 'Sound'), ('Mileage', 'Liter', 'Doors', 'Leather'), ('Mileage', 'Liter', 'Cruise', 'Sound'), ('Mileage', 'Liter', 'Cruise', 'Leather'), ('Mileage', 'Liter', 'Sound', 'Leather'), ('Mileage', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Cruise'), ('Cylinder', 'Liter', 'Doors', 'Sound'), ('Cylinder', 'Liter', 'Doors', 'Leather'), ('Cylinder', 'Liter', 'Cruise', 'Sound'), ('Cylinder', 'Liter', 'Cruise', 'Leather'), ('Cylinder', 'Liter', 'Sound', 'Leather'), ('Cylinder', 'Doors', 'Cruise', 'Sound'), ('Cylinder', 'Doors', 'Cruise', 'Leather'), ('Cylinder', 'Doors', 'Sound', 'Leather'), ('Cylinder', 'Cruise', 'Sound', 'Leather'), ('Liter', 'Doors', 'Cruise', 'Sound'), ('Liter', 'Doors', 'Cruise', 'Leather'), ('Liter', 'Doors', 'Sound', 'Leather'), ('Liter', 'Cruise', 'Sound', 'Leather'), ('Doors', 'Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Liter', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound'), ('Cylinder', 'Liter', 'Doors', 'Cruise', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Liter', 'Doors', 'Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Doors', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Liter', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Cylinder', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Mileage', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), ('Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather')], [('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather')]]\n"
+      "<itertools.chain object at 0x1096513c8>\n",
+      "(('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.4462643536728379)\n",
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "[ -1.69747832e-01   3.79237893e+03  -7.87220732e+02  -1.54274585e+03\n",
+      "   6.28899715e+03  -1.99379528e+03   3.34936162e+03] 6758.7551436\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "choices = []\n",
+    "\n",
+    "def combos(list_of_series):\n",
+    "    combos = []\n",
+    "    x = len(list_of_series) + 1\n",
+    "    for num in range(2,x):\n",
+    "        combos.append(list(itertools.combinations(list_of_series, num)))\n",
+    "        x -= 1\n",
+    "    return itertools.chain(*combos)\n",
+    "\n",
+    "combos = combos(dependant_variables)\n",
+    "print(combos)\n",
+    "\n",
+    "def regression_for(combo):\n",
+    "    combo = list(combo)\n",
+    "    df = set_one.loc[:, combo + ['Price']]\n",
+    "    df.dropna(inplace=True)\n",
+    "    input_data = df[combo]\n",
+    "    price = df['Price']\n",
+    "    regr = linear_model.LinearRegression()\n",
+    "    regr.fit(input_data, price)\n",
+    "    return regr, regr.score(input_data, price)\n",
+    "\n",
+    "\n",
+    "for combo in combos:\n",
+    "    regr, score = regression_for(combo)\n",
+    "    choices.append((combo, score))\n",
+    "                   \n",
+    "best = sorted(choices, key=lambda x: x[1])[-1]\n",
+    "print(best)\n",
+    "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
+    "regr, score = regression_for(best[0])\n",
+    "print(regr.coef_, regr.intercept_)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Based on the data set used in the previous example,  mileage, cylinders, liters, doors, cruise, sound, and leather still yields the most accurate prediction, 44.6%.  Next I will incorporate make, model, trim, and type into my data set and see what combination yields the most accurate result\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Type\n",
+       "Convertible     50\n",
+       "Coupe          140\n",
+       "Hatchback       60\n",
+       "Sedan          490\n",
+       "Wagon           64\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set_two = df\n",
+    "\n",
+    "\n",
+    "car_type = set_two.sort('Type')\n",
+    "car_type.groupby('Type').size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "Found array with 0 sample(s) (shape=(0, 21)) while a minimum of 1 is required.",
+     "data": {
+      "text/plain": [
+       "Make\n",
+       "Buick         80\n",
+       "Cadillac      80\n",
+       "Chevrolet    320\n",
+       "Pontiac      150\n",
+       "SAAB         114\n",
+       "Saturn        60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "car_make = set_two.sort('Make')\n",
+    "car_make.groupby('Make').size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "car_model = set_two.sort('Model')\n",
+    "#car_model.groupby('Model').size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "car_Trim = set_two.sort('Trim')\n",
+    "#car_Trim.groupby('Trim').size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-148-2677388250b1>, line 3)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-91-399d309eba09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcombo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcombos\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregression_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-91-399d309eba09>\u001b[0m in \u001b[0;36mregression_for\u001b[0;34m(combo)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mregr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, n_jobs)\u001b[0m\n\u001b[1;32m    374\u001b[0m             \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 376\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         X, y, X_mean, y_mean, X_std = self._center_data(\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric)\u001b[0m\n\u001b[1;32m    442\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    443\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m                     ensure_min_features)\n\u001b[0m\u001b[1;32m    445\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features)\u001b[0m\n\u001b[1;32m    358\u001b[0m             raise ValueError(\"Found array with %d sample(s) (shape=%s) while a\"\n\u001b[1;32m    359\u001b[0m                              \u001b[0;34m\" minimum of %d is required.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m                              % (n_samples, shape_repr, ensure_min_samples))\n\u001b[0m\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_features\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0, 21)) while a minimum of 1 is required."
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-148-2677388250b1>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    })\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
    "source": [
+    "set_temp = set_two.replace({'Type': {'Convertible': 1, 'Coupe': 2, 'Hatchback': 3, 'Sedan': 4, 'Wagon': 5},\n",
+    "                           'Make': {'Buick': 1, 'Cadillac': 2, 'Chevrolet': 3, 'Pontiac': 4, 'SAAB': 5,'Saturn': 6},\n",
+    "                            'Model'\n",
+    "                           })\n",
+    "set_temp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Make</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Trim</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17314.103129</td>\n",
+       "      <td>8221</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17542.036083</td>\n",
+       "      <td>9135</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16218.847862</td>\n",
+       "      <td>13196</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16336.913140</td>\n",
+       "      <td>16342</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16339.170324</td>\n",
+       "      <td>19832</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Price  Mileage   Make    Model      Trim   Type  Cylinder  Liter  \\\n",
+       "0  17314.103129     8221  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "1  17542.036083     9135  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "2  16218.847862    13196  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "3  16336.913140    16342  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "4  16339.170324    19832  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "\n",
+       "   Doors  Cruise  Sound  Leather  \n",
+       "0      4       1      1        1  \n",
+       "1      4       1      1        0  \n",
+       "2      4       1      1        0  \n",
+       "3      4       1      0        0  \n",
+       "4      4       1      0        1  "
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set_two.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<itertools.chain object at 0x10966a6a0>\n",
+      "(('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.4462643536728379)\n",
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "[ -1.69747832e-01   3.79237893e+03  -7.87220732e+02  -1.54274585e+03\n",
+      "   6.28899715e+03  -1.99379528e+03   3.34936162e+03] 6758.7551436\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "dependant_variables = list(set_one.columns)\n",
+    "dependant_variables.remove('Price')\n",
+    "\n",
+    "\n",
+    "\n",
     "choices = []\n",
     "\n",
     "def combos(list_of_series):\n",
@@ -779,7 +1085,7 @@
     "    for num in range(2,x):\n",
     "        combos.append(list(itertools.combinations(list_of_series, num)))\n",
     "        x -= 1\n",
-    "    return combos\n",
+    "    return itertools.chain(*combos)\n",
     "\n",
     "combos = combos(dependant_variables)\n",
     "print(combos)\n",
@@ -801,10 +1107,10 @@
     "                   \n",
     "best = sorted(choices, key=lambda x: x[1])[-1]\n",
     "print(best)\n",
+    "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
     "regr, score = regression_for(best[0])\n",
     "print(regr.coef_, regr.intercept_)\n",
-    "print(choices)\n",
-    "combos(dependant_variables)"
+    "\n"
    ]
   },
   {
@@ -814,9 +1120,7 @@
     "collapsed": false
    },
    "outputs": [],
-   "source": [
-    "\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -827,6 +1131,15 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -847,7 +1160,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },

From 539d56b3f38003b00ce39dbf83746d54f90f495e Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Wed, 24 Jun 2015 00:05:34 -0400
Subject: [PATCH 05/13] Made a function that takes a groupby object and turns
 the group into a key with a unique value

