Projet_Python_For_Data_Science.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Projet_Python_For_Data_Science.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPHMmJbnzQnYyLMfbJel1+B",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AIKevin/Deep-Learning/blob/master/Projet_Python_For_Data_Science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ariwjAC_At-",
        "colab_type": "text"
      },
      "source": [
        "#Récupération des données en Shell\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0WI_2AaJbpg",
        "colab_type": "code",
        "outputId": "2fc7cf6c-8bc6-4b97-d122-8bab0fc81c9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "import requests as req\n",
        "import zipfile, io\n",
        "import pandas as pd\n",
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00341/HAPT%20Data%20Set.zip\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-02-09 19:55:50--  https://archive.ics.uci.edu/ml/machine-learning-databases/00341/HAPT%20Data%20Set.zip\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 79596192 (76M) [application/x-httpd-php]\n",
            "Saving to: ‘HAPT Data Set.zip’\n",
            "\n",
            "HAPT Data Set.zip   100%[===================>]  75.91M  60.6MB/s    in 1.3s    \n",
            "\n",
            "2020-02-09 19:55:51 (60.6 MB/s) - ‘HAPT Data Set.zip’ saved [79596192/79596192]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQASsar8JiT_",
        "colab_type": "code",
        "outputId": "88f25f20-c809-4498-bf8d-b5f83eb7bca6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!unzip 'HAPT Data Set.zip'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  HAPT Data Set.zip\n",
            "  inflating: features_info.txt       \n",
            "  inflating: README.txt              \n",
            "  inflating: RawData/acc_exp01_user01.txt  \n",
            "  inflating: RawData/acc_exp02_user01.txt  \n",
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            "  inflating: RawData/acc_exp09_user05.txt  \n",
            "  inflating: RawData/acc_exp10_user05.txt  \n",
            "  inflating: RawData/acc_exp11_user06.txt  \n",
            "  inflating: RawData/acc_exp12_user06.txt  \n",
            "  inflating: RawData/acc_exp13_user07.txt  \n",
            "  inflating: RawData/acc_exp14_user07.txt  \n",
            "  inflating: RawData/acc_exp15_user08.txt  \n",
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            "  inflating: RawData/acc_exp60_user30.txt  \n",
            "  inflating: RawData/acc_exp61_user30.txt  \n",
            "  inflating: RawData/gyro_exp01_user01.txt  \n",
            "  inflating: RawData/gyro_exp02_user01.txt  \n",
            "  inflating: RawData/gyro_exp03_user02.txt  \n",
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            "  inflating: RawData/gyro_exp10_user05.txt  \n",
            "  inflating: RawData/gyro_exp11_user06.txt  \n",
            "  inflating: RawData/gyro_exp12_user06.txt  \n",
            "  inflating: RawData/gyro_exp13_user07.txt  \n",
            "  inflating: RawData/gyro_exp14_user07.txt  \n",
            "  inflating: RawData/gyro_exp15_user08.txt  \n",
            "  inflating: RawData/gyro_exp16_user08.txt  \n",
            "  inflating: RawData/gyro_exp17_user09.txt  \n",
            "  inflating: RawData/gyro_exp18_user09.txt  \n",
            "  inflating: RawData/gyro_exp19_user10.txt  \n",
            "  inflating: RawData/gyro_exp20_user10.txt  \n",
            "  inflating: RawData/gyro_exp21_user10.txt  \n",
            "  inflating: RawData/gyro_exp22_user11.txt  \n",
            "  inflating: RawData/gyro_exp23_user11.txt  \n",
            "  inflating: RawData/gyro_exp24_user12.txt  \n",
            "  inflating: RawData/gyro_exp25_user12.txt  \n",
            "  inflating: RawData/gyro_exp26_user13.txt  \n",
            "  inflating: RawData/gyro_exp27_user13.txt  \n",
            "  inflating: RawData/gyro_exp28_user14.txt  \n",
            "  inflating: RawData/gyro_exp29_user14.txt  \n",
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            "  inflating: RawData/gyro_exp31_user15.txt  \n",
            "  inflating: RawData/gyro_exp32_user16.txt  \n",
            "  inflating: RawData/gyro_exp33_user16.txt  \n",
            "  inflating: RawData/gyro_exp34_user17.txt  \n",
            "  inflating: RawData/gyro_exp35_user17.txt  \n",
            "  inflating: RawData/gyro_exp36_user18.txt  \n",
            "  inflating: RawData/gyro_exp37_user18.txt  \n",
            "  inflating: RawData/gyro_exp38_user19.txt  \n",
            "  inflating: RawData/gyro_exp39_user19.txt  \n",
            "  inflating: RawData/gyro_exp40_user20.txt  \n",
            "  inflating: RawData/gyro_exp41_user20.txt  \n",
            "  inflating: RawData/gyro_exp42_user21.txt  \n",
            "  inflating: RawData/gyro_exp43_user21.txt  \n",
            "  inflating: RawData/gyro_exp44_user22.txt  \n",
            "  inflating: RawData/gyro_exp45_user22.txt  \n",
            "  inflating: RawData/gyro_exp46_user23.txt  \n",
            "  inflating: RawData/gyro_exp47_user23.txt  \n",
            "  inflating: RawData/gyro_exp48_user24.txt  \n",
            "  inflating: RawData/gyro_exp49_user24.txt  \n",
            "  inflating: RawData/gyro_exp50_user25.txt  \n",
            "  inflating: RawData/gyro_exp51_user25.txt  \n",
            "  inflating: RawData/gyro_exp52_user26.txt  \n",
            "  inflating: RawData/gyro_exp53_user26.txt  \n",
            "  inflating: RawData/gyro_exp54_user27.txt  \n",
            "  inflating: RawData/gyro_exp55_user27.txt  \n",
            "  inflating: RawData/gyro_exp56_user28.txt  \n",
            "  inflating: RawData/gyro_exp57_user28.txt  \n",
            "  inflating: RawData/gyro_exp58_user29.txt  \n",
            "  inflating: RawData/gyro_exp59_user29.txt  \n",
            "  inflating: RawData/gyro_exp60_user30.txt  \n",
            "  inflating: RawData/gyro_exp61_user30.txt  \n",
            "  inflating: RawData/labels.txt      \n",
            "  inflating: Test/subject_id_test.txt  \n",
            "  inflating: Test/X_test.txt         \n",
            "  inflating: Test/y_test.txt         \n",
            "  inflating: Train/subject_id_train.txt  \n",
            "  inflating: Train/X_train.txt       \n",
            "  inflating: Train/y_train.txt       \n",
            "  inflating: activity_labels.txt     \n",
            "  inflating: features.txt            \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeRg4hspJnV7",
        "colab_type": "code",
        "outputId": "484f3e90-1437-487a-acfe-9805073cd57f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " activity_labels.txt   features.txt\t    RawData\t sample_data   Train\n",
            " features_info.txt    'HAPT Data Set.zip'   README.txt\t Test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubMpNdq8NsV_",
        "colab_type": "text"
      },
      "source": [
        "#Présentation des données"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pmnCRlcJxvc",
        "colab_type": "code",
        "outputId": "6a5336b5-28eb-4d08-8f35-4428b69da2fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "!cat activity_labels.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 WALKING           \n",
