Cell Analysis & Prediction-TPOT-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries and load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1330,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['atoll_schema', 'site_version', 'site_name', 'fuze_site_id',\n",
       "       'site_record_id', 'latitude_degrees', 'longitude_degrees',\n",
       "       'street_address', 'city', 'county', 'state', 'zip_code', 'sector',\n",
       "       'azimuth', 'antenna_model', 'antenna_manufacturer', 'antenna_max_gain',\n",
       "       'antenna_centerline', 'lte_market', 'enodeb_id', 'max_power',\n",
       "       'main_propagation_model', 'radio_manufacturer', 'radio_model',\n",
       "       'extended_calculation_radius', 'enodeb_eutrancellfdd', 'channel_number',\n",
       "       'pci', 'antenna_h_bw', 'mechanical_tilt', 'electrical_tilt', 'ret',\n",
       "       'altitude_amsl', 'tac', 'cell_id', 'technology', 'band_class',\n",
       "       'trans_cell_type', 'site_record_name', 'site_number', 'site_code',\n",
       "       'location_name', 'fips_county_code', 'search_ring_id',\n",
       "       'peoplesoft_location_code', 'site_on_air_date', 'c_ran_hub_name',\n",
       "       'address_description', 'network_territory', 'network_market',\n",
       "       'network_sub_market', 'network_local_market', 'site_altitude_amsl',\n",
       "       'site_surveyed_altitude_amsl', 'site_support_structure_height',\n",
       "       'site_structure_type', 'temporary_site', 'lra_partner',\n",
       "       'network_extender_capable', 'spm_project_id', 'band_info', 'channel_bw',\n",
       "       'combined_cell_code', 'antenna_latitude', 'antenna_longitude',\n",
       "       'number_of_tx_antenna_ports', 'number_of_rx_antenna_ports',\n",
       "       'tx_record_id', 'xmtr_location', 'duplex_type', 'coverage_type',\n",
       "       'threshold', 'fcc_market_number', 'sum'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tmp_alb_tx_sums_202004021132.csv')\n",
    "df.site_version.astype('category')\n",
    "df= df[df['site_version']== '0000']\n",
    "#check the decoration\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First things first: analysing 'Sum'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = df.corr()\n",
    "f, ax = plt.subplots(figsize=(20, 15))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sum correlation matrix\n",
    "k = 10 #number of variables for heatmap\n",
    "cols = corrmat.nlargest(k, 'sum')['sum'].index\n",
    "cm = np.corrcoef(df[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "f, ax = plt.subplots(figsize=(20, 15))\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 20}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We see that 'antenna_centerline','extended_calculation_radius', 'site_support_structure_height' are somewhat correlated with the'Sum'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['atoll_schema',\n",
       " 'site_version',\n",
       " 'fuze_site_id',\n",
       " 'site_record_id',\n",
       " 'street_address',\n",
       " 'city',\n",
       " 'zip_code',\n",
       " 'sector',\n",
       " 'antenna_manufacturer',\n",
       " 'lte_market',\n",
       " 'enodeb_id',\n",
       " 'radio_manufacturer',\n",
       " 'enodeb_eutrancellfdd',\n",
       " 'channel_number',\n",
       " 'cell_id',\n",
       " 'technology',\n",
       " 'site_record_name',\n",
       " 'site_number',\n",
       " 'site_code',\n",
       " 'location_name',\n",
       " 'fips_county_code',\n",
       " 'search_ring_id',\n",
       " 'peoplesoft_location_code',\n",
       " 'site_on_air_date',\n",
       " 'c_ran_hub_name',\n",
       " 'address_description',\n",
       " 'network_territory',\n",
       " 'network_market',\n",
       " 'network_sub_market',\n",
       " 'network_local_market',\n",
       " 'site_structure_type',\n",
       " 'temporary_site',\n",
       " 'lra_partner',\n",
       " 'spm_project_id',\n",
       " 'combined_cell_code',\n",
       " 'tx_record_id',\n",
       " 'xmtr_location',\n",
       " 'duplex_type',\n",
       " 'coverage_type',\n",
       " 'threshold',\n",
       " 'fcc_market_number']"
      ]
     },
     "execution_count": 1334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns to be deleted\n",
    "['atoll_schema', 'site_version','fuze_site_id','site_record_id','street_address', 'city','zip_code', 'sector','antenna_manufacturer'\n",
    ",'lte_market', 'enodeb_id','radio_manufacturer','enodeb_eutrancellfdd', 'channel_number','cell_id', 'technology','site_record_name', 'site_number', 'site_code',\n",
    "'location_name', 'fips_county_code', 'search_ring_id','peoplesoft_location_code', 'site_on_air_date', 'c_ran_hub_name'\n",
    ",'address_description','network_territory', 'network_market','network_sub_market', 'network_local_market','site_structure_type','temporary_site','lra_partner',\n",
    "'spm_project_id','combined_cell_code','tx_record_id','xmtr_location', 'duplex_type', 'coverage_type',\n",
    "'threshold', 'fcc_market_number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.137000e+03\n",
       "mean     5.685331e+08\n",
       "std      1.227489e+09\n",
       "min      9.001435e+02\n",
       "25%      3.005578e+07\n",
       "50%      1.627407e+08\n",
       "75%      5.791687e+08\n",
       "max      2.926708e+10\n",
       "Name: sum, dtype: float64"
      ]
     },
     "execution_count": 1335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sum'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1336,
   "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>Total Missing Count</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>fcc_market_number</td>\n",
       "      <td>5137</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c_ran_hub_name</td>\n",
       "      <td>5012</td>\n",
       "      <td>97.566673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>site_on_air_date</td>\n",
       "      <td>4716</td>\n",
       "      <td>91.804555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>site_structure_type</td>\n",
       "      <td>4696</td>\n",
       "      <td>91.415223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>search_ring_id</td>\n",
       "      <td>4659</td>\n",
       "      <td>90.694958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>threshold</td>\n",
       "      <td>4647</td>\n",
       "      <td>90.461359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>coverage_type</td>\n",
       "      <td>4647</td>\n",
       "      <td>90.461359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duplex_type</td>\n",
       "      <td>4647</td>\n",
       "      <td>90.461359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>xmtr_location</td>\n",
