HumanActivityRecognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "colab": {
      "name": "HumanActivityRecognition.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.2"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6gaC5fkConn"
      },
      "source": [
        "\n",
        "\n"
      ],
      "id": "y6gaC5fkConn",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPPMIk0Blxc2",
        "outputId": "ca09717c-77ff-4e15-f208-569f42f29e5a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "HPPMIk0Blxc2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "received-inclusion"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import  matplotlib.pyplot as plt\n",
        "import os\n",
        "from glob import glob \n",
        "\n"
      ],
      "id": "received-inclusion",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "centered-difference"
      },
      "source": [
        ""
      ],
      "id": "centered-difference",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "palestinian-pharmacology",
        "outputId": "a4acba8c-93a2-40da-f79c-8284c24d5e2d"
      },
      "source": [
        "\n",
        "\n",
        "from glob import glob\n",
        "stock_files =sorted(glob(os.path.join(\"/content/drive/MyDrive/My Dataset/DataSet/Participant_*.csv\")))  \n",
        "\n",
        "stock_files\n",
        "\n"
      ],
      "id": "palestinian-pharmacology",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/My Dataset/DataSet/Participant_1.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_10.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_2.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_3.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_4.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_5.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_6.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_7.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_8.csv',\n",
              " '/content/drive/MyDrive/My Dataset/DataSet/Participant_9.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "agreed-hormone",
        "outputId": "58ac9597-8f5a-4a1c-a4ed-fd241d3d2b5c"
      },
      "source": [
        "#load a file  into memory\n",
        "p='/content/drive/MyDrive/My Dataset/DataSet/Participant_1.csv'\n",
        "df=pd.read_csv(p,header=1)\n",
        "df.head()\n",
        "\n"
      ],
      "id": "agreed-hormone",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>time_stamp</th>\n",
              "      <th>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Lx</th>\n",
              "      <th>Ly</th>\n",
              "      <th>Lz</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "      <th>Mx</th>\n",
              "      <th>My</th>\n",
              "      <th>Mz</th>\n",
              "      <th>Unnamed: 13</th>\n",
              "      <th>time_stamp.1</th>\n",
              "      <th>Ax.1</th>\n",
              "      <th>Ay.1</th>\n",
              "      <th>Az.1</th>\n",
              "      <th>Lx.1</th>\n",
              "      <th>Ly.1</th>\n",
              "      <th>Lz.1</th>\n",
              "      <th>Gx.1</th>\n",
              "      <th>Gy.1</th>\n",
              "      <th>Gz.1</th>\n",
              "      <th>Mx.1</th>\n",
              "      <th>My.1</th>\n",
              "      <th>Mz.1</th>\n",
              "      <th>Unnamed: 27</th>\n",
              "      <th>time_stamp.2</th>\n",
              "      <th>Ax.2</th>\n",
              "      <th>Ay.2</th>\n",
              "      <th>Az.2</th>\n",
              "      <th>Lx.2</th>\n",
              "      <th>Ly.2</th>\n",
              "      <th>Lz.2</th>\n",
              "      <th>Gx.2</th>\n",
              "      <th>Gy.2</th>\n",
              "      <th>Gz.2</th>\n",
              "      <th>Mx.2</th>\n",
              "      <th>My.2</th>\n",
              "      <th>Mz.2</th>\n",
              "      <th>Unnamed: 41</th>\n",
              "      <th>time_stamp.3</th>\n",
              "      <th>Ax.3</th>\n",
              "      <th>Ay.3</th>\n",
              "      <th>Az.3</th>\n",
              "      <th>Lx.3</th>\n",
              "      <th>Ly.3</th>\n",
              "      <th>Lz.3</th>\n",
              "      <th>Gx.3</th>\n",
              "      <th>Gy.3</th>\n",
              "      <th>Gz.3</th>\n",
              "      <th>Mx.3</th>\n",
              "      <th>My.3</th>\n",
              "      <th>Mz.3</th>\n",
              "      <th>Unnamed: 55</th>\n",
              "      <th>time_stamp.4</th>\n",
              "      <th>Ax.4</th>\n",
              "      <th>Ay.4</th>\n",
              "      <th>Az.4</th>\n",
              "      <th>Lx.4</th>\n",
              "      <th>Ly.4</th>\n",
              "      <th>Lz.4</th>\n",
              "      <th>Gx.4</th>\n",
              "      <th>Gy.4</th>\n",
              "      <th>Gz.4</th>\n",
              "      <th>Mx.4</th>\n",
              "      <th>My.4</th>\n",
              "      <th>Mz.4</th>\n",
              "      <th>Unnamed: 69</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-1.81150</td>\n",
              "      <td>-14.873</td>\n",
              "      <td>-1.34840</td>\n",
              "      <td>-1.26910</td>\n",
              "      <td>-5.1057</td>\n",
              "      <td>-0.66445</td>\n",
              "      <td>-0.53206</td>\n",
              "      <td>-3.18690</td>\n",
              "      <td>0.23976</td>\n",
              "      <td>12.72</td>\n",
              "      <td>40.74</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-1.19860</td>\n",
              "      <td>-13.852</td>\n",
              "      <td>3.7865</td>\n",
              "      <td>-1.76980</td>\n",
              "      <td>-4.0625</td>\n",
              "      <td>3.89850</td>\n",
              "      <td>-3.6255</td>\n",
              "      <td>1.07390</td>\n",
              "      <td>0.13622</td>\n",
              "      <td>18.84</td>\n",
              "      <td>55.92</td>\n",
              "      <td>-6.72</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.17706</td>\n",
              "      <td>-10.569</td>\n",
              "      <td>1.8251</td>\n",
              "      <td>-7.5051</td>\n",
              "      <td>-4.7736</td>\n",
              "      <td>-0.062476</td>\n",
              "      <td>-0.36652</td>\n",
              "      <td>1.27060</td>\n",
              "      <td>-0.52412</td>\n",
              "      <td>-30.30</td>\n",
              "      <td>31.08</td>\n",
              "      <td>3.96</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.79220</td>\n",
              "      <td>-12.5720</td>\n",
              "      <td>-4.3177</td>\n",
              "      <td>-0.098712</td>\n",
              "      <td>-3.385700</td>\n",
              "      <td>-2.46480</td>\n",
              "      <td>0.021075</td>\n",
              "      <td>1.02990</td>\n",
              "      <td>1.22690</td>\n",
              "      <td>-18.72</td>\n",
              "      <td>31.74</td>\n",
              "      <td>17.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>4.3177</td>\n",
              "      <td>-2.36990</td>\n",
              "      <td>-0.43585</td>\n",
              "      <td>-5.3574</td>\n",
              "      <td>-0.95749</td>\n",
              "      <td>0.318860</td>\n",
              "      <td>0.52565</td>\n",
              "      <td>-0.18815</td>\n",
              "      <td>0.285880</td>\n",
              "      <td>-22.20</td>\n",
              "      <td>6.48</td>\n",
              "      <td>4.56</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.24517</td>\n",
              "      <td>-14.070</td>\n",
              "      <td>-0.84446</td>\n",
              "      <td>0.70147</td>\n",
              "      <td>-4.2969</td>\n",
              "      <td>-0.17199</td>\n",
              "      <td>-0.25229</td>\n",
              "      <td>-1.79660</td>\n",
              "      <td>0.40745</td>\n",
              "      <td>12.54</td>\n",
              "      <td>40.74</td>\n",
              "      <td>-6.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-2.38360</td>\n",
              "      <td>-16.590</td>\n",
              "      <td>2.9965</td>\n",
              "      <td>-2.70250</td>\n",
              "      <td>-6.7890</td>\n",
              "      <td>2.86820</td>\n",
              "      <td>-3.4899</td>\n",
              "      <td>0.39034</td>\n",
              "      <td>0.40073</td>\n",
              "      <td>18.60</td>\n",
              "      <td>55.86</td>\n",
              "      <td>-6.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.16344</td>\n",
              "      <td>-12.190</td>\n",
              "      <td>2.0703</td>\n",
              "      <td>-7.3573</td>\n",
              "      <td>-6.2618</td>\n",
              "      <td>-0.042442</td>\n",
              "      <td>-0.54459</td>\n",
              "      <td>1.28070</td>\n",
              "      <td>0.34025</td>\n",
              "      <td>-31.02</td>\n",
              "      <td>29.88</td>\n",
              "      <td>3.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.38360</td>\n",
              "      <td>-10.9920</td>\n",
              "      <td>-4.6037</td>\n",
              "      <td>-0.404440</td>\n",
              "      <td>-1.774500</td>\n",
              "      <td>-2.74830</td>\n",
              "      <td>0.201890</td>\n",
              "      <td>0.53573</td>\n",
              "      <td>1.32280</td>\n",
              "      <td>-18.84</td>\n",
              "      <td>31.74</td>\n",
              "      <td>17.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>5.1213</td>\n",
              "      <td>-2.19290</td>\n",
              "      <td>-0.70826</td>\n",
              "      <td>-4.5352</td>\n",
              "      <td>-0.67629</td>\n",
              "      <td>0.081656</td>\n",
              "      <td>0.43616</td>\n",
              "      <td>0.16371</td>\n",
              "      <td>0.198230</td>\n",
              "      <td>-21.90</td>\n",
              "      <td>7.14</td>\n",
              "      <td>5.34</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.57205</td>\n",
              "      <td>-14.628</td>\n",
              "      <td>-1.75700</td>\n",
              "      <td>-0.21760</td>\n",
              "      <td>-4.8531</td>\n",
              "      <td>-1.05650</td>\n",
              "      <td>-1.04920</td>\n",
              "      <td>0.29138</td>\n",
              "      <td>0.29230</td>\n",
              "      <td>12.42</td>\n",
              "      <td>40.68</td>\n",
              "      <td>-8.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-4.63090</td>\n",
              "      <td>-16.603</td>\n",
              "      <td>2.0703</td>\n",
              "      <td>-4.34550</td>\n",
              "      <td>-6.8081</td>\n",
              "      <td>1.68890</td>\n",
              "      <td>-3.3851</td>\n",
              "      <td>-1.99200</td>\n",
              "      <td>1.12280</td>\n",
              "      <td>18.72</td>\n",
              "      <td>55.80</td>\n",
              "      <td>-4.26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.57205</td>\n",
              "      <td>-12.626</td>\n",
              "      <td>2.2474</td>\n",
              "      <td>-6.7655</td>\n",
              "      <td>-6.5138</td>\n",
              "      <td>0.017921</td>\n",
              "      <td>-0.70952</td>\n",
              "      <td>1.15820</td>\n",
              "      <td>0.99724</td>\n",
              "      <td>-31.26</td>\n",
              "      <td>29.22</td>\n",
              "      <td>3.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>1.56630</td>\n",
              "      <td>-9.0848</td>\n",
              "      <td>-4.5764</td>\n",