            "2 WALKING_UPSTAIRS  \n",
            "3 WALKING_DOWNSTAIRS\n",
            "4 SITTING           \n",
            "5 STANDING          \n",
            "6 LAYING            \n",
            "7 STAND_TO_SIT      \n",
            "8 SIT_TO_STAND      \n",
            "9 SIT_TO_LIE        \n",
            "10 LIE_TO_SIT        \n",
            "11 STAND_TO_LIE      \n",
            "12 LIE_TO_STAND      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khqE6004K6hC",
        "colab_type": "code",
        "outputId": "0fba77e3-c759-4d23-a1dc-d61c80fb9815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!cat features_info.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature Selection \r\n",
            "=================\r\n",
            "\r\n",
            "The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz. \r\n",
            "\r\n",
            "Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag). \r\n",
            "\r\n",
            "Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals). \r\n",
            "\r\n",
            "These signals were used to estimate variables of the feature vector for each pattern:  \r\n",
            "'-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.\r\n",
            "\r\n",
            "tBodyAcc-XYZ\r\n",
            "tGravityAcc-XYZ\r\n",
            "tBodyAccJerk-XYZ\r\n",
            "tBodyGyro-XYZ\r\n",
            "tBodyGyroJerk-XYZ\r\n",
            "tBodyAccMag\r\n",
            "tGravityAccMag\r\n",
            "tBodyAccJerkMag\r\n",
            "tBodyGyroMag\r\n",
            "tBodyGyroJerkMag\r\n",
            "fBodyAcc-XYZ\r\n",
            "fBodyAccJerk-XYZ\r\n",
            "fBodyGyro-XYZ\r\n",
            "fBodyAccMag\r\n",
            "fBodyAccJerkMag\r\n",
            "fBodyGyroMag\r\n",
            "fBodyGyroJerkMag\r\n",
            "\r\n",
            "The set of variables that were estimated from these signals are: \r\n",
            "\r\n",
            "mean(): Mean value\r\n",
            "std(): Standard deviation\r\n",
            "mad(): Median absolute deviation \r\n",
            "max(): Largest value in array\r\n",
            "min(): Smallest value in array\r\n",
            "sma(): Signal magnitude area\r\n",
            "energy(): Energy measure. Sum of the squares divided by the number of values. \r\n",
            "iqr(): Interquartile range \r\n",
            "entropy(): Signal entropy\r\n",
            "arCoeff(): Autorregresion coefficients with Burg order equal to 4\r\n",
            "correlation(): correlation coefficient between two signals\r\n",
            "maxInds(): index of the frequency component with largest magnitude\r\n",
            "meanFreq(): Weighted average of the frequency components to obtain a mean frequency\r\n",
            "skewness(): skewness of the frequency domain signal \r\n",
            "kurtosis(): kurtosis of the frequency domain signal \r\n",
            "bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window.\r\n",
            "angle(): Angle between to vectors.\r\n",
            "\r\n",
            "Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:\r\n",
            "\r\n",
            "gravityMean\r\n",
            "tBodyAccMean\r\n",
            "tBodyAccJerkMean\r\n",
            "tBodyGyroMean\r\n",
            "tBodyGyroJerkMean\r\n",
            "\r\n",
            "The complete list of variables of each feature vector is available in 'features.txt'\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_qPQArgOZ5m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = open('features.txt').read().splitlines()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlJ3lK6vOkeB",
        "colab_type": "code",
        "outputId": "20166b1a-5fbd-4ef5-c693-063ed3a4a9cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print(features)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['tBodyAcc-Mean-1                ', 'tBodyAcc-Mean-2                ', 'tBodyAcc-Mean-3                ', 'tBodyAcc-STD-1                 ', 'tBodyAcc-STD-2                 ', 'tBodyAcc-STD-3                 ', 'tBodyAcc-Mad-1                 ', 'tBodyAcc-Mad-2                 ', 'tBodyAcc-Mad-3                 ', 'tBodyAcc-Max-1                 ', 'tBodyAcc-Max-2                 ', 'tBodyAcc-Max-3                 ', 'tBodyAcc-Min-1                 ', 'tBodyAcc-Min-2                 ', 'tBodyAcc-Min-3                 ', 'tBodyAcc-SMA-1                 ', 'tBodyAcc-Energy-1              ', 'tBodyAcc-Energy-2              ', 'tBodyAcc-Energy-3              ', 'tBodyAcc-IQR-1                 ', 'tBodyAcc-IQR-2                 ', 'tBodyAcc-IQR-3                 ', 'tBodyAcc-ropy-1                ', 'tBodyAcc-ropy-1                ', 'tBodyAcc-ropy-1                ', 'tBodyAcc-ARCoeff-1             ', 'tBodyAcc-ARCoeff-2             ', 'tBodyAcc-ARCoeff-3             ', 'tBodyAcc-ARCoeff-4             ', 'tBodyAcc-ARCoeff-5             ', 'tBodyAcc-ARCoeff-6             ', 'tBodyAcc-ARCoeff-7             ', 'tBodyAcc-ARCoeff-8             ', 'tBodyAcc-ARCoeff-9             ', 'tBodyAcc-ARCoeff-10            ', 'tBodyAcc-ARCoeff-11            ', 'tBodyAcc-ARCoeff-12            ', 'tBodyAcc-Correlation-1         ', 'tBodyAcc-Correlation-2         ', 'tBodyAcc-Correlation-3         ', 'tGravityAcc-Mean-1             ', 'tGravityAcc-Mean-2             ', 'tGravityAcc-Mean-3             ', 'tGravityAcc-STD-1              ', 'tGravityAcc-STD-2              ', 'tGravityAcc-STD-3              ', 'tGravityAcc-Mad-1              ', 'tGravityAcc-Mad-2              ', 'tGravityAcc-Mad-3              ', 'tGravityAcc-Max-1              ', 'tGravityAcc-Max-2              ', 'tGravityAcc-Max-3              ', 'tGravityAcc-Min-1              ', 'tGravityAcc-Min-2              ', 'tGravityAcc-Min-3              ', 'tGravityAcc-SMA-1              ', 'tGravityAcc-Energy-1           ', 'tGravityAcc-Energy-2           ', 'tGravityAcc-Energy-3           ', 'tGravityAcc-IQR-1              ', 'tGravityAcc-IQR-2              ', 'tGravityAcc-IQR-3              ', 'tGravityAcc-ropy-1             ', 'tGravityAcc-ropy-1             ', 'tGravityAcc-ropy-1             ', 'tGravityAcc-ARCoeff-1          ', 'tGravityAcc-ARCoeff-2          ', 'tGravityAcc-ARCoeff-3          ', 'tGravityAcc-ARCoeff-4          ', 'tGravityAcc-ARCoeff-5          ', 'tGravityAcc-ARCoeff-6          ', 'tGravityAcc-ARCoeff-7          ', 'tGravityAcc-ARCoeff-8          ', 'tGravityAcc-ARCoeff-9          ', 'tGravityAcc-ARCoeff-10         ', 'tGravityAcc-ARCoeff-11         ', 'tGravityAcc-ARCoeff-12         ', 'tGravityAcc-Correlation-1      ', 'tGravityAcc-Correlation-2      ', 'tGravityAcc-Correlation-3      ', 'tBodyAccJerk-Mean-1            ', 'tBodyAccJerk-Mean-2            ', 'tBodyAccJerk-Mean-3            ', 'tBodyAccJerk-STD-1             ', 'tBodyAccJerk-STD-2             ', 'tBodyAccJerk-STD-3             ', 'tBodyAccJerk-Mad-1             ', 'tBodyAccJerk-Mad-2             ', 'tBodyAccJerk-Mad-3             ', 'tBodyAccJerk-Max-1             ', 'tBodyAccJerk-Max-2             ', 'tBodyAccJerk-Max-3             ', 'tBodyAccJerk-Min-1             ', 'tBodyAccJerk-Min-2             ', 'tBodyAccJerk-Min-3             ', 'tBodyAccJerk-SMA-1             ', 'tBodyAccJerk-Energy-1          ', 'tBodyAccJerk-Energy-2          ', 'tBodyAccJerk-Energy-3          ', 