       "      <td>4647</td>\n",
       "      <td>90.461359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>address_description</td>\n",
       "      <td>4470</td>\n",
       "      <td>87.015768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>spm_project_id</td>\n",
       "      <td>3881</td>\n",
       "      <td>75.549932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radio_manufacturer</td>\n",
       "      <td>2607</td>\n",
       "      <td>50.749465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ret</td>\n",
       "      <td>1634</td>\n",
       "      <td>31.808449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>altitude_amsl</td>\n",
       "      <td>1139</td>\n",
       "      <td>22.172474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>site_altitude_amsl</td>\n",
       "      <td>1139</td>\n",
       "      <td>22.172474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lra_partner</td>\n",
       "      <td>307</td>\n",
       "      <td>5.976251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>peoplesoft_location_code</td>\n",
       "      <td>94</td>\n",
       "      <td>1.829862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fuze_site_id</td>\n",
       "      <td>64</td>\n",
       "      <td>1.245863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>site_surveyed_altitude_amsl</td>\n",
       "      <td>64</td>\n",
       "      <td>1.245863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>street_address</td>\n",
       "      <td>35</td>\n",
       "      <td>0.681332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mechanical_tilt</td>\n",
       "      <td>27</td>\n",
       "      <td>0.525599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trans_cell_type</td>\n",
       "      <td>25</td>\n",
       "      <td>0.486665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>enodeb_eutrancellfdd</td>\n",
       "      <td>20</td>\n",
       "      <td>0.389332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cell_id</td>\n",
       "      <td>17</td>\n",
       "      <td>0.330932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>location_name</td>\n",
       "      <td>10</td>\n",
       "      <td>0.194666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>network_territory</td>\n",
       "      <td>9</td>\n",
       "      <td>0.175200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>network_local_market</td>\n",
       "      <td>9</td>\n",
       "      <td>0.175200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>network_market</td>\n",
       "      <td>9</td>\n",
       "      <td>0.175200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>network_sub_market</td>\n",
       "      <td>9</td>\n",
       "      <td>0.175200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fips_county_code</td>\n",
       "      <td>9</td>\n",
       "      <td>0.175200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>enodeb_id</td>\n",
       "      <td>7</td>\n",
       "      <td>0.136266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lte_market</td>\n",
       "      <td>7</td>\n",
       "      <td>0.136266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>antenna_h_bw</td>\n",
       "      <td>7</td>\n",
       "      <td>0.136266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>antenna_manufacturer</td>\n",
       "      <td>7</td>\n",
       "      <td>0.136266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>city</td>\n",
       "      <td>6</td>\n",
       "      <td>0.116800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>tac</td>\n",
       "      <td>5</td>\n",
       "      <td>0.097333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sector</td>\n",
       "      <td>3</td>\n",
       "      <td>0.058400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Total Missing Count     Percent\n",
       "fcc_market_number                           5137  100.000000\n",
       "c_ran_hub_name                              5012   97.566673\n",
       "site_on_air_date                            4716   91.804555\n",
       "site_structure_type                         4696   91.415223\n",
       "search_ring_id                              4659   90.694958\n",
       "threshold                                   4647   90.461359\n",
       "coverage_type                               4647   90.461359\n",
       "duplex_type                                 4647   90.461359\n",
       "xmtr_location                               4647   90.461359\n",
       "address_description                         4470   87.015768\n",
       "spm_project_id                              3881   75.549932\n",
       "radio_manufacturer                          2607   50.749465\n",
       "ret                                         1634   31.808449\n",
       "altitude_amsl                               1139   22.172474\n",
       "site_altitude_amsl                          1139   22.172474\n",
       "lra_partner                                  307    5.976251\n",
       "peoplesoft_location_code                      94    1.829862\n",
       "fuze_site_id                                  64    1.245863\n",
       "site_surveyed_altitude_amsl                   64    1.245863\n",
       "street_address                                35    0.681332\n",
       "mechanical_tilt                               27    0.525599\n",
       "trans_cell_type                               25    0.486665\n",
       "enodeb_eutrancellfdd                          20    0.389332\n",
       "cell_id                                       17    0.330932\n",
       "location_name                                 10    0.194666\n",
       "network_territory                              9    0.175200\n",
       "network_local_market                           9    0.175200\n",
       "network_market                                 9    0.175200\n",
       "network_sub_market                             9    0.175200\n",
       "fips_county_code                               9    0.175200\n",
       "enodeb_id                                      7    0.136266\n",
       "lte_market                                     7    0.136266\n",
       "antenna_h_bw                                   7    0.136266\n",
       "antenna_manufacturer                           7    0.136266\n",
       "city                                           6    0.116800\n",
       "tac                                            5    0.097333\n",
       "sector                                         3    0.058400"
      ]
     },
     "execution_count": 1336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing data\n",