---
 How Much is Your Car Worth.ipynb | 179 ++++++++++++++-----------------
 1 file changed, 81 insertions(+), 98 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index a81e4aa..cb2ebaf 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 168,
    "metadata": {
     "collapsed": false
    },
@@ -12,6 +12,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn import linear_model\n",
+    "from collections import defaultdict\n",
     "\n"
    ]
   },
@@ -807,117 +808,99 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_dict(groupby_obj):\n",
+    "    keys = []\n",
+    "    val = 1\n",
+    "    x = len(groupby_obj) + 1\n",
+    "    while val < x:\n",
+    "        for key in groupby_obj.groups:\n",
+    "            keys.append((key, val))\n",
+    "            val += 1\n",
+    "    return {el:v for el,v in keys}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Type\n",
-       "Convertible     50\n",
-       "Coupe          140\n",
-       "Hatchback       60\n",
-       "Sedan          490\n",
-       "Wagon           64\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "set_two = df\n",
     "\n",
     "\n",
     "car_type = set_two.sort('Type')\n",
-    "car_type.groupby('Type').size()"
+    "c_type = car_type.groupby('Type')\n",
+    "\n",
+    "type_d = get_dict(c_type)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 190,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Make\n",
-       "Buick         80\n",
-       "Cadillac      80\n",
-       "Chevrolet    320\n",
-       "Pontiac      150\n",
-       "SAAB         114\n",
-       "Saturn        60\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "car_make = set_two.sort('Make')\n",
-    "car_make.groupby('Make').size()"
+    "car_make.groupby('Make').size()\n",
+    "make = car_make.groupby('Make')\n",
+    "make_d = get_dict(make)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 185,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [],
    "source": [
+    "\n",
     "car_model = set_two.sort('Model')\n",
-    "#car_model.groupby('Model').size()"
+    "model = car_model.groupby('Model')\n",
+    "model_d = get_dict(model)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 193,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "car_Trim = set_two.sort('Trim')\n",
-    "#car_Trim.groupby('Trim').size()"
+    "trim = car_Trim.groupby('Trim')\n",
+    "trim_d = get_dict(trim)\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 196,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-148-2677388250b1>, line 3)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-148-2677388250b1>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    })\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "set_temp = set_two.replace({'Type': {'Convertible': 1, 'Coupe': 2, 'Hatchback': 3, 'Sedan': 4, 'Wagon': 5},\n",
-    "                           'Make': {'Buick': 1, 'Cadillac': 2, 'Chevrolet': 3, 'Pontiac': 4, 'SAAB': 5,'Saturn': 6},\n",
-    "                            'Model'\n",
-    "                           })\n",
-    "set_temp.head()"
+    "set_two = set_two.replace({'Type': type_d, 'Make': make_d, 'Model': model_d, 'Trim': trim_d\n",
+    "                           })"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 197,
    "metadata": {
     "collapsed": false
    },
@@ -949,10 +932,10 @@
        "      <th>0</th>\n",
        "      <td>17314.103129</td>\n",
        "      <td>8221</td>\n",
-       "      <td>Buick</td>\n",
-       "      <td>Century</td>\n",
-       "      <td>Sedan 4D</td>\n",
-       "      <td>Sedan</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -964,10 +947,10 @@
        "      <th>1</th>\n",
        "      <td>17542.036083</td>\n",
        "      <td>9135</td>\n",
-       "      <td>Buick</td>\n",
-       "      <td>Century</td>\n",
-       "      <td>Sedan 4D</td>\n",
-       "      <td>Sedan</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -979,10 +962,10 @@
        "      <th>2</th>\n",
        "      <td>16218.847862</td>\n",
        "      <td>13196</td>\n",
-       "      <td>Buick</td>\n",
-       "      <td>Century</td>\n",
-       "      <td>Sedan 4D</td>\n",
-       "      <td>Sedan</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -994,10 +977,10 @@
        "      <th>3</th>\n",
        "      <td>16336.913140</td>\n",
        "      <td>16342</td>\n",
-       "      <td>Buick</td>\n",
-       "      <td>Century</td>\n",
-       "      <td>Sedan 4D</td>\n",
-       "      <td>Sedan</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1009,10 +992,10 @@
        "      <th>4</th>\n",
        "      <td>16339.170324</td>\n",
        "      <td>19832</td>\n",
-       "      <td>Buick</td>\n",
-       "      <td>Century</td>\n",
-       "      <td>Sedan 4D</td>\n",
-       "      <td>Sedan</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1025,22 +1008,22 @@
        "</div>"
       ],
       "text/plain": [
-       "          Price  Mileage   Make    Model      Trim   Type  Cylinder  Liter  \\\n",
-       "0  17314.103129     8221  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
-       "1  17542.036083     9135  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
-       "2  16218.847862    13196  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
-       "3  16336.913140    16342  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
-       "4  16339.170324    19832  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "          Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
+       "0  17314.103129     8221     3     10    24     4         6    3.1      4   \n",
+       "1  17542.036083     9135     3     10    24     4         6    3.1      4   \n",
+       "2  16218.847862    13196     3     10    24     4         6    3.1      4   \n",
+       "3  16336.913140    16342     3     10    24     4         6    3.1      4   \n",
+       "4  16339.170324    19832     3     10    24     4         6    3.1      4   \n",
        "\n",
-       "   Doors  Cruise  Sound  Leather  \n",
-       "0      4       1      1        1  \n",
-       "1      4       1      1        0  \n",
-       "2      4       1      1        0  \n",
-       "3      4       1      0        0  \n",
-       "4      4       1      0        1  "
+       "   Cruise  Sound  Leather  \n",
+       "0       1      1        1  \n",
+       "1       1      1        0  \n",
+       "2       1      1        0  \n",
+       "3       1      0        0  \n",
+       "4       1      0        1  "
       ]
      },
-     "execution_count": 149,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }

From 2aae933336e47c7f5a2dda1ff80f21479dd07699 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Wed, 24 Jun 2015 01:00:28 -0400
Subject: [PATCH 06/13] So I found my bug and fixed it in a sloppy function and
 got an accuracy rate of 63% with certain paramaters, I will attempt to have
 my new improved function fixed in the morning and make it less sloppy

---
 How Much is Your Car Worth.ipynb | 454 +++++++++++++++++++++++++------
 1 file changed, 365 insertions(+), 89 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index cb2ebaf..cfa9abf 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
@@ -238,7 +238,7 @@
      "data": {
       "image/png": 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SuDI6fw1wKs7EF5vv3gXcFJ2z2B/XNd/hFqiEtKTo/uhcv7fffZYWNS/G3rSz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\nNgHz/PFhJOa7K4Ero/PWAKf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GYRhGaTChZBiGYZQGE0qGYRhGaTChZBiGYZQG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\nDMMwSoMJJcMwDKM0/P/SLjHrjQG0QwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x106428128>"
+       "<matplotlib.figure.Figure at 0x1064f3be0>"
       ]
      },
      "metadata": {},
@@ -322,7 +322,7 @@
      "data": {
       "image/png": 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QV8KSSyiHyoW/9Qwb1fMmn1ZOMi9+1g2asFCntGVgSqnV7YnmUm4dFf9zSt42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\nZkmdF1qmuuf8eu7WuuapjKmO/U5qULS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QzoirgS/741LJqKp7gTuAfwVeAPap6qYi5ZzK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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10955e400>"
+       "<matplotlib.figure.Figure at 0x10947df60>"
       ]
      },
      "metadata": {},
@@ -349,7 +349,7 @@
      "data": {
       "image/png": 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yz/fvJ3Pbhdkbku+dpvGi1+i8MLlrm8b7J4WLaKMkrps0DqiYn3Hvij2e1AnW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\nGKXBhJJhGIZRGv4v47RO3mstTn0AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1094f6748>"
+       "<matplotlib.figure.Figure at 0x10941d0b8>"
       ]
      },
      "metadata": {},
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -504,7 +504,7 @@
        "4      4       1      0        1  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -609,7 +609,7 @@
        "4  16339.170324    19832         6    3.1      4       1      0        1"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -633,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -664,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -689,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -728,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 75,
    "metadata": {
     "collapsed": false
    },
@@ -743,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 76,
    "metadata": {
     "collapsed": false
    },
@@ -752,7 +752,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<itertools.chain object at 0x1096513c8>\n",
+      "<itertools.chain object at 0x109a7b320>\n",
       "(('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.4462643536728379)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
@@ -808,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "collapsed": true
    },
@@ -827,11 +827,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 67,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Convertible': 5, 'Coupe': 4, 'Hatchback': 3, 'Sedan': 1, 'Wagon': 2}"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "set_two = df\n",
     "\n",
@@ -839,68 +850,343 @@
     "car_type = set_two.sort('Type')\n",
     "c_type = car_type.groupby('Type')\n",
     "\n",
-    "type_d = get_dict(c_type)"
+    "type_d = get_dict(c_type)\n",
+    "type_d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 68,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Buick': 1,\n",
+       " 'Cadillac': 2,\n",
+       " 'Chevrolet': 6,\n",
+       " 'Pontiac': 4,\n",
+       " 'SAAB': 3,\n",
+       " 'Saturn': 5}"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "car_make = set_two.sort('Make')\n",
     "car_make.groupby('Make').size()\n",
     "make = car_make.groupby('Make')\n",
-    "make_d = get_dict(make)"
+    "make_d = get_dict(make)\n",
+    "make_d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 69,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'9-2X AWD': 7,\n",
+       " '9_3': 6,\n",
+       " '9_3 HO': 12,\n",
+       " '9_5': 18,\n",
+       " '9_5 HO': 27,\n",
+       " 'AVEO': 31,\n",
+       " 'Bonneville': 13,\n",