              "      <td>-1.003600</td>\n",
              "      <td>0.201530</td>\n",
              "      <td>-2.75150</td>\n",
              "      <td>0.287720</td>\n",
              "      <td>0.10049</td>\n",
              "      <td>1.20710</td>\n",
              "      <td>-19.02</td>\n",
              "      <td>31.68</td>\n",
              "      <td>17.52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>5.6116</td>\n",
              "      <td>-2.12480</td>\n",
              "      <td>-1.26670</td>\n",
              "      <td>-4.0408</td>\n",
              "      <td>-0.55974</td>\n",
              "      <td>-0.523690</td>\n",
              "      <td>0.32284</td>\n",
              "      <td>0.43127</td>\n",
              "      <td>0.211970</td>\n",
              "      <td>-21.60</td>\n",
              "      <td>7.50</td>\n",
              "      <td>5.64</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.69464</td>\n",
              "      <td>-12.939</td>\n",
              "      <td>-3.09180</td>\n",
              "      <td>-0.32273</td>\n",
              "      <td>-3.1786</td>\n",
              "      <td>-2.21970</td>\n",
              "      <td>-2.37200</td>\n",
              "      <td>1.00820</td>\n",
              "      <td>0.34972</td>\n",
              "      <td>12.30</td>\n",
              "      <td>40.74</td>\n",
              "      <td>-8.52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-4.78070</td>\n",
              "      <td>-16.453</td>\n",
              "      <td>1.1577</td>\n",
              "      <td>-4.19540</td>\n",
              "      <td>-6.6731</td>\n",
              "      <td>0.74084</td>\n",
              "      <td>-3.1017</td>\n",
              "      <td>-3.04390</td>\n",
              "      <td>0.79138</td>\n",
              "      <td>18.84</td>\n",
              "      <td>55.62</td>\n",
              "      <td>-3.12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>1.21220</td>\n",
              "      <td>-12.476</td>\n",
              "      <td>2.2882</td>\n",
              "      <td>-5.9716</td>\n",
              "      <td>-6.1990</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>-0.78191</td>\n",
              "      <td>1.10200</td>\n",
              "      <td>1.33380</td>\n",
              "      <td>-31.44</td>\n",
              "      <td>28.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.61292</td>\n",
              "      <td>-8.5263</td>\n",
              "      <td>-2.9284</td>\n",
              "      <td>-2.827100</td>\n",
              "      <td>0.859490</td>\n",
              "      <td>-1.14670</td>\n",
              "      <td>0.175930</td>\n",
              "      <td>-0.21441</td>\n",
              "      <td>0.90469</td>\n",
              "      <td>-18.96</td>\n",
              "      <td>31.86</td>\n",
              "      <td>17.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>6.0611</td>\n",
              "      <td>-1.47100</td>\n",
              "      <td>-2.47890</td>\n",
              "      <td>-3.5908</td>\n",
              "      <td>0.15795</td>\n",
              "      <td>-1.880200</td>\n",
              "      <td>0.20403</td>\n",
              "      <td>0.72846</td>\n",
              "      <td>0.164930</td>\n",
              "      <td>-21.12</td>\n",
              "      <td>8.40</td>\n",
              "      <td>6.12</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.87170</td>\n",
              "      <td>-12.000</td>\n",
              "      <td>-1.56630</td>\n",
              "      <td>1.13740</td>\n",
              "      <td>-2.2390</td>\n",
              "      <td>-0.65476</td>\n",
              "      <td>-2.79010</td>\n",
              "      <td>0.65485</td>\n",
              "      <td>0.22724</td>\n",
              "      <td>12.24</td>\n",
              "      <td>40.80</td>\n",
              "      <td>-8.88</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.92618</td>\n",
              "      <td>-12.190</td>\n",
              "      <td>-1.0624</td>\n",
              "      <td>-0.18116</td>\n",
              "      <td>-2.4144</td>\n",
              "      <td>-1.28500</td>\n",
              "      <td>-2.7587</td>\n",
              "      <td>-4.46020</td>\n",
              "      <td>0.69364</td>\n",
              "      <td>19.38</td>\n",
              "      <td>55.44</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.24740</td>\n",
              "      <td>-11.387</td>\n",
              "      <td>1.9886</td>\n",
              "      <td>-4.6603</td>\n",
              "      <td>-4.8230</td>\n",
              "      <td>-0.329740</td>\n",
              "      <td>-0.82436</td>\n",
              "      <td>0.90561</td>\n",
              "      <td>2.15600</td>\n",
              "      <td>-31.56</td>\n",
              "      <td>28.32</td>\n",
              "      <td>3.30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-1.18500</td>\n",
              "      <td>-9.3436</td>\n",
              "      <td>-2.2337</td>\n",
              "      <td>-3.209900</td>\n",
              "      <td>0.085583</td>\n",
              "      <td>-0.45569</td>\n",
              "      <td>-0.033292</td>\n",
              "      <td>-0.48625</td>\n",
              "      <td>0.60720</td>\n",
              "      <td>-18.84</td>\n",
              "      <td>32.10</td>\n",
              "      <td>17.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>7.7091</td>\n",
              "      <td>-0.77636</td>\n",
              "      <td>-3.54130</td>\n",
              "      <td>-1.9450</td>\n",
              "      <td>0.85796</td>\n",
              "      <td>-2.995300</td>\n",
              "      <td>0.09896</td>\n",
              "      <td>0.94379</td>\n",
              "      <td>0.064752</td>\n",
              "      <td>-20.94</td>\n",
              "      <td>8.70</td>\n",
              "      <td>6.30</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     time_stamp       Ax      Ay       Az  ...   Mx.4  My.4  Mz.4  Unnamed: 69\n",
              "0  1.390000e+12 -1.81150 -14.873 -1.34840  ... -22.20  6.48  4.56      walking\n",
              "1  1.390000e+12  0.24517 -14.070 -0.84446  ... -21.90  7.14  5.34      walking\n",
              "2  1.390000e+12 -0.57205 -14.628 -1.75700  ... -21.60  7.50  5.64      walking\n",
              "3  1.390000e+12 -0.69464 -12.939 -3.09180  ... -21.12  8.40  6.12      walking\n",
              "4  1.390000e+12  0.87170 -12.000 -1.56630  ... -20.94  8.70  6.30      walking\n",
              "\n",
              "[5 rows x 70 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "posted-focus",
        "outputId": "870bb77b-1c93-4782-df0c-2e33d83535ec"
      },
      "source": [
        "df['Unnamed: 69'].unique()\n"
      ],
      "id": "posted-focus",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upstairs',\n",
              "       'downstairs'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "subjective-continuity"
      },
      "source": [
        "# Visualize the difference in change in values "
      ],
      "id": "subjective-continuity"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "composite-kernel",
        "outputId": "58ad3370-9bf0-4b96-c526-5cccc23fe065"
      },
      "source": [
        "# change in values while walking\n",
        "\n",
        "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"walking\"][:100], label='X-axis')\n",
        "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"walking\"][:100], label='Y-axis')\n",
        "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"walking\"][:100], label='Z-axis')\n",
        "plt.legend()"
      ],
      "id": "composite-kernel",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fce3dd0c910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "perceived-ebony",
        "outputId": "7c3d1b66-8a18-4df0-d1ff-7c807493d98c"
      },
      "source": [
        "# change in values while standing\n",
        "\n",
        "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"standing\"][:100], label='X-axis')\n",
        "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"standing\"][:100], label='Y-axis')\n",
        "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"standing\"][:100], label='Z-axis')\n",
        "plt.legend()\n"
      ],
      "id": "perceived-ebony",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fce38024d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sorted-discrimination"
      },
      "source": [
        "# change in values while biking\n",
        "\n",
        "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"biking\"][:100], label='X-axis')\n",
        "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"biking\"][:100], label='Y-axis')\n",
        "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"biking\"][:100], label='Z-axis')\n",
        "plt.legend()"
      ],
      "id": "sorted-discrimination"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "fuzzy-acceptance",
        "outputId": "520bf38a-29a2-400e-acb7-bcb563701418"
      },
      "source": [
        "# change in values while jogging\n",
        "\n",
        "plt.plot(np.arange(0,100),df.Ax[df['Unnamed: 69']==\"jogging\"][:100], label='X-axis')\n",
        "plt.plot(np.arange(0,100),df.Ay[df['Unnamed: 69']==\"jogging\"][:100], label='Y-axis')\n",
        "plt.plot(np.arange(0,100),df.Az[df['Unnamed: 69']==\"jogging\"][:100], label='Z-axis')\n",
        "plt.legend()"
      ],
      "id": "fuzzy-acceptance",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fce3802b1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "compressed-bedroom"
      },
      "source": [
        "# Load and concatenate all files into one dataframe"
      ],
      "id": "compressed-bedroom"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "american-skating",
        "outputId": "f68b586d-b2dd-402b-b99e-286fa6b22117"
      },
      "source": [
        "\n",
        "df=pd.DataFrame()\n",
        "path='/content/drive/MyDrive/My Dataset'\n",
        "\n",
        "for file in stock_files:\n",
        "    df_temp=pd.read_csv(file,header=1)\n",
        "    df=pd.concat([df,df_temp], sort=False)\n",
        "df\n"
      ],
      "id": "american-skating",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>time_stamp</th>\n",
              "      <th>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Lx</th>\n",
              "      <th>Ly</th>\n",
              "      <th>Lz</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "      <th>Mx</th>\n",
              "      <th>My</th>\n",
              "      <th>Mz</th>\n",
              "      <th>Unnamed: 13</th>\n",
              "      <th>time_stamp.1</th>\n",
              "      <th>Ax.1</th>\n",
              "      <th>Ay.1</th>\n",
              "      <th>Az.1</th>\n",
              "      <th>Lx.1</th>\n",
              "      <th>Ly.1</th>\n",
              "      <th>Lz.1</th>\n",
              "      <th>Gx.1</th>\n",
              "      <th>Gy.1</th>\n",
              "      <th>Gz.1</th>\n",
              "      <th>Mx.1</th>\n",
              "      <th>My.1</th>\n",
              "      <th>Mz.1</th>\n",
              "      <th>Unnamed: 27</th>\n",
              "      <th>time_stamp.2</th>\n",
              "      <th>Ax.2</th>\n",
              "      <th>Ay.2</th>\n",
              "      <th>Az.2</th>\n",
              "      <th>Lx.2</th>\n",
              "      <th>Ly.2</th>\n",
              "      <th>Lz.2</th>\n",
              "      <th>Gx.2</th>\n",
              "      <th>Gy.2</th>\n",
              "      <th>Gz.2</th>\n",
              "      <th>Mx.2</th>\n",
              "      <th>My.2</th>\n",
              "      <th>Mz.2</th>\n",
              "      <th>Unnamed: 41</th>\n",