    "total = df.isnull().sum().sort_values(ascending=False)\n",
    "percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent*100], axis=1, keys=['Total Missing Count', 'Percent'])\n",
    "missing_data[missing_data['Total Missing Count'] !=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percent missing data by feature')"
      ]
     },
     "execution_count": 1337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(15, 12))\n",
    "plt.xticks(rotation='90')\n",
    "sns.barplot(x=percent.index, y=percent)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.ylabel('Percent of missing values', fontsize=15)\n",
    "plt.title('Percent missing data by feature', fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1338,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dealing with missing data\n",
    "df = df.drop((missing_data[missing_data['Total Missing Count'] > 1000]).index,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1339,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to treat missing values\n",
    "def fill_na_with_mean_or_mode(col):\n",
    "    if col.dtype == int or col.dtype == float:\n",
    "        return col.fillna(round(col.median()), inplace=True)\n",
    "    else:\n",
    "        return col.fillna(col.mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#passed my function through a list comprehension so i could fill all null columns with one line of code\n",
    "[fill_na_with_mean_or_mode(df[col]) for col in ['lra_partner','peoplesoft_location_code','fuze_site_id','site_surveyed_altitude_amsl',\n",
    "                                                'street_address','trans_cell_type','mechanical_tilt','enodeb_eutrancellfdd','cell_id',\n",
    "                                               'location_name','network_market','fips_county_code','network_territory','network_local_market',\n",
    "                                               'network_sub_market','lte_market','enodeb_id','antenna_h_bw','antenna_manufacturer','tac',\n",
    "                                               'city','sector','max_power','channel_bw','band_class','channel_number','pci']];\n",
    "df.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In summary, to handle missing data, we deleted all the variables with missing data >1000 and for remaining we replaced with median/mode values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1341,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleting data points which are outliers\n",
    "#df.sort_values(by = 'sum', ascending = False)[:2]\n",
    "df = df.drop(df[df['fuze_site_id'] == 480809.0].index)\n",
    "#df.sort_values(by = 'site_support_structure_height', ascending = False)[:3]\n",
    "df = df.drop(df[df['fuze_site_id'] == 355319.0].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The max value with highest 'sum' seem strange and they are not following the crowd. We can speculate why this is happening.Therefore, we'll define them as outliers and delete them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows     :  5128\n",
      "Columns  :  59\n"
     ]
    }
   ],
   "source": [
    "print (\"Rows     : \" ,df.shape[0])\n",
    "print (\"Columns  : \" ,df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['atoll_schema', 'site_version', 'site_name', 'street_address', 'city',\n",
       "       'county', 'state', 'antenna_model', 'antenna_manufacturer',\n",
       "       'main_propagation_model', 'radio_model', 'enodeb_eutrancellfdd',\n",
       "       'technology', 'band_class', 'trans_cell_type', 'site_record_name',\n",
       "       'site_number', 'site_code', 'location_name', 'network_territory',\n",
       "       'network_market', 'network_sub_market', 'network_local_market',\n",
       "       'temporary_site', 'lra_partner', 'network_extender_capable',\n",
       "       'band_info', 'combined_cell_code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(object).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['atoll_schema',\n",
       " 'site_version',\n",
       " 'site_name',\n",
       " 'fuze_site_id',\n",
       " 'site_record_id',\n",
       " 'street_address',\n",
       " 'city',\n",
       " 'zip_code',\n",
       " 'sector',\n",
       " 'antenna_manufacturer',\n",
       " 'lte_market',\n",
       " 'enodeb_id',\n",
       " 'radio_manufacturer',\n",
       " 'enodeb_eutrancellfdd',\n",
       " 'channel_number',\n",
       " 'cell_id',\n",
       " 'technology',\n",
       " 'site_record_name',\n",
       " 'site_number',\n",
       " 'site_code',\n",
       " 'location_name',\n",
       " 'fips_county_code',\n",
       " 'search_ring_id',\n",
       " 'peoplesoft_location_code',\n",
       " 'site_on_air_date',\n",
       " 'c_ran_hub_name',\n",
       " 'address_description',\n",
       " 'network_territory',\n",
       " 'network_market',\n",
       " 'network_sub_market',\n",
       " 'network_local_market',\n",
       " 'site_structure_type',\n",
       " 'temporary_site',\n",
       " 'lra_partner',\n",
       " 'spm_project_id',\n",
       " 'combined_cell_code',\n",
       " 'tx_record_id',\n",
       " 'xmtr_location',\n",
       " 'duplex_type',\n",
       " 'coverage_type',\n",
       " 'threshold',\n",
       " 'fcc_market_number']"
      ]
     },
     "execution_count": 1344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns to be deleted\n",
    "['atoll_schema', 'site_version','site_name','fuze_site_id','site_record_id','street_address', 'city','zip_code', 'sector','antenna_manufacturer'\n",
    ",'lte_market', 'enodeb_id','radio_manufacturer','enodeb_eutrancellfdd', 'channel_number','cell_id', 'technology','site_record_name', 'site_number', 'site_code',\n",
    "'location_name', 'fips_county_code', 'search_ring_id','peoplesoft_location_code', 'site_on_air_date', 'c_ran_hub_name'\n",
    ",'address_description','network_territory', 'network_market','network_sub_market', 'network_local_market','site_structure_type','temporary_site','lra_partner',\n",
    "'spm_project_id','combined_cell_code','tx_record_id','xmtr_location', 'duplex_type', 'coverage_type',\n",
    "'threshold', 'fcc_market_number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1345,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "\n",
    "categorical_columns=['county', 'antenna_model',\n",
    "       'main_propagation_model', 'radio_model',  'band_class', 'trans_cell_type', 'network_extender_capable',\n",
    "       'band_info']\n",
    "column_trans = make_column_transformer((OrdinalEncoder(), categorical_columns))\n",
    "df_transformed=column_trans.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1346,