+       " 'CST-V': 21,\n",
+       " 'CTS': 25,\n",
+       " 'Cavalier': 26,\n",
+       " 'Century': 30,\n",
+       " 'Classic': 28,\n",
+       " 'Cobalt': 14,\n",
+       " 'Corvette': 24,\n",
+       " 'Deville': 3,\n",
+       " 'G6': 4,\n",
+       " 'GTO': 17,\n",
+       " 'Grand Am': 8,\n",
+       " 'Grand Prix': 23,\n",
+       " 'Impala': 1,\n",
+       " 'Ion': 29,\n",
+       " 'L Series': 15,\n",
+       " 'Lacrosse': 19,\n",
+       " 'Lesabre': 5,\n",
+       " 'Malibu': 2,\n",
+       " 'Monte Carlo': 16,\n",
+       " 'Park Avenue': 11,\n",
+       " 'STS-V6': 20,\n",
+       " 'STS-V8': 10,\n",
+       " 'Sunfire': 9,\n",
+       " 'Vibe': 32,\n",
+       " 'XLR-V8': 22}"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "car_model = set_two.sort('Model')\n",
     "model = car_model.groupby('Model')\n",
-    "model_d = get_dict(model)\n"
+    "model_d = get_dict(model)\n",
+    "model_d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 71,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "47"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "car_Trim = set_two.sort('Trim')\n",
     "trim = car_Trim.groupby('Trim')\n",
     "trim_d = get_dict(trim)\n",
-    "\n"
+    "len(trim_d)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 73,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "set_two = set_two.replace({'Type': type_d, 'Make': make_d, 'Model': model_d, 'Trim': trim_d\n",
-    "                           })"
+    "final_data = set_two.replace({'Type': type_d, 'Make': make_d, 'Model': model_d, 'Trim': trim_d\n",
+    "                           })\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Make</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Trim</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>16507.070267</td>\n",
+       "      <td>16229</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>16175.957604</td>\n",
+       "      <td>19095</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>801</th>\n",
+       "      <td>15731.132897</td>\n",
+       "      <td>20484</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>802</th>\n",
+       "      <td>15118.893228</td>\n",
+       "      <td>25979</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>803</th>\n",
+       "      <td>13585.636802</td>\n",
+       "      <td>35662</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
+       "799  16507.070267    16229     5     15    28     1         6      3      4   \n",
+       "800  16175.957604    19095     5     15    28     1         6      3      4   \n",
+       "801  15731.132897    20484     5     15    28     1         6      3      4   \n",
+       "802  15118.893228    25979     5     15    28     1         6      3      4   \n",
+       "803  13585.636802    35662     5     15    28     1         6      3      4   \n",
+       "\n",
+       "     Cruise  Sound  Leather  \n",
+       "799       1      0        0  \n",
+       "800       1      1        0  \n",
+       "801       1      1        0  \n",
+       "802       1      1        0  \n",
+       "803       1      0        0  "
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_data = final_data.dropna()\n",
+    "final_data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Found array with 0 sample(s) (shape=(0, 2)) while a minimum of 1 is required.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-81-dcc66ef019d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0moptimization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-81-dcc66ef019d6>\u001b[0m in \u001b[0;36moptimization\u001b[0;34m(df, param)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcombos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcombo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregression_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-81-dcc66ef019d6>\u001b[0m in \u001b[0;36mregression_for\u001b[0;34m(combo, param)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mregr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, n_jobs)\u001b[0m\n\u001b[1;32m    374\u001b[0m             \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 376\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         X, y, X_mean, y_mean, X_std = self._center_data(\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric)\u001b[0m\n\u001b[1;32m    442\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    443\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m                     ensure_min_features)\n\u001b[0m\u001b[1;32m    445\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features)\u001b[0m\n\u001b[1;32m    358\u001b[0m             raise ValueError(\"Found array with %d sample(s) (shape=%s) while a\"\n\u001b[1;32m    359\u001b[0m                              \u001b[0;34m\" minimum of %d is required.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m                              % (n_samples, shape_repr, ensure_min_samples))\n\u001b[0m\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_features\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0, 2)) while a minimum of 1 is required."
+     ]
+    }
+   ],
+   "source": [
+    "final_data = final_data\n",
+    "\n",
+    "\n",
+    "def combos(list_of_series):\n",
+    "    combos = []\n",
+    "    x = len(list_of_series) + 1\n",
+    "    for num in range(2,x):\n",
+    "        combos.append(list(itertools.combinations(list_of_series, num)))\n",
+    "        x -= 1\n",
+    "    return itertools.chain(*combos)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def regression_for(combo, param='Price'):\n",
+    "    combo = list(combo)\n",
+    "    df = set_one.loc[:, combo + [param]]\n",
+    "    df.dropna(inplace=True)\n",
+    "    input_data = df[combo]\n",
+    "    price = df[param]\n",
+    "    regr = linear_model.LinearRegression()\n",
+    "    regr.fit(input_data, price)\n",
+    "    return regr, regr.score(input_data, price)\n",
+    "\n",
+    "\n",
+    "def optimization(df, param='Price'):\n",
+    "    df = list(df.columns)\n",
+    "    df.remove(param)\n",
+    "    choices = []\n",
+    "    series = combos(df)\n",
+    "    for combo in series:\n",
+    "        regr, score = regression_for(combo)\n",
+    "        choices.append((combo, score))\n",
+    "    return \n",
+    "optimization(final_data)    \n",
+    "    \n",
+    "\n",
+    "print(best)\n",
+    "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
+    "\n",
+    "print(regr.coef_, regr.intercept_)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
    "metadata": {
     "collapsed": false
    },
@@ -932,10 +1218,10 @@
        "      <th>0</th>\n",
        "      <td>17314.103129</td>\n",
        "      <td>8221</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -947,10 +1233,10 @@
        "      <th>1</th>\n",
        "      <td>17542.036083</td>\n",
        "      <td>9135</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -962,10 +1248,10 @@
        "      <th>2</th>\n",
        "      <td>16218.847862</td>\n",
        "      <td>13196</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -977,10 +1263,10 @@
        "      <th>3</th>\n",
        "      <td>16336.913140</td>\n",
        "      <td>16342</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -992,10 +1278,10 @@
        "      <th>4</th>\n",
        "      <td>16339.170324</td>\n",
        "      <td>19832</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1009,11 +1295,11 @@
       ],
       "text/plain": [
        "          Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
-       "0  17314.103129     8221     3     10    24     4         6    3.1      4   \n",
-       "1  17542.036083     9135     3     10    24     4         6    3.1      4   \n",
-       "2  16218.847862    13196     3     10    24     4         6    3.1      4   \n",
-       "3  16336.913140    16342     3     10    24     4         6    3.1      4   \n",
-       "4  16339.170324    19832     3     10    24     4         6    3.1      4   \n",
+       "0  17314.103129     8221     1     30    29     1         6    3.1      4   \n",
+       "1  17542.036083     9135     1     30    29     1         6    3.1      4   \n",
+       "2  16218.847862    13196     1     30    29     1         6    3.1      4   \n",
+       "3  16336.913140    16342     1     30    29     1         6    3.1      4   \n",
+       "4  16339.170324    19832     1     30    29     1         6    3.1      4   \n",
        "\n",
        "   Cruise  Sound  Leather  \n",
        "0       1      1        1  \n",
@@ -1023,18 +1309,18 @@
        "4       1      0        1  "
       ]
      },
-     "execution_count": 197,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "set_two.head()"
+    "final_data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 97,
    "metadata": {
     "collapsed": false
    },
@@ -1043,25 +1329,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<itertools.chain object at 0x10966a6a0>\n",
-      "(('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.4462643536728379)\n",
+      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.63439499346624129)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
       "\n",
-      "[ -1.69747832e-01   3.79237893e+03  -7.87220732e+02  -1.54274585e+03\n",
-      "   6.28899715e+03  -1.99379528e+03   3.34936162e+03] 6758.7551436\n"
+      "[ -1.76357292e-01  -2.38954232e+03   2.80570409e+01   1.08593374e+02\n",
+      "   3.68330872e+03   1.66848539e+03   1.72267708e+03   3.92400657e+03\n",
+      "   3.05461512e+03  -6.12539483e+02   3.25233458e+03] -7827.87937733\n"
      ]
     }
    ],
    "source": [
-    "\n",
-    "dependant_variables = list(set_one.columns)\n",
-    "dependant_variables.remove('Price')\n",
-    "\n",
     "\n",
     "\n",
     "choices = []\n",
     "\n",
+    "dependant_variable = list(final_data.columns)\n",
+    "dependant_variable.remove('Price')\n",
+    "\n",
     "def combos(list_of_series):\n",
     "    combos = []\n",
     "    x = len(list_of_series) + 1\n",
@@ -1070,12 +1355,11 @@
     "        x -= 1\n",
     "    return itertools.chain(*combos)\n",
     "\n",
-    "combos = combos(dependant_variables)\n",
-    "print(combos)\n",
+    "combos = combos(dependant_variable)\n",
     "\n",
     "def regression_for(combo):\n",
     "    combo = list(combo)\n",
-    "    df = set_one.loc[:, combo + ['Price']]\n",
+    "    df = final_data.loc[:, combo + ['Price']]\n",
     "    df.dropna(inplace=True)\n",
     "    input_data = df[combo]\n",
     "    price = df['Price']\n",
@@ -1097,31 +1381,23 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
-   "source": []
+   "source": [
+    "##Found my bug it is in my regression_for function... the dataframe is hardcoded and threw an error"
+   ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 13,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
-   "source": []
+   "source": [
+    "#I have gotten my accuracy to 63% using these paramaters: \n",
+    "### Mileage, Make, Model, Trim, Type, Cylinder, Liter, Doors, Cruise, Sound, Leather "
+   ]
   },
   {
    "cell_type": "code",

