modeling_final-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = pd.read_csv('trainingData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WAP001</th>\n",
       "      <th>WAP002</th>\n",
       "      <th>WAP003</th>\n",
       "      <th>WAP004</th>\n",
       "      <th>WAP005</th>\n",
       "      <th>WAP006</th>\n",
       "      <th>WAP007</th>\n",
       "      <th>WAP008</th>\n",
       "      <th>WAP009</th>\n",
       "      <th>WAP010</th>\n",
       "      <th>...</th>\n",
       "      <th>WAP520</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>FLOOR</th>\n",
       "      <th>BUILDINGID</th>\n",
       "      <th>SPACEID</th>\n",
       "      <th>RELATIVEPOSITION</th>\n",
       "      <th>USERID</th>\n",
       "      <th>PHONEID</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>-7541.2643</td>\n",
       "      <td>4.864921e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1371713733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>-7536.6212</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1371713691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-97</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>-7519.1524</td>\n",
       "      <td>4.864950e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1371714095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>-7524.5704</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1371713807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>-7632.1436</td>\n",
       "      <td>4.864982e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1369909710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 529 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WAP001  WAP002  WAP003  WAP004  WAP005  WAP006  WAP007  WAP008  WAP009  \\\n",
       "0     100     100     100     100     100     100     100     100     100   \n",
       "1     100     100     100     100     100     100     100     100     100   \n",
       "2     100     100     100     100     100     100     100     -97     100   \n",
       "3     100     100     100     100     100     100     100     100     100   \n",
       "4     100     100     100     100     100     100     100     100     100   \n",
       "\n",
       "   WAP010  ...  WAP520  LONGITUDE      LATITUDE  FLOOR  BUILDINGID  SPACEID  \\\n",
       "0     100  ...     100 -7541.2643  4.864921e+06      2           1      106   \n",
       "1     100  ...     100 -7536.6212  4.864934e+06      2           1      106   \n",
       "2     100  ...     100 -7519.1524  4.864950e+06      2           1      103   \n",
       "3     100  ...     100 -7524.5704  4.864934e+06      2           1      102   \n",
       "4     100  ...     100 -7632.1436  4.864982e+06      0           0      122   \n",
       "\n",
       "   RELATIVEPOSITION  USERID  PHONEID   TIMESTAMP  \n",
       "0                 2       2       23  1371713733  \n",
       "1                 2       2       23  1371713691  \n",
       "2                 2       2       23  1371714095  \n",
       "3                 2       2       23  1371713807  \n",
       "4                 2      11       13  1369909710  \n",
       "\n",
       "[5 rows x 529 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df.drop(columns=['PHONEID','TIMESTAMP','SPACEID','RELATIVEPOSITION'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WAP001</th>\n",
       "      <th>WAP002</th>\n",
       "      <th>WAP003</th>\n",
       "      <th>WAP004</th>\n",
       "      <th>WAP005</th>\n",
       "      <th>WAP006</th>\n",
       "      <th>WAP007</th>\n",
       "      <th>WAP008</th>\n",
       "      <th>WAP009</th>\n",
       "      <th>WAP010</th>\n",
       "      <th>...</th>\n",
       "      <th>WAP516</th>\n",
       "      <th>WAP517</th>\n",
       "      <th>WAP518</th>\n",
       "      <th>WAP519</th>\n",
       "      <th>WAP520</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>FLOOR</th>\n",
       "      <th>BUILDINGID</th>\n",
       "      <th>USERID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-7541.2643</td>\n",
       "      <td>4.864921e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-7536.6212</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-97</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-7519.1524</td>\n",
       "      <td>4.864950e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-7524.5704</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-7632.1436</td>\n",
       "      <td>4.864982e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 525 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WAP001  WAP002  WAP003  WAP004  WAP005  WAP006  WAP007  WAP008  WAP009  \\\n",
       "0     100     100     100     100     100     100     100     100     100   \n",
       "1     100     100     100     100     100     100     100     100     100   \n",