   "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>county</th>\n",
       "      <th>antenna_model</th>\n",
       "      <th>main_propagation_model</th>\n",
       "      <th>radio_model</th>\n",
       "      <th>band_class</th>\n",
       "      <th>trans_cell_type</th>\n",
       "      <th>network_extender_capable</th>\n",
       "      <th>band_info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>941</td>\n",
       "      <td>5</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>217</td>\n",
       "      <td>16</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>938</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>669</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>938</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5123</td>\n",
       "      <td>29</td>\n",
       "      <td>120</td>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5124</td>\n",
       "      <td>29</td>\n",
       "      <td>120</td>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5125</td>\n",
       "      <td>15</td>\n",
       "      <td>448</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5126</td>\n",
       "      <td>15</td>\n",
       "      <td>448</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5127</td>\n",
       "      <td>15</td>\n",
       "      <td>448</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5128 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      county  antenna_model  main_propagation_model  radio_model  band_class  \\\n",
       "0         30            941                       5           79           2   \n",
       "1          7            217                      16           91           1   \n",
       "2         33            938                       4           98           1   \n",
       "3          2            669                      21            4           2   \n",
       "4         33            938                       4           98           1   \n",
       "...      ...            ...                     ...          ...         ...   \n",
       "5123      29            120                       5           46           2   \n",
       "5124      29            120                       5           46           2   \n",
       "5125      15            448                       3           98           5   \n",
       "5126      15            448                       3           98           5   \n",
       "5127      15            448                       3           98           5   \n",
       "\n",
       "      trans_cell_type  network_extender_capable  band_info  \n",
       "0                   2                         1         15  \n",
       "1                   2                         1          0  \n",
       "2                   2                         1          0  \n",
       "3                   2                         1         16  \n",
       "4                   2                         1          0  \n",
       "...               ...                       ...        ...  \n",
       "5123                2                         1         17  \n",
       "5124                2                         1         17  \n",
       "5125                2                         1          2  \n",
       "5126                2                         1          2  \n",
       "5127                2                         1          2  \n",
       "\n",
       "[5128 rows x 8 columns]"
      ]
     },
     "execution_count": 1346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trans = pd.DataFrame(df_transformed,columns=categorical_columns)\n",
    "#converting to integer datatype\n",
    "df_trans=df_trans.astype(int)\n",
    "df_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1347,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_number=df.select_dtypes(np.number)\n",
    "#df=pd.concat([df_number,df_trans],axis=1)\n",
    "#numnerical_columns=['latitude_degrees','longitude_degrees',\n",
    "#       'azimuth','antenna_max_gain', 'antenna_centerline','max_power', 'extended_calculation_radius', 'channel_number', 'pci',\n",
    "#       'antenna_h_bw', 'mechanical_tilt', 'electrical_tilt', 'tac','site_surveyed_altitude_amsl', 'site_support_structure_height',\n",
    "#       'channel_bw', 'antenna_latitude', 'antenna_longitude',\n",
    "#       'number_of_tx_antenna_ports', 'number_of_rx_antenna_ports', 'sum']\n",
    "#df_number = pd.DataFrame(df_number,columns=numnerical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1348,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reset_index()\n",
    "df.update(df_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'atoll_schema', 'site_version', 'site_name', 'fuze_site_id',\n",
       "       'site_record_id', 'latitude_degrees', 'longitude_degrees',\n",
       "       'street_address', 'city', 'county', 'state', 'zip_code', 'sector',\n",
       "       'azimuth', 'antenna_model', 'antenna_manufacturer', 'antenna_max_gain',\n",
       "       'antenna_centerline', 'lte_market', 'enodeb_id', 'max_power',\n",
       "       'main_propagation_model', 'radio_model', 'extended_calculation_radius',\n",
       "       'enodeb_eutrancellfdd', 'channel_number', 'pci', 'antenna_h_bw',\n",
       "       'mechanical_tilt', 'electrical_tilt', 'tac', 'cell_id', 'technology',\n",
       "       'band_class', 'trans_cell_type', 'site_record_name', 'site_number',\n",
       "       'site_code', 'location_name', 'fips_county_code',\n",
       "       'peoplesoft_location_code', 'network_territory', 'network_market',\n",
       "       'network_sub_market', 'network_local_market',\n",
       "       'site_surveyed_altitude_amsl', 'site_support_structure_height',\n",
       "       'temporary_site', 'lra_partner', 'network_extender_capable',\n",
       "       'band_info', 'channel_bw', 'combined_cell_code', 'antenna_latitude',\n",
       "       'antenna_longitude', 'number_of_tx_antenna_ports',\n",
       "       'number_of_rx_antenna_ports', 'tx_record_id', 'sum'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2048,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[['azimuth', 'antenna_model', 'antenna_max_gain',\n",
    "       'antenna_centerline', 'max_power',\n",
    "       'main_propagation_model', 'extended_calculation_radius',\n",
    "        'pci', 'antenna_h_bw',\n",
    "       'mechanical_tilt', 'electrical_tilt', 'tac',  \n",
    "       'site_surveyed_altitude_amsl', 'site_support_structure_height',\n",
    "       'band_info', 'channel_bw', 'sum']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2049,
   "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>azimuth</th>\n",
       "      <th>antenna_max_gain</th>\n",