From 64db0496bdb602aa58db6718b8b33db191952401 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Wed, 24 Jun 2015 08:55:04 -0400
Subject: [PATCH 07/13] next to final hard mode Car Worth

---
 How Much is Your Car Worth.ipynb | 357 ++++++++++---------------------
 1 file changed, 116 insertions(+), 241 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index cfa9abf..1ccb995 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -13,14 +13,15 @@
     "import numpy as np\n",
     "from sklearn import linear_model\n",
     "from collections import defaultdict\n",
+    "\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -72,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -83,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -206,7 +207,7 @@
        "4      4       1      0        1  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -217,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -229,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -238,7 +239,7 @@
      "data": {
       "image/png": 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SuDI6fw1wKs7EF5vv3gXcFJ2z2B/XNd/hFqiEtKTo/uhcv7fffZYWNS/G3rSz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\nNgHz/PFhJOa7K4Ero/PWAKf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GYRhGaTChZBiGYZQGE0qGYRhGaTChZBiGYZQG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\nDMMwSoMJJcMwDKM0/P/SLjHrjQG0QwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1064f3be0>"
+       "<matplotlib.figure.Figure at 0x1063f0c88>"
       ]
      },
      "metadata": {},
@@ -256,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -270,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -301,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -313,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -322,7 +323,7 @@
      "data": {
       "image/png": 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QV8KSSyiHyoW/9Qwb1fMmn1ZOMi9+1g2asFCntGVgSqnV7YnmUm4dFf9zSt42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\nZkmdF1qmuuf8eu7WuuapjKmO/U5qULS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QzoirgS/741LJqKp7gTuAfwVeAPap6qYi5ZzK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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10947df60>"
+       "<matplotlib.figure.Figure at 0x10949ca90>"
       ]
      },
      "metadata": {},
@@ -340,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -349,7 +350,7 @@
      "data": {
       "image/png": 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yz/fvJ3Pbhdkbku+dpvGi1+i8MLlrm8b7J4WLaKMkrps0DqiYn3Hvij2e1AnW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\nGKXBhJJhGIZRGv4v47RO3mstTn0AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10941d0b8>"
+       "<matplotlib.figure.Figure at 0x10953a5c0>"
       ]
      },
      "metadata": {},
@@ -381,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -504,7 +505,7 @@
        "4      4       1      0        1  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -609,7 +610,7 @@
        "4  16339.170324    19832         6    3.1      4       1      0        1"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
@@ -633,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -664,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -689,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -728,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -743,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -752,7 +753,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<itertools.chain object at 0x109a7b320>\n",
+      "<itertools.chain object at 0x1094be3c8>\n",
       "(('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.4462643536728379)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
@@ -808,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {
     "collapsed": true
    },
@@ -827,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -835,10 +836,10 @@
     {
      "data": {
       "text/plain": [
-       "{'Convertible': 5, 'Coupe': 4, 'Hatchback': 3, 'Sedan': 1, 'Wagon': 2}"
+       "{'Convertible': 3, 'Coupe': 5, 'Hatchback': 4, 'Sedan': 2, 'Wagon': 1}"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -856,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -865,14 +866,14 @@
      "data": {
       "text/plain": [
        "{'Buick': 1,\n",
-       " 'Cadillac': 2,\n",
-       " 'Chevrolet': 6,\n",
+       " 'Cadillac': 6,\n",
+       " 'Chevrolet': 3,\n",
        " 'Pontiac': 4,\n",
-       " 'SAAB': 3,\n",
+       " 'SAAB': 2,\n",
        " 'Saturn': 5}"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -887,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -896,41 +897,41 @@
     {
      "data": {
       "text/plain": [
-       "{'9-2X AWD': 7,\n",
-       " '9_3': 6,\n",
-       " '9_3 HO': 12,\n",
-       " '9_5': 18,\n",
-       " '9_5 HO': 27,\n",
-       " 'AVEO': 31,\n",
-       " 'Bonneville': 13,\n",
-       " 'CST-V': 21,\n",
-       " 'CTS': 25,\n",
-       " 'Cavalier': 26,\n",
-       " 'Century': 30,\n",
-       " 'Classic': 28,\n",
+       "{'9-2X AWD': 2,\n",
+       " '9_3': 24,\n",
+       " '9_3 HO': 29,\n",
+       " '9_5': 13,\n",
+       " '9_5 HO': 22,\n",
+       " 'AVEO': 26,\n",
+       " 'Bonneville': 19,\n",
+       " 'CST-V': 11,\n",
+       " 'CTS': 10,\n",
+       " 'Cavalier': 31,\n",
+       " 'Century': 28,\n",
+       " 'Classic': 15,\n",
        " 'Cobalt': 14,\n",
-       " 'Corvette': 24,\n",
-       " 'Deville': 3,\n",
-       " 'G6': 4,\n",
-       " 'GTO': 17,\n",
-       " 'Grand Am': 8,\n",
-       " 'Grand Prix': 23,\n",
-       " 'Impala': 1,\n",
-       " 'Ion': 29,\n",
-       " 'L Series': 15,\n",
-       " 'Lacrosse': 19,\n",
-       " 'Lesabre': 5,\n",
-       " 'Malibu': 2,\n",
-       " 'Monte Carlo': 16,\n",
-       " 'Park Avenue': 11,\n",
-       " 'STS-V6': 20,\n",
-       " 'STS-V8': 10,\n",
-       " 'Sunfire': 9,\n",
-       " 'Vibe': 32,\n",
-       " 'XLR-V8': 22}"
+       " 'Corvette': 18,\n",
+       " 'Deville': 27,\n",
+       " 'G6': 32,\n",
+       " 'GTO': 30,\n",
+       " 'Grand Am': 17,\n",
+       " 'Grand Prix': 1,\n",
+       " 'Impala': 21,\n",
+       " 'Ion': 7,\n",
+       " 'L Series': 5,\n",
+       " 'Lacrosse': 6,\n",
+       " 'Lesabre': 16,\n",
+       " 'Malibu': 20,\n",
+       " 'Monte Carlo': 12,\n",
+       " 'Park Avenue': 9,\n",
+       " 'STS-V6': 3,\n",
+       " 'STS-V8': 25,\n",
+       " 'Sunfire': 8,\n",
+       " 'Vibe': 4,\n",
+       " 'XLR-V8': 23}"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -945,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -956,7 +957,7 @@
        "47"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -970,7 +971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -983,7 +984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1016,9 +1017,9 @@
        "      <td>16507.070267</td>\n",
        "      <td>16229</td>\n",
        "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>28</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1031,9 +1032,9 @@
        "      <td>16175.957604</td>\n",
        "      <td>19095</td>\n",
        "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>28</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1046,9 +1047,9 @@
        "      <td>15731.132897</td>\n",
        "      <td>20484</td>\n",
        "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>28</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1061,9 +1062,9 @@
        "      <td>15118.893228</td>\n",
        "      <td>25979</td>\n",
        "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>28</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1076,9 +1077,9 @@
        "      <td>13585.636802</td>\n",
        "      <td>35662</td>\n",
        "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>28</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1092,11 +1093,11 @@
       ],
       "text/plain": [
        "            Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
-       "799  16507.070267    16229     5     15    28     1         6      3      4   \n",
-       "800  16175.957604    19095     5     15    28     1         6      3      4   \n",
-       "801  15731.132897    20484     5     15    28     1         6      3      4   \n",
-       "802  15118.893228    25979     5     15    28     1         6      3      4   \n",
-       "803  13585.636802    35662     5     15    28     1         6      3      4   \n",
+       "799  16507.070267    16229     5      5     3     2         6      3      4   \n",
+       "800  16175.957604    19095     5      5     3     2         6      3      4   \n",
+       "801  15731.132897    20484     5      5     3     2         6      3      4   \n",
+       "802  15118.893228    25979     5      5     3     2         6      3      4   \n",
+       "803  13585.636802    35662     5      5     3     2         6      3      4   \n",
        "\n",
        "     Cruise  Sound  Leather  \n",
        "799       1      0        0  \n",
@@ -1106,7 +1107,7 @@
        "803       1      0        0  "
       ]
      },
-     "execution_count": 74,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1118,25 +1119,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "Found array with 0 sample(s) (shape=(0, 2)) while a minimum of 1 is required.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-81-dcc66ef019d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0moptimization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-81-dcc66ef019d6>\u001b[0m in \u001b[0;36moptimization\u001b[0;34m(df, param)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcombos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcombo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregression_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-81-dcc66ef019d6>\u001b[0m in \u001b[0;36mregression_for\u001b[0;34m(combo, param)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mregr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, n_jobs)\u001b[0m\n\u001b[1;32m    374\u001b[0m             \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 376\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         X, y, X_mean, y_mean, X_std = self._center_data(\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric)\u001b[0m\n\u001b[1;32m    442\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    443\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m                     ensure_min_features)\n\u001b[0m\u001b[1;32m    445\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features)\u001b[0m\n\u001b[1;32m    358\u001b[0m             raise ValueError(\"Found array with %d sample(s) (shape=%s) while a\"\n\u001b[1;32m    359\u001b[0m                              \u001b[0;34m\" minimum of %d is required.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m                              % (n_samples, shape_repr, ensure_min_samples))\n\u001b[0m\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_features\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0, 2)) while a minimum of 1 is required."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.64467245708191956)\n",
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "[ -1.82046837e-01   8.76680639e+02   9.75977085e+01   1.14367642e+02\n",
+      "  -5.52609936e+03   2.95486002e+03   3.73882301e+02  -7.53596934e+03\n",
+      "   3.86292841e+03  -5.50034254e+02   2.63703568e+03] 38029.283591\n"
      ]
     }
    ],
@@ -1154,9 +1152,9 @@
     "\n",
     "\n",
     "\n",
-    "def regression_for(combo, param='Price'):\n",
+    "def regression_for(combo, param='Price', data_frame=final_data):\n",
     "    combo = list(combo)\n",
-    "    df = set_one.loc[:, combo + [param]]\n",
+    "    df = data_frame.loc[:, combo + [param]]\n",
     "    df.dropna(inplace=True)\n",
     "    input_data = df[combo]\n",
     "    price = df[param]\n",
@@ -1173,7 +1171,7 @@
     "    for combo in series:\n",
     "        regr, score = regression_for(combo)\n",
     "        choices.append((combo, score))\n",
-    "    return \n",
+    "    return best\n",
     "optimization(final_data)    \n",
     "    \n",
     "\n",
@@ -1186,141 +1184,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Price</th>\n",
-       "      <th>Mileage</th>\n",
-       "      <th>Make</th>\n",
-       "      <th>Model</th>\n",
-       "      <th>Trim</th>\n",
-       "      <th>Type</th>\n",
-       "      <th>Cylinder</th>\n",
-       "      <th>Liter</th>\n",
-       "      <th>Doors</th>\n",
-       "      <th>Cruise</th>\n",
-       "      <th>Sound</th>\n",
-       "      <th>Leather</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>17314.103129</td>\n",
-       "      <td>8221</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>17542.036083</td>\n",
-       "      <td>9135</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>16218.847862</td>\n",
-       "      <td>13196</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16336.913140</td>\n",
-       "      <td>16342</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>16339.170324</td>\n",
-       "      <td>19832</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
-       "0  17314.103129     8221     1     30    29     1         6    3.1      4   \n",
-       "1  17542.036083     9135     1     30    29     1         6    3.1      4   \n",
-       "2  16218.847862    13196     1     30    29     1         6    3.1      4   \n",
-       "3  16336.913140    16342     1     30    29     1         6    3.1      4   \n",
-       "4  16339.170324    19832     1     30    29     1         6    3.1      4   \n",
-       "\n",
-       "   Cruise  Sound  Leather  \n",
-       "0       1      1        1  \n",
-       "1       1      1        0  \n",
-       "2       1      1        0  \n",
-       "3       1      0        0  \n",
-       "4       1      0        1  "
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "final_data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -1329,13 +1204,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.63439499346624129)\n",
+      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.64467245708191956)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
       "\n",
-      "[ -1.76357292e-01  -2.38954232e+03   2.80570409e+01   1.08593374e+02\n",
-      "   3.68330872e+03   1.66848539e+03   1.72267708e+03   3.92400657e+03\n",
-      "   3.05461512e+03  -6.12539483e+02   3.25233458e+03] -7827.87937733\n"
+      "[ -1.82046837e-01   8.76680639e+02   9.75977085e+01   1.14367642e+02\n",
+      "  -5.52609936e+03   2.95486002e+03   3.73882301e+02  -7.53596934e+03\n",
+      "   3.86292841e+03  -5.50034254e+02   2.63703568e+03] 38029.283591\n"
      ]
     }
    ],

From 00a961e4f59da762da767d05c9a294da097d7be4 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Wed, 24 Jun 2015 08:56:39 -0400
Subject: [PATCH 08/13]  Finished Hard Mode

---
 How Much is Your Car Worth.ipynb | 65 ++++++++------------------------
 1 file changed, 16 insertions(+), 49 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index 1ccb995..831faff 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -1119,7 +1119,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "final_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -1139,7 +1150,10 @@
     }
    ],
    "source": [
-    "final_data = final_data\n",
+    "\n",
+    "\n",
+    "choices = []\n",
+    "inal_data = final_data\n",
     "\n",
     "\n",
     "def combos(list_of_series):\n",
@@ -1179,45 +1193,7 @@
     "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
     "\n",
     "print(regr.coef_, regr.intercept_)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "final_data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.64467245708191956)\n",
-      "\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "\n",
-      "[ -1.82046837e-01   8.76680639e+02   9.75977085e+01   1.14367642e+02\n",
-      "  -5.52609936e+03   2.95486002e+03   3.73882301e+02  -7.53596934e+03\n",
-      "   3.86292841e+03  -5.50034254e+02   2.63703568e+03] 38029.283591\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
     "\n",
-    "choices = []\n",
     "\n",
     "dependant_variable = list(final_data.columns)\n",
     "dependant_variable.remove('Price')\n",
@@ -1255,15 +1231,6 @@
     "\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "##Found my bug it is in my regression_for function... the dataframe is hardcoded and threw an error"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {

From a02924c4745a7c3ca0bfef057d419a20285e15dc Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Wed, 24 Jun 2015 13:56:42 -0400
Subject: [PATCH 09/13] cool