              "      <th>time_stamp.3</th>\n",
              "      <th>Ax.3</th>\n",
              "      <th>Ay.3</th>\n",
              "      <th>Az.3</th>\n",
              "      <th>Lx.3</th>\n",
              "      <th>Ly.3</th>\n",
              "      <th>Lz.3</th>\n",
              "      <th>Gx.3</th>\n",
              "      <th>Gy.3</th>\n",
              "      <th>Gz.3</th>\n",
              "      <th>Mx.3</th>\n",
              "      <th>My.3</th>\n",
              "      <th>Mz.3</th>\n",
              "      <th>Unnamed: 55</th>\n",
              "      <th>time_stamp.4</th>\n",
              "      <th>Ax.4</th>\n",
              "      <th>Ay.4</th>\n",
              "      <th>Az.4</th>\n",
              "      <th>Lx.4</th>\n",
              "      <th>Ly.4</th>\n",
              "      <th>Lz.4</th>\n",
              "      <th>Gx.4</th>\n",
              "      <th>Gy.4</th>\n",
              "      <th>Gz.4</th>\n",
              "      <th>Mx.4</th>\n",
              "      <th>My.4</th>\n",
              "      <th>Mz.4</th>\n",
              "      <th>Unnamed: 69</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-1.81150</td>\n",
              "      <td>-14.8730</td>\n",
              "      <td>-1.34840</td>\n",
              "      <td>-1.269100</td>\n",
              "      <td>-5.105700</td>\n",
              "      <td>-0.664450</td>\n",
              "      <td>-0.532060</td>\n",
              "      <td>-3.18690</td>\n",
              "      <td>0.239760</td>\n",
              "      <td>12.72</td>\n",
              "      <td>40.74</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-1.19860</td>\n",
              "      <td>-13.8520</td>\n",
              "      <td>3.786500</td>\n",
              "      <td>-1.76980</td>\n",
              "      <td>-4.06250</td>\n",
              "      <td>3.89850</td>\n",
              "      <td>-3.62550</td>\n",
              "      <td>1.07390</td>\n",
              "      <td>0.136220</td>\n",
              "      <td>18.84</td>\n",
              "      <td>55.92</td>\n",
              "      <td>-6.72</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.17706</td>\n",
              "      <td>-10.5690</td>\n",
              "      <td>1.82510</td>\n",
              "      <td>-7.5051</td>\n",
              "      <td>-4.7736</td>\n",
              "      <td>-0.062476</td>\n",
              "      <td>-0.366520</td>\n",
              "      <td>1.27060</td>\n",
              "      <td>-0.52412</td>\n",
              "      <td>-30.30</td>\n",
              "      <td>31.08</td>\n",
              "      <td>3.96</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.79220</td>\n",
              "      <td>-12.5720</td>\n",
              "      <td>-4.3177</td>\n",
              "      <td>-0.098712</td>\n",
              "      <td>-3.385700</td>\n",
              "      <td>-2.46480</td>\n",
              "      <td>0.021075</td>\n",
              "      <td>1.029900</td>\n",
              "      <td>1.22690</td>\n",
              "      <td>-18.72</td>\n",
              "      <td>31.74</td>\n",
              "      <td>17.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>4.3177</td>\n",
              "      <td>-2.36990</td>\n",
              "      <td>-0.43585</td>\n",
              "      <td>-5.35740</td>\n",
              "      <td>-0.95749</td>\n",
              "      <td>0.318860</td>\n",
              "      <td>0.52565</td>\n",
              "      <td>-0.18815</td>\n",
              "      <td>0.285880</td>\n",
              "      <td>-22.20</td>\n",
              "      <td>6.48</td>\n",
              "      <td>4.56</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.24517</td>\n",
              "      <td>-14.0700</td>\n",
              "      <td>-0.84446</td>\n",
              "      <td>0.701470</td>\n",
              "      <td>-4.296900</td>\n",
              "      <td>-0.171990</td>\n",
              "      <td>-0.252290</td>\n",
              "      <td>-1.79660</td>\n",
              "      <td>0.407450</td>\n",
              "      <td>12.54</td>\n",
              "      <td>40.74</td>\n",
              "      <td>-6.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-2.38360</td>\n",
              "      <td>-16.5900</td>\n",
              "      <td>2.996500</td>\n",
              "      <td>-2.70250</td>\n",
              "      <td>-6.78900</td>\n",
              "      <td>2.86820</td>\n",
              "      <td>-3.48990</td>\n",
              "      <td>0.39034</td>\n",
              "      <td>0.400730</td>\n",
              "      <td>18.60</td>\n",
              "      <td>55.86</td>\n",
              "      <td>-6.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.16344</td>\n",
              "      <td>-12.1900</td>\n",
              "      <td>2.07030</td>\n",
              "      <td>-7.3573</td>\n",
              "      <td>-6.2618</td>\n",
              "      <td>-0.042442</td>\n",
              "      <td>-0.544590</td>\n",
              "      <td>1.28070</td>\n",
              "      <td>0.34025</td>\n",
              "      <td>-31.02</td>\n",
              "      <td>29.88</td>\n",
              "      <td>3.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.38360</td>\n",
              "      <td>-10.9920</td>\n",
              "      <td>-4.6037</td>\n",
              "      <td>-0.404440</td>\n",
              "      <td>-1.774500</td>\n",
              "      <td>-2.74830</td>\n",
              "      <td>0.201890</td>\n",
              "      <td>0.535730</td>\n",
              "      <td>1.32280</td>\n",
              "      <td>-18.84</td>\n",
              "      <td>31.74</td>\n",
              "      <td>17.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>5.1213</td>\n",
              "      <td>-2.19290</td>\n",
              "      <td>-0.70826</td>\n",
              "      <td>-4.53520</td>\n",
              "      <td>-0.67629</td>\n",
              "      <td>0.081656</td>\n",
              "      <td>0.43616</td>\n",
              "      <td>0.16371</td>\n",
              "      <td>0.198230</td>\n",
              "      <td>-21.90</td>\n",
              "      <td>7.14</td>\n",
              "      <td>5.34</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.57205</td>\n",
              "      <td>-14.6280</td>\n",
              "      <td>-1.75700</td>\n",
              "      <td>-0.217600</td>\n",
              "      <td>-4.853100</td>\n",
              "      <td>-1.056500</td>\n",
              "      <td>-1.049200</td>\n",
              "      <td>0.29138</td>\n",
              "      <td>0.292300</td>\n",
              "      <td>12.42</td>\n",
              "      <td>40.68</td>\n",
              "      <td>-8.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-4.63090</td>\n",
              "      <td>-16.6030</td>\n",
              "      <td>2.070300</td>\n",
              "      <td>-4.34550</td>\n",
              "      <td>-6.80810</td>\n",
              "      <td>1.68890</td>\n",
              "      <td>-3.38510</td>\n",
              "      <td>-1.99200</td>\n",
              "      <td>1.122800</td>\n",
              "      <td>18.72</td>\n",
              "      <td>55.80</td>\n",
              "      <td>-4.26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.57205</td>\n",
              "      <td>-12.6260</td>\n",
              "      <td>2.24740</td>\n",
              "      <td>-6.7655</td>\n",
              "      <td>-6.5138</td>\n",
              "      <td>0.017921</td>\n",
              "      <td>-0.709520</td>\n",
              "      <td>1.15820</td>\n",
              "      <td>0.99724</td>\n",
              "      <td>-31.26</td>\n",
              "      <td>29.22</td>\n",
              "      <td>3.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>1.56630</td>\n",
              "      <td>-9.0848</td>\n",
              "      <td>-4.5764</td>\n",
              "      <td>-1.003600</td>\n",
              "      <td>0.201530</td>\n",
              "      <td>-2.75150</td>\n",
              "      <td>0.287720</td>\n",
              "      <td>0.100490</td>\n",
              "      <td>1.20710</td>\n",
              "      <td>-19.02</td>\n",
              "      <td>31.68</td>\n",
              "      <td>17.52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>5.6116</td>\n",
              "      <td>-2.12480</td>\n",
              "      <td>-1.26670</td>\n",
              "      <td>-4.04080</td>\n",
              "      <td>-0.55974</td>\n",
              "      <td>-0.523690</td>\n",
              "      <td>0.32284</td>\n",
              "      <td>0.43127</td>\n",
              "      <td>0.211970</td>\n",
              "      <td>-21.60</td>\n",
              "      <td>7.50</td>\n",
              "      <td>5.64</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.69464</td>\n",
              "      <td>-12.9390</td>\n",
              "      <td>-3.09180</td>\n",
              "      <td>-0.322730</td>\n",
              "      <td>-3.178600</td>\n",
              "      <td>-2.219700</td>\n",
              "      <td>-2.372000</td>\n",
              "      <td>1.00820</td>\n",
              "      <td>0.349720</td>\n",
              "      <td>12.30</td>\n",
              "      <td>40.74</td>\n",
              "      <td>-8.52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-4.78070</td>\n",
              "      <td>-16.4530</td>\n",
              "      <td>1.157700</td>\n",
              "      <td>-4.19540</td>\n",
              "      <td>-6.67310</td>\n",
              "      <td>0.74084</td>\n",
              "      <td>-3.10170</td>\n",
              "      <td>-3.04390</td>\n",
              "      <td>0.791380</td>\n",
              "      <td>18.84</td>\n",
              "      <td>55.62</td>\n",
              "      <td>-3.12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>1.21220</td>\n",
              "      <td>-12.4760</td>\n",
              "      <td>2.28820</td>\n",
              "      <td>-5.9716</td>\n",
              "      <td>-6.1990</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>-0.781910</td>\n",
              "      <td>1.10200</td>\n",
              "      <td>1.33380</td>\n",
              "      <td>-31.44</td>\n",
              "      <td>28.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.61292</td>\n",
              "      <td>-8.5263</td>\n",
              "      <td>-2.9284</td>\n",
              "      <td>-2.827100</td>\n",
              "      <td>0.859490</td>\n",
              "      <td>-1.14670</td>\n",
              "      <td>0.175930</td>\n",
              "      <td>-0.214410</td>\n",
              "      <td>0.90469</td>\n",
              "      <td>-18.96</td>\n",
              "      <td>31.86</td>\n",
              "      <td>17.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>6.0611</td>\n",
              "      <td>-1.47100</td>\n",
              "      <td>-2.47890</td>\n",
              "      <td>-3.59080</td>\n",
              "      <td>0.15795</td>\n",
              "      <td>-1.880200</td>\n",
              "      <td>0.20403</td>\n",
              "      <td>0.72846</td>\n",
              "      <td>0.164930</td>\n",
              "      <td>-21.12</td>\n",
              "      <td>8.40</td>\n",
              "      <td>6.12</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.87170</td>\n",
              "      <td>-12.0000</td>\n",
              "      <td>-1.56630</td>\n",
              "      <td>1.137400</td>\n",
              "      <td>-2.239000</td>\n",
              "      <td>-0.654760</td>\n",
              "      <td>-2.790100</td>\n",
              "      <td>0.65485</td>\n",
              "      <td>0.227240</td>\n",
              "      <td>12.24</td>\n",