       "      <th>antenna_centerline</th>\n",
       "      <th>max_power</th>\n",
       "      <th>main_propagation_model</th>\n",
       "      <th>extended_calculation_radius</th>\n",
       "      <th>pci</th>\n",
       "      <th>antenna_h_bw</th>\n",
       "      <th>tac</th>\n",
       "      <th>site_surveyed_altitude_amsl</th>\n",
       "      <th>site_support_structure_height</th>\n",
       "      <th>band_info</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>15.562</td>\n",
       "      <td>37.0</td>\n",
       "      <td>49.542425</td>\n",
       "      <td>5</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>287.0</td>\n",
       "      <td>81.75</td>\n",
       "      <td>6404.0</td>\n",
       "      <td>7177.6</td>\n",
       "      <td>41.000001</td>\n",
       "      <td>15</td>\n",
       "      <td>8.263681e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>13.300</td>\n",
       "      <td>38.0</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>16</td>\n",
       "      <td>29.999801</td>\n",
       "      <td>165.0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>6658.0</td>\n",
       "      <td>4189.0</td>\n",
       "      <td>45.011525</td>\n",
       "      <td>0</td>\n",
       "      <td>2.184412e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>13.540</td>\n",
       "      <td>120.0</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>285.0</td>\n",
       "      <td>79.75</td>\n",
       "      <td>6403.0</td>\n",
       "      <td>5439.0</td>\n",
       "      <td>231.059161</td>\n",
       "      <td>0</td>\n",
       "      <td>1.614895e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>16.000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>21</td>\n",
       "      <td>29.999801</td>\n",
       "      <td>202.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>6660.0</td>\n",
       "      <td>3623.5</td>\n",
       "      <td>197.050453</td>\n",
       "      <td>16</td>\n",
       "      <td>2.872214e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>165</td>\n",
       "      <td>13.540</td>\n",
       "      <td>120.0</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>286.0</td>\n",
       "      <td>79.75</td>\n",
       "      <td>6403.0</td>\n",
       "      <td>5439.0</td>\n",
       "      <td>231.059161</td>\n",
       "      <td>0</td>\n",
       "      <td>1.407915e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5123</td>\n",
       "      <td>230</td>\n",
       "      <td>15.600</td>\n",
       "      <td>88.0</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>5</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>109.0</td>\n",
       "      <td>82.75</td>\n",
       "      <td>6403.0</td>\n",
       "      <td>4601.5</td>\n",
       "      <td>90.023050</td>\n",
       "      <td>17</td>\n",
       "      <td>2.454942e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5124</td>\n",
       "      <td>350</td>\n",
       "      <td>15.600</td>\n",
       "      <td>88.0</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>5</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>110.0</td>\n",
       "      <td>82.75</td>\n",
       "      <td>6403.0</td>\n",
       "      <td>4601.5</td>\n",
       "      <td>90.023050</td>\n",
       "      <td>17</td>\n",
       "      <td>3.250458e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5125</td>\n",
       "      <td>30</td>\n",
       "      <td>15.590</td>\n",
       "      <td>168.0</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>3</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>6658.0</td>\n",
       "      <td>6575.5</td>\n",
       "      <td>180.000006</td>\n",
       "      <td>2</td>\n",
       "      <td>2.962542e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5126</td>\n",
       "      <td>240</td>\n",
       "      <td>15.590</td>\n",
       "      <td>168.0</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>3</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>6658.0</td>\n",
       "      <td>6575.5</td>\n",
       "      <td>180.000006</td>\n",
       "      <td>2</td>\n",
       "      <td>1.680068e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5127</td>\n",
       "      <td>320</td>\n",
       "      <td>15.590</td>\n",
       "      <td>157.6</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>3</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>5.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>6658.0</td>\n",
       "      <td>6575.5</td>\n",
       "      <td>180.000006</td>\n",
       "      <td>2</td>\n",
       "      <td>2.351062e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5128 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      azimuth  antenna_max_gain  antenna_centerline  max_power  \\\n",
       "0         240            15.562                37.0  49.542425   \n",
       "1         240            13.300                38.0  47.781513   \n",
       "2          45            13.540               120.0  46.000000   \n",
       "3         120            16.000               100.0  47.781513   \n",
       "4         165            13.540               120.0  46.000000   \n",
       "...       ...               ...                 ...        ...   \n",
       "5123      230            15.600                88.0  47.781513   \n",
       "5124      350            15.600                88.0  47.781513   \n",
       "5125       30            15.590               168.0  47.781513   \n",
       "5126      240            15.590               168.0  47.781513   \n",
       "5127      320            15.590               157.6  47.781513   \n",
       "\n",
       "     main_propagation_model  extended_calculation_radius    pci  antenna_h_bw  \\\n",
       "0                         5                    99.999751  287.0         81.75   \n",
       "1                        16                    29.999801  165.0         71.00   \n",
       "2                         4                    99.999751  285.0         79.75   \n",
       "3                        21                    29.999801  202.0         81.00   \n",
       "4                         4                    99.999751  286.0         79.75   \n",
       "...                     ...                          ...    ...           ...   \n",
       "5123                      5                    99.999751  109.0         82.75   \n",
       "5124                      5                    99.999751  110.0         82.75   \n",
       "5125                      3                    99.999751    3.0         64.00   \n",
       "5126                      3                    99.999751    4.0         64.00   \n",