---
 How Much is Your Car Worth.ipynb | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index 831faff..162052d 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -230,16 +230,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SuDI6fw1wKs7EF5vv3gXcFJ2z2B/XNd/hFqiEtKTo/uhcv7fffZYWNS/G3rSz\nJv9bCl8UhT+6IGgquxhzWc8ycwUvuypvtkxvwOz+WqjJHFbQ2LK0tbkKM/0Gf3O1UtvqT4UOCv2x\n0GtsszdXfl9R58xQSK0+h+rnMVa/zPiDRf92uyWN9zmULeE8r+NxUnO5T4ENPBTvWYd7q/4hcCbO\n0WGFz7+SakeHabhX4V+SODr8xAsoodrRIQioizBHh4y2l2NNR/KPO2ND5EHX8j8wYzu/Dr0QhFFl\nW5tzCKAq/M9ANOczsC/JH9LEkUEjYVTLhMhtqT2i1M09VZnEVlbH4xvY468fqcwL3nyLo3tMaF+o\n1G8iK6qFCaXmf9e9adrsRaF0IvCQFzSPAh/1+UO45fBZLuErcTaRTfGDJXEJfwq4Mcrvw3kRBpfw\nBZ3s3G5Ijf5p8nrLy6Pc5trS6uLULK/E/j3Jhnt9IzAYCaXgBZjlYt4fNKKV9ddP1YvHF+a5wr0W\nqzsvnrMKXnvjEyTZ68oaB6XtxDPutlSWl7582obmUm7RDStDmsxCybe/xuA7uMENcMtbGoyau18e\nu8Y2HognOlBmaxFpc1y/Orf2mepc22PvvYUKM3dWr+Maq1NqUW46Ht98/3dN6p7xthaLtZmFuC0+\nn7qCtFPPuNuSCaVxlFt0w8qQJrtQyuiP1BxFPOBN/B8qv3ms8c19tCKoapu21njhs9gLnoHdbh4o\nrXH0a8okt68y/E5WG8I9BtTNlc3VbJPaYk08CVsLWZT9G8g2gxb1jLsx9bJwzmvcbGY7dGMS4byu\nBj8Fn5tS6WH2JeAtbbrLaEZwzqy8Ki+wOl5dcVDUwLLRWp5/0dbnwzDwOudRB429D9PehY/udd59\nN/a5/hnbUXalL+9uoM9Zl78CvB63rflY3x7sgtSGzzentkzfiLNkf0ThRHEhKN+HC1SSZgvwHuCW\nl5P7t+4R5z0yY2+7j4vIhmavNxLUPBNbp2hpW4aEaUpRXwytrf0W3i4zW3pH1ezFnrQQHSJ5M481\nlsoyk+tmbEicGFqfxE/fP6s+0bneYWHA3+uEjPudkNKmgidfOjLFoQqLfDmnqVtwHDs8tCf23Xi0\nnOw+6U0NwdLYM9dcyi26YWVI3S6U6g2KrZcVPNTi+YoBTW9VPrG6hnmq+l5w2YNjtlmq0SBY+X3W\nQttYmPVvHm+/UxHRIXgSxia+WZohkNNOBLe5Ppqv2QIsDqdUOxL5xH4DzQulVl4e2v17tVRcMqHU\nhZ3bobputjSSAAAgAElEQVQ3fCNtZRBIyhtzMR7bmqI99c0UeiNZ9XKDelWYn3FtElg50Mbu2mu8\n0K2Y5xmt1U/Z2lbsDJDeRXb2SKUQPEOrBXJ19GzGPPCq2vrCRN3m2/Gbqt23lc+kRtlVrvZF/x9Z\nGtfvRHMpt+iGlSF1t1CqPyCMx4yS55tstpmtWjOpHrxCfLhmImnXC/yqWr2nUwjKGpeZteV5LW1r\nrB4ZQmShQv9o7WCxtZ9HjWfXkhfcBP4nWniRaT7MUashkZqph2lexSQTSl3YuZ2peyOhVC4vqNrC\npp5mo2ODVz0h2+C7lZXCoF99UNUN2UJp6IX6fZ21OHZORhSIhermlBYqzFMXiPUCbVYLTQ24K1t9\nwRjf82k1CG/zAWFraX91fitNWAG6c+4KdC7o50A/ATqt6PqM47eiuZRbdMPKkLpbKDWaSymXUHJ1\nyjTLpdYT1a53c9pQ+pra0Ryad7yop20N7HaRGYZS5YRo4FpDkDX/LGrPscVCayJrsMa7XUkrkTJa\nEmANf7tl/H3X7iedBfrxpK4V6fyi6zeO34vmUa65hHc52tDltLUAqZ2hb3ulW/QdACeLDK9N3L5r\n19u3bxxutSeSBEi9I8rf/UHgbrjZu4Xv3gu7V1Zfn67T7r3w4cdc8NNd13u39Fe7KD9bcJG0nvs9\nMKv1ujbDRuCgk2D1FHd869lwOM7H4JZxBNYdWu7cwMNzYbr/XTUoY8f1cMvpcGMTv7GXVoI20dfd\njwjTgPcDVwNz6pz6FC5OpwGmKeUp8cuSmOBCyBzqk34j16yoEVSZrpqZW8jeJI/qoKVjpjOqPOay\nTERhN93+zbGTQXUd0/efvTlZHFs1n7Qno4yUl1pFvVLmu7DQNh3dYY46E2FrGsNEtI5abZjIuRm/\nk1Kb70CngL4TdHMNbShOt4IeXfT/4gT/jzWXcotuWBlSLwuldvzTtjLgjKPMF6rju2WFBmrchkR4\nzNxJxW6vIWjpBerMaxVbpGfN06ysFDT9e7I2H6xRr5UZm/TtSTzm+vYlcfP6swKsxi7u0dzbkDrX\n8Wlb3dzV0AtJxIhMs6A269rezt9KPr/fcjo6gAroWaAPNCGEvgP6+iL7Modno7mUW3TDypB6WyhN\nzOaeDFTjdxGvN2i0a96gckDNWqAa5rCa8ZgbjITEYMqlOwRBDVpTdb1q1PcFJyzTc2lzsupUo4x4\njdLtXqgN7Km92Hl2pqv9eJ+VJQX0ZND7mhBCPwQ9o+j65tsXaB7l2pyS0YCh5XDxdPhfwLUAU2DZ\np5oNO+ND1twNF/e5jfQ2nSkin1DVa9wZ6Xmay/bCtOHK+aW9wy4Uzz3AX9ep5+rpbnut36S+24ib\n43ket+tJIxZOyQ77cz8u/1qAYVg2DPf6etGg7CnDcBBuLg1fznuAGU3UJ/Bbf12oG33wtw/BowOw\n7NXJeSt82VumwIFraGH+Tcc9X9ebiHAsbu+g9zQ49QngvwLfUUWbK7vZEFqTjKKlbRkSPaopMWbO\nan7bAarCxdQKO9R4war7fnBDxmLZisWpSRl9m1Nmt2Be21dpxurPmIuZsSExZcX3q5rH0Wj+agRn\nPttT+f1CTSJzL9dkX6N0P2SVHbZFT7u9z9DsbShCgNW4jLH5o5T5bqH/e3VUxsyd7rwLfHnBNBm2\nz8hemGyp1u9/zE27kSa0DfT9oAeP/3+zXKbScbRBcym36IaVIfWiUKr80TcXjqbGP8rKWtG3acK0\n58xW2UItJdBWZocfSu8Au1xhzkuuLf2pyAD9eypD+pyv2aF64sG7fw/07Xd5QwrHpgd1H/Zn5k6Y\nM1pZVr12ZZnqFqfOPTa6T9ig7wJNTHPTX3D1OkGTtVS3++NQx6M1uc9STebM1lTUqbXfTefMd52+\nX/X967ppx+kA6ArQGe25b/e4std5dppLuUU3rAypN4XSeIJqZl9Dze22G4cM8l5rGYP3YCqG3aBf\nYJpewzK4v3I+J/39mlSZac2w1lYQ4XPYyjwu9xV+0Hfeikk9XxkN+ss1e9FtaFemwMqoe7xR30CU\nF84NThHpsuar05hiYTXXC6naz72eEOj023sR2gLoNNDLQHc0IYiuAz20LP+fZUs9J5SAI4EfAI8B\nPweW+fwhYB3ZO89ehdtFdhNwTpQfdp7dDNwQ5ffhYvyHnWeP7mTnFvuDaffi01qRuetH2XbX9e2r\nHDyDN1paozk2o6yZLyUCI+te59eoa3Cl7tucMs+NJGa0IORqDfrBFTvUMyyEvd0fHxwJhSCkBne5\n4zWaODEEIdSnMGuf855Lm/JO8/U4Vl18vHDPRZot/BZH58X5s0czYvI1jHjR6Pl3+jfavnvoQaDf\nakIAKR100zbzXZ1yC2zQPOAkfzwT+AXwWuA64GM+fwXwaX+8CLd1+sHAAtyCM/HfPQi8yR/fB5zn\njy8DvuiPLwS+0cnOLfgHc27ltt399dyYmxq0su/ReGM9Ktb5hHVDMzY4LSUeuOPoB6GssM4naBNZ\ng3OjdoSYeWNa357E/FVLEzkhEjQn1BAAwUx2Wko4DfnzF/lrj/XnXODvOccLjzC3NajJNhTL/fdB\nA12syXzR2HNR5x4eNNS4ToO7xvPS0cz39X8HrZvg8hBKODftnzYphAp10x5vv5Ul9ZxQymjgd4Cz\nvBY01+fNAzb546uAFdH5a4DFwGHAE1H+RcDN0Tmn+uOpwPOd7NyC+3Nl4s58gSZzSuHNX2sMTK39\no5C5KLX+NheMOQKkhUGW+W7mTsbWDVVF5s7cQ6jx4DtjQ+W24oNabVqboZX7FWWZIIMZ8Az/+eqU\n8AhzP6o1opH7+wQX9rAIdnl0v3hrjRN9fhCY/eq2XA+C1TlJ1O73poL3thTBeyJv/O3SFkC/0KQQ\nUtBPFP2/2Supp4WS13x+jQvH8rsoX8Jn4PPAu6PvvgxcgDPdrYvy3wzc6483AodH3z0FDHWqcwvo\nRy9Q+jdTc6O42HylVQNT6/cac1LYkDgq1B9gksExvW4ozNXEjg6VmwvW1gKyPPGy21htdozNeMEx\noZanXajTHIVDIgGyXGFYq+8bNMmwTXra+eFYTQRZ3B9nRMJo0F8bmw/nqJs/6o/y+hUGXoA5+4Iw\nr+yf+s+IGtp1/d9BO9bBtRoAVv+mBSG0HXRK0f+bvZjyGjcLX6ckIjOBbwMfUtXfi8jYd6qqIqId\nqseq6ON6VV3fifu2C78e6C63VudmkthyFwA3Eq1tmQIfHoUT/fqbxrHwXNkzroFpx4ECL2+DgSPj\nLcSBJyq3UN84Hb7yzyJzdsO+bXDIr6rXYpyM20o88Cjw+H648WD3eQUuRt1zYzHYNGMdTWXbAZbt\ndeudCPVLtXHH9fDon8AVfUnegVH4QFT/ValeOBHYOwq3THH9CfBRX7+LgduB4zJ6T/fAR0bg5Vmu\nOlf6/KW4pS8vAn8GfAhnvQ5c5b9fCkwBBv21S6NzbgZewhkUAA4BVvtt5a84GEb/p/9/2uD653Oh\nf8a2bK98HkPL3TMN97ijr7nYd+Mn63mmEeHPgO+1UOxsVXZNqGJGFSKyBFiS+40KlrQH436QH47y\nNgHz/PFhJOa7K4Ero/PWAKf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N8eocA8I0BaFymqrQPFA4m2lXaN7LkImwubc4GVypWVSpPUzJusm9KTOsUSme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\nBuyKB9BIqZRLdxC5T0drOh0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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1063f0c88>"
+       "<matplotlib.figure.Figure at 0x1095282b0>"
       ]
      },
      "metadata": {},
@@ -250,7 +250,7 @@
     "plt.scatter(mileage_p['Mileage'], mileage_p['Price'])\n",
     "plt.ylabel('Price')\n",
     "plt.xlabel('Mileage')\n",
-    "plt.plot(mileage_p.Mileage, func_one(mileage_p.Mileage), linewidth=2)\n",
+    "plt.plot(mileage_p.Price, func_one(mileage_p.Price), linewidth=2)\n",
     "\n",
     "plt.show()"
    ]
@@ -1237,18 +1237,18 @@
     "collapsed": true
    },
    "source": [
-    "#I have gotten my accuracy to 63% using these paramaters: \n",
-    "### Mileage, Make, Model, Trim, Type, Cylinder, Liter, Doors, Cruise, Sound, Leather "
+    "#I have gotten my accuracy to 64.5% using these paramaters: \n",
+    "## Mileage, Make, Model, Trim, Type, Cylinder, Liter, Doors, Cruise, Sound, Leather "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
-   "source": []
+   "source": [
+    "Make a function that iterates over all posiible splits of data frame "
+   ]
   },
   {
    "cell_type": "code",

