DataPreprocessing_Smoothing_updated.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e815c15",
   "metadata": {},
   "source": [
    "## Data Pre-processing (2021 Problem Statement)\n",
    "\n",
    "Preliminaries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0c5326d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "258be9aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "AP_train_path = './data/AP_info.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79fb1a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Wifi_train_path ='./data/training_dataset.csv'\n",
    "Wifi_test_path ='./data/verification_dataset.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa2258e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read dataset [Reading the csv files and storing in form of dataset]\n",
    "AP_train=pd.read_csv(AP_train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af35a76f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AP SSID</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Hight Difference(m)</th>\n",
       "      <th>Indoor/Outdoor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>35.157320</td>\n",
       "      <td>136.926447</td>\n",
       "      <td>0</td>\n",
       "      <td>Outdoor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>35.157347</td>\n",
       "      <td>136.926807</td>\n",
       "      <td>0</td>\n",
       "      <td>Outdoor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>35.157202</td>\n",
       "      <td>136.926827</td>\n",
       "      <td>0</td>\n",
       "      <td>Outdoor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35.157165</td>\n",
       "      <td>136.926470</td>\n",
       "      <td>0</td>\n",
       "      <td>Outdoor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AP SSID   Latitude   Longitude  Hight Difference(m) Indoor/Outdoor\n",
       "0        1  35.157320  136.926447                    0        Outdoor\n",
       "1        2  35.157347  136.926807                    0        Outdoor\n",
       "2        3  35.157202  136.926827                    0        Outdoor\n",
       "3        4  35.157165  136.926470                    0        Outdoor"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AP_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eacbff39",
   "metadata": {},
   "source": [
    "Transforming the categorical data to numerical ones using one-hot-encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64f47101",
   "metadata": {},
   "outputs": [],
   "source": [
    "AP_train = pd.concat([AP_train,pd.get_dummies(AP_train['Indoor/Outdoor'])['Outdoor']], axis=1, join='inner').drop(columns=['Indoor/Outdoor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d92907eb",
   "metadata": {},
   "outputs": [
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       "      <th>AP SSID</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Hight Difference(m)</th>\n",
       "      <th>Outdoor</th>\n",
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      "text/plain": [
       "   AP SSID   Latitude   Longitude  Hight Difference(m)  Outdoor\n",
       "0        1  35.157320  136.926447                    0        1\n",
       "1        2  35.157347  136.926807                    0        1\n",
       "2        3  35.157202  136.926827                    0        1\n",
       "3        4  35.157165  136.926470                    0        1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AP_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd3da917",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(Wifi_train_path)\n",
    "test = pd.read_csv(Wifi_test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4602cf7",
   "metadata": {},
   "outputs": [
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       "      <td>-41</td>\n",
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      ],
      "text/plain": [
       "   No.  TimeStamp(UNIX)  Latitude   Longitude  SSID  Channel  RSSI(dBm)\n",
       "0    1       1631687696  35.15714  136.926306     1       11        -41\n",
       "1    2       1631687697  35.15714  136.926306     1       11        -41\n",
       "2    3       1631687697  35.15714  136.926306     1       11        -41\n",
       "3    4       1631687697  35.15714  136.926306     1       11        -41\n",
       "4    5       1631687697  35.15714  136.926306     1       11        -41"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ce12218",
   "metadata": {},
   "outputs": [
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       "   TimeStamp(UNIX)  Latitude   Longitude  SSID  Channel  RSSI(dBm)\n",
       "0       1631687696  35.15714  136.926306     1       11        -41\n",
       "1       1631687697  35.15714  136.926306     1       11        -41\n",
       "2       1631687697  35.15714  136.926306     1       11        -41\n",
       "3       1631687697  35.15714  136.926306     1       11        -41\n",
       "4       1631687697  35.15714  136.926306     1       11        -41"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(columns=['No.'], inplace=True)\n",
    "test.drop(columns=['No.'], inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8783aa1a",
   "metadata": {},
   "outputs": [
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       "      <td>1631688763</td>\n",
       "      <td>35.157236</td>\n",
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       "      <td>4</td>\n",
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       "      <td>-52</td>\n",
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       "      <th>5196</th>\n",
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       "      <td>1</td>\n",
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       "      <th>5197</th>\n",
       "      <td>1631688763</td>\n",
       "      <td>35.157236</td>\n",
       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5198</th>\n",
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       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
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       "      <td>-52</td>\n",