       "5127                      3                    99.999751    5.0         64.00   \n",
       "\n",
       "         tac  site_surveyed_altitude_amsl  site_support_structure_height  \\\n",
       "0     6404.0                       7177.6                      41.000001   \n",
       "1     6658.0                       4189.0                      45.011525   \n",
       "2     6403.0                       5439.0                     231.059161   \n",
       "3     6660.0                       3623.5                     197.050453   \n",
       "4     6403.0                       5439.0                     231.059161   \n",
       "...      ...                          ...                            ...   \n",
       "5123  6403.0                       4601.5                      90.023050   \n",
       "5124  6403.0                       4601.5                      90.023050   \n",
       "5125  6658.0                       6575.5                     180.000006   \n",
       "5126  6658.0                       6575.5                     180.000006   \n",
       "5127  6658.0                       6575.5                     180.000006   \n",
       "\n",
       "     band_info           sum  \n",
       "0           15  8.263681e+08  \n",
       "1            0  2.184412e+08  \n",
       "2            0  1.614895e+08  \n",
       "3           16  2.872214e+08  \n",
       "4            0  1.407915e+08  \n",
       "...        ...           ...  \n",
       "5123        17  2.454942e+08  \n",
       "5124        17  3.250458e+08  \n",
       "5125         2  2.962542e+07  \n",
       "5126         2  1.680068e+07  \n",
       "5127         2  2.351062e+07  \n",
       "\n",
       "[5128 rows x 13 columns]"
      ]
     },
     "execution_count": 2049,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separating Features and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2078,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labels are the values we want to predict\n",
    "labels = np.array(df['sum'])\n",
    "# Remove the labels from the features\n",
    "# axis 1 refers to the columns\n",
    "features= df.drop('sum', axis = 1)\n",
    "# Saving feature names for later use\n",
    "feature_list = list(features.columns)\n",
    "# Convert to numpy array\n",
    "#features = np.array(features)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "features = scaler.fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2079,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.86017932,  0.48178166, -0.78257528, ...,  1.57834637,\n",
       "        -0.84085252,  0.53695923],\n",
       "       [ 0.86017932,  0.04001192, -0.76882208, ..., -0.66213864,\n",
       "        -0.79281804, -1.1071992 ],\n",
       "       [-0.88775229,  0.08688405,  0.35893987, ...,  0.27495775,\n",
       "         1.43493939, -1.1071992 ],\n",
       "       ...,\n",
       "       [-1.02220857,  0.48725008,  1.01909321, ...,  1.12696578,\n",
       "         0.82355075, -0.88797808],\n",
       "       [ 0.86017932,  0.48725008,  1.01909321, ...,  1.12696578,\n",
       "         0.82355075, -0.88797808],\n",
       "       [ 1.57727947,  0.48725008,  0.87605999, ...,  1.12696578,\n",
       "         0.82355075, -0.88797808]])"
      ]
     },
     "execution_count": 2079,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Train and Test Data using Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2080,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (4102, 12)\n",
      "Training Labels Shape: (4102,)\n",
      "Testing Features Shape: (1026, 12)\n",
      "Testing Labels Shape: (1026,)\n"
     ]
    }
   ],
   "source": [
    "# Using Skicit-learn to split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.20, random_state = 42)\n",
    "print('Training Features Shape:', train_features.shape)\n",
    "print('Training Labels Shape:', train_labels.shape)\n",
    "print('Testing Features Shape:', test_features.shape)\n",
    "print('Testing Labels Shape:', test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model - Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2081,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 2081,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Instantiate model with default parameters\n",
    "rf = RandomForestRegressor()\n",
    "# Train the model on training data\n",
    "rf.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2082,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predictions = rf.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2083,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.76889676e+08, 3.04215513e+06, 2.12280936e+09, ...,\n",
       "       3.99199050e+08, 7.39941989e+08, 2.23785113e+08])"
      ]
     },
     "execution_count": 2083,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2084,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.50069998e+08, 4.15519452e+06, 1.53194203e+09, ...,\n",
       "       1.20549800e+09, 8.69101930e+08, 2.73293775e+08])"
      ]
     },
     "execution_count": 2084,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2085,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.714393425624343"
      ]
     },
     "execution_count": 2085,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "r2_score(test_labels, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2086,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "572681745.1159502"
      ]
     },
     "execution_count": 2086,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse=mean_squared_error(test_labels, predictions)\n",
    "rmse= np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying GridSearchCV and RandomSearchCV to find best parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_features': 'sqrt', 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfc = RandomForestRegressor()\n",
    "param_grid = { \n",
    "    'n_estimators': [200,500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "}\n",
    "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
    "CV_rfc.fit(features, labels)\n",
    "print(CV_rfc.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RandomSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 500, 'max_features': 'sqrt', 'max_depth': None}\n"
     ]
    }
   ],
   "source": [
    "#RandomSearchCV\n",
    "rfc = RandomForestRegressor()\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "# specify parameters and distributions to sample from\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"max_features\": ['auto', 'sqrt', 'log2'],\n",
    "              \"n_estimators\": [200,500]}\n",
    "random_search = RandomizedSearchCV(rfc, param_distributions=param_dist,\n",