              "      <td>40.80</td>\n",
              "      <td>-8.88</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-0.92618</td>\n",
              "      <td>-12.1900</td>\n",
              "      <td>-1.062400</td>\n",
              "      <td>-0.18116</td>\n",
              "      <td>-2.41440</td>\n",
              "      <td>-1.28500</td>\n",
              "      <td>-2.75870</td>\n",
              "      <td>-4.46020</td>\n",
              "      <td>0.693640</td>\n",
              "      <td>19.38</td>\n",
              "      <td>55.44</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.24740</td>\n",
              "      <td>-11.3870</td>\n",
              "      <td>1.98860</td>\n",
              "      <td>-4.6603</td>\n",
              "      <td>-4.8230</td>\n",
              "      <td>-0.329740</td>\n",
              "      <td>-0.824360</td>\n",
              "      <td>0.90561</td>\n",
              "      <td>2.15600</td>\n",
              "      <td>-31.56</td>\n",
              "      <td>28.32</td>\n",
              "      <td>3.30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-1.18500</td>\n",
              "      <td>-9.3436</td>\n",
              "      <td>-2.2337</td>\n",
              "      <td>-3.209900</td>\n",
              "      <td>0.085583</td>\n",
              "      <td>-0.45569</td>\n",
              "      <td>-0.033292</td>\n",
              "      <td>-0.486250</td>\n",
              "      <td>0.60720</td>\n",
              "      <td>-18.84</td>\n",
              "      <td>32.10</td>\n",
              "      <td>17.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>7.7091</td>\n",
              "      <td>-0.77636</td>\n",
              "      <td>-3.54130</td>\n",
              "      <td>-1.94500</td>\n",
              "      <td>0.85796</td>\n",
              "      <td>-2.995300</td>\n",
              "      <td>0.09896</td>\n",
              "      <td>0.94379</td>\n",
              "      <td>0.064752</td>\n",
              "      <td>-20.94</td>\n",
              "      <td>8.70</td>\n",
              "      <td>6.30</td>\n",
              "      <td>walking</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <th>62995</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-3.11910</td>\n",
              "      <td>-9.0167</td>\n",
              "      <td>-1.51190</td>\n",
              "      <td>2.289100</td>\n",
              "      <td>-0.836530</td>\n",
              "      <td>-1.422500</td>\n",
              "      <td>0.430050</td>\n",
              "      <td>-0.50946</td>\n",
              "      <td>0.111790</td>\n",
              "      <td>15.30</td>\n",
              "      <td>27.24</td>\n",
              "      <td>-15.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>3.86820</td>\n",
              "      <td>-9.9020</td>\n",
              "      <td>0.354130</td>\n",
              "      <td>3.55870</td>\n",
              "      <td>-0.18493</td>\n",
              "      <td>-0.93169</td>\n",
              "      <td>-0.20617</td>\n",
              "      <td>0.28253</td>\n",
              "      <td>0.230910</td>\n",
              "      <td>29.34</td>\n",
              "      <td>47.94</td>\n",
              "      <td>0.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>4.41300</td>\n",
              "      <td>-7.4095</td>\n",
              "      <td>0.42223</td>\n",
              "      <td>1.7435</td>\n",
              "      <td>2.0216</td>\n",
              "      <td>0.737300</td>\n",
              "      <td>-0.706470</td>\n",
              "      <td>1.86830</td>\n",
              "      <td>1.32340</td>\n",
              "      <td>29.46</td>\n",
              "      <td>44.22</td>\n",
              "      <td>-7.08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.47890</td>\n",
              "      <td>-8.5808</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>1.965300</td>\n",
              "      <td>1.044700</td>\n",
              "      <td>-0.85143</td>\n",
              "      <td>0.082772</td>\n",
              "      <td>-0.083078</td>\n",
              "      <td>0.53420</td>\n",
              "      <td>21.78</td>\n",
              "      <td>45.36</td>\n",
              "      <td>10.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>9.0575</td>\n",
              "      <td>-1.94770</td>\n",
              "      <td>-1.28030</td>\n",
              "      <td>0.54756</td>\n",
              "      <td>2.91870</td>\n",
              "      <td>-1.543100</td>\n",
              "      <td>-0.74678</td>\n",
              "      <td>0.31857</td>\n",
              "      <td>0.103540</td>\n",
              "      <td>27.06</td>\n",
              "      <td>27.30</td>\n",
              "      <td>-76.92</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62996</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-4.56280</td>\n",
              "      <td>-7.5321</td>\n",
              "      <td>0.70826</td>\n",
              "      <td>0.843960</td>\n",
              "      <td>0.648940</td>\n",
              "      <td>0.615980</td>\n",
              "      <td>0.197920</td>\n",
              "      <td>-0.67959</td>\n",
              "      <td>-0.092546</td>\n",
              "      <td>14.94</td>\n",
              "      <td>27.00</td>\n",
              "      <td>-16.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>3.94990</td>\n",
              "      <td>-9.5206</td>\n",
              "      <td>0.054481</td>\n",
              "      <td>3.63060</td>\n",
              "      <td>0.20627</td>\n",
              "      <td>-1.15210</td>\n",
              "      <td>-0.25137</td>\n",
              "      <td>0.32223</td>\n",
              "      <td>0.107210</td>\n",
              "      <td>29.58</td>\n",
              "      <td>47.82</td>\n",
              "      <td>0.60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>4.33130</td>\n",
              "      <td>-5.5843</td>\n",
              "      <td>-0.17706</td>\n",
              "      <td>1.8355</td>\n",
              "      <td>3.8941</td>\n",
              "      <td>0.139460</td>\n",
              "      <td>-0.423640</td>\n",
              "      <td>1.70460</td>\n",
              "      <td>1.10470</td>\n",
              "      <td>30.48</td>\n",
              "      <td>43.80</td>\n",
              "      <td>-6.54</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>1.34840</td>\n",
              "      <td>-7.3005</td>\n",
              "      <td>-2.1112</td>\n",
              "      <td>0.970760</td>\n",
              "      <td>2.331200</td>\n",
              "      <td>-0.30611</td>\n",
              "      <td>-0.022602</td>\n",
              "      <td>-0.306350</td>\n",
              "      <td>0.53665</td>\n",
              "      <td>21.66</td>\n",
              "      <td>44.88</td>\n",
              "      <td>10.62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>9.1665</td>\n",
              "      <td>-2.17930</td>\n",
              "      <td>-1.29390</td>\n",
              "      <td>0.65620</td>\n",
              "      <td>2.68710</td>\n",
              "      <td>-1.546200</td>\n",
              "      <td>-0.35308</td>\n",
              "      <td>0.35125</td>\n",
              "      <td>0.221440</td>\n",
              "      <td>26.94</td>\n",
              "      <td>27.30</td>\n",
              "      <td>-76.80</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62997</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-5.40730</td>\n",
              "      <td>-7.8045</td>\n",
              "      <td>0.87170</td>\n",
              "      <td>-0.022973</td>\n",
              "      <td>0.389340</td>\n",
              "      <td>0.668480</td>\n",
              "      <td>0.193340</td>\n",
              "      <td>-0.65088</td>\n",
              "      <td>-0.208000</td>\n",
              "      <td>14.64</td>\n",
              "      <td>27.12</td>\n",
              "      <td>-16.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>3.43230</td>\n",
              "      <td>-9.4798</td>\n",
              "      <td>-0.313270</td>\n",
              "      <td>3.09120</td>\n",
              "      <td>0.24997</td>\n",
              "      <td>-1.49070</td>\n",
              "      <td>-0.13531</td>\n",
              "      <td>0.25351</td>\n",
              "      <td>-0.073609</td>\n",
              "      <td>29.76</td>\n",
              "      <td>47.64</td>\n",
              "      <td>0.96</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>4.41300</td>\n",
              "      <td>-5.3528</td>\n",
              "      <td>-0.36775</td>\n",
              "      <td>1.9846</td>\n",
              "      <td>4.1434</td>\n",
              "      <td>-0.059238</td>\n",
              "      <td>-0.071471</td>\n",
              "      <td>0.90927</td>\n",
              "      <td>0.86834</td>\n",
              "      <td>31.86</td>\n",
              "      <td>43.44</td>\n",
              "      <td>-5.88</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.68102</td>\n",
              "      <td>-6.7693</td>\n",
              "      <td>-2.0839</td>\n",
              "      <td>0.408010</td>\n",
              "      <td>2.860900</td>\n",
              "      <td>-0.25252</td>\n",
              "      <td>-0.125840</td>\n",
              "      <td>-0.260530</td>\n",
              "      <td>0.51740</td>\n",
              "      <td>21.90</td>\n",
              "      <td>44.76</td>\n",
              "      <td>10.68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>10.0380</td>\n",
              "      <td>-3.21440</td>\n",
              "      <td>-1.51190</td>\n",
              "      <td>1.54690</td>\n",
              "      <td>1.68460</td>\n",
              "      <td>-1.771800</td>\n",
              "      <td>-0.28711</td>\n",
              "      <td>0.19487</td>\n",
              "      <td>0.210750</td>\n",
              "      <td>26.58</td>\n",
              "      <td>27.12</td>\n",
              "      <td>-77.04</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62998</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-6.04740</td>\n",
              "      <td>-8.1177</td>\n",
              "      <td>0.25879</td>\n",
              "      <td>-0.695410</td>\n",
              "      <td>0.094103</td>\n",
              "      <td>-0.044593</td>\n",
              "      <td>0.188760</td>\n",
              "      <td>-0.50824</td>\n",
              "      <td>-0.164630</td>\n",
              "      <td>14.58</td>\n",
              "      <td>27.24</td>\n",
              "      <td>-16.74</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.43800</td>\n",
              "      <td>-9.5615</td>\n",
              "      <td>-0.858080</td>\n",
              "      <td>2.04180</td>\n",
              "      <td>0.17223</td>\n",
              "      <td>-1.98430</td>\n",
              "      <td>-0.16768</td>\n",
              "      <td>0.29963</td>\n",
              "      <td>-0.204330</td>\n",
              "      <td>29.76</td>\n",
              "      <td>47.46</td>\n",
              "      <td>1.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>4.75350</td>\n",
              "      <td>-7.1234</td>\n",
              "      <td>-0.64016</td>\n",
              "      <td>2.5049</td>\n",
              "      <td>2.4179</td>\n",
              "      <td>-0.364150</td>\n",
              "      <td>0.075747</td>\n",
              "      <td>0.20892</td>\n",
              "      <td>0.75381</td>\n",
              "      <td>33.78</td>\n",
              "      <td>43.08</td>\n",
              "      <td>-4.74</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>0.76274</td>\n",
              "      <td>-6.8647</td>\n",
              "      <td>-2.2882</td>\n",