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       "    <tr>\n",
       "      <th>5199</th>\n",
       "      <td>1631688763</td>\n",
       "      <td>35.157236</td>\n",
       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-52</td>\n",
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       "<p>5200 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "      TimeStamp(UNIX)   Latitude   Longitude  SSID  Channel  RSSI(dBm)\n",
       "0          1631687696  35.157140  136.926306     1       11        -41\n",
       "1          1631687697  35.157140  136.926306     1       11        -41\n",
       "2          1631687697  35.157140  136.926306     1       11        -41\n",
       "3          1631687697  35.157140  136.926306     1       11        -41\n",
       "4          1631687697  35.157140  136.926306     1       11        -41\n",
       "...               ...        ...         ...   ...      ...        ...\n",
       "5195       1631688763  35.157236  136.926489     4        1        -52\n",
       "5196       1631688763  35.157236  136.926489     4        1        -52\n",
       "5197       1631688763  35.157236  136.926489     4        1        -52\n",
       "5198       1631688763  35.157236  136.926489     4        1        -52\n",
       "5199       1631688763  35.157236  136.926489     4        1        -52\n",
       "\n",
       "[5200 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6da45690",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "for x in train['TimeStamp(UNIX)']:\n",
    "    #print(train['TimeStamp(UNIX)'])\n",
    "    train['TimeStamp(UNIX)'] = datetime.datetime.fromtimestamp(x)\n",
    "for x in test['TimeStamp(UNIX)']:\n",
    "    #print(train['TimeStamp(UNIX)'])\n",
    "    test['TimeStamp(UNIX)'] = datetime.datetime.fromtimestamp(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "91296c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.rename(columns = {'TimeStamp(UNIX)':'TimeStamp'}, inplace = True)\n",
    "test.rename(columns = {'TimeStamp(UNIX)':'TimeStamp'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e969d732",
   "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>TimeStamp</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>SSID</th>\n",
       "      <th>Channel</th>\n",
       "      <th>RSSI(dBm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157140</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157140</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157140</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157140</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157140</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5195</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157236</td>\n",
       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5196</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157236</td>\n",
       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5197</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157236</td>\n",
       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5198</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157236</td>\n",
       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5199</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.157236</td>\n",
       "      <td>136.926489</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               TimeStamp   Latitude   Longitude  SSID  Channel  RSSI(dBm)\n",
       "0    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41\n",
       "1    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41\n",
       "2    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41\n",
       "3    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41\n",
       "4    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41\n",
       "...                  ...        ...         ...   ...      ...        ...\n",
       "5195 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52\n",
       "5196 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52\n",
       "5197 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52\n",
       "5198 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52\n",
       "5199 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52\n",
       "\n",
       "[5200 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1144e4c2",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "lRSSI=list(train['RSSI(dBm)'])\n",
    "plt.plot(lRSSI, color=\"blue\", label=\"Original Data\")\n",
    "plt.xlabel(\"No. of Observations\")\n",
    "plt.ylabel(\"RSSI (dBm)\")\n",
    "plt.legend(loc='best',fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cf36e2",
   "metadata": {},
   "source": [
    "Smoothening of the data is required as the RSSI values tend to change abruptly over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ffb70383",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating simple moving average\n",
    "# using .rolling(window).mean() ,\n",
    "# with window size = 80\n",
    "RSSIMA = (train['RSSI(dBm)']).rolling(80).mean()\n",
    " \n",
    "# removing all the NULL values using\n",
    "# dropna() method\n",
    "RSSIMA.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "583dd375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79     -41.0\n",
       "80     -41.0\n",
       "81     -41.0\n",
       "82     -41.0\n",
       "83     -41.0\n",
       "        ... \n",
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       "5198   -52.0\n",
       "5199   -52.0\n",
       "Name: RSSI(dBm), Length: 5121, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RSSIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f52ae367",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['RSSIMA'] = RSSIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "98411556",
   "metadata": {},
   "outputs": [
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       "               TimeStamp   Latitude   Longitude  SSID  Channel  RSSI(dBm)  \\\n",
       "0    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "1    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "2    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "3    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "4    2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "...                  ...        ...         ...   ...      ...        ...   \n",
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       "5196 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "5197 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "5198 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "5199 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "\n",
       "      RSSIMA  \n",
       "0        NaN  \n",
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "64d7f97a",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "               TimeStamp   Latitude   Longitude  SSID  Channel  RSSI(dBm)  \\\n",
       "79   2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "80   2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "81   2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "82   2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "83   2021-09-15 06:52:43  35.157140  136.926306     1       11        -41   \n",
       "...                  ...        ...         ...   ...      ...        ...   \n",
       "5195 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "5196 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "5197 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "5198 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "5199 2021-09-15 06:52:43  35.157236  136.926489     4        1        -52   \n",
       "\n",
       "      RSSIMA  \n",
       "79     -41.0  \n",
       "80     -41.0  \n",
       "81     -41.0  \n",
       "82     -41.0  \n",
       "83     -41.0  \n",
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       "\n",
       "[5121 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainSM = train.dropna()\n",
    "trainSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e425d620",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lRSSI=list(trainSM['RSSI(dBm)'])\n",
    "lMoving_avg=list(trainSM['RSSIMA'])\n",
    "plt.plot(lRSSI, color=\"blue\", label=\"Original Data\")\n",
    "plt.plot(lMoving_avg, color=\"orange\", label=\"Smoothened Data\")\n",
    "plt.title(\"Data Smoothening\")\n",
    "plt.xlabel(\"No. of Observations\")\n",
    "plt.ylabel(\"RSSI (dBm)\")\n",
    "plt.legend(loc='best',fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf2345e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_average(inp):\n",
    "  moving_avg= []\n",
    "  i = 0\n",
    "  window_size=90\n",
    "  while i < len(list(inp[\"RSSI(dBm)\"])) - window_size + 1:\n",
    "      this_window = list(inp[\"RSSI(dBm)\"][i : i + window_size])\n",
    "      window_average = sum(this_window) / window_size\n",
    "      moving_avg.append(window_average)\n",
    "      i += 1\n",
    "  for z in range(len(list(inp[\"RSSI(dBm)\"]))-len(moving_avg)):\n",
    "    moving_avg.append(moving_avg[-1])\n",
    "    \n",
    "  return moving_avg\n",
    "\n",
    "RSSI_MA90 = moving_average(trainSM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bac76bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5121"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(RSSI_MA90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2f606d71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_993/649016603.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  trainSM['RSSI_MA90'] = RSSI_MA90\n"
     ]
    }
   ],
   "source": [
    "trainSM['RSSI_MA90'] = RSSI_MA90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "413a9d47",
   "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>TimeStamp</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>SSID</th>\n",
       "      <th>Channel</th>\n",
       "      <th>RSSI(dBm)</th>\n",
       "      <th>RSSIMA</th>\n",
       "      <th>RSSI_MA90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.15714</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>-42.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.15714</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>-42.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.15714</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>-42.577778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.15714</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>-42.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2021-09-15 06:52:43</td>\n",
       "      <td>35.15714</td>\n",
       "      <td>136.926306</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-41</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>-42.622222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp  Latitude   Longitude  SSID  Channel  RSSI(dBm)  \\\n",
       "79 2021-09-15 06:52:43  35.15714  136.926306     1       11        -41   \n",
       "80 2021-09-15 06:52:43  35.15714  136.926306     1       11        -41   \n",
       "81 2021-09-15 06:52:43  35.15714  136.926306     1       11        -41   \n",
       "82 2021-09-15 06:52:43  35.15714  136.926306     1       11        -41   \n",
       "83 2021-09-15 06:52:43  35.15714  136.926306     1       11        -41   \n",
       "\n",
       "    RSSIMA  RSSI_MA90  \n",
       "79   -41.0 -42.533333  \n",
       "80   -41.0 -42.555556  \n",
       "81   -41.0 -42.577778  \n",
       "82   -41.0 -42.600000  \n",
       "83   -41.0 -42.622222  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainSM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03f404bf",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "lRSSI=list(trainSM['RSSI(dBm)'])\n",
    "lMoving_avg=list(trainSM['RSSI_MA90'])\n",
    "plt.plot(lRSSI, color=\"blue\", label=\"Original Data\")\n",
    "plt.plot(lMoving_avg, color=\"orange\", label=\"Smoothened Data\")\n",
    "plt.title(\"Data Smoothening\")\n",
    "plt.xlabel(\"No. of Observations\")\n",
    "plt.ylabel(\"RSSI (dBm)\")\n",
    "plt.legend(loc='best',fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cac73208",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}