    "                                   n_iter=3, cv=5, iid=False, random_state=42)\n",
    "random_search.fit(features, labels)\n",
    "print(random_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying model with best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1903,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = RandomForestRegressor(max_features= 'sqrt',n_estimators = 500,random_state=42)\n",
    "# Train the model on training data\n",
    "regressor.fit(train_features, train_labels)\n",
    "predictions1 = regressor.predict(test_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1904,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.08939914e+08, 3.36515205e+06, 2.12188488e+09, ...,\n",
       "       1.02163818e+09, 5.19820575e+08, 2.04520304e+08])"
      ]
     },
     "execution_count": 1904,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1905,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.50069998e+08, 4.15519452e+06, 1.53194203e+09, ...,\n",
       "       1.20549800e+09, 8.69101930e+08, 2.73293775e+08])"
      ]
     },
     "execution_count": 1905,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root Mean Squared Error (RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1906,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The root mean squared error is  628573390.6778504\n"
     ]
    }
   ],
   "source": [
    "mse=mean_squared_error(test_labels, predictions1)\n",
    "rmse= np.sqrt(mse)\n",
    "print('The root mean squared error is ',rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R-Squared Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1907,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6559246937448966"
      ]
     },
     "execution_count": 1907,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "r2_score(test_labels, predictions1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### R-squared value 65% indicates that the model explains 65 percent variability of the response data around its mean (how close the data are to the fitted regression line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6.500700e+08</td>\n",
       "      <td>4.063442e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.155195e+06</td>\n",
       "      <td>3.064378e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.531942e+09</td>\n",
       "      <td>1.973429e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.944061e+08</td>\n",
       "      <td>2.099434e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.878759e+08</td>\n",
       "      <td>8.488751e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1021</td>\n",
       "      <td>2.317214e+09</td>\n",
       "      <td>2.331877e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1022</td>\n",
       "      <td>1.830249e+07</td>\n",
       "      <td>6.853226e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1023</td>\n",
       "      <td>1.205498e+09</td>\n",
       "      <td>9.171071e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1024</td>\n",
       "      <td>8.691019e+08</td>\n",
       "      <td>4.251460e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1025</td>\n",
       "      <td>2.732938e+08</td>\n",
       "      <td>2.012379e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual     Predicted\n",
       "0     6.500700e+08  4.063442e+08\n",
       "1     4.155195e+06  3.064378e+06\n",
       "2     1.531942e+09  1.973429e+09\n",
       "3     1.944061e+08  2.099434e+08\n",
       "4     8.878759e+08  8.488751e+08\n",
       "...            ...           ...\n",
       "1021  2.317214e+09  2.331877e+09\n",
       "1022  1.830249e+07  6.853226e+07\n",
       "1023  1.205498e+09  9.171071e+08\n",
       "1024  8.691019e+08  4.251460e+08\n",
       "1025  2.732938e+08  2.012379e+08\n",
       "\n",
       "[1026 rows x 2 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actualvspredicted= pd.DataFrame({'Actual': test_labels,'Predicted': predictions1})\n",
    "actualvspredicted.to_csv('Test_predictions.csv')\n",
    "actualvspredicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TPOTRegressor \n",
    "### Use TPOT regressor to find the best model algorithm that fits the data. TPOT uses genetic algorithm based search internally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1908,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size = .20, random_state = 55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1909,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import TPOT \n",
    "import timeit\n",
    "from tpot import TPOTRegressor\n",
    "# instantiate tpot \n",
    "tpot = TPOTRegressor(verbosity=3,  \n",
    "                    random_state=55, \n",
    "                    scoring='r2',\n",
    "                    n_jobs=-1,\n",
    "                    generations=10, \n",
    "                    population_size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the TPOT optimization process on the given training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1910,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 operators have been imported by TPOT.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcbc6482d91f4f43b117b37e20c4c619",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Optimization Progress', max=220, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current Pareto front scores:\n",
      "-1\t0.6366410011365855\tXGBRegressor(input_matrix, XGBRegressor__learning_rate=0.5, XGBRegressor__max_depth=6, XGBRegressor__min_child_weight=20, XGBRegressor__n_estimators=100, XGBRegressor__nthread=1, XGBRegressor__objective=reg:squarederror, XGBRegressor__subsample=0.8)\n",
      "\n",
      "_pre_test decorator: _random_mutation_operator: num_test=0 Found array with 0 feature(s) (shape=(50, 0)) while a minimum of 1 is required..\n",
      "Generation 2 - Current Pareto front scores:\n",
      "-1\t0.6366410011365855\tXGBRegressor(input_matrix, XGBRegressor__learning_rate=0.5, XGBRegressor__max_depth=6, XGBRegressor__min_child_weight=20, XGBRegressor__n_estimators=100, XGBRegressor__nthread=1, XGBRegressor__objective=reg:squarederror, XGBRegressor__subsample=0.8)\n",
      "\n",
      "\n",
      "\n",
      "TPOT closed during evaluation in one generation.\n",
      "WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.\n",
      "\n",
      "\n",
      "TPOT closed prematurely. Will use the current best pipeline.\n",
      "0.626848227802411\n"
     ]
    }
   ],
   "source": [
    "#Run the TPOT optimization process on the given training data.\n",
    "tpot.fit(X_train, y_train)\n",
    "print(tpot.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6914580957689291"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpot.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('featureunion',\n",
       "                 FeatureUnion(n_jobs=None,\n",
       "                              transformer_list=[('pipeline',\n",
       "                                                 Pipeline(memory=None,\n",
       "                                                          steps=[('stackingestimator',\n",
       "                                                                  StackingEstimator(estimator=GradientBoostingRegressor(alpha=0.95,\n",
       "                                                                                                                        criterion='friedman_mse',\n",
       "                                                                                                                        init=None,\n",
       "                                                                                                                        learning_rate=0.1,\n",
       "                                                                                                                        loss='huber',\n",
       "                                                                                                                        max_depth=7,\n",
       "                                                                                                                        max_features=0.9500000000000001,\n",
       "                                                                                                                        max_leaf_nodes=None,\n",
       "                                                                                                                        min_imp...\n",
       "                ('randomforestregressor',\n",
       "                 RandomForestRegressor(bootstrap=True, criterion='mse',\n",
       "                                       max_depth=None, max_features=0.1,\n",
       "                                       max_leaf_nodes=None,\n",
       "                                       min_impurity_decrease=0.0,\n",
       "                                       min_impurity_split=None,\n",
       "                                       min_samples_leaf=2, min_samples_split=2,\n",
       "                                       min_weight_fraction_leaf=0.0,\n",
       "                                       n_estimators=100, n_jobs=None,\n",
       "                                       oob_score=False, random_state=55,\n",
       "                                       verbose=0, warm_start=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpot.fitted_pipeline_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "626da4c22a4a4f59a62c65d4b0e17590",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Optimization Progress', max=2550, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.5743405679516097\n",
      "Generation 2 - Current best internal CV score: 0.5743405679516097\n",
      "Generation 4 - Current best internal CV score: 0.5816036363518491\n",
      "Generation 5 - Current best internal CV score: 0.6194351932633365\n",
      "Generation 6 - Current best internal CV score: 0.6194351932633365\n",
      "Generation 7 - Current best internal CV score: 0.6194351932633365\n",
      "Generation 8 - Current best internal CV score: 0.6194351932633365\n"
     ]
    }
   ],
   "source": [
    "tpot = TPOTRegressor(generations=50, population_size=50,scoring='r2', verbosity=2)\n",
    "tpot.fit(train_features, train_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2089,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6997855688444348\n"
     ]
    }
   ],
   "source": [
    "print(tpot.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### R-squared value 71% indicates that the model explains 71 percent variability of the response data around its mean (how close the data are to the fitted regression line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the optimized pipeline to predict the target values for a feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.56384483e+09, 7.85634110e+07, 1.88577276e+09, ...,\n",
       "       2.51447715e+08, 6.53947119e+08, 2.13601489e+07])"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_tpot=tpot.predict(X_test)\n",
    "predictions_tpot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.654634e+09</td>\n",
       "      <td>1.563845e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.544750e+08</td>\n",
       "      <td>7.856341e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.836121e+09</td>\n",
       "      <td>1.885773e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.612585e+08</td>\n",
       "      <td>1.935056e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.098521e+05</td>\n",
       "      <td>1.659847e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1021</td>\n",
       "      <td>2.152384e+07</td>\n",
       "      <td>1.247513e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1022</td>\n",
       "      <td>4.914010e+08</td>\n",
       "      <td>2.834507e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1023</td>\n",
       "      <td>6.396089e+07</td>\n",
       "      <td>2.514477e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1024</td>\n",
       "      <td>6.049662e+08</td>\n",
       "      <td>6.539471e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1025</td>\n",
       "      <td>1.941141e+07</td>\n",
       "      <td>2.136015e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual     Predicted\n",
       "0     1.654634e+09  1.563845e+09\n",
       "1     1.544750e+08  7.856341e+07\n",
       "2     1.836121e+09  1.885773e+09\n",
       "3     2.612585e+08  1.935056e+08\n",
       "4     1.098521e+05  1.659847e+08\n",
       "...            ...           ...\n",
       "1021  2.152384e+07  1.247513e+07\n",
       "1022  4.914010e+08  2.834507e+08\n",
       "1023  6.396089e+07  2.514477e+08\n",
       "1024  6.049662e+08  6.539471e+08\n",
       "1025  1.941141e+07  2.136015e+07\n",
       "\n",
       "[1026 rows x 2 columns]"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actualvspredicted= pd.DataFrame({'Actual': y_test,'Predicted': predictions_tpot})\n",
    "actualvspredicted.to_csv('Test_predictions_TPOT.csv')\n",
    "actualvspredicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the scikit learn code for best regressor and dispay the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [],
   "source": [
    "tpot.export('tpot_cell_analysis.py')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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