              "      <td>0.606280</td>\n",
              "      <td>2.758200</td>\n",
              "      <td>-0.40523</td>\n",
              "      <td>-0.161880</td>\n",
              "      <td>-0.039095</td>\n",
              "      <td>0.43952</td>\n",
              "      <td>21.78</td>\n",
              "      <td>44.46</td>\n",
              "      <td>10.86</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>9.6296</td>\n",
              "      <td>-4.22230</td>\n",
              "      <td>-1.70250</td>\n",
              "      <td>1.14830</td>\n",
              "      <td>0.69598</td>\n",
              "      <td>-1.924200</td>\n",
              "      <td>-0.32742</td>\n",
              "      <td>-0.16066</td>\n",
              "      <td>0.114230</td>\n",
              "      <td>26.46</td>\n",
              "      <td>27.18</td>\n",
              "      <td>-76.92</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62999</th>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>-6.12920</td>\n",
              "      <td>-8.3493</td>\n",
              "      <td>-1.26670</td>\n",
              "      <td>-0.798490</td>\n",
              "      <td>-0.127550</td>\n",
              "      <td>-1.663900</td>\n",
              "      <td>0.081856</td>\n",
              "      <td>-0.45662</td>\n",
              "      <td>-0.041539</td>\n",
              "      <td>14.28</td>\n",
              "      <td>27.18</td>\n",
              "      <td>-17.34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>2.77860</td>\n",
              "      <td>-10.0380</td>\n",
              "      <td>0.490330</td>\n",
              "      <td>2.28560</td>\n",
              "      <td>-0.29845</td>\n",
              "      <td>-0.54154</td>\n",
              "      <td>-0.43768</td>\n",
              "      <td>0.27764</td>\n",
              "      <td>-0.260530</td>\n",
              "      <td>29.70</td>\n",
              "      <td>47.34</td>\n",
              "      <td>1.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>4.72630</td>\n",
              "      <td>-8.4719</td>\n",
              "      <td>-0.39499</td>\n",
              "      <td>2.5824</td>\n",
              "      <td>1.0941</td>\n",
              "      <td>-0.135490</td>\n",
              "      <td>0.114230</td>\n",
              "      <td>-0.43127</td>\n",
              "      <td>0.67531</td>\n",
              "      <td>34.86</td>\n",
              "      <td>42.90</td>\n",
              "      <td>-4.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>1.07600</td>\n",
              "      <td>-6.9872</td>\n",
              "      <td>-2.4244</td>\n",
              "      <td>0.968590</td>\n",
              "      <td>2.629600</td>\n",
              "      <td>-0.50732</td>\n",
              "      <td>-0.174400</td>\n",
              "      <td>0.116680</td>\n",
              "      <td>0.29932</td>\n",
              "      <td>21.60</td>\n",
              "      <td>44.52</td>\n",
              "      <td>10.92</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.390000e+12</td>\n",
              "      <td>8.8668</td>\n",
              "      <td>-4.38580</td>\n",
              "      <td>-2.09750</td>\n",
              "      <td>0.39406</td>\n",
              "      <td>0.55024</td>\n",
              "      <td>-2.233000</td>\n",
              "      <td>-0.36469</td>\n",
              "      <td>-0.29230</td>\n",
              "      <td>0.063225</td>\n",
              "      <td>26.28</td>\n",
              "      <td>27.06</td>\n",
              "      <td>-76.80</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>630000 rows × 70 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         time_stamp       Ax       Ay       Az  ...   Mx.4   My.4   Mz.4  Unnamed: 69\n",
              "0      1.390000e+12 -1.81150 -14.8730 -1.34840  ... -22.20   6.48   4.56      walking\n",
              "1      1.390000e+12  0.24517 -14.0700 -0.84446  ... -21.90   7.14   5.34      walking\n",
              "2      1.390000e+12 -0.57205 -14.6280 -1.75700  ... -21.60   7.50   5.64      walking\n",
              "3      1.390000e+12 -0.69464 -12.9390 -3.09180  ... -21.12   8.40   6.12      walking\n",
              "4      1.390000e+12  0.87170 -12.0000 -1.56630  ... -20.94   8.70   6.30      walking\n",
              "...             ...      ...      ...      ...  ...    ...    ...    ...          ...\n",
              "62995  1.390000e+12 -3.11910  -9.0167 -1.51190  ...  27.06  27.30 -76.92   downstairs\n",
              "62996  1.390000e+12 -4.56280  -7.5321  0.70826  ...  26.94  27.30 -76.80   downstairs\n",
              "62997  1.390000e+12 -5.40730  -7.8045  0.87170  ...  26.58  27.12 -77.04   downstairs\n",
              "62998  1.390000e+12 -6.04740  -8.1177  0.25879  ...  26.46  27.18 -76.92   downstairs\n",
              "62999  1.390000e+12 -6.12920  -8.3493 -1.26670  ...  26.28  27.06 -76.80   downstairs\n",
              "\n",
              "[630000 rows x 70 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8Zxw4gy3CeV"
      },
      "source": [
        ""
      ],
      "id": "b8Zxw4gy3CeV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "familiar-bottom",
        "outputId": "4f58a122-c191-444e-e65b-8708d7c5bc80"
      },
      "source": [
        "\n",
        "for i in df.columns:\n",
        "    print(i)"
      ],
      "id": "familiar-bottom",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time_stamp\n",
            "Ax\n",
            "Ay\n",
            "Az\n",
            "Lx\n",
            "Ly\n",
            "Lz\n",
            "Gx\n",
            "Gy\n",
            "Gz\n",
            "Mx\n",
            "My\n",
            "Mz\n",
            "Unnamed: 13\n",
            "time_stamp.1\n",
            "Ax.1\n",
            "Ay.1\n",
            "Az.1\n",
            "Lx.1\n",
            "Ly.1\n",
            "Lz.1\n",
            "Gx.1\n",
            "Gy.1\n",
            "Gz.1\n",
            "Mx.1\n",
            "My.1\n",
            "Mz.1\n",
            "Unnamed: 27\n",
            "time_stamp.2\n",
            "Ax.2\n",
            "Ay.2\n",
            "Az.2\n",
            "Lx.2\n",
            "Ly.2\n",
            "Lz.2\n",
            "Gx.2\n",
            "Gy.2\n",
            "Gz.2\n",
            "Mx.2\n",
            "My.2\n",
            "Mz.2\n",
            "Unnamed: 41\n",
            "time_stamp.3\n",
            "Ax.3\n",
            "Ay.3\n",
            "Az.3\n",
            "Lx.3\n",
            "Ly.3\n",
            "Lz.3\n",
            "Gx.3\n",
            "Gy.3\n",
            "Gz.3\n",
            "Mx.3\n",
            "My.3\n",
            "Mz.3\n",
            "Unnamed: 55\n",
            "time_stamp.4\n",
            "Ax.4\n",
            "Ay.4\n",
            "Az.4\n",
            "Lx.4\n",
            "Ly.4\n",
            "Lz.4\n",
            "Gx.4\n",
            "Gy.4\n",
            "Gz.4\n",
            "Mx.4\n",
            "My.4\n",
            "Mz.4\n",
            "Unnamed: 69\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "pressing-round",
        "outputId": "36922fee-7bc1-44e1-c075-c52803928238"
      },
      "source": [
        "# split out left and right pocket data\n",
        "\n",
        "left_pocket = df[df.columns[1:10]]\n",
        "left_pocket"
      ],
      "id": "pressing-round",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Lx</th>\n",
              "      <th>Ly</th>\n",
              "      <th>Lz</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.81150</td>\n",
              "      <td>-14.8730</td>\n",
              "      <td>-1.34840</td>\n",
              "      <td>-1.269100</td>\n",
              "      <td>-5.105700</td>\n",
              "      <td>-0.664450</td>\n",
              "      <td>-0.532060</td>\n",
              "      <td>-3.18690</td>\n",
              "      <td>0.239760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.24517</td>\n",
              "      <td>-14.0700</td>\n",
              "      <td>-0.84446</td>\n",
              "      <td>0.701470</td>\n",
              "      <td>-4.296900</td>\n",
              "      <td>-0.171990</td>\n",
              "      <td>-0.252290</td>\n",
              "      <td>-1.79660</td>\n",
              "      <td>0.407450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.57205</td>\n",
              "      <td>-14.6280</td>\n",
              "      <td>-1.75700</td>\n",
              "      <td>-0.217600</td>\n",
              "      <td>-4.853100</td>\n",
              "      <td>-1.056500</td>\n",
              "      <td>-1.049200</td>\n",
              "      <td>0.29138</td>\n",
              "      <td>0.292300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.69464</td>\n",
              "      <td>-12.9390</td>\n",
              "      <td>-3.09180</td>\n",
              "      <td>-0.322730</td>\n",
              "      <td>-3.178600</td>\n",
              "      <td>-2.219700</td>\n",
              "      <td>-2.372000</td>\n",
              "      <td>1.00820</td>\n",
              "      <td>0.349720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.87170</td>\n",
              "      <td>-12.0000</td>\n",
              "      <td>-1.56630</td>\n",
              "      <td>1.137400</td>\n",
              "      <td>-2.239000</td>\n",
              "      <td>-0.654760</td>\n",
              "      <td>-2.790100</td>\n",
              "      <td>0.65485</td>\n",
              "      <td>0.227240</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62995</th>\n",
              "      <td>-3.11910</td>\n",
              "      <td>-9.0167</td>\n",
              "      <td>-1.51190</td>\n",
              "      <td>2.289100</td>\n",
              "      <td>-0.836530</td>\n",
              "      <td>-1.422500</td>\n",
              "      <td>0.430050</td>\n",
              "      <td>-0.50946</td>\n",
              "      <td>0.111790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62996</th>\n",
              "      <td>-4.56280</td>\n",
              "      <td>-7.5321</td>\n",
              "      <td>0.70826</td>\n",
              "      <td>0.843960</td>\n",
              "      <td>0.648940</td>\n",
              "      <td>0.615980</td>\n",
              "      <td>0.197920</td>\n",
              "      <td>-0.67959</td>\n",
              "      <td>-0.092546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62997</th>\n",
              "      <td>-5.40730</td>\n",
              "      <td>-7.8045</td>\n",
              "      <td>0.87170</td>\n",
              "      <td>-0.022973</td>\n",
              "      <td>0.389340</td>\n",
              "      <td>0.668480</td>\n",
              "      <td>0.193340</td>\n",
              "      <td>-0.65088</td>\n",
              "      <td>-0.208000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62998</th>\n",
              "      <td>-6.04740</td>\n",
              "      <td>-8.1177</td>\n",
              "      <td>0.25879</td>\n",
              "      <td>-0.695410</td>\n",
              "      <td>0.094103</td>\n",
              "      <td>-0.044593</td>\n",
              "      <td>0.188760</td>\n",
              "      <td>-0.50824</td>\n",
              "      <td>-0.164630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62999</th>\n",
              "      <td>-6.12920</td>\n",
              "      <td>-8.3493</td>\n",
              "      <td>-1.26670</td>\n",
              "      <td>-0.798490</td>\n",
              "      <td>-0.127550</td>\n",
              "      <td>-1.663900</td>\n",
              "      <td>0.081856</td>\n",
              "      <td>-0.45662</td>\n",
              "      <td>-0.041539</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>630000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Ax       Ay       Az  ...        Gx       Gy        Gz\n",