From 1baaedf996f3efb1cbf337e40681c85ad2d4a569 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Wed, 24 Jun 2015 15:37:51 -0400
Subject: [PATCH 10/13] working

---
 How Much is Your Car Worth.ipynb | 331 +++++++++++++++++++++++++------
 1 file changed, 269 insertions(+), 62 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index 162052d..dd0e1e1 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 61,
    "metadata": {
     "collapsed": false
    },
@@ -12,6 +12,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn import linear_model\n",
+    "from sklearn.cross_validation import train_test_split \n",
     "from collections import defaultdict\n",
     "\n",
     "\n"
@@ -19,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false
    },
@@ -744,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 63,
    "metadata": {
     "collapsed": false
    },
@@ -753,7 +754,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<itertools.chain object at 0x1094be3c8>\n",
+      "<itertools.chain object at 0x1093fd320>\n",
       "(('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.4462643536728379)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
@@ -1119,18 +1120,141 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Make</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Trim</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17314.103129</td>\n",
+       "      <td>8221</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17542.036083</td>\n",
+       "      <td>9135</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16218.847862</td>\n",
+       "      <td>13196</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16336.913140</td>\n",
+       "      <td>16342</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16339.170324</td>\n",
+       "      <td>19832</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
+       "0  17314.103129     8221     1     28    10     2         6    3.1      4   \n",
+       "1  17542.036083     9135     1     28    10     2         6    3.1      4   \n",
+       "2  16218.847862    13196     1     28    10     2         6    3.1      4   \n",
+       "3  16336.913140    16342     1     28    10     2         6    3.1      4   \n",
+       "4  16339.170324    19832     1     28    10     2         6    3.1      4   \n",
+       "\n",
+       "   Cruise  Sound  Leather  \n",
+       "0       1      1        1  \n",
+       "1       1      1        0  \n",
+       "2       1      1        0  \n",
+       "3       1      0        0  \n",
+       "4       1      0        1  "
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "final_data.head()"
+    "final_data.head()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 69,
    "metadata": {
     "collapsed": false
    },
@@ -1142,62 +1266,17 @@
       "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.64467245708191956)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "\n",
-      "[ -1.82046837e-01   8.76680639e+02   9.75977085e+01   1.14367642e+02\n",
-      "  -5.52609936e+03   2.95486002e+03   3.73882301e+02  -7.53596934e+03\n",
-      "   3.86292841e+03  -5.50034254e+02   2.63703568e+03] 38029.283591\n"
+      "\n"
      ]
     }
    ],
    "source": [
-    "\n",
-    "\n",
-    "choices = []\n",
-    "inal_data = final_data\n",
-    "\n",
-    "\n",
-    "def combos(list_of_series):\n",
-    "    combos = []\n",
-    "    x = len(list_of_series) + 1\n",
-    "    for num in range(2,x):\n",
-    "        combos.append(list(itertools.combinations(list_of_series, num)))\n",
-    "        x -= 1\n",
-    "    return itertools.chain(*combos)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def regression_for(combo, param='Price', data_frame=final_data):\n",
-    "    combo = list(combo)\n",
-    "    df = data_frame.loc[:, combo + [param]]\n",
-    "    df.dropna(inplace=True)\n",
-    "    input_data = df[combo]\n",
-    "    price = df[param]\n",
-    "    regr = linear_model.LinearRegression()\n",
-    "    regr.fit(input_data, price)\n",
-    "    return regr, regr.score(input_data, price)\n",
-    "\n",
-    "\n",
-    "def optimization(df, param='Price'):\n",
-    "    df = list(df.columns)\n",
-    "    df.remove(param)\n",
-    "    choices = []\n",
-    "    series = combos(df)\n",
-    "    for combo in series:\n",
-    "        regr, score = regression_for(combo)\n",
-    "        choices.append((combo, score))\n",
-    "    return best\n",
-    "optimization(final_data)    \n",
-    "    \n",
-    "\n",
-    "print(best)\n",
-    "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
-    "\n",
-    "print(regr.coef_, regr.intercept_)\n",
-    "\n",
     "\n",
     "dependant_variable = list(final_data.columns)\n",
     "dependant_variable.remove('Price')\n",
     "\n",
+    "type(final_data)\n",
+    "    \n",
     "def combos(list_of_series):\n",
     "    combos = []\n",
     "    x = len(list_of_series) + 1\n",
@@ -1224,13 +1303,35 @@
     "    choices.append((combo, score))\n",
     "                   \n",
     "best = sorted(choices, key=lambda x: x[1])[-1]\n",
+    "\n",
     "print(best)\n",
     "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
     "regr, score = regression_for(best[0])\n",
-    "print(regr.coef_, regr.intercept_)\n",
+    "#print(regr.coef_, regr.intercept_)\n",
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "unexpected EOF while parsing (<ipython-input-53-afb2c6dbce2a>, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-53-afb2c6dbce2a>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    #a_train, a_test, b_train, b_test = train_test_split(df, df[[]], test_size=0.33)\u001b[0m\n\u001b[0m                                                                                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(list(best[0]))\n",
+    "#a_train, a_test, b_train, b_test = train_test_split(df, df.columns, test_size=0.33)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1252,12 +1353,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Found arrays with inconsistent numbers of samples: [ 12 804]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-54-3e3b056c9fef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.33\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_sets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcombos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_of_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-54-3e3b056c9fef>\u001b[0m in \u001b[0;36mmake_sets\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfinal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfinal_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_sets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.33\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_sets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m   1806\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtrain_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1807\u001b[0m         \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1808\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1809\u001b[0m     \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1810\u001b[0m     cv = ShuffleSplit(n_samples, test_size=test_size,\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         raise ValueError(\"Found arrays with inconsistent numbers of samples: \"\n\u001b[0;32m--> 174\u001b[0;31m                          \"%s\" % str(uniques))\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Found arrays with inconsistent numbers of samples: [ 12 804]"
+     ]
+    }
+   ],
+   "source": [
+    "final_data = final_data\n",
+    "def make_sets(df):\n",
+    "    train, test = train_test_split(df, df.columns, test_size=0.33)\n",
+    "    return train, test\n",
+    "train, test = make_sets(final_data)\n",
+    "\n",
+    "def combos(list_of_series):\n",
+    "    combos = []\n",
+    "    x = len(list_of_series) + 1\n",
+    "    for num in range(2,x):\n",
+    "        combos.append(list(itertools.combinations(list_of_series, num)))\n",
+    "        x -= 1\n",
+    "    return itertools.chain(*combos)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def regression_for(combo, param='Price', data_frame=final_data):\n",
+    "    combo = list(combo)\n",
+    "    df = data_frame.loc[:, combo + [param]]\n",
+    "    df.dropna(inplace=True)\n",
+    "    input_data = df[combo]\n",
+    "    price = df[param]\n",
+    "    regr = linear_model.LinearRegression()\n",
+    "    regr.fit(input_data, price)\n",
+    "    return regr, regr.score(input_data, price)\n",
+    "\n",
+    "\n",
+    "def optimization(df, param='Price'):\n",
+    "    df = list(df.columns)\n",
+    "    df.remove(param)\n",
+    "    choices = []\n",
+    "    series = combos(df)\n",
+    "    for combo in series:\n",
+    "        regr, score = regression_for(combo)\n",
+    "        choices.append((combo, score))\n",
+    "    return choices\n",
+    "\n",
+    "\n",
+    "\n",
+    "best = sorted(optimization(df), key=lambda x: x[1])[-1]\n",
+    "\n",
+    "\n",
+    "\n",
+    "    \n",
+    "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
+    "\n",
+    "print(regr.coef_, regr.intercept_)"
+   ]
   },
   {
    "cell_type": "code",
@@ -1266,7 +1431,49 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "choices = []\n",
+    "inal_data = final_data\n",
+    "\n",
+    "\n",
+    "def combos(list_of_series):\n",
+    "    combos = []\n",
+    "    x = len(list_of_series) + 1\n",
+    "    for num in range(2,x):\n",
+    "        combos.append(list(itertools.combinations(list_of_series, num)))\n",
+    "        x -= 1\n",
+    "    return itertools.chain(*combos)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def regression_for(combo, param='Price', data_frame=final_data):\n",
+    "    combo = list(combo)\n",
+    "    df = data_frame.loc[:, combo + [param]]\n",
+    "    df.dropna(inplace=True)\n",
+    "    input_data = df[combo]\n",
+    "    price = df[param]\n",
+    "    regr = linear_model.LinearRegression()\n",
+    "    regr.fit(input_data, price)\n",
+    "    return regr, regr.score(input_data, price)\n",
+    "\n",
+    "\n",
+    "def optimization(df, param='Price'):\n",
+    "    df = list(df.columns)\n",
+    "    df.remove(param)\n",
+    "    choices = []\n",
+    "    series = combos(df)\n",
+    "    for combo in series:\n",
+    "        regr, score = regression_for(combo)\n",
+    "        choices.append((combo, score))\n",
+    "    return best\n",
+    "optimization(final_data)    \n",
+    "    \n",
+    "\n",
+    "print(best)\n",
+    "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
+    "\n",
+    "print(regr.coef_, regr.intercept_)\n"
+   ]
   },
   {
    "cell_type": "code",

From bdbff1e7d98bb0713eea2696df1215590bcde113 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Wed, 24 Jun 2015 19:35:29 -0400
Subject: [PATCH 11/13] ran through some of the homework I didnt get to last
 night and understand much better