'tBodyAccJerk-IQR-1             ', 'tBodyAccJerk-IQR-2             ', 'tBodyAccJerk-IQR-3             ', 'tBodyAccJerk-ropy-1            ', 'tBodyAccJerk-ropy-1            ', 'tBodyAccJerk-ropy-1            ', 'tBodyAccJerk-ARCoeff-1         ', 'tBodyAccJerk-ARCoeff-2         ', 'tBodyAccJerk-ARCoeff-3         ', 'tBodyAccJerk-ARCoeff-4         ', 'tBodyAccJerk-ARCoeff-5         ', 'tBodyAccJerk-ARCoeff-6         ', 'tBodyAccJerk-ARCoeff-7         ', 'tBodyAccJerk-ARCoeff-8         ', 'tBodyAccJerk-ARCoeff-9         ', 'tBodyAccJerk-ARCoeff-10        ', 'tBodyAccJerk-ARCoeff-11        ', 'tBodyAccJerk-ARCoeff-12        ', 'tBodyAccJerk-Correlation-1     ', 'tBodyAccJerk-Correlation-2     ', 'tBodyAccJerk-Correlation-3     ', 'tBodyGyro-Mean-1               ', 'tBodyGyro-Mean-2               ', 'tBodyGyro-Mean-3               ', 'tBodyGyro-STD-1                ', 'tBodyGyro-STD-2                ', 'tBodyGyro-STD-3                ', 'tBodyGyro-Mad-1                ', 'tBodyGyro-Mad-2                ', 'tBodyGyro-Mad-3                ', 'tBodyGyro-Max-1                ', 'tBodyGyro-Max-2                ', 'tBodyGyro-Max-3                ', 'tBodyGyro-Min-1                ', 'tBodyGyro-Min-2                ', 'tBodyGyro-Min-3                ', 'tBodyGyro-SMA-1                ', 'tBodyGyro-Energy-1             ', 'tBodyGyro-Energy-2             ', 'tBodyGyro-Energy-3             ', 'tBodyGyro-IQR-1                ', 'tBodyGyro-IQR-2                ', 'tBodyGyro-IQR-3                ', 'tBodyGyro-ropy-1               ', 'tBodyGyro-ropy-1               ', 'tBodyGyro-ropy-1               ', 'tBodyGyro-ARCoeff-1            ', 'tBodyGyro-ARCoeff-2            ', 'tBodyGyro-ARCoeff-3            ', 'tBodyGyro-ARCoeff-4            ', 'tBodyGyro-ARCoeff-5            ', 'tBodyGyro-ARCoeff-6            ', 'tBodyGyro-ARCoeff-7            ', 'tBodyGyro-ARCoeff-8            ', 'tBodyGyro-ARCoeff-9            ', 'tBodyGyro-ARCoeff-10           ', 'tBodyGyro-ARCoeff-11           ', 'tBodyGyro-ARCoeff-12           ', 'tBodyGyro-Correlation-1        ', 'tBodyGyro-Correlation-2        ', 'tBodyGyro-Correlation-3        ', 'tBodyGyroJerk-Mean-1           ', 'tBodyGyroJerk-Mean-2           ', 'tBodyGyroJerk-Mean-3           ', 'tBodyGyroJerk-STD-1            ', 'tBodyGyroJerk-STD-2            ', 'tBodyGyroJerk-STD-3            ', 'tBodyGyroJerk-Mad-1            ', 'tBodyGyroJerk-Mad-2            ', 'tBodyGyroJerk-Mad-3            ', 'tBodyGyroJerk-Max-1            ', 'tBodyGyroJerk-Max-2            ', 'tBodyGyroJerk-Max-3            ', 'tBodyGyroJerk-Min-1            ', 'tBodyGyroJerk-Min-2            ', 'tBodyGyroJerk-Min-3            ', 'tBodyGyroJerk-SMA-1            ', 'tBodyGyroJerk-Energy-1         ', 'tBodyGyroJerk-Energy-2         ', 'tBodyGyroJerk-Energy-3         ', 'tBodyGyroJerk-IQR-1            ', 'tBodyGyroJerk-IQR-2            ', 'tBodyGyroJerk-IQR-3            ', 'tBodyGyroJerk-ropy-1           ', 'tBodyGyroJerk-ropy-1           ', 'tBodyGyroJerk-ropy-1           ', 'tBodyGyroJerk-ARCoeff-1        ', 'tBodyGyroJerk-ARCoeff-2        ', 'tBodyGyroJerk-ARCoeff-3        ', 'tBodyGyroJerk-ARCoeff-4        ', 'tBodyGyroJerk-ARCoeff-5        ', 'tBodyGyroJerk-ARCoeff-6        ', 'tBodyGyroJerk-ARCoeff-7        ', 'tBodyGyroJerk-ARCoeff-8        ', 'tBodyGyroJerk-ARCoeff-9        ', 'tBodyGyroJerk-ARCoeff-10       ', 'tBodyGyroJerk-ARCoeff-11       ', 'tBodyGyroJerk-ARCoeff-12       ', 'tBodyGyroJerk-Correlation-1    ', 'tBodyGyroJerk-Correlation-2    ', 'tBodyGyroJerk-Correlation-3    ', 'tBodyAccMag-Mean-1             ', 'tBodyAccMag-STD-1              ', 'tBodyAccMag-Mad-1              ', 'tBodyAccMag-Max-1              ', 'tBodyAccMag-Min-1              ', 'tBodyAccMag-SMA-1              ', 'tBodyAccMag-Energy-1           ', 'tBodyAccMag-IQR-1              ', 'tBodyAccMag-ropy-1             ', 'tBodyAccMag-ARCoeff-1          ', 'tBodyAccMag-ARCoeff-2          ', 'tBodyAccMag-ARCoeff-3          ', 'tBodyAccMag-ARCoeff-4          ', 'tGravityAccMag-Mean-1          ', 'tGravityAccMag-STD-1           ', 'tGravityAccMag-Mad-1           ', 'tGravityAccMag-Max-1           ', 'tGravityAccMag-Min-1           ', 'tGravityAccMag-SMA-1           ', 'tGravityAccMag-Energy-1        ', 'tGravityAccMag-IQR-1           ', 'tGravityAccMag-ropy-1          ', 'tGravityAccMag-ARCoeff-1       ', 'tGravityAccMag-ARCoeff-2       ', 'tGravityAccMag-ARCoeff-3       ', 'tGravityAccMag-ARCoeff-4       ', 'tBodyAccJerkMag-Mean-1         ', 'tBodyAccJerkMag-STD-1          ', 'tBodyAccJerkMag-Mad-1          ', 'tBodyAccJerkMag-Max-1          ', 'tBodyAccJerkMag-Min-1          ', 'tBodyAccJerkMag-SMA-1          ', 'tBodyAccJerkMag-Energy-1       ', 'tBodyAccJerkMag-IQR-1          ', 'tBodyAccJerkMag-ropy-1         ', 'tBodyAccJerkMag-ARCoeff-1      ', 'tBodyAccJerkMag-ARCoeff-2      ', 'tBodyAccJerkMag-ARCoeff-3      ', 'tBodyAccJerkMag-ARCoeff-4      ', 'tBodyGyroMag-Mean-1            ', 'tBodyGyroMag-STD-1             ', 'tBodyGyroMag-Mad-1             ', 'tBodyGyroMag-Max-1             ', 'tBodyGyroMag-Min-1             ', 'tBodyGyroMag-SMA-1             ', 'tBodyGyroMag-Energy-1          ', 'tBodyGyroMag-IQR-1             ', 'tBodyGyroMag-ropy-1            ', 'tBodyGyroMag-ARCoeff-1         ', 'tBodyGyroMag-ARCoeff-2         ', 'tBodyGyroMag-ARCoeff-3         ', 'tBodyGyroMag-ARCoeff-4         ', 'tBodyGyroJerkMag-Mean-1        ', 'tBodyGyroJerkMag-STD-1         ', 'tBodyGyroJerkMag-Mad-1         ', 'tBodyGyroJerkMag-Max-1         ', 'tBodyGyroJerkMag-Min-1         ', 'tBodyGyroJerkMag-SMA-1         ', 'tBodyGyroJerkMag-Energy-1      ', 'tBodyGyroJerkMag-IQR-1         ', 'tBodyGyroJerkMag-ropy-1        ', 'tBodyGyroJerkMag-ARCoeff-1     ', 'tBodyGyroJerkMag-ARCoeff-2     ', 'tBodyGyroJerkMag-ARCoeff-3     ', 'tBodyGyroJerkMag-ARCoeff-4     ', 'fBodyAcc-Mean-1                ', 'fBodyAcc-Mean-2                ', 'fBodyAcc-Mean-3                ', 'fBodyAcc-STD-1                 ', 'fBodyAcc-STD-2                 ', 'fBodyAcc-STD-3                 ', 'fBodyAcc-Mad-1                 ', 'fBodyAcc-Mad-2                 ', 'fBodyAcc-Mad-3                 ', 'fBodyAcc-Max-1                 ', 'fBodyAcc-Max-2                 ', 'fBodyAcc-Max-3                 ', 'fBodyAcc-Min-1                 ', 'fBodyAcc-Min-2                 ', 'fBodyAcc-Min-3                 ', 'fBodyAcc-SMA-1                 ', 'fBodyAcc-Energy-1              ', 'fBodyAcc-Energy-2              ', 'fBodyAcc-Energy-3              ', 'fBodyAcc-IQR-1                 ', 'fBodyAcc-IQR-2                 ', 'fBodyAcc-IQR-3                 ', 'fBodyAcc-ropy-1                ', 'fBodyAcc-ropy-1                ', 'fBodyAcc-ropy-1                ', 'fBodyAcc-MaxInds-1             ', 'fBodyAcc-MaxInds-2             ', 'fBodyAcc-MaxInds-3             ', 'fBodyAcc-MeanFreq-1            ', 'fBodyAcc-MeanFreq-2            ', 'fBodyAcc-MeanFreq-3            ', 'fBodyAcc-Skewness-1            ', 'fBodyAcc-Kurtosis-1            ', 'fBodyAcc-Skewness-1            ', 'fBodyAcc-Kurtosis-1            ', 'fBodyAcc-Skewness-1            ', 'fBodyAcc-Kurtosis-1            ', 'fBodyAcc-BandsEnergyOld-1      ', 'fBodyAcc-BandsEnergyOld-2      ', 'fBodyAcc-BandsEnergyOld-3      ', 'fBodyAcc-BandsEnergyOld-4      ', 'fBodyAcc-BandsEnergyOld-5      ', 'fBodyAcc-BandsEnergyOld-6      ', 'fBodyAcc-BandsEnergyOld-7      ', 'fBodyAcc-BandsEnergyOld-8      ', 'fBodyAcc-BandsEnergyOld-9      ', 'fBodyAcc-BandsEnergyOld-10     ', 'fBodyAcc-BandsEnergyOld-11     ', 'fBodyAcc-BandsEnergyOld-12     ', 'fBodyAcc-BandsEnergyOld-13     ', 'fBodyAcc-BandsEnergyOld-14     ', 'fBodyAcc-BandsEnergyOld-15     ', 'fBodyAcc-BandsEnergyOld-16     ', 'fBodyAcc-BandsEnergyOld-17     ', 'fBodyAcc-BandsEnergyOld-18     ', 'fBodyAcc-BandsEnergyOld-19     ', 'fBodyAcc-BandsEnergyOld-20     ', 'fBodyAcc-BandsEnergyOld-21     ', 'fBodyAcc-BandsEnergyOld-22     ', 'fBodyAcc-BandsEnergyOld-23     ', 'fBodyAcc-BandsEnergyOld-24     ', 'fBodyAcc-BandsEnergyOld-25     ', 'fBodyAcc-BandsEnergyOld-26     ', 'fBodyAcc-BandsEnergyOld-27     ', 'fBodyAcc-BandsEnergyOld-28     ', 'fBodyAcc-BandsEnergyOld-29     ', 'fBodyAcc-BandsEnergyOld-30     ', 'fBodyAcc-BandsEnergyOld-31     ', 'fBodyAcc-BandsEnergyOld-32     ', 'fBodyAcc-BandsEnergyOld-33     ', 'fBodyAcc-BandsEnergyOld-34     ', 'fBodyAcc-BandsEnergyOld-35     ', 'fBodyAcc-BandsEnergyOld-36     ', 'fBodyAcc-BandsEnergyOld-37     ', 'fBodyAcc-BandsEnergyOld-38     ', 'fBodyAcc-BandsEnergyOld-39     ', 'fBodyAcc-BandsEnergyOld-40     ', 'fBodyAcc-BandsEnergyOld-41     ', 'fBodyAcc-BandsEnergyOld-42     ', 'fBodyAccJerk-Mean-1            ', 'fBodyAccJerk-Mean-2            ', 'fBodyAccJerk-Mean-3            ', 'fBodyAccJerk-STD-1             ', 'fBodyAccJerk-STD-2             ', 'fBodyAccJerk-STD-3             ', 'fBodyAccJerk-Mad-1             ', 'fBodyAccJerk-Mad-2             ', 'fBodyAccJerk-Mad-3             ', 'fBodyAccJerk-Max-1             ', 'fBodyAccJerk-Max-2             ', 'fBodyAccJerk-Max-3             ', 'fBodyAccJerk-Min-1             ', 'fBodyAccJerk-Min-2             ', 'fBodyAccJerk-Min-3             ', 'fBodyAccJerk-SMA-1             ', 'fBodyAccJerk-Energy-1          ', 'fBodyAccJerk-Energy-2          ', 'fBodyAccJerk-Energy-3          ', 'fBodyAccJerk-IQR-1             ', 'fBodyAccJerk-IQR-2             ', 'fBodyAccJerk-IQR-3             ', 'fBodyAccJerk-ropy-1            ', 'fBodyAccJerk-ropy-1            ', 'fBodyAccJerk-ropy-1            ', 'fBodyAccJerk-MaxInds-1         ', 'fBodyAccJerk-MaxInds-2         ', 'fBodyAccJerk-MaxInds-3         ', 'fBodyAccJerk-MeanFreq-1        ', 'fBodyAccJerk-MeanFreq-2        ', 'fBodyAccJerk-MeanFreq-3        ', 'fBodyAccJerk-Skewness-1        ', 'fBodyAccJerk-Kurtosis-1        ', 'fBodyAccJerk-Skewness-1        ', 'fBodyAccJerk-Kurtosis-1        ', 'fBodyAccJerk-Skewness-1        ', 'fBodyAccJerk-Kurtosis-1        ', 'fBodyAccJerk-BandsEnergyOld-1  ', 'fBodyAccJerk-BandsEnergyOld-2  ', 'fBodyAccJerk-BandsEnergyOld-3  ', 'fBodyAccJerk-BandsEnergyOld-4  ', 'fBodyAccJerk-BandsEnergyOld-5  ', 'fBodyAccJerk-BandsEnergyOld-6  ', 'fBodyAccJerk-BandsEnergyOld-7  ', 'fBodyAccJerk-BandsEnergyOld-8  ', 'fBodyAccJerk-BandsEnergyOld-9  ', 'fBodyAccJerk-BandsEnergyOld-10 ', 'fBodyAccJerk-BandsEnergyOld-11 ', 'fBodyAccJerk-BandsEnergyOld-12 ', 'fBodyAccJerk-BandsEnergyOld-13 ', 'fBodyAccJerk-BandsEnergyOld-14 ', 'fBodyAccJerk-BandsEnergyOld-15 ', 'fBodyAccJerk-BandsEnergyOld-16 ', 'fBodyAccJerk-BandsEnergyOld-17 ', 'fBodyAccJerk-BandsEnergyOld-18 ', 'fBodyAccJerk-BandsEnergyOld-19 ', 'fBodyAccJerk-BandsEnergyOld-20 ', 'fBodyAccJerk-BandsEnergyOld-21 ', 'fBodyAccJerk-BandsEnergyOld-22 ', 'fBodyAccJerk-BandsEnergyOld-23 ', 'fBodyAccJerk-BandsEnergyOld-24 ', 'fBodyAccJerk-BandsEnergyOld-25 ', 'fBodyAccJerk-BandsEnergyOld-26 ', 'fBodyAccJerk-BandsEnergyOld-27 ', 'fBodyAccJerk-BandsEnergyOld-28 ', 'fBodyAccJerk-BandsEnergyOld-29 ', 'fBodyAccJerk-BandsEnergyOld-30 ', 'fBodyAccJerk-BandsEnergyOld-31 ', 'fBodyAccJerk-BandsEnergyOld-32 ', 'fBodyAccJerk-BandsEnergyOld-33 ', 'fBodyAccJerk-BandsEnergyOld-34 ', 'fBodyAccJerk-BandsEnergyOld-35 ', 'fBodyAccJerk-BandsEnergyOld-36 ', 'fBodyAccJerk-BandsEnergyOld-37 ', 'fBodyAccJerk-BandsEnergyOld-38 ', 'fBodyAccJerk-BandsEnergyOld-39 ', 'fBodyAccJerk-BandsEnergyOld-40 ', 'fBodyAccJerk-BandsEnergyOld-41 ', 'fBodyAccJerk-BandsEnergyOld-42 ', 'fBodyGyro-Mean-1               ', 'fBodyGyro-Mean-2               ', 'fBodyGyro-Mean-3               ', 'fBodyGyro-STD-1                ', 'fBodyGyro-STD-2                ', 'fBodyGyro-STD-3                ', 'fBodyGyro-Mad-1                ', 'fBodyGyro-Mad-2                ', 'fBodyGyro-Mad-3                ', 'fBodyGyro-Max-1                ', 'fBodyGyro-Max-2                ', 'fBodyGyro-Max-3                ', 'fBodyGyro-Min-1                ', 'fBodyGyro-Min-2                ', 'fBodyGyro-Min-3                ', 'fBodyGyro-SMA-1                ', 'fBodyGyro-Energy-1             ', 'fBodyGyro-Energy-2             ', 'fBodyGyro-Energy-3             ', 'fBodyGyro-IQR-1                ', 'fBodyGyro-IQR-2                ', 'fBodyGyro-IQR-3                ', 'fBodyGyro-ropy-1               ', 'fBodyGyro-ropy-1               ', 'fBodyGyro-ropy-1               ', 'fBodyGyro-MaxInds-1            ', 'fBodyGyro-MaxInds-2            ', 'fBodyGyro-MaxInds-3            ', 'fBodyGyro-MeanFreq-1           ', 'fBodyGyro-MeanFreq-2           ', 'fBodyGyro-MeanFreq-3           ', 'fBodyGyro-Skewness-1           ', 'fBodyGyro-Kurtosis-1           ', 'fBodyGyro-Skewness-1           ', 'fBodyGyro-Kurtosis-1           ', 'fBodyGyro-Skewness-1           ', 'fBodyGyro-Kurtosis-1           ', 'fBodyGyro-BandsEnergyOld-1     ', 'fBodyGyro-BandsEnergyOld-2     ', 'fBodyGyro-BandsEnergyOld-3     ', 'fBodyGyro-BandsEnergyOld-4     ', 'fBodyGyro-BandsEnergyOld-5     ', 'fBodyGyro-BandsEnergyOld-6     ', 'fBodyGyro-BandsEnergyOld-7     ', 'fBodyGyro-BandsEnergyOld-8     ', 'fBodyGyro-BandsEnergyOld-9     ', 'fBodyGyro-BandsEnergyOld-10    ', 'fBodyGyro-BandsEnergyOld-11    ', 'fBodyGyro-BandsEnergyOld-12    ', 'fBodyGyro-BandsEnergyOld-13    ', 'fBodyGyro-BandsEnergyOld-14    ', 'fBodyGyro-BandsEnergyOld-15    ', 'fBodyGyro-BandsEnergyOld-16    ', 'fBodyGyro-BandsEnergyOld-17    ', 'fBodyGyro-BandsEnergyOld-18    ', 'fBodyGyro-BandsEnergyOld-19    ', 'fBodyGyro-BandsEnergyOld-20    ', 'fBodyGyro-BandsEnergyOld-21    ', 'fBodyGyro-BandsEnergyOld-22    ', 'fBodyGyro-BandsEnergyOld-23    ', 'fBodyGyro-BandsEnergyOld-24    ', 'fBodyGyro-BandsEnergyOld-25    ', 'fBodyGyro-BandsEnergyOld-26    ', 'fBodyGyro-BandsEnergyOld-27    ', 'fBodyGyro-BandsEnergyOld-28    ', 'fBodyGyro-BandsEnergyOld-29    ', 'fBodyGyro-BandsEnergyOld-30    ', 'fBodyGyro-BandsEnergyOld-31    ', 'fBodyGyro-BandsEnergyOld-32    ', 'fBodyGyro-BandsEnergyOld-33    ', 'fBodyGyro-BandsEnergyOld-34    ', 'fBodyGyro-BandsEnergyOld-35    ', 'fBodyGyro-BandsEnergyOld-36    ', 'fBodyGyro-BandsEnergyOld-37    ', 'fBodyGyro-BandsEnergyOld-38    ', 'fBodyGyro-BandsEnergyOld-39    ', 'fBodyGyro-BandsEnergyOld-40    ', 'fBodyGyro-BandsEnergyOld-41    ', 'fBodyGyro-BandsEnergyOld-42    ', 'fBodyAccMag-Mean-1             ', 'fBodyAccMag-STD-1              ', 'fBodyAccMag-Mad-1              ', 'fBodyAccMag-Max-1              ', 'fBodyAccMag-Min-1              ', 'fBodyAccMag-SMA-1              ', 'fBodyAccMag-Energy-1           ', 'fBodyAccMag-IQR-1              ', 'fBodyAccMag-ropy-1             ', 'fBodyAccMag-MaxInds-1          ', 'fBodyAccMag-MeanFreq-1         ', 'fBodyAccMag-Skewness-1         ', 'fBodyAccMag-Kurtosis-1         ', 'fBodyAccJerkMag-Mean-1         ', 'fBodyAccJerkMag-STD-1          ', 'fBodyAccJerkMag-Mad-1          ', 'fBodyAccJerkMag-Max-1          ', 'fBodyAccJerkMag-Min-1          ', 