              "0     -1.81150 -14.8730 -1.34840  ... -0.532060 -3.18690  0.239760\n",
              "1      0.24517 -14.0700 -0.84446  ... -0.252290 -1.79660  0.407450\n",
              "2     -0.57205 -14.6280 -1.75700  ... -1.049200  0.29138  0.292300\n",
              "3     -0.69464 -12.9390 -3.09180  ... -2.372000  1.00820  0.349720\n",
              "4      0.87170 -12.0000 -1.56630  ... -2.790100  0.65485  0.227240\n",
              "...        ...      ...      ...  ...       ...      ...       ...\n",
              "62995 -3.11910  -9.0167 -1.51190  ...  0.430050 -0.50946  0.111790\n",
              "62996 -4.56280  -7.5321  0.70826  ...  0.197920 -0.67959 -0.092546\n",
              "62997 -5.40730  -7.8045  0.87170  ...  0.193340 -0.65088 -0.208000\n",
              "62998 -6.04740  -8.1177  0.25879  ...  0.188760 -0.50824 -0.164630\n",
              "62999 -6.12920  -8.3493 -1.26670  ...  0.081856 -0.45662 -0.041539\n",
              "\n",
              "[630000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "quick-allocation",
        "outputId": "16841d85-7364-45c5-f84b-624387049223"
      },
      "source": [
        "right_pocket = df[df.columns[15:24]]\n",
        "right_pocket.columns=left_pocket.columns\n",
        "right_pocket"
      ],
      "id": "quick-allocation",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Lx</th>\n",
              "      <th>Ly</th>\n",
              "      <th>Lz</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.19860</td>\n",
              "      <td>-13.8520</td>\n",
              "      <td>3.786500</td>\n",
              "      <td>-1.76980</td>\n",
              "      <td>-4.06250</td>\n",
              "      <td>3.89850</td>\n",
              "      <td>-3.62550</td>\n",
              "      <td>1.07390</td>\n",
              "      <td>0.136220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.38360</td>\n",
              "      <td>-16.5900</td>\n",
              "      <td>2.996500</td>\n",
              "      <td>-2.70250</td>\n",
              "      <td>-6.78900</td>\n",
              "      <td>2.86820</td>\n",
              "      <td>-3.48990</td>\n",
              "      <td>0.39034</td>\n",
              "      <td>0.400730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-4.63090</td>\n",
              "      <td>-16.6030</td>\n",
              "      <td>2.070300</td>\n",
              "      <td>-4.34550</td>\n",
              "      <td>-6.80810</td>\n",
              "      <td>1.68890</td>\n",
              "      <td>-3.38510</td>\n",
              "      <td>-1.99200</td>\n",
              "      <td>1.122800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-4.78070</td>\n",
              "      <td>-16.4530</td>\n",
              "      <td>1.157700</td>\n",
              "      <td>-4.19540</td>\n",
              "      <td>-6.67310</td>\n",
              "      <td>0.74084</td>\n",
              "      <td>-3.10170</td>\n",
              "      <td>-3.04390</td>\n",
              "      <td>0.791380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.92618</td>\n",
              "      <td>-12.1900</td>\n",
              "      <td>-1.062400</td>\n",
              "      <td>-0.18116</td>\n",
              "      <td>-2.41440</td>\n",
              "      <td>-1.28500</td>\n",
              "      <td>-2.75870</td>\n",
              "      <td>-4.46020</td>\n",
              "      <td>0.693640</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62995</th>\n",
              "      <td>3.86820</td>\n",
              "      <td>-9.9020</td>\n",
              "      <td>0.354130</td>\n",
              "      <td>3.55870</td>\n",
              "      <td>-0.18493</td>\n",
              "      <td>-0.93169</td>\n",
              "      <td>-0.20617</td>\n",
              "      <td>0.28253</td>\n",
              "      <td>0.230910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62996</th>\n",
              "      <td>3.94990</td>\n",
              "      <td>-9.5206</td>\n",
              "      <td>0.054481</td>\n",
              "      <td>3.63060</td>\n",
              "      <td>0.20627</td>\n",
              "      <td>-1.15210</td>\n",
              "      <td>-0.25137</td>\n",
              "      <td>0.32223</td>\n",
              "      <td>0.107210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62997</th>\n",
              "      <td>3.43230</td>\n",
              "      <td>-9.4798</td>\n",
              "      <td>-0.313270</td>\n",
              "      <td>3.09120</td>\n",
              "      <td>0.24997</td>\n",
              "      <td>-1.49070</td>\n",
              "      <td>-0.13531</td>\n",
              "      <td>0.25351</td>\n",
              "      <td>-0.073609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62998</th>\n",
              "      <td>2.43800</td>\n",
              "      <td>-9.5615</td>\n",
              "      <td>-0.858080</td>\n",
              "      <td>2.04180</td>\n",
              "      <td>0.17223</td>\n",
              "      <td>-1.98430</td>\n",
              "      <td>-0.16768</td>\n",
              "      <td>0.29963</td>\n",
              "      <td>-0.204330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62999</th>\n",
              "      <td>2.77860</td>\n",
              "      <td>-10.0380</td>\n",
              "      <td>0.490330</td>\n",
              "      <td>2.28560</td>\n",
              "      <td>-0.29845</td>\n",
              "      <td>-0.54154</td>\n",
              "      <td>-0.43768</td>\n",
              "      <td>0.27764</td>\n",
              "      <td>-0.260530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>630000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Ax       Ay        Az       Lx  ...       Lz       Gx       Gy        Gz\n",
              "0     -1.19860 -13.8520  3.786500 -1.76980  ...  3.89850 -3.62550  1.07390  0.136220\n",
              "1     -2.38360 -16.5900  2.996500 -2.70250  ...  2.86820 -3.48990  0.39034  0.400730\n",
              "2     -4.63090 -16.6030  2.070300 -4.34550  ...  1.68890 -3.38510 -1.99200  1.122800\n",
              "3     -4.78070 -16.4530  1.157700 -4.19540  ...  0.74084 -3.10170 -3.04390  0.791380\n",
              "4     -0.92618 -12.1900 -1.062400 -0.18116  ... -1.28500 -2.75870 -4.46020  0.693640\n",
              "...        ...      ...       ...      ...  ...      ...      ...      ...       ...\n",
              "62995  3.86820  -9.9020  0.354130  3.55870  ... -0.93169 -0.20617  0.28253  0.230910\n",
              "62996  3.94990  -9.5206  0.054481  3.63060  ... -1.15210 -0.25137  0.32223  0.107210\n",
              "62997  3.43230  -9.4798 -0.313270  3.09120  ... -1.49070 -0.13531  0.25351 -0.073609\n",
              "62998  2.43800  -9.5615 -0.858080  2.04180  ... -1.98430 -0.16768  0.29963 -0.204330\n",
              "62999  2.77860 -10.0380  0.490330  2.28560  ... -0.54154 -0.43768  0.27764 -0.260530\n",
              "\n",
              "[630000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "cordless-hanging",
        "outputId": "7d7dd8e6-3a16-4ce6-fa77-3832cb125392"
      },
      "source": [
        "#concat left and right split in one dataframe\n",
        "train_df=pd.concat([left_pocket,right_pocket],sort=False)\n",
        "train_df"
      ],
      "id": "cordless-hanging",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Lx</th>\n",
              "      <th>Ly</th>\n",
              "      <th>Lz</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.81150</td>\n",
              "      <td>-14.8730</td>\n",
              "      <td>-1.348400</td>\n",
              "      <td>-1.26910</td>\n",
              "      <td>-5.10570</td>\n",
              "      <td>-0.66445</td>\n",
              "      <td>-0.53206</td>\n",
              "      <td>-3.18690</td>\n",
              "      <td>0.239760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.24517</td>\n",
              "      <td>-14.0700</td>\n",
              "      <td>-0.844460</td>\n",
              "      <td>0.70147</td>\n",
              "      <td>-4.29690</td>\n",
              "      <td>-0.17199</td>\n",
              "      <td>-0.25229</td>\n",
              "      <td>-1.79660</td>\n",
              "      <td>0.407450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.57205</td>\n",
              "      <td>-14.6280</td>\n",
              "      <td>-1.757000</td>\n",
              "      <td>-0.21760</td>\n",
              "      <td>-4.85310</td>\n",
              "      <td>-1.05650</td>\n",
              "      <td>-1.04920</td>\n",
              "      <td>0.29138</td>\n",
              "      <td>0.292300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.69464</td>\n",
              "      <td>-12.9390</td>\n",
              "      <td>-3.091800</td>\n",
              "      <td>-0.32273</td>\n",
              "      <td>-3.17860</td>\n",
              "      <td>-2.21970</td>\n",
              "      <td>-2.37200</td>\n",
              "      <td>1.00820</td>\n",
              "      <td>0.349720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.87170</td>\n",
              "      <td>-12.0000</td>\n",
              "      <td>-1.566300</td>\n",
              "      <td>1.13740</td>\n",
              "      <td>-2.23900</td>\n",
              "      <td>-0.65476</td>\n",
              "      <td>-2.79010</td>\n",
              "      <td>0.65485</td>\n",
              "      <td>0.227240</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62995</th>\n",
              "      <td>3.86820</td>\n",
              "      <td>-9.9020</td>\n",
              "      <td>0.354130</td>\n",
              "      <td>3.55870</td>\n",
              "      <td>-0.18493</td>\n",
              "      <td>-0.93169</td>\n",
              "      <td>-0.20617</td>\n",
              "      <td>0.28253</td>\n",
              "      <td>0.230910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62996</th>\n",
              "      <td>3.94990</td>\n",
              "      <td>-9.5206</td>\n",
              "      <td>0.054481</td>\n",
              "      <td>3.63060</td>\n",
              "      <td>0.20627</td>\n",
              "      <td>-1.15210</td>\n",
              "      <td>-0.25137</td>\n",
              "      <td>0.32223</td>\n",
              "      <td>0.107210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62997</th>\n",
              "      <td>3.43230</td>\n",
              "      <td>-9.4798</td>\n",
              "      <td>-0.313270</td>\n",
              "      <td>3.09120</td>\n",
              "      <td>0.24997</td>\n",
              "      <td>-1.49070</td>\n",
              "      <td>-0.13531</td>\n",
              "      <td>0.25351</td>\n",
              "      <td>-0.073609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62998</th>\n",
              "      <td>2.43800</td>\n",
              "      <td>-9.5615</td>\n",
              "      <td>-0.858080</td>\n",
              "      <td>2.04180</td>\n",
              "      <td>0.17223</td>\n",
              "      <td>-1.98430</td>\n",
              "      <td>-0.16768</td>\n",
              "      <td>0.29963</td>\n",
              "      <td>-0.204330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62999</th>\n",
              "      <td>2.77860</td>\n",
              "      <td>-10.0380</td>\n",
              "      <td>0.490330</td>\n",
              "      <td>2.28560</td>\n",
              "      <td>-0.29845</td>\n",
              "      <td>-0.54154</td>\n",
              "      <td>-0.43768</td>\n",
              "      <td>0.27764</td>\n",
              "      <td>-0.260530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1260000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Ax       Ay        Az       Lx  ...       Lz       Gx       Gy        Gz\n",