---
 Simple Linear Regression.ipynb | 417 ++++++++++++++++++++++++++++++++-
 1 file changed, 411 insertions(+), 6 deletions(-)

diff --git a/Simple Linear Regression.ipynb b/Simple Linear Regression.ipynb
index 65d531a..954b7a3 100644
--- a/Simple Linear Regression.ipynb	
+++ b/Simple Linear Regression.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -11,7 +11,19 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from sklearn import linear_model"
+    "from sklearn import linear_model\n",
+    "from sklearn.cross_validation import train_test_split as tts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
    ]
   },
   {
@@ -42,6 +54,73 @@
     "df = pd.DataFrame(ground_cricket_data)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chirps/Second</th>\n",
+       "      <th>Ground Temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>88.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>71.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.8</td>\n",
+       "      <td>93.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>18.4</td>\n",
+       "      <td>84.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.1</td>\n",
+       "      <td>80.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Chirps/Second  Ground Temperature\n",
+       "0           20.0                88.6\n",
+       "1           16.0                71.6\n",
+       "2           19.8                93.3\n",
+       "3           18.4                84.3\n",
+       "4           17.1                80.6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -56,6 +135,136 @@
     "5. Interpolate data:  With a listening device, you discovered that on a particular morning the crickets were chirping at a rate of 18 chirps per second.  What was the approximate ground temperature that morning? "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "train, test = tts(df, test_size=.33)\n",
+    "tr_chirp = train[['Chirps/Second']]\n",
+    "tr_temp = train['Ground Temperature']\n",
+    "te_chirp = test[['Chirps/Second']]\n",
+    "te_temp = test['Ground Temperature']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr = linear_model.LinearRegression()\n",
+    "regr.fit(tr_chirp, tr_temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10940bc18>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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zjufRijWExNYSlwBXAd8A3u4SN2sNF7mNicRGEmcBvwYeJM3BL45gVeZoZl3DoxUbFYlx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\nnIvczKzk/j/MKn9glfhPlgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10923e048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chirps = df[['Chirps/Second']]\n",
+    "temp = df['Ground Temperature']\n",
+    "\n",
+    "\n",
+    "plt.scatter(chirps, temp)\n",
+    "plt.plot(te_chirp, regr.predict(te_chirp))\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percent Accuracy: 49.54197051874848%\n"
+     ]
+    }
+   ],
+   "source": [
+    "x =regr.score(te_chirp, te_temp) * 10 ** 2\n",
+    "print('Percent Accuracy: {}%'.format(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -74,15 +283,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "df = pd.read_fwf(\"brain_body.txt\")"
+    "bbw = pd.read_fwf(\"brain_body.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brain</th>\n",
+       "      <th>Body</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.385</td>\n",
+       "      <td>44.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.480</td>\n",
+       "      <td>15.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.350</td>\n",
+       "      <td>8.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>465.000</td>\n",
+       "      <td>423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>36.330</td>\n",
+       "      <td>119.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Brain   Body\n",
+       "0    3.385   44.5\n",
+       "1    0.480   15.5\n",
+       "2    1.350    8.1\n",
+       "3  465.000  423.0\n",
+       "4   36.330  119.5"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bbw.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "train_b, test_b = tts(bbw, test_size=.33)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tr_brain = train_b[['Brain']]\n",
+    "tr_body = train_b['Body']\n",
+    "te_brain = test_b[['Brain']]\n",
+    "te_body = test_b['Body']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.80770330208679952"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regrb = linear_model.LinearRegression()\n",
+    "\n",
+    "regrb.fit(tr_brain, tr_body)\n",
+    "\n",
+    "regrb.score(te_brain, te_body)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x109428780>]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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5ADgtgn0cCNaq3CmYbYPEUOCltPoQcLxPbW2tzp2CWQ8kPks1ECZFcKwDwdqBOwWzHImx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\nzKzCoWBmZhUOBTMzq3AomJlZxf8HXK7lnr8bhK8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1094d1908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(bbw[['Brain']], bbw['Body'])\n",
+    "plt.plot(te_brain, regrb.predict(te_brain) )"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -109,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -136,7 +541,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.4.2"
   }
  },
  "nbformat": 4,

From fecad33925d11386018361ea8f4501f0479a7310 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Tue, 21 Jul 2015 18:43:10 -0400
Subject: [PATCH 12/13] removed error from notebook

---
 How Much is Your Car Worth.ipynb | 298 +++++++++++++++----------------
 1 file changed, 148 insertions(+), 150 deletions(-)

diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index dd0e1e1..53c3b0d 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -208,7 +208,7 @@
        "4      4       1      0        1  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -240,7 +240,7 @@
      "data": {
       "image/png": 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N8eocA8I0BaFymqrQPFA4m2lXaN7LkImwubc4GVypWVSpPUzJusm9KTOsUSme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\nBuyKB9BIqZRLdxC5T0drOh0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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1095282b0>"
+       "<matplotlib.figure.Figure at 0x1063c8b70>"
       ]
      },
      "metadata": {},
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -324,7 +324,7 @@
      "data": {
       "image/png": 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QV8KSSyiHyoW/9Qwb1fMmn1ZOMi9+1g2asFCntGVgSqnV7YnmUm4dFf9zSt42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\nZkmdF1qmuuf8eu7WuuapjKmO/U5qULS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QzoirgS/741LJqKp7gTuAfwVeAPap6qYi5ZzK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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10949ca90>"
+       "<matplotlib.figure.Figure at 0x1093ada90>"
       ]
      },
      "metadata": {},
@@ -342,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -351,7 +351,7 @@
      "data": {
       "image/png": 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yz/fvJ3Pbhdkbku+dpvGi1+i8MLlrm8b7J4WLaKMkrps0DqiYn3Hvij2e1AnW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\nGKXBhJJhGIZRGv4v47RO3mstTn0AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10953a5c0>"
+       "<matplotlib.figure.Figure at 0x1093ad080>"
       ]
      },
      "metadata": {},
@@ -383,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -506,7 +506,7 @@
        "4      4       1      0        1  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -611,7 +611,7 @@
        "4  16339.170324    19832         6    3.1      4       1      0        1"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -691,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -730,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -745,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -754,7 +754,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<itertools.chain object at 0x1093fd320>\n",
+      "<itertools.chain object at 0x109444978>\n",
       "(('Mileage', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.4462643536728379)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
@@ -810,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {
     "collapsed": true
    },
@@ -829,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -837,10 +837,10 @@
     {
      "data": {
       "text/plain": [
-       "{'Convertible': 3, 'Coupe': 5, 'Hatchback': 4, 'Sedan': 2, 'Wagon': 1}"
+       "{'Convertible': 3, 'Coupe': 5, 'Hatchback': 1, 'Sedan': 4, 'Wagon': 2}"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -858,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -866,15 +866,15 @@
     {
      "data": {
       "text/plain": [
-       "{'Buick': 1,\n",
-       " 'Cadillac': 6,\n",
-       " 'Chevrolet': 3,\n",
+       "{'Buick': 2,\n",
+       " 'Cadillac': 5,\n",
+       " 'Chevrolet': 6,\n",
        " 'Pontiac': 4,\n",
-       " 'SAAB': 2,\n",
-       " 'Saturn': 5}"
+       " 'SAAB': 1,\n",
+       " 'Saturn': 3}"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -889,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -898,41 +898,41 @@
     {
      "data": {
       "text/plain": [
-       "{'9-2X AWD': 2,\n",
-       " '9_3': 24,\n",
-       " '9_3 HO': 29,\n",
-       " '9_5': 13,\n",
-       " '9_5 HO': 22,\n",
-       " 'AVEO': 26,\n",
-       " 'Bonneville': 19,\n",
-       " 'CST-V': 11,\n",
-       " 'CTS': 10,\n",
-       " 'Cavalier': 31,\n",
-       " 'Century': 28,\n",
-       " 'Classic': 15,\n",
-       " 'Cobalt': 14,\n",
-       " 'Corvette': 18,\n",
-       " 'Deville': 27,\n",
-       " 'G6': 32,\n",
-       " 'GTO': 30,\n",
-       " 'Grand Am': 17,\n",
-       " 'Grand Prix': 1,\n",
-       " 'Impala': 21,\n",
-       " 'Ion': 7,\n",
-       " 'L Series': 5,\n",
-       " 'Lacrosse': 6,\n",
-       " 'Lesabre': 16,\n",
-       " 'Malibu': 20,\n",
-       " 'Monte Carlo': 12,\n",
-       " 'Park Avenue': 9,\n",
-       " 'STS-V6': 3,\n",
-       " 'STS-V8': 25,\n",
-       " 'Sunfire': 8,\n",
-       " 'Vibe': 4,\n",
-       " 'XLR-V8': 23}"
+       "{'9-2X AWD': 13,\n",
+       " '9_3': 21,\n",
+       " '9_3 HO': 30,\n",
+       " '9_5': 8,\n",
+       " '9_5 HO': 4,\n",
+       " 'AVEO': 19,\n",
+       " 'Bonneville': 32,\n",
+       " 'CST-V': 25,\n",
+       " 'CTS': 9,\n",
+       " 'Cavalier': 18,\n",
+       " 'Century': 7,\n",
+       " 'Classic': 11,\n",
+       " 'Cobalt': 27,\n",
+       " 'Corvette': 16,\n",
+       " 'Deville': 20,\n",
+       " 'G6': 14,\n",
+       " 'GTO': 28,\n",
+       " 'Grand Am': 31,\n",
+       " 'Grand Prix': 10,\n",
+       " 'Impala': 12,\n",
+       " 'Ion': 5,\n",
+       " 'L Series': 3,\n",
+       " 'Lacrosse': 15,\n",
+       " 'Lesabre': 2,\n",
+       " 'Malibu': 22,\n",
+       " 'Monte Carlo': 6,\n",
+       " 'Park Avenue': 29,\n",
+       " 'STS-V6': 23,\n",
+       " 'STS-V8': 26,\n",
+       " 'Sunfire': 1,\n",
+       " 'Vibe': 17,\n",
+       " 'XLR-V8': 24}"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -947,7 +947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -958,7 +958,7 @@
        "47"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -972,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -985,7 +985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1017,10 +1017,10 @@
        "      <th>799</th>\n",
        "      <td>16507.070267</td>\n",
        "      <td>16229</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1032,10 +1032,10 @@
        "      <th>800</th>\n",
        "      <td>16175.957604</td>\n",
        "      <td>19095</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1047,10 +1047,10 @@
        "      <th>801</th>\n",
        "      <td>15731.132897</td>\n",
        "      <td>20484</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1062,10 +1062,10 @@
        "      <th>802</th>\n",
        "      <td>15118.893228</td>\n",
        "      <td>25979</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1077,10 +1077,10 @@
        "      <th>803</th>\n",
        "      <td>13585.636802</td>\n",
        "      <td>35662</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
@@ -1094,11 +1094,11 @@
       ],
       "text/plain": [
        "            Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
-       "799  16507.070267    16229     5      5     3     2         6      3      4   \n",
-       "800  16175.957604    19095     5      5     3     2         6      3      4   \n",
-       "801  15731.132897    20484     5      5     3     2         6      3      4   \n",
-       "802  15118.893228    25979     5      5     3     2         6      3      4   \n",
-       "803  13585.636802    35662     5      5     3     2         6      3      4   \n",
+       "799  16507.070267    16229     3      3    45     4         6      3      4   \n",
+       "800  16175.957604    19095     3      3    45     4         6      3      4   \n",
+       "801  15731.132897    20484     3      3    45     4         6      3      4   \n",
+       "802  15118.893228    25979     3      3    45     4         6      3      4   \n",
+       "803  13585.636802    35662     3      3    45     4         6      3      4   \n",
        "\n",
        "     Cruise  Sound  Leather  \n",
        "799       1      0        0  \n",
@@ -1108,7 +1108,7 @@
        "803       1      0        0  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1120,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -1152,10 +1152,10 @@
        "      <th>0</th>\n",
        "      <td>17314.103129</td>\n",
        "      <td>8221</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>10</td>\n",
        "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1167,10 +1167,10 @@
        "      <th>1</th>\n",
        "      <td>17542.036083</td>\n",
        "      <td>9135</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>10</td>\n",
        "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1182,10 +1182,10 @@
        "      <th>2</th>\n",
        "      <td>16218.847862</td>\n",
        "      <td>13196</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>10</td>\n",
        "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1197,10 +1197,10 @@
        "      <th>3</th>\n",
        "      <td>16336.913140</td>\n",
        "      <td>16342</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>10</td>\n",
        "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1212,10 +1212,10 @@
        "      <th>4</th>\n",
        "      <td>16339.170324</td>\n",
        "      <td>19832</td>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>10</td>\n",
        "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>4</td>\n",
@@ -1229,11 +1229,11 @@
       ],
       "text/plain": [
        "          Price  Mileage  Make  Model  Trim  Type  Cylinder  Liter  Doors  \\\n",
-       "0  17314.103129     8221     1     28    10     2         6    3.1      4   \n",
-       "1  17542.036083     9135     1     28    10     2         6    3.1      4   \n",
-       "2  16218.847862    13196     1     28    10     2         6    3.1      4   \n",
-       "3  16336.913140    16342     1     28    10     2         6    3.1      4   \n",
-       "4  16339.170324    19832     1     28    10     2         6    3.1      4   \n",
+       "0  17314.103129     8221     2      7    17     4         6    3.1      4   \n",
+       "1  17542.036083     9135     2      7    17     4         6    3.1      4   \n",
+       "2  16218.847862    13196     2      7    17     4         6    3.1      4   \n",
+       "3  16336.913140    16342     2      7    17     4         6    3.1      4   \n",
+       "4  16339.170324    19832     2      7    17     4         6    3.1      4   \n",
        "\n",
        "   Cruise  Sound  Leather  \n",
        "0       1      1        1  \n",
@@ -1243,7 +1243,7 @@
        "4       1      0        1  "
       ]
      },
-     "execution_count": 72,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1254,7 +1254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -1263,7 +1263,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.64467245708191956)\n",
+      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.62448899040747941)\n",
       "\n",
       "----------------------------------------------------------------------------------------------------\n",
       "\n"
@@ -1313,17 +1313,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "ename": "SyntaxError",
-     "evalue": "unexpected EOF while parsing (<ipython-input-53-afb2c6dbce2a>, line 2)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-53-afb2c6dbce2a>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    #a_train, a_test, b_train, b_test = train_test_split(df, df[[]], test_size=0.33)\u001b[0m\n\u001b[0m                                                                                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather']\n"
      ]
     }
    ],
@@ -1352,34 +1351,17 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 54,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Found arrays with inconsistent numbers of samples: [ 12 804]",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-54-3e3b056c9fef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.33\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_sets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcombos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_of_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-54-3e3b056c9fef>\u001b[0m in \u001b[0;36mmake_sets\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfinal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfinal_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_sets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.33\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_sets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m   1806\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtrain_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1807\u001b[0m         \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1808\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1809\u001b[0m     \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1810\u001b[0m     cv = ShuffleSplit(n_samples, test_size=test_size,\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/lancerogers/Iron_Yard/homework/linear-regression/linear-regression/.direnv/python-3.4.2/lib/python3.4/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         raise ValueError(\"Found arrays with inconsistent numbers of samples: \"\n\u001b[0;32m--> 174\u001b[0;31m                          \"%s\" % str(uniques))\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Found arrays with inconsistent numbers of samples: [ 12 804]"
-     ]
-    }
-   ],
    "source": [
+    "<!----\n",
     "final_data = final_data\n",
     "def make_sets(df):\n",
     "    train, test = train_test_split(df, df.columns, test_size=0.33)\n",
     "    return train, test\n",
-    "train, test = make_sets(final_data)\n",
+    "#train, test = make_sets(final_data)\n",
     "\n",
     "def combos(list_of_series):\n",
     "    combos = []\n",
@@ -1421,17 +1403,33 @@
     "    \n",
     "print('\\n' + 50 * len(best) * '-' + '\\n')\n",
     "\n",
-    "print(regr.coef_, regr.intercept_)"
+    "print(regr.coef_, regr.intercept_)\n",
+    "-->"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(('Mileage', 'Make', 'Model', 'Trim', 'Type', 'Cylinder', 'Liter', 'Doors', 'Cruise', 'Sound', 'Leather'), 0.62448899040747941)\n",
+      "\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "[ -1.78288796e-01  -2.30475011e+03   1.23586041e+02  -9.13140802e+01\n",
+      "  -2.20756434e+03   4.95954978e+03  -7.03194039e+02  -3.03269773e+03\n",
+      "   3.45680314e+03  -3.78689401e+02   3.39003845e+03] 24369.7163568\n"
+     ]
+    }
+   ],
    "source": [
+    "\n",
     "choices = []\n",
     "inal_data = final_data\n",
     "\n",