       "2     100     100     100     100     100     100     100     -97     100   \n",
       "3     100     100     100     100     100     100     100     100     100   \n",
       "4     100     100     100     100     100     100     100     100     100   \n",
       "\n",
       "   WAP010  ...  WAP516  WAP517  WAP518  WAP519  WAP520  LONGITUDE  \\\n",
       "0     100  ...     100     100     100     100     100 -7541.2643   \n",
       "1     100  ...     100     100     100     100     100 -7536.6212   \n",
       "2     100  ...     100     100     100     100     100 -7519.1524   \n",
       "3     100  ...     100     100     100     100     100 -7524.5704   \n",
       "4     100  ...     100     100     100     100     100 -7632.1436   \n",
       "\n",
       "       LATITUDE  FLOOR  BUILDINGID  USERID  \n",
       "0  4.864921e+06      2           1       2  \n",
       "1  4.864934e+06      2           1       2  \n",
       "2  4.864950e+06      2           1       2  \n",
       "3  4.864934e+06      2           1       2  \n",
       "4  4.864982e+06      0           0      11  \n",
       "\n",
       "[5 rows x 525 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dad88a8780>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(full_df['LONGITUDE'], full_df['LATITUDE'], 'go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = full_df[full_df.columns[full_df.sum() !=1993700]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df.iloc[:,:-5] = abs(full_df.iloc[:,:-5])+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change \"No signal\" values (101) to \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df.replace(101,0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WAP001</th>\n",
       "      <th>WAP002</th>\n",
       "      <th>WAP005</th>\n",
       "      <th>WAP006</th>\n",
       "      <th>WAP007</th>\n",
       "      <th>WAP008</th>\n",
       "      <th>WAP009</th>\n",
       "      <th>WAP010</th>\n",
       "      <th>WAP011</th>\n",
       "      <th>WAP012</th>\n",
       "      <th>...</th>\n",
       "      <th>WAP515</th>\n",
       "      <th>WAP516</th>\n",
       "      <th>WAP517</th>\n",
       "      <th>WAP518</th>\n",
       "      <th>WAP519</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>FLOOR</th>\n",
       "      <th>BUILDINGID</th>\n",
       "      <th>USERID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7541.2643</td>\n",
       "      <td>4.864921e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7536.6212</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7519.1524</td>\n",
       "      <td>4.864950e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7524.5704</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7632.1436</td>\n",
       "      <td>4.864982e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 470 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WAP001  WAP002  WAP005  WAP006  WAP007  WAP008  WAP009  WAP010  WAP011  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0      98       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   WAP012  ...  WAP515  WAP516  WAP517  WAP518  WAP519  LONGITUDE  \\\n",
       "0       0  ...       0       0       0       0       0 -7541.2643   \n",
       "1       0  ...       0       0       0       0       0 -7536.6212   \n",
       "2       0  ...       0       0       0       0       0 -7519.1524   \n",
       "3       0  ...       0       0       0       0       0 -7524.5704   \n",
       "4       0  ...       0       0       0       0       0 -7632.1436   \n",
       "\n",
       "       LATITUDE  FLOOR  BUILDINGID  USERID  \n",
       "0  4.864921e+06      2           1       2  \n",
       "1  4.864934e+06      2           1       2  \n",
       "2  4.864950e+06      2           1       2  \n",
       "3  4.864934e+06      2           1       2  \n",
       "4  4.864982e+06      0           0      11  \n",
       "\n",
       "[5 rows x 470 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change coordinates to range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WAP001</th>\n",
       "      <th>WAP002</th>\n",
       "      <th>WAP005</th>\n",
       "      <th>WAP006</th>\n",
       "      <th>WAP007</th>\n",
       "      <th>WAP008</th>\n",
       "      <th>WAP009</th>\n",
       "      <th>WAP010</th>\n",
       "      <th>WAP011</th>\n",
       "      <th>WAP012</th>\n",
       "      <th>...</th>\n",
       "      <th>WAP517</th>\n",
       "      <th>WAP518</th>\n",
       "      <th>WAP519</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>FLOOR</th>\n",
       "      <th>BUILDINGID</th>\n",
       "      <th>USERID</th>\n",
       "      <th>ABS_LONGITUDE</th>\n",
       "      <th>ABS_LATITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7541.2643</td>\n",
       "      <td>4.864921e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150.0741</td>\n",
       "      <td>175.033184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7536.6212</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>154.7172</td>\n",
       "      <td>188.480184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7519.1524</td>\n",
       "      <td>4.864950e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172.1860</td>\n",
       "      <td>203.787184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7524.5704</td>\n",
       "      <td>4.864934e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>166.7680</td>\n",
       "      <td>188.347984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7632.1436</td>\n",
       "      <td>4.864982e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>59.1948</td>\n",
       "      <td>236.472084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 472 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WAP001  WAP002  WAP005  WAP006  WAP007  WAP008  WAP009  WAP010  WAP011  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0      98       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   WAP012  ...  WAP517  WAP518  WAP519  LONGITUDE      LATITUDE  FLOOR  \\\n",
       "0       0  ...       0       0       0 -7541.2643  4.864921e+06      2   \n",
       "1       0  ...       0       0       0 -7536.6212  4.864934e+06      2   \n",
       "2       0  ...       0       0       0 -7519.1524  4.864950e+06      2   \n",
       "3       0  ...       0       0       0 -7524.5704  4.864934e+06      2   \n",
       "4       0  ...       0       0       0 -7632.1436  4.864982e+06      0   \n",
       "\n",
       "   BUILDINGID  USERID  ABS_LONGITUDE  ABS_LATITUDE  \n",
       "0           1       2       150.0741    175.033184  \n",
       "1           1       2       154.7172    188.480184  \n",
       "2           1       2       172.1860    203.787184  \n",
       "3           1       2       166.7680    188.347984  \n",
       "4           0      11        59.1948    236.472084  \n",
       "\n",
       "[5 rows x 472 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert Longitude and Latitude values to absolute values\n",
    "\n",
    "# Training Data\n",
    "full_df['ABS_LONGITUDE'] = full_df['LONGITUDE'] - full_df['LONGITUDE'].min()\n",
    "full_df['ABS_LATITUDE'] = full_df['LATITUDE'] - full_df['LATITUDE'].min()\n",
    "full_df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390.5194099061191\n",
      "270.94278402719647\n"
     ]
    }
   ],
   "source": [
    "print (full_df['ABS_LONGITUDE'].max())\n",
    "print (full_df['ABS_LATITUDE'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1da81708d68>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(full_df['ABS_LONGITUDE'], full_df['ABS_LATITUDE'], 'go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "WAPS = full_df.iloc[:,:-7].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WAPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Longitude with KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = WAPS\n",
    "y = full_df['ABS_LONGITUDE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = neighbors.KNeighborsRegressor(n_neighbors = 5)\n",
    "model.fit(X_train, y_train)  #fit the model\n",
    "pred=model.predict(X_test) #make prediction on test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE is:  11.028721660213957\n"
     ]
    }
   ],
   "source": [
    "error = sqrt(mse) #calculate rmse\n",
    "print('RMSE is: ',error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared is:  0.991878549638283\n"
     ]
    }
   ],
   "source": [
    "print(\"R-Squared is: \", r2_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([276.46493001, 347.49418   ,   8.51994   , ..., 352.08086   ,\n",
       "       361.24576439, 346.60130863])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Longitude with KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = full_df['ABS_LATITUDE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = neighbors.KNeighborsRegressor(n_neighbors = 5)\n",
    "model.fit(X_train, y_train)  #fit the model\n",
    "pred2=model.predict(X_test) #make prediction on test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse2 = mean_squared_error(y_test,pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE is:  7.252867262450976\n"
     ]
    }
   ],
   "source": [
    "error = sqrt(mse2) #calculate rmse\n",
    "print('RMSE is: ',error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared is:  0.9882971345939633\n"
     ]
    }
   ],
   "source": [
    "print(\"R-Squared is: \", r2_score(y_test,pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 17.70644403, 211.71660413, 188.09890403, ...,   6.72755042,\n",
       "        97.41232195, 166.37846403])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1da9408a9e8>]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pred2,pred,'go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}