              "0     -1.81150 -14.8730 -1.348400 -1.26910  ... -0.66445 -0.53206 -3.18690  0.239760\n",
              "1      0.24517 -14.0700 -0.844460  0.70147  ... -0.17199 -0.25229 -1.79660  0.407450\n",
              "2     -0.57205 -14.6280 -1.757000 -0.21760  ... -1.05650 -1.04920  0.29138  0.292300\n",
              "3     -0.69464 -12.9390 -3.091800 -0.32273  ... -2.21970 -2.37200  1.00820  0.349720\n",
              "4      0.87170 -12.0000 -1.566300  1.13740  ... -0.65476 -2.79010  0.65485  0.227240\n",
              "...        ...      ...       ...      ...  ...      ...      ...      ...       ...\n",
              "62995  3.86820  -9.9020  0.354130  3.55870  ... -0.93169 -0.20617  0.28253  0.230910\n",
              "62996  3.94990  -9.5206  0.054481  3.63060  ... -1.15210 -0.25137  0.32223  0.107210\n",
              "62997  3.43230  -9.4798 -0.313270  3.09120  ... -1.49070 -0.13531  0.25351 -0.073609\n",
              "62998  2.43800  -9.5615 -0.858080  2.04180  ... -1.98430 -0.16768  0.29963 -0.204330\n",
              "62999  2.77860 -10.0380  0.490330  2.28560  ... -0.54154 -0.43768  0.27764 -0.260530\n",
              "\n",
              "[1260000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "located-split",
        "outputId": "fc1e2a08-cc88-4c14-b11d-673050a69806"
      },
      "source": [
        "# restore labels in dataframe\n",
        "labels=pd.concat([df['Unnamed: 69'],df['Unnamed: 69']],axis=0, sort=False)\n",
        "labels.columns=['Activity']\n",
        "train_df['Activity']=labels\n",
        "train_df"
      ],
      "id": "located-split",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Lx</th>\n",
              "      <th>Ly</th>\n",
              "      <th>Lz</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "      <th>Activity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.81150</td>\n",
              "      <td>-14.8730</td>\n",
              "      <td>-1.348400</td>\n",
              "      <td>-1.26910</td>\n",
              "      <td>-5.10570</td>\n",
              "      <td>-0.66445</td>\n",
              "      <td>-0.53206</td>\n",
              "      <td>-3.18690</td>\n",
              "      <td>0.239760</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.24517</td>\n",
              "      <td>-14.0700</td>\n",
              "      <td>-0.844460</td>\n",
              "      <td>0.70147</td>\n",
              "      <td>-4.29690</td>\n",
              "      <td>-0.17199</td>\n",
              "      <td>-0.25229</td>\n",
              "      <td>-1.79660</td>\n",
              "      <td>0.407450</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.57205</td>\n",
              "      <td>-14.6280</td>\n",
              "      <td>-1.757000</td>\n",
              "      <td>-0.21760</td>\n",
              "      <td>-4.85310</td>\n",
              "      <td>-1.05650</td>\n",
              "      <td>-1.04920</td>\n",
              "      <td>0.29138</td>\n",
              "      <td>0.292300</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.69464</td>\n",
              "      <td>-12.9390</td>\n",
              "      <td>-3.091800</td>\n",
              "      <td>-0.32273</td>\n",
              "      <td>-3.17860</td>\n",
              "      <td>-2.21970</td>\n",
              "      <td>-2.37200</td>\n",
              "      <td>1.00820</td>\n",
              "      <td>0.349720</td>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.87170</td>\n",
              "      <td>-12.0000</td>\n",
              "      <td>-1.566300</td>\n",
              "      <td>1.13740</td>\n",
              "      <td>-2.23900</td>\n",
              "      <td>-0.65476</td>\n",
              "      <td>-2.79010</td>\n",
              "      <td>0.65485</td>\n",
              "      <td>0.227240</td>\n",
              "      <td>walking</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62995</th>\n",
              "      <td>3.86820</td>\n",
              "      <td>-9.9020</td>\n",
              "      <td>0.354130</td>\n",
              "      <td>3.55870</td>\n",
              "      <td>-0.18493</td>\n",
              "      <td>-0.93169</td>\n",
              "      <td>-0.20617</td>\n",
              "      <td>0.28253</td>\n",
              "      <td>0.230910</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62996</th>\n",
              "      <td>3.94990</td>\n",
              "      <td>-9.5206</td>\n",
              "      <td>0.054481</td>\n",
              "      <td>3.63060</td>\n",
              "      <td>0.20627</td>\n",
              "      <td>-1.15210</td>\n",
              "      <td>-0.25137</td>\n",
              "      <td>0.32223</td>\n",
              "      <td>0.107210</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62997</th>\n",
              "      <td>3.43230</td>\n",
              "      <td>-9.4798</td>\n",
              "      <td>-0.313270</td>\n",
              "      <td>3.09120</td>\n",
              "      <td>0.24997</td>\n",
              "      <td>-1.49070</td>\n",
              "      <td>-0.13531</td>\n",
              "      <td>0.25351</td>\n",
              "      <td>-0.073609</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62998</th>\n",
              "      <td>2.43800</td>\n",
              "      <td>-9.5615</td>\n",
              "      <td>-0.858080</td>\n",
              "      <td>2.04180</td>\n",
              "      <td>0.17223</td>\n",
              "      <td>-1.98430</td>\n",
              "      <td>-0.16768</td>\n",
              "      <td>0.29963</td>\n",
              "      <td>-0.204330</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62999</th>\n",
              "      <td>2.77860</td>\n",
              "      <td>-10.0380</td>\n",
              "      <td>0.490330</td>\n",
              "      <td>2.28560</td>\n",
              "      <td>-0.29845</td>\n",
              "      <td>-0.54154</td>\n",
              "      <td>-0.43768</td>\n",
              "      <td>0.27764</td>\n",
              "      <td>-0.260530</td>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1260000 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Ax       Ay        Az  ...       Gy        Gz    Activity\n",
              "0     -1.81150 -14.8730 -1.348400  ... -3.18690  0.239760     walking\n",
              "1      0.24517 -14.0700 -0.844460  ... -1.79660  0.407450     walking\n",
              "2     -0.57205 -14.6280 -1.757000  ...  0.29138  0.292300     walking\n",
              "3     -0.69464 -12.9390 -3.091800  ...  1.00820  0.349720     walking\n",
              "4      0.87170 -12.0000 -1.566300  ...  0.65485  0.227240     walking\n",
              "...        ...      ...       ...  ...      ...       ...         ...\n",
              "62995  3.86820  -9.9020  0.354130  ...  0.28253  0.230910  downstairs\n",
              "62996  3.94990  -9.5206  0.054481  ...  0.32223  0.107210  downstairs\n",
              "62997  3.43230  -9.4798 -0.313270  ...  0.25351 -0.073609  downstairs\n",
              "62998  2.43800  -9.5615 -0.858080  ...  0.29963 -0.204330  downstairs\n",
              "62999  2.77860 -10.0380  0.490330  ...  0.27764 -0.260530  downstairs\n",
              "\n",
              "[1260000 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "graduate-boutique",
        "outputId": "243bb4f8-54c7-47f8-d011-8736ccd1f519"
      },
      "source": [
        "\n",
        "#check for unique labels\n",
        "train_df.Activity.unique()"
      ],
      "id": "graduate-boutique",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upstairs',\n",
              "       'downstairs', 'upsatirs'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "natural-proportion",
        "outputId": "cffa1790-6456-450b-950f-64227c10e1c0"
      },
      "source": [
        "train_df.Activity.loc[(train_df.Activity == 'upsatirs')] = 'upstairs'\n",
        "train_df.Activity.unique()"
      ],
      "id": "natural-proportion",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  iloc._setitem_with_indexer(indexer, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upstairs',\n",
              "       'downstairs'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "transparent-physics"
      },
      "source": [
        "# Preprocessing\n"
      ],
      "id": "transparent-physics"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "published-collect"
      },
      "source": [
        "# Split the data and labels "
      ],
      "id": "published-collect"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "excellent-taste"
      },
      "source": [
        "X_train = train_df[train_df.columns[:9]]\n",
        "y_train = train_df[train_df.columns[9:10]]\n",
        "\n"
      ],
      "id": "excellent-taste",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "recent-monaco",
        "outputId": "12f99c11-f1d7-4357-d1bf-bb4b65b66e0a"
      },
      "source": [
        "\n",
        "X_train"
      ],
      "id": "recent-monaco",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Lx</th>\n",
              "      <th>Ly</th>\n",
              "      <th>Lz</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.81150</td>\n",
              "      <td>-14.8730</td>\n",
              "      <td>-1.348400</td>\n",
              "      <td>-1.26910</td>\n",
              "      <td>-5.10570</td>\n",
              "      <td>-0.66445</td>\n",
              "      <td>-0.53206</td>\n",
              "      <td>-3.18690</td>\n",
              "      <td>0.239760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.24517</td>\n",
              "      <td>-14.0700</td>\n",
              "      <td>-0.844460</td>\n",
              "      <td>0.70147</td>\n",
              "      <td>-4.29690</td>\n",
              "      <td>-0.17199</td>\n",
              "      <td>-0.25229</td>\n",
              "      <td>-1.79660</td>\n",
              "      <td>0.407450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.57205</td>\n",
              "      <td>-14.6280</td>\n",
              "      <td>-1.757000</td>\n",
              "      <td>-0.21760</td>\n",
              "      <td>-4.85310</td>\n",
              "      <td>-1.05650</td>\n",
              "      <td>-1.04920</td>\n",
              "      <td>0.29138</td>\n",
              "      <td>0.292300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.69464</td>\n",
              "      <td>-12.9390</td>\n",
              "      <td>-3.091800</td>\n",
              "      <td>-0.32273</td>\n",
              "      <td>-3.17860</td>\n",
              "      <td>-2.21970</td>\n",
              "      <td>-2.37200</td>\n",
              "      <td>1.00820</td>\n",
              "      <td>0.349720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.87170</td>\n",
              "      <td>-12.0000</td>\n",
              "      <td>-1.566300</td>\n",
              "      <td>1.13740</td>\n",
              "      <td>-2.23900</td>\n",
              "      <td>-0.65476</td>\n",
              "      <td>-2.79010</td>\n",
              "      <td>0.65485</td>\n",
              "      <td>0.227240</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62995</th>\n",
              "      <td>3.86820</td>\n",
              "      <td>-9.9020</td>\n",
              "      <td>0.354130</td>\n",
              "      <td>3.55870</td>\n",
              "      <td>-0.18493</td>\n",
              "      <td>-0.93169</td>\n",
              "      <td>-0.20617</td>\n",
              "      <td>0.28253</td>\n",
              "      <td>0.230910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62996</th>\n",
              "      <td>3.94990</td>\n",
              "      <td>-9.5206</td>\n",
              "      <td>0.054481</td>\n",