From c24bb7af8c2f959928c8f490111a7bea8f70d0e6 Mon Sep 17 00:00:00 2001
From: Lance Rogers <lancekincaid1994@gmail.com>
Date: Tue, 21 Jul 2015 20:17:41 -0400
Subject: [PATCH 13/13] annotated ipython notebook

---
 Simple Linear Regression.ipynb | 205 +++++++++++++++++++++++++++------
 1 file changed, 168 insertions(+), 37 deletions(-)

diff --git a/Simple Linear Regression.ipynb b/Simple Linear Regression.ipynb
index 954b7a3..1411dac 100644
--- a/Simple Linear Regression.ipynb	
+++ b/Simple Linear Regression.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -112,7 +112,7 @@
        "4           17.1                80.6"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -163,7 +163,7 @@
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,17 +174,17 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "source": [
+    "Below is a scatter plot of chirps per second based on ground temperature for crickets"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -192,18 +192,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10940bc18>]"
+       "<matplotlib.text.Text at 0x10a46e710>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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zjufRijWExNYSlwBXAd8A3u4SN2sNF7mNicRGEmcBvwYeJM3BL45gVeZoZl3DoxUbFYlx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\nnIvczKzk/j/MKn9glfhPlgAAAABJRU5ErkJggg==\n",
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sHmswvIgJ66qF5icrSSDqJPE04D2UDt9vAB+yqbUoYkTdaqkdFNFvJF4FLAD+B9jF5rqG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\nEREDLEkgImKA/R9lXqXyj4LhwAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10923e048>"
+       "<matplotlib.figure.Figure at 0x10a3a1908>"
       ]
      },
      "metadata": {},
@@ -217,14 +217,13 @@
     "\n",
     "plt.scatter(chirps, temp)\n",
     "plt.plot(te_chirp, regr.predict(te_chirp))\n",
-    "\n",
-    "\n",
-    "\n"
+    "plt.xlabel('Chirps per second')\n",
+    "plt.ylabel('Ground temp in Farenheit')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -233,7 +232,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Percent Accuracy: 49.54197051874848%\n"
+      "Percent Accuracy: 50.495659663845885%\n"
      ]
     }
    ],
@@ -283,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -294,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -350,7 +349,7 @@
        "4   36.330  119.5"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -361,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 38,
    "metadata": {
     "collapsed": true
    },
@@ -372,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 39,
    "metadata": {
     "collapsed": true
    },
@@ -386,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -394,10 +393,10 @@
     {
      "data": {
       "text/plain": [
-       "0.80770330208679952"
+       "0.85785104063030027"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -410,9 +409,16 @@
     "regrb.score(te_brain, te_body)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This data shows to be acurate 80%-90%  of the time. Though by looking at the graph, there are 2 extreme outliers that scew our data immensely.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -420,18 +426,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x109428780>]"
+       "<matplotlib.text.Text at 0x10a67d278>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Xp7ykS4Afk/pTPkUaNmxmFSUxgtS8tTywZwT3lByS9QL1NHkNBA4DdshFfwAujIg3Ghxb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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1094d1908>"
+       "<matplotlib.figure.Figure at 0x10a75c0f0>"
       ]
      },
      "metadata": {},
@@ -440,7 +446,9 @@
    ],
    "source": [
     "plt.scatter(bbw[['Brain']], bbw['Body'])\n",
-    "plt.plot(te_brain, regrb.predict(te_brain) )"
+    "plt.plot(te_brain, regrb.predict(te_brain) )\n",
+    "plt.xlabel('Mamalian Brain weight')\n",
+    "plt.ylabel('Mamalian Body weight')"
    ]
   },
   {
@@ -514,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -523,6 +531,129 @@
     "df = pd.read_fwf(\"salary.txt\", header=None, \n",
     "                 names=[\"Sex\", \"Rank\", \"Year\", \"Degree\", \"YSdeg\", \"Salary\"])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "task_two_data = df[[\"Sex\", \"Rank\", 'Year', 'Degree', 'YSdeg']]\n",
+    "salary = df[['Salary']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85471806744109691"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear_regression = linear_model.LinearRegression()\n",
+    "linear_regression.fit(task_two_data, salary)\n",
+    "linear_regression.score(task_two_data, salary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the R^2 score, this is a good set of data to make predictions with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "predict_model = linear_regression.predict([0, 3, 25, 1, 35]) - linear_regression.predict([1, 3, 25, 1, 35])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the difference in the sexs, denoted by position 0 in the predict list, is not zero then sex is a factor in salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-1241.7924996]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(predict_model)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the small sample size sex is a factory in salary, but the dataset is so small and the source is not known, no conclusion can be drawn from my findings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {