dataPreparation-Tutorial+(1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Data Exploration and Preprocessing\n",
    "\n",
    "We will use the Yelp Review dataset included in the \"daily_weather.csv\" file.\n",
    "We will explore and then preprocess the dataset, dealing with missing values.\n",
    "\n",
    "\n",
    "We start by reading the csv file into a dataframe (make sure to give the correct path when you run this). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import sys\n",
    "from flask import Flask\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "\n",
    "# Store the csv in a dataframe\n",
    "df = pd.read_csv('/Users/ropatel/Desktop/book1.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#items = pandas.read_csv('C:\\\\Users\\Bassi\\\\Desktop\\\\testworks.csv', sep = '\\t', \n",
    "      #  quoting = csv.QUOTE_NONE, names = [\"time, type, business_id\"])\n",
    "                        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#print the contents of the dataframe\n",
    "\n",
    "#print(items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that some of the cells have missing values, denoted as 'NaN'. Since it's impossible to see how many of those exist by visually exploring the dataset, we instead print out a total count of the non-empty cells for each attribute. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Business_ID        20\n",
       "Location           20\n",
       "Positive_review    20\n",
       "Stars              20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()\n",
    "\n",
    "# NOTE: I just put in 0s for in the actual CSV file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observce that indeed, most of the attributes have missing values. \n",
    "\n",
    "We can also view the summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.50000</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>34.05000</td>\n",
       "      <td>2.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.91608</td>\n",
       "      <td>1.424411</td>\n",
       "      <td>26.35881</td>\n",
       "      <td>1.503505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.75000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>13.50000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.50000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.50000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.25000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>4.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>85.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Business_ID   Location  Positive_review      Stars\n",
       "count     20.00000  20.000000         20.00000  20.000000\n",
       "mean      10.50000   2.850000         34.05000   2.950000\n",
       "std        5.91608   1.424411         26.35881   1.503505\n",
       "min        1.00000   1.000000          1.00000   1.000000\n",
       "25%        5.75000   2.000000         13.50000   2.000000\n",
       "50%       10.50000   3.000000         22.50000   3.000000\n",
       "75%       15.25000   4.000000         56.00000   4.250000\n",
       "max       20.00000   5.000000         85.00000   5.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "\n",
    "#or for an alternative view...\n",
    "#df.describe().transpose() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Time to do something about all these missing values.\n",
    "\n",
    "Let's look closer at one variable, 'air_temp_9am':\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df.shape()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "One approach is to drop the rows that contain the missing values.\n",
    "\n",
    "(notice that in order to avoid messing up with our original dataframe, we save the resulting dataset in a new dataframe df2) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df2 = df.dropna(subset=['years'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see the total number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#df2.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's clear the entire data frame from missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df3 = df.dropna()\n",
    "#df3.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How was the dataset affected by this? Let's look at the mean and standard deviation of the attributes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df3.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation\n",
    "\n",
    "An alternative approach to missing values is to impute them. \n",
    "\n",
    "The following snippet demonstrates how to replace missing values, encoded as np.nan, using the mean value of the column (axis 0) that contain the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "\n",
    "imputed_DF = pd.DataFrame(imp.fit_transform(df))\n",
    "imputed_DF.columns = df.columns\n",
    "imputed_DF.index = df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how much did the attributes' stats change..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Business_ID  Location  Positive_review  Stars\n",
       "0           1.0       2.0             50.0    3.0\n",
       "1           2.0       3.0              1.0    2.0\n",
       "2           3.0       2.0              6.0    5.0\n",
       "3           4.0       5.0             15.0    1.0\n",
       "4           5.0       1.0             85.0    5.0\n",
       "5           6.0       4.0             12.0    3.0\n",
       "6           7.0       2.0             65.0    5.0\n",
       "7           8.0       3.0             12.0    4.0\n",
       "8           9.0       5.0             45.0    1.0\n",
       "9          10.0       1.0             10.0    2.0\n",
       "10         11.0       5.0             15.0    3.0\n",
       "11         12.0       4.0             20.0    5.0\n",
       "12         13.0       5.0             14.0    1.0\n",
       "13         14.0       1.0             25.0    2.0\n",
       "14         15.0       2.0             54.0    3.0\n",
       "15         16.0       3.0             62.0    2.0\n",
       "16         17.0       2.0             78.0    2.0\n",
       "17         18.0       3.0             32.0    4.0\n",
       "18         19.0       1.0             15.0    1.0\n",
       "19         20.0       3.0             65.0    5.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# STILL NEED TO DO:\n",
    "\n",
    "# This will fill in the missing values in the CSV, so we somehow need to use this imputed dataframe to work with for the \n",
    "# rest of the code\n",
    "\n",
    "#imputed_DF.describe()\n",
    "imputed_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#n_stuff = df.Business_ID.unique().shape[0]\n",
    "#print (\"Numer of bussiness ids: \" + str(n_stuff))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBtgMMINMBjhBhiMcAMMRrgBBvN/5GcqwGKzrxAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113569610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Show the layout for the location column, I did this just to see what it looks like (Note: we can show all the column values) \n",
    "plt.hist(imputed_DF[\"Location\"])\n",
    "plt.hist(imputed_DF[\"Stars\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Set the clusters = 5\n",
    "kmeans_model = KMeans(n_clusters=5, random_state=1)\n",
    "\n",
    "# good_columns = df._get_numberic_data()\n",
    "# Fit the model with the dataframe df\n",
    "kmeans_model.fit(df)\n",
    "\n",
    "#Get the cluster assignments\n",
    "labels = kmeans_model.labels_\n",
    "\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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIikUDGIiEgKFYOIiKT4P44BsqkBuVMiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11718e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#############just coloumn\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "# Reduce the dimensionality of data without losing data\n",
    "# Use Principal Component Analysis to accomplish\n",
    "pca_2 = PCA(2)\n",
    "\n",
    "# Fit the PCA model with the dataframe\n",
    "plot_columns = pca_2.fit_transform(df)\n",
    "plt.scatter(x=plot_columns[:,0], y=plot_columns[:,1], c=labels)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Show the columns that have a strong correlation to the NEW_location_TEST (We might not need to do this for our project, \n",
    "# since, we are using the reviews, stars, and locations as our columns)\n",
    "#df.corr()[\"NEW_location_TEST\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Location', 'Positive_review', 'Stars']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This just gets the columns that we need, and removes the one we don't (i.e. only use location, reviews, and starts)\n",
    "columns = df.columns.tolist()\n",
    "columns = [c for c in columns if c not in [\"Business_ID\"]]\n",
    "\n",
    "#Print out the contents \n",
    "columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create new csv file and show test data\n",
    "#Right now I am only using target=\"NEW_location_TEST\" as the target variable which is what we will predict to\n",
    "target = \"NEW_location_TEST\"\n",
    "\n",
    "# This is what we actually need to set it to, so basically just cross out target=\"NEW_location_TEST\" and put target = \"NEW_stars\")\n",
    "target1 = \"NEW_stars\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 4)\n",
      "(4, 4)\n",
      "(10, 4)\n",
      "(10, 4)\n",
      "(18, 4)\n",
      "(2, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Train our dataset. It is split up into 80% training and 20% testing\n",
    "train = imputed_DF.sample(frac=0.8, random_state=1)\n",
    "\n",
    "# TO DO: df.dropna() business id ...........................\n",
    "train1 = df.sample(frac=0.5, random_state=1)\n",
    "train2 = df.sample(frac=0.9, random_state=1)\n",
    "\n",
    "# This line of code just gets whatever is not in the training set (i.e. 20%)\n",
    "test = df.loc[~df.index.isin(train.index)]\n",
    "test1 = df.loc[~df.index.isin(train1.index)]\n",
    "test2 = df.loc[~df.index.isin(train2.index)]\n",
    "\n",
    "# Print out the training set. (16, 6) means 16 rows and 6 columns (80%)\n",
    "print(train.shape)\n",
    "\n",
    "# Print out the test set. (4,6) means 4 rows and 6 columns (20%)\n",
    "print(test.shape)\n",
    "\n",
    "print(train1.shape)\n",
    "\n",
    "print(test1.shape)\n",
    "\n",
    "print(train2.shape)\n",
    "\n",
    "print(test2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Business_ID  Location  Positive_review  Stars\n",
       "3           4.0       5.0             15.0    1.0\n",
       "16         17.0       2.0             78.0    2.0\n",
       "6           7.0       2.0             65.0    5.0\n",
       "10         11.0       5.0             15.0    3.0\n",
       "2           3.0       2.0              6.0    5.0\n",
       "14         15.0       2.0             54.0    3.0\n",
       "4           5.0       1.0             85.0    5.0\n",
       "17         18.0       3.0             32.0    4.0\n",
       "7           8.0       3.0             12.0    4.0\n",
       "1           2.0       3.0              1.0    2.0\n",
       "13         14.0       1.0             25.0    2.0\n",
       "0           1.0       2.0             50.0    3.0\n",
       "19         20.0       3.0             65.0    5.0\n",
       "18         19.0       1.0             15.0    1.0\n",
       "9          10.0       1.0             10.0    2.0\n",
       "15         16.0       3.0             62.0    2.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the actual contents of the training set\n",
    "train\n",
    "\n",
    "# from flask import Flask\n",
    "# app = Flask(__name__)\n",
    "\n",
    "# @app.route(\"/\")\n",
    "# def hello():\n",
    "#     return \"test2.shape\"\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     app.run()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Business_ID  Location  Positive_review  Stars\n",
       "5             6         4               12      3\n",
       "8             9         5               45      1\n",
       "11           12         4               20      5\n",
       "12           13         5               14      1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the actual contents of the test set\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Business_ID  Location  Positive_review  Stars\n",
       "3             4         5               15      1\n",
       "16           17         2               78      2\n",
       "6             7         2               65      5\n",
       "10           11         5               15      3\n",
       "2             3         2                6      5\n",
       "14           15         2               54      3\n",
       "4             5         1               85      5\n",
       "17           18         3               32      4\n",
       "7             8         3               12      4\n",
       "1             2         3                1      2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Business_ID  Location  Positive_review  Stars\n",
       "0             1         2               50      3\n",
       "5             6         4               12      3\n",
       "8             9         5               45      1\n",
       "9            10         1               10      2\n",
       "11           12         4               20      5\n",
       "12           13         5               14      1\n",
       "13           14         1               25      2\n",
       "15           16         3               62      2\n",
       "18           19         1               15      1\n",
       "19           20         3               65      5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Business_ID  Location  Positive_review  Stars\n",
       "3             4         5               15      1\n",
       "16           17         2               78      2\n",
       "6             7         2               65      5\n",
       "10           11         5               15      3\n",
       "2             3         2                6      5\n",
       "14           15         2               54      3\n",
       "4             5         1               85      5\n",
       "17           18         3               32      4\n",
       "7             8         3               12      4\n",
       "1             2         3                1      2\n",
       "13           14         1               25      2\n",
       "0             1         2               50      3\n",
       "19           20         3               65      5\n",
       "18           19         1               15      1\n",
       "9            10         1               10      2\n",
       "15           16         3               62      2\n",
       "8             9         5               45      1\n",
       "12           13         5               14      1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Positive_review</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Business_ID  Location  Positive_review  Stars\n",
       "5             6         4               12      3\n",
       "11           12         4               20      5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'NEW_location_TEST'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-cdbf5483ae79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Fit the linear regression model with the training set (Note: columns = \"reviews, location, stars\"), target = \"NEW_location_test\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mmodel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2057\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2058\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2059\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2064\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2065\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2066\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2068\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1384\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1385\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1386\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1387\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3542\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3543\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3544\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3545\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2134\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2135\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2136\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4433)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'NEW_location_TEST'"
     ]
    }
   ],
   "source": [
    "# We will use linear regressiong model to predict for our target variable\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Create a linear regression model\n",
    "model = LinearRegression()\n",
    "model1 = LinearRegression()\n",
    "model2 = LinearRegression()\n",
    "\n",
    "# Fit the linear regression model with the training set (Note: columns = \"reviews, location, stars\"), target = \"NEW_location_test\"\n",
    "model.fit(train[columns], train[target])\n",
    "model1.fit(train1[columns], train1[target])\n",
    "model2.fit(train2[columns], train2[target])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "# Now call predict on the trained model and pass in the test set\n",
    "predictions = model.predict(test[columns]) #three different cases 80%\n",
    "predictions1 = model1.predict(test1[columns]) #three different cases 50%\n",
    "predictions2 = model2.predict(test2[columns])  #three different cases\n",
    "\n",
    "# This shows the 4 outputs for the NEW_location_TEST column\n",
    "print(\"Linear Regression output for first model is:\\n\", predictions)\n",
    "plt.plot(predictions)\n",
    "plt.show()\n",
    "\n",
    "#TO DO: put range case statements so we can assign stars accordingly\n",
    "\n",
    "#mean_squared_error(predictions, test[columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Linear Regression output for second model is:\\n\", predictions1)\n",
    "plt.plot(predictions1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Linear Regression output for third model is:\\n\", predictions2)\n",
    "plt.plot(predictions2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Print out the mean squared error\n",
    "mean_squared_error(predictions, test[target])\n",
    "mean_squared_error(predictions1, test1[target])\n",
    "mean_squared_error(predictions2, test2[target])\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Random Forest starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfmodel = RandomForestRegressor(n_estimators=10, min_samples_leaf=10, random_state=1)\n",
    "rfmodel1 = RandomForestRegressor(n_estimators=100, min_samples_leaf=20, random_state=1)\n",
    "rfmodel2 = RandomForestRegressor(n_estimators=50, min_samples_leaf=5, random_state=1)\n",
    "\n",
    "rfmodel.fit(train[columns], train[target])\n",
    "rfmodel1.fit(train1[columns], train1[target])\n",
    "rfmodel2.fit(train2[columns], train2[target])\n",
    "\n",
    "rfpredictions = rfmodel.predict(test[columns])\n",
    "rfpredictions1 = rfmodel1.predict(test1[columns])\n",
    "rfpredictions2 = rfmodel2.predict(test2[columns])\n",
    "\n",
    "print(\"Random Forest for first model output is:\\n\", rfpredictions)\n",
    "plt.plot(rfpredictions)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Random Forest for second model output is:\\n\", rfpredictions1)\n",
    "plt.plot(rfpredictions1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Random Forest for third model output is:\\n\", rfpredictions2)\n",
    "plt.plot(rfpredictions2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean_squared_error(rfpredictions, test[target])\n",
    "mean_squared_error(rfpredictions1, test1[target])\n",
    "mean_squared_error(rfpredictions2, test2[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(algorithm='auto', leaf_size=3, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
    "                     weights='uniform')\n",
    "knn1 = KNeighborsClassifier(algorithm='auto', leaf_size=4, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=2, p=2,\n",
    "                     weights='uniform')\n",
    "knn2 = KNeighborsClassifier(algorithm='auto', leaf_size=5, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
    "                     weights='uniform')\n",
    "\n",
    "\n",
    "\n",
    "knn.fit(train[columns], train[target]) \n",
    "knn1.fit(train1[columns], train1[target]) \n",
    "knn2.fit(train2[columns], train2[target]) \n",
    "\n",
    "knnpredictions = knn.predict(test[columns])\n",
    "knnpredictions1 = knn1.predict(test1[columns])\n",
    "knnpredictions2 = knn2.predict(test2[columns])\n",
    "\n",
    "\n",
    "# This shows the 4 outputs for the NEW_location_TEST column\n",
    "print(\"KNN Regression output for first model is:\\n\", knnpredictions)\n",
    "plt.plot(knnpredictions)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"KNN Regression output for second model is:\\n\", knnpredictions1)\n",
    "plt.plot(knnpredictions1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"KNN Regression output for third model is:\\n\", knnpredictions2)\n",
    "plt.plot(knnpredictions2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean_squared_error(knnpredictions, test[target])\n",
    "mean_squared_error(knnpredictions1, test1[target])\n",
    "mean_squared_error(knnpredictions2, test2[target])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}