              "      <td>3.63060</td>\n",
              "      <td>0.20627</td>\n",
              "      <td>-1.15210</td>\n",
              "      <td>-0.25137</td>\n",
              "      <td>0.32223</td>\n",
              "      <td>0.107210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62997</th>\n",
              "      <td>3.43230</td>\n",
              "      <td>-9.4798</td>\n",
              "      <td>-0.313270</td>\n",
              "      <td>3.09120</td>\n",
              "      <td>0.24997</td>\n",
              "      <td>-1.49070</td>\n",
              "      <td>-0.13531</td>\n",
              "      <td>0.25351</td>\n",
              "      <td>-0.073609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62998</th>\n",
              "      <td>2.43800</td>\n",
              "      <td>-9.5615</td>\n",
              "      <td>-0.858080</td>\n",
              "      <td>2.04180</td>\n",
              "      <td>0.17223</td>\n",
              "      <td>-1.98430</td>\n",
              "      <td>-0.16768</td>\n",
              "      <td>0.29963</td>\n",
              "      <td>-0.204330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62999</th>\n",
              "      <td>2.77860</td>\n",
              "      <td>-10.0380</td>\n",
              "      <td>0.490330</td>\n",
              "      <td>2.28560</td>\n",
              "      <td>-0.29845</td>\n",
              "      <td>-0.54154</td>\n",
              "      <td>-0.43768</td>\n",
              "      <td>0.27764</td>\n",
              "      <td>-0.260530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1260000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Ax       Ay        Az       Lx  ...       Lz       Gx       Gy        Gz\n",
              "0     -1.81150 -14.8730 -1.348400 -1.26910  ... -0.66445 -0.53206 -3.18690  0.239760\n",
              "1      0.24517 -14.0700 -0.844460  0.70147  ... -0.17199 -0.25229 -1.79660  0.407450\n",
              "2     -0.57205 -14.6280 -1.757000 -0.21760  ... -1.05650 -1.04920  0.29138  0.292300\n",
              "3     -0.69464 -12.9390 -3.091800 -0.32273  ... -2.21970 -2.37200  1.00820  0.349720\n",
              "4      0.87170 -12.0000 -1.566300  1.13740  ... -0.65476 -2.79010  0.65485  0.227240\n",
              "...        ...      ...       ...      ...  ...      ...      ...      ...       ...\n",
              "62995  3.86820  -9.9020  0.354130  3.55870  ... -0.93169 -0.20617  0.28253  0.230910\n",
              "62996  3.94990  -9.5206  0.054481  3.63060  ... -1.15210 -0.25137  0.32223  0.107210\n",
              "62997  3.43230  -9.4798 -0.313270  3.09120  ... -1.49070 -0.13531  0.25351 -0.073609\n",
              "62998  2.43800  -9.5615 -0.858080  2.04180  ... -1.98430 -0.16768  0.29963 -0.204330\n",
              "62999  2.77860 -10.0380  0.490330  2.28560  ... -0.54154 -0.43768  0.27764 -0.260530\n",
              "\n",
              "[1260000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "favorite-westminster",
        "outputId": "7349af8f-33b5-4e57-cac1-6b36134d168e"
      },
      "source": [
        "y_train"
      ],
      "id": "favorite-westminster",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Activity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>walking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62995</th>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62996</th>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62997</th>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62998</th>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62999</th>\n",
              "      <td>downstairs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1260000 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Activity\n",
              "0         walking\n",
              "1         walking\n",
              "2         walking\n",
              "3         walking\n",
              "4         walking\n",
              "...           ...\n",
              "62995  downstairs\n",
              "62996  downstairs\n",
              "62997  downstairs\n",
              "62998  downstairs\n",
              "62999  downstairs\n",
              "\n",
              "[1260000 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "armed-harvey",
        "outputId": "1af755c7-064e-4360-e4b5-10fbbb94c442"
      },
      "source": [
        "y_train.Activity.unique()"
      ],
      "id": "armed-harvey",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['walking', 'standing', 'jogging', 'sitting', 'biking', 'upstairs',\n",
              "       'downstairs'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "narrative-devon"
      },
      "source": [
        "# Encode labels into number"
      ],
      "id": "narrative-devon"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "physical-anchor",
        "outputId": "8b52af8e-2759-4004-de76-595739cdad07"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder=LabelEncoder()\n",
        "y_train=encoder.fit_transform(y_train)\n",
        "y_train[:10]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "id": "physical-anchor",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:251: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 6, 6, 6, 6, 6, 6, 6, 6, 6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "allied-shape"
      },
      "source": [
        "# Split training data into training and validation data"
      ],
      "id": "allied-shape"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diagnostic-communications",
        "outputId": "6369f987-5dbd-41f4-f34b-9c5abb3bb530"
      },
      "source": [
        "def train_test_split(X, y, split_size=0.8):\n",
        "    split= int(len(X) * split_size)\n",
        "    train_x = X[:split]\n",
        "    train_y = y[:split]\n",
        "    test_x = X[split:]\n",
        "    test_y = y[split: ]\n",
        "    return train_x, test_x, train_y, test_y\n",
        "\n",
        "X_train,X_test,y_train, y_test =train_test_split(X_train, y_train)\n",
        "\n",
        "print(\"X_train shape \", X_train.shape)\n",
        "print(\"Y_train shape \", y_train.shape)\n",
        "print(\"X_test shape \", X_test.shape)\n",
        "print(\"y_test shape \", y_test.shape)"
      ],
      "id": "diagnostic-communications",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape  (1008000, 9)\n",
            "Y_train shape  (1008000,)\n",
            "X_test shape  (252000, 9)\n",
            "y_test shape  (252000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "matched-vessel"
      },
      "source": [
        "# Convert dataset into time series sequence"
      ],
      "id": "matched-vessel"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acute-civilization"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
        "\n",
        "n_time_steps = 100\n",
        "n_features = 9\n",
        "\n",
        "train_gen = TimeseriesGenerator(X_train.to_numpy(), y_train, length=n_time_steps, batch_size=1024)\n",
        "test_gen = TimeseriesGenerator(X_test.to_numpy(), y_test, length=n_time_steps, batch_size=1024)\n",
        "\n"
      ],
      "id": "acute-civilization",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ql-5jtVzoFwj"
      },
      "source": [
        "Create and compile LSTM model\n"
      ],
      "id": "ql-5jtVzoFwj"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "disabled-stadium"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Flatten, LSTM\n",
        "from keras.regularizers import l2\n",
        "from keras.optimizers import Adam"
      ],
      "id": "disabled-stadium",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edPYFs8uoe3P",
        "outputId": "984b9d0c-5425-48d7-92b3-7c8eae3ec607"
      },
      "source": [
        "model=Sequential()\n",
        "model.add(LSTM(32, return_sequences=True, input_shape = (n_time_steps, n_features),\n",
        "              kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='lstm_1'))\n",
        "model.add(Flatten(name='flatten'))\n",
        "model.add(Dense(64, activation='relu',kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='dense_1' ))\n",
        "model.add(Dense(len(np.unique(y_train)), activation='softmax', \n",
        "                kernel_regularizer = l2(0.000001), bias_regularizer = l2(0.000001), name='output'))\n",
        "model.summary()"
      ],
      "id": "edPYFs8uoe3P",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 100, 32)           5376      \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 3200)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                204864    \n",
            "_________________________________________________________________\n",
            "output (Dense)               (None, 7)                 455       \n",
            "=================================================================\n",
            "Total params: 210,695\n",
            "Trainable params: 210,695\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mq3eB_tQFK28"
      },
      "source": [
        "\n",
        "#compile\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
      ],
      "id": "mq3eB_tQFK28",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNFahs_9FknP"
      },
      "source": [
        "# prepare callbacks\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "callbacks= [ModelCheckpoint('model.h5', save_weights_only=False, save_best_only=True, verbose=1)]\n"
      ],
      "id": "rNFahs_9FknP",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APkHksu9FXZY"
      },
      "source": [
        "Start Training\n"
      ],
      "id": "APkHksu9FXZY"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dh5cV80FZRE",
        "outputId": "d170b60a-2d4a-4c2c-da12-75e1105f5245"
      },
      "source": [
        "history = model.fit_generator(train_gen,steps_per_epoch=len(train_gen), epochs=4, validation_data=TimeseriesGenerator(X_test.to_numpy(), y_test, length=n_time_steps, batch_size=1024),callbacks=callbacks)\n",
        "\n"
      ],
      "id": "7dh5cV80FZRE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "985/985 [==============================] - 328s 331ms/step - loss: 0.7310 - accuracy: 0.7538 - val_loss: 0.1220 - val_accuracy: 0.9617\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.12202, saving model to model.h5\n",
            "Epoch 2/4\n",
            "985/985 [==============================] - 333s 338ms/step - loss: 0.1043 - accuracy: 0.9733 - val_loss: 0.1325 - val_accuracy: 0.9501\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 0.12202\n",
            "Epoch 3/4\n",
            "985/985 [==============================] - 336s 341ms/step - loss: 0.0778 - accuracy: 0.9808 - val_loss: 0.1681 - val_accuracy: 0.9447\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.12202\n",
            "Epoch 4/4\n",
            "985/985 [==============================] - 329s 334ms/step - loss: 0.0736 - accuracy: 0.9791 - val_loss: 0.5957 - val_accuracy: 0.9372\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 0.12202\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSuk4hzB7-de"
      },
      "source": [
        "# stopping training here\n",
        "\n",
        "# loading the best saved model \n",
        "\n",
        "from keras.models import load_model\n",
        "model=load_model('model.h5')\n",
        "model.summary()"
      ],
      "id": "BSuk4hzB7-de",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrhYoHG40eyg"
      },
      "source": [
        ""
      ],
      "id": "BrhYoHG40eyg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnesZhFp0vH4"
      },
      "source": [
        ""
      ],
      "id": "FnesZhFp0vH4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZ0IkiId02Q-"
      },
      "source": [
        ""
      ],
      "id": "RZ0IkiId02Q-",
      "execution_count": null,
      "outputs": []
    }
  ]
}