'fBodyAccJerkMag-SMA-1          ', 'fBodyAccJerkMag-Energy-1       ', 'fBodyAccJerkMag-IQR-1          ', 'fBodyAccJerkMag-ropy-1         ', 'fBodyAccJerkMag-MaxInds-1      ', 'fBodyAccJerkMag-MeanFreq-1     ', 'fBodyAccJerkMag-Skewness-1     ', 'fBodyAccJerkMag-Kurtosis-1     ', 'fBodyGyroMag-Mean-1            ', 'fBodyGyroMag-STD-1             ', 'fBodyGyroMag-Mad-1             ', 'fBodyGyroMag-Max-1             ', 'fBodyGyroMag-Min-1             ', 'fBodyGyroMag-SMA-1             ', 'fBodyGyroMag-Energy-1          ', 'fBodyGyroMag-IQR-1             ', 'fBodyGyroMag-ropy-1            ', 'fBodyGyroMag-MaxInds-1         ', 'fBodyGyroMag-MeanFreq-1        ', 'fBodyGyroMag-Skewness-1        ', 'fBodyGyroMag-Kurtosis-1        ', 'fBodyGyroJerkMag-Mean-1        ', 'fBodyGyroJerkMag-STD-1         ', 'fBodyGyroJerkMag-Mad-1         ', 'fBodyGyroJerkMag-Max-1         ', 'fBodyGyroJerkMag-Min-1         ', 'fBodyGyroJerkMag-SMA-1         ', 'fBodyGyroJerkMag-Energy-1      ', 'fBodyGyroJerkMag-IQR-1         ', 'fBodyGyroJerkMag-ropy-1        ', 'fBodyGyroJerkMag-MaxInds-1     ', 'fBodyGyroJerkMag-MeanFreq-1    ', 'fBodyGyroJerkMag-Skewness-1    ', 'fBodyGyroJerkMag-Kurtosis-1    ', 'tBodyAcc-AngleWRTGravity-1     ', 'tBodyAccJerk-AngleWRTGravity-1 ', 'tBodyGyro-AngleWRTGravity-1    ', 'tBodyGyroJerk-AngleWRTGravity-1', 'tXAxisAcc-AngleWRTGravity-1    ', 'tYAxisAcc-AngleWRTGravity-1    ', 'tZAxisAcc-AngleWRTGravity-1    ']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZaoJrt5ME5y",
        "colab_type": "code",
        "outputId": "f0684332-ad92-4ed2-95b8-10ca88ccfd99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "X_train = pd.read_csv('/content/Train/X_train.txt',sep=' ', header=None)   \n",
        "X_train.columns = features          \n",
        "X_train.head(5)"
      ],
      "execution_count": 0,
      "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>tBodyAcc-Mean-1</th>\n",
              "      <th>tBodyAcc-Mean-2</th>\n",
              "      <th>tBodyAcc-Mean-3</th>\n",
              "      <th>tBodyAcc-STD-1</th>\n",
              "      <th>tBodyAcc-STD-2</th>\n",
              "      <th>tBodyAcc-STD-3</th>\n",
              "      <th>tBodyAcc-Mad-1</th>\n",
              "      <th>tBodyAcc-Mad-2</th>\n",
              "      <th>tBodyAcc-Mad-3</th>\n",
              "      <th>tBodyAcc-Max-1</th>\n",
              "      <th>tBodyAcc-Max-2</th>\n",
              "      <th>tBodyAcc-Max-3</th>\n",
              "      <th>tBodyAcc-Min-1</th>\n",
              "      <th>tBodyAcc-Min-2</th>\n",
              "      <th>tBodyAcc-Min-3</th>\n",
              "      <th>tBodyAcc-SMA-1</th>\n",
              "      <th>tBodyAcc-Energy-1</th>\n",
              "      <th>tBodyAcc-Energy-2</th>\n",
              "      <th>tBodyAcc-Energy-3</th>\n",
              "      <th>tBodyAcc-IQR-1</th>\n",
              "      <th>tBodyAcc-IQR-2</th>\n",
              "      <th>tBodyAcc-IQR-3</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-2</th>\n",
              "      <th>tBodyAcc-ARCoeff-3</th>\n",
              "      <th>tBodyAcc-ARCoeff-4</th>\n",
              "      <th>tBodyAcc-ARCoeff-5</th>\n",
              "      <th>tBodyAcc-ARCoeff-6</th>\n",
              "      <th>tBodyAcc-ARCoeff-7</th>\n",
              "      <th>tBodyAcc-ARCoeff-8</th>\n",
              "      <th>tBodyAcc-ARCoeff-9</th>\n",
              "      <th>tBodyAcc-ARCoeff-10</th>\n",
              "      <th>tBodyAcc-ARCoeff-11</th>\n",
              "      <th>tBodyAcc-ARCoeff-12</th>\n",
              "      <th>tBodyAcc-Correlation-1</th>\n",
              "      <th>tBodyAcc-Correlation-2</th>\n",
              "      <th>tBodyAcc-Correlation-3</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyAccJerkMag-Energy-1</th>\n",
              "      <th>fBodyAccJerkMag-IQR-1</th>\n",
              "      <th>fBodyAccJerkMag-ropy-1</th>\n",
              "      <th>fBodyAccJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyAccJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyAccJerkMag-Skewness-1</th>\n",
              "      <th>fBodyAccJerkMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroMag-Mean-1</th>\n",
              "      <th>fBodyGyroMag-STD-1</th>\n",
              "      <th>fBodyGyroMag-Mad-1</th>\n",
              "      <th>fBodyGyroMag-Max-1</th>\n",
              "      <th>fBodyGyroMag-Min-1</th>\n",
              "      <th>fBodyGyroMag-SMA-1</th>\n",
              "      <th>fBodyGyroMag-Energy-1</th>\n",
              "      <th>fBodyGyroMag-IQR-1</th>\n",
              "      <th>fBodyGyroMag-ropy-1</th>\n",
              "      <th>fBodyGyroMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroMag-Skewness-1</th>\n",
              "      <th>fBodyGyroMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mean-1</th>\n",
              "      <th>fBodyGyroJerkMag-STD-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mad-1</th>\n",
              "      <th>fBodyGyroJerkMag-Max-1</th>\n",
              "      <th>fBodyGyroJerkMag-Min-1</th>\n",
              "      <th>fBodyGyroJerkMag-SMA-1</th>\n",
              "      <th>fBodyGyroJerkMag-Energy-1</th>\n",
              "      <th>fBodyGyroJerkMag-IQR-1</th>\n",
              "      <th>fBodyGyroJerkMag-ropy-1</th>\n",
              "      <th>fBodyGyroJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroJerkMag-Skewness-1</th>\n",
              "      <th>fBodyGyroJerkMag-Kurtosis-1</th>\n",
              "      <th>tBodyAcc-AngleWRTGravity-1</th>\n",
              "      <th>tBodyAccJerk-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyro-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyroJerk-AngleWRTGravity-1</th>\n",
              "      <th>tXAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tYAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tZAxisAcc-AngleWRTGravity-1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.043580</td>\n",
              "      <td>-0.005970</td>\n",
              "      <td>-0.035054</td>\n",
              "      <td>-0.995381</td>\n",
              "      <td>-0.988366</td>\n",
              "      <td>-0.937382</td>\n",
              "      <td>-0.995007</td>\n",
              "      <td>-0.988816</td>\n",
              "      <td>-0.953325</td>\n",
              "      <td>-0.794796</td>\n",
              "      <td>-0.744893</td>\n",
              "      <td>-0.648447</td>\n",
              "      <td>0.841796</td>\n",
              "      <td>0.708440</td>\n",
              "      <td>0.651716</td>\n",
              "      <td>-0.975752</td>\n",
              "      <td>-0.999950</td>\n",
              "      <td>-0.999888</td>\n",
              "      <td>-0.998014</td>\n",
              "      <td>-0.993999</td>\n",
              "      <td>-0.991980</td>\n",
              "      <td>-0.970970</td>\n",
              "      <td>-0.547095</td>\n",
              "      <td>-0.700974</td>\n",
              "      <td>-0.622697</td>\n",
              "      <td>0.921884</td>\n",
              "      <td>-0.719483</td>\n",
              "      <td>0.342168</td>\n",
              "      <td>-0.161318</td>\n",
              "      <td>0.266049</td>\n",
              "      <td>-0.274351</td>\n",
              "      <td>0.267205</td>\n",
              "      <td>-0.020958</td>\n",
              "      <td>0.382610</td>\n",
              "      <td>-0.501748</td>\n",
              "      <td>0.512463</td>\n",
              "      <td>-0.206337</td>\n",
              "      <td>0.376778</td>\n",
              "      <td>0.435172</td>\n",
              "      <td>0.660199</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999918</td>\n",
              "      <td>-0.991736</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.349260</td>\n",
              "      <td>-0.517127</td>\n",
              "      <td>-0.801006</td>\n",
              "      <td>-0.980135</td>\n",
              "      <td>-0.961301</td>\n",
              "      <td>-0.974129</td>\n",
              "      <td>-0.956013</td>\n",
              "      <td>-0.989894</td>\n",
              "      <td>-0.980135</td>\n",
              "      <td>-0.999240</td>\n",
              "      <td>-0.992673</td>\n",
              "      <td>-0.701291</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.132480</td>\n",
              "      <td>0.565697</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>-0.991994</td>\n",
              "      <td>-0.990877</td>\n",
              "      <td>-0.990169</td>\n",
              "      <td>-0.992521</td>\n",
              "      <td>-0.991044</td>\n",
              "      <td>-0.991994</td>\n",
              "      <td>-0.999937</td>\n",
              "      <td>-0.990537</td>\n",
              "      <td>-0.871306</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.012236</td>\n",
              "      <td>-0.314848</td>\n",
              "      <td>-0.713308</td>\n",
              "      <td>-0.112754</td>\n",
              "      <td>0.030400</td>\n",
              "      <td>-0.464761</td>\n",
              "      <td>-0.018446</td>\n",
              "      <td>-0.841559</td>\n",
              "      <td>0.179913</td>\n",
              "      <td>-0.051718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.039480</td>\n",
              "      <td>-0.002131</td>\n",
              "      <td>-0.029067</td>\n",
              "      <td>-0.998348</td>\n",
              "      <td>-0.982945</td>\n",
              "      <td>-0.971273</td>\n",
              "      <td>-0.998702</td>\n",
              "      <td>-0.983315</td>\n",
              "      <td>-0.974000</td>\n",
              "      <td>-0.802537</td>\n",
              "      <td>-0.736338</td>\n",
              "      <td>-0.712415</td>\n",
              "      <td>0.838758</td>\n",
              "      <td>0.708440</td>\n",
              "      <td>0.659340</td>\n",
              "      <td>-0.987427</td>\n",
              "      <td>-0.999993</td>\n",
              "      <td>-0.999826</td>\n",
              "      <td>-0.999411</td>\n",
              "      <td>-0.998918</td>\n",
              "      <td>-0.985482</td>\n",
              "      <td>-0.973481</td>\n",
              "      <td>-0.781973</td>\n",
              "      <td>-0.534604</td>\n",
              "      <td>-0.593165</td>\n",
              "      <td>0.607435</td>\n",
              "      <td>-0.266783</td>\n",
              "      <td>0.275882</td>\n",
              "      <td>0.200417</td>\n",
              "      <td>0.131266</td>\n",
              "      <td>-0.149017</td>\n",
              "      <td>0.292436</td>\n",
              "      <td>-0.192986</td>\n",
              "      <td>0.217496</td>\n",
              "      <td>-0.089175</td>\n",
              "      <td>0.059909</td>\n",
              "      <td>-0.236609</td>\n",
              "      <td>-0.012696</td>\n",
              "      <td>-0.072711</td>\n",
              "      <td>0.578649</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999867</td>\n",
              "      <td>-0.991506</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.841270</td>\n",
              "      <td>0.533688</td>\n",
              "      <td>-0.625993</td>\n",
              "      <td>-0.898311</td>\n",
              "      <td>-0.988296</td>\n",
              "      <td>-0.983313</td>\n",
              "      <td>-0.982951</td>\n",
              "      <td>-0.987406</td>\n",
              "      <td>-0.992134</td>\n",
              "      <td>-0.988296</td>\n",
              "      <td>-0.999811</td>\n",
              "      <td>-0.993996</td>\n",
              "      <td>-0.720683</td>\n",
              "      <td>-0.948718</td>\n",
              "      <td>-0.268979</td>\n",
              "      <td>-0.364219</td>\n",
              "      <td>-0.723724</td>\n",
              "      <td>-0.995857</td>\n",
              "      <td>-0.996580</td>\n",
              "      <td>-0.995671</td>\n",
              "      <td>-0.996939</td>\n",
              "      <td>-0.994436</td>\n",
              "      <td>-0.995857</td>\n",
              "      <td>-0.999981</td>\n",
              "      <td>-0.994623</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.202804</td>\n",
              "      <td>-0.603199</td>\n",
              "      <td>-0.860677</td>\n",
              "      <td>0.053477</td>\n",
              "      <td>-0.007435</td>\n",
              "      <td>-0.732626</td>\n",
              "      <td>0.703511</td>\n",
              "      <td>-0.845092</td>\n",
              "      <td>0.180261</td>\n",
              "      <td>-0.047436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.039978</td>\n",
              "      <td>-0.005153</td>\n",
              "      <td>-0.022651</td>\n",
              "      <td>-0.995482</td>\n",
              "      <td>-0.977314</td>\n",
              "      <td>-0.984760</td>\n",
              "      <td>-0.996415</td>\n",
              "      <td>-0.975835</td>\n",
              "      <td>-0.985973</td>\n",
              "      <td>-0.798477</td>\n",
              "      <td>-0.736338</td>\n",
              "      <td>-0.712415</td>\n",
              "      <td>0.834002</td>\n",
              "      <td>0.705008</td>\n",
              "      <td>0.674551</td>\n",
              "      <td>-0.988528</td>\n",
              "      <td>-0.999972</td>\n",
              "      <td>-0.999719</td>\n",
              "      <td>-0.999803</td>\n",
              "      <td>-0.996898</td>\n",
              "      <td>-0.976781</td>\n",
              "      <td>-0.986754</td>\n",
              "      <td>-0.688176</td>\n",
              "      <td>-0.520514</td>\n",
              "      <td>-0.593165</td>\n",
              "      <td>0.272262</td>\n",
              "      <td>-0.056424</td>\n",
              "      <td>0.322283</td>\n",
              "      <td>-0.273292</td>\n",
              "      <td>0.037180</td>\n",
              "      <td>-0.133612</td>\n",
              "      <td>0.332487</td>\n",
              "      <td>-0.240491</td>\n",
              "      <td>0.348733</td>\n",
              "      <td>-0.195409</td>\n",
              "      <td>0.229436</td>\n",
              "      <td>-0.316816</td>\n",
              "      <td>-0.123889</td>\n",
              "      <td>-0.181137</td>\n",
              "      <td>0.608219</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999845</td>\n",
              "      <td>-0.987029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.904762</td>\n",
              "      <td>0.661975</td>\n",
              "      <td>-0.725887</td>\n",
              "      <td>-0.926663</td>\n",
              "      <td>-0.989255</td>\n",
              "      <td>-0.986019</td>\n",
              "      <td>-0.984533</td>\n",
              "      <td>-0.991701</td>\n",
              "      <td>-0.995857</td>\n",
              "      <td>-0.989255</td>\n",
              "      <td>-0.999854</td>\n",
              "      <td>-0.993256</td>\n",
              "      <td>-0.736521</td>\n",
              "      <td>-0.794872</td>\n",
              "      <td>-0.212429</td>\n",
              "      <td>-0.564868</td>\n",
              "      <td>-0.874594</td>\n",
              "      <td>-0.995034</td>\n",
              "      <td>-0.995308</td>\n",
              "      <td>-0.994868</td>\n",
              "      <td>-0.996133</td>\n",
              "      <td>-0.995863</td>\n",
              "      <td>-0.995034</td>\n",
              "      <td>-0.999973</td>\n",
              "      <td>-0.993834</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>0.440079</td>\n",
              "      <td>-0.404427</td>\n",
              "      <td>-0.761847</td>\n",
              "      <td>-0.118559</td>\n",
              "      <td>0.177899</td>\n",
              "      <td>0.100699</td>\n",
              "      <td>0.808529</td>\n",
              "      <td>-0.849230</td>\n",
              "      <td>0.180610</td>\n",
              "      <td>-0.042271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.039785</td>\n",
              "      <td>-0.011809</td>\n",
              "      <td>-0.028916</td>\n",
              "      <td>-0.996194</td>\n",
              "      <td>-0.988569</td>\n",
              "      <td>-0.993256</td>\n",
              "      <td>-0.996994</td>\n",
              "      <td>-0.988526</td>\n",
              "      <td>-0.993135</td>\n",
              "      <td>-0.798477</td>\n",
              "      <td>-0.752778</td>\n",
              "      <td>-0.722186</td>\n",
              "      <td>0.834002</td>\n",
              "      <td>0.705008</td>\n",
              "      <td>0.673208</td>\n",
              "      <td>-0.990389</td>\n",
              "      <td>-0.999978</td>\n",
              "      <td>-0.999783</td>\n",
              "      <td>-0.999815</td>\n",
              "      <td>-0.996949</td>\n",
              "      <td>-0.989437</td>\n",
              "      <td>-0.992440</td>\n",
              "      <td>-0.715103</td>\n",
              "      <td>-0.860988</td>\n",
              "      <td>-0.916429</td>\n",
              "      <td>0.062816</td>\n",
              "      <td>0.082940</td>\n",
              "      <td>0.200566</td>\n",
              "      <td>-0.378262</td>\n",
              "      <td>0.090063</td>\n",
              "      <td>-0.209264</td>\n",
              "      <td>0.316530</td>\n",
              "      <td>-0.090862</td>\n",
              "      <td>0.396383</td>\n",
              "      <td>-0.353643</td>\n",
              "      <td>0.503754</td>\n",
              "      <td>-0.490389</td>\n",
              "      <td>-0.304759</td>\n",
              "      <td>-0.362708</td>\n",
              "      <td>0.506602</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999894</td>\n",
              "      <td>-0.988427</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.680038</td>\n",
              "      <td>-0.702305</td>\n",
              "      <td>-0.907781</td>\n",
              "      <td>-0.989413</td>\n",
              "      <td>-0.987827</td>\n",
              "      <td>-0.987057</td>\n",
              "      <td>-0.987801</td>\n",
              "      <td>-0.996334</td>\n",
              "      <td>-0.989413</td>\n",
              "      <td>-0.999876</td>\n",
              "      <td>-0.989153</td>\n",
              "      <td>-0.720891</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.043398</td>\n",
              "      <td>-0.257142</td>\n",
              "      <td>-0.516341</td>\n",
              "      <td>-0.995224</td>\n",
              "      <td>-0.995417</td>\n",
              "      <td>-0.995951</td>\n",
              "      <td>-0.995346</td>\n",
              "      <td>-0.995728</td>\n",
              "      <td>-0.995224</td>\n",
              "      <td>-0.999974</td>\n",
              "      <td>-0.995305</td>\n",
              "      <td>-0.955696</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.430891</td>\n",
              "      <td>-0.138373</td>\n",
              "      <td>-0.491604</td>\n",
              "      <td>-0.036788</td>\n",
              "      <td>-0.012892</td>\n",
              "      <td>0.640011</td>\n",
              "      <td>-0.485366</td>\n",
              "      <td>-0.848947</td>\n",
              "      <td>0.181907</td>\n",
              "      <td>-0.040826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.038758</td>\n",
              "      <td>-0.002289</td>\n",
              "      <td>-0.023863</td>\n",
              "      <td>-0.998241</td>\n",
              "      <td>-0.986774</td>\n",
              "      <td>-0.993115</td>\n",
              "      <td>-0.998216</td>\n",
              "      <td>-0.986479</td>\n",
              "      <td>-0.993825</td>\n",
              "      <td>-0.801982</td>\n",
              "      <td>-0.746505</td>\n",
              "      <td>-0.717858</td>\n",
              "      <td>0.838581</td>\n",
              "      <td>0.705854</td>\n",
              "      <td>0.673208</td>\n",
              "      <td>-0.995057</td>\n",
              "      <td>-0.999992</td>\n",
              "      <td>-0.999882</td>\n",
              "      <td>-0.999908</td>\n",
              "      <td>-0.997772</td>\n",
              "      <td>-0.987726</td>\n",
              "      <td>-0.995109</td>\n",
              "      <td>-0.836774</td>\n",
              "      <td>-0.589200</td>\n",
              "      <td>-0.773771</td>\n",
              "      <td>0.312105</td>\n",
              "      <td>-0.095254</td>\n",
              "      <td>0.194399</td>\n",
              "      <td>-0.007998</td>\n",
              "      <td>0.266740</td>\n",
              "      <td>-0.318965</td>\n",
              "      <td>0.409731</td>\n",
              "      <td>-0.224589</td>\n",
              "      <td>0.520354</td>\n",
              "      <td>-0.319167</td>\n",
              "      <td>0.234376</td>\n",
              "      <td>-0.102650</td>\n",
              "      <td>-0.154974</td>\n",
              "      <td>-0.189796</td>\n",
              "      <td>0.598515</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999941</td>\n",
              "      <td>-0.994542</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.560592</td>\n",
              "      <td>-0.529957</td>\n",
              "      <td>-0.857124</td>\n",
              "      <td>-0.991433</td>\n",
              "      <td>-0.989051</td>\n",
              "      <td>-0.987932</td>\n",
              "      <td>-0.992145</td>\n",
              "      <td>-0.998404</td>\n",
              "      <td>-0.991433</td>\n",
              "      <td>-0.999902</td>\n",
              "      <td>-0.989339</td>\n",
              "      <td>-0.763372</td>\n",
              "      <td>-0.897436</td>\n",
              "      <td>-0.270529</td>\n",
              "      <td>-0.539596</td>\n",
              "      <td>-0.833661</td>\n",
              "      <td>-0.995096</td>\n",
              "      <td>-0.995645</td>\n",
              "      <td>-0.995508</td>\n",
              "      <td>-0.995683</td>\n",
              "      <td>-0.997414</td>\n",
              "      <td>-0.995096</td>\n",
              "      <td>-0.999974</td>\n",
              "      <td>-0.995566</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.137735</td>\n",
              "      <td>-0.366214</td>\n",
              "      <td>-0.702490</td>\n",
              "      <td>0.123320</td>\n",
              "      <td>0.122542</td>\n",
              "      <td>0.693578</td>\n",
              "      <td>-0.615971</td>\n",
              "      <td>-0.848164</td>\n",
              "      <td>0.185124</td>\n",
              "      <td>-0.037080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 561 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   tBodyAcc-Mean-1                  ...  tZAxisAcc-AngleWRTGravity-1    \n",
              "0                         0.043580  ...                        -0.051718\n",
              "1                         0.039480  ...                        -0.047436\n",
              "2                         0.039978  ...                        -0.042271\n",
              "3                         0.039785  ...                        -0.040826\n",
              "4                         0.038758  ...                        -0.037080\n",
              "\n",
              "[5 rows x 561 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exuze1IXMLIo",
        "colab_type": "code",
        "outputId": "309c0c29-fddb-4301-9092-96623f3aeda1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "X_train.describe()"
      ],
      "execution_count": 0,
      "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>tBodyAcc-Mean-1</th>\n",
              "      <th>tBodyAcc-Mean-2</th>\n",
              "      <th>tBodyAcc-Mean-3</th>\n",
              "      <th>tBodyAcc-STD-1</th>\n",
              "      <th>tBodyAcc-STD-2</th>\n",
              "      <th>tBodyAcc-STD-3</th>\n",
              "      <th>tBodyAcc-Mad-1</th>\n",
              "      <th>tBodyAcc-Mad-2</th>\n",
              "      <th>tBodyAcc-Mad-3</th>\n",
              "      <th>tBodyAcc-Max-1</th>\n",
              "      <th>tBodyAcc-Max-2</th>\n",
              "      <th>tBodyAcc-Max-3</th>\n",
              "      <th>tBodyAcc-Min-1</th>\n",
              "      <th>tBodyAcc-Min-2</th>\n",
              "      <th>tBodyAcc-Min-3</th>\n",
              "      <th>tBodyAcc-SMA-1</th>\n",
              "      <th>tBodyAcc-Energy-1</th>\n",
              "      <th>tBodyAcc-Energy-2</th>\n",
              "      <th>tBodyAcc-Energy-3</th>\n",
              "      <th>tBodyAcc-IQR-1</th>\n",
              "      <th>tBodyAcc-IQR-2</th>\n",
              "      <th>tBodyAcc-IQR-3</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-2</th>\n",
              "      <th>tBodyAcc-ARCoeff-3</th>\n",
              "      <th>tBodyAcc-ARCoeff-4</th>\n",
              "      <th>tBodyAcc-ARCoeff-5</th>\n",
              "      <th>tBodyAcc-ARCoeff-6</th>\n",
              "      <th>tBodyAcc-ARCoeff-7</th>\n",
              "      <th>tBodyAcc-ARCoeff-8</th>\n",
              "      <th>tBodyAcc-ARCoeff-9</th>\n",
              "      <th>tBodyAcc-ARCoeff-10</th>\n",
              "      <th>tBodyAcc-ARCoeff-11</th>\n",
              "      <th>tBodyAcc-ARCoeff-12</th>\n",
              "      <th>tBodyAcc-Correlation-1</th>\n",
              "      <th>tBodyAcc-Correlation-2</th>\n",
              "      <th>tBodyAcc-Correlation-3</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyAccJerkMag-Energy-1</th>\n",
              "      <th>fBodyAccJerkMag-IQR-1</th>\n",
              "      <th>fBodyAccJerkMag-ropy-1</th>\n",
              "      <th>fBodyAccJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyAccJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyAccJerkMag-Skewness-1</th>\n",
              "      <th>fBodyAccJerkMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroMag-Mean-1</th>\n",
              "      <th>fBodyGyroMag-STD-1</th>\n",
              "      <th>fBodyGyroMag-Mad-1</th>\n",
              "      <th>fBodyGyroMag-Max-1</th>\n",
              "      <th>fBodyGyroMag-Min-1</th>\n",
              "      <th>fBodyGyroMag-SMA-1</th>\n",
              "      <th>fBodyGyroMag-Energy-1</th>\n",
              "      <th>fBodyGyroMag-IQR-1</th>\n",
              "      <th>fBodyGyroMag-ropy-1</th>\n",
              "      <th>fBodyGyroMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroMag-Skewness-1</th>\n",
              "      <th>fBodyGyroMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mean-1</th>\n",
              "      <th>fBodyGyroJerkMag-STD-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mad-1</th>\n",
              "      <th>fBodyGyroJerkMag-Max-1</th>\n",
              "      <th>fBodyGyroJerkMag-Min-1</th>\n",
              "      <th>fBodyGyroJerkMag-SMA-1</th>\n",
              "      <th>fBodyGyroJerkMag-Energy-1</th>\n",
              "      <th>fBodyGyroJerkMag-IQR-1</th>\n",
              "      <th>fBodyGyroJerkMag-ropy-1</th>\n",
              "      <th>fBodyGyroJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroJerkMag-Skewness-1</th>\n",
              "      <th>fBodyGyroJerkMag-Kurtosis-1</th>\n",
              "      <th>tBodyAcc-AngleWRTGravity-1</th>\n",
              "      <th>tBodyAccJerk-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyro-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyroJerk-AngleWRTGravity-1</th>\n",
              "      <th>tXAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tYAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tZAxisAcc-AngleWRTGravity-1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.038759</td>\n",
              "      <td>-0.000647</td>\n",
              "      <td>-0.018155</td>\n",
              "      <td>-0.599017</td>\n",
              "      <td>-0.634424</td>\n",
              "      <td>-0.691270</td>\n",
              "      <td>-0.623886</td>\n",
              "      <td>-0.657884</td>\n",
              "      <td>-0.740154</td>\n",
              "      <td>-0.360200</td>\n",
              "      <td>-0.492537</td>\n",
              "      <td>-0.472255</td>\n",
              "      <td>0.559253</td>\n",
              "      <td>0.401466</td>\n",
              "      <td>0.443246</td>\n",
              "      <td>-0.649103</td>\n",
              "      <td>-0.825839</td>\n",
              "      <td>-0.895025</td>\n",
              "      <td>-0.926409</td>\n",
              "      <td>-0.676961</td>\n",
              "      <td>-0.737241</td>\n",
              "      <td>-0.794020</td>\n",
              "      <td>-0.298958</td>\n",
              "      <td>-0.181455</td>\n",
              "      <td>-0.195345</td>\n",
              "      <td>-0.125491</td>\n",
              "      <td>0.114611</td>\n",
              "      <td>-0.013326</td>\n",
              "      <td>0.035385</td>\n",
              "      <td>-0.018562</td>\n",
              "      <td>-0.030709</td>\n",
              "      <td>0.173692</td>\n",
              "      <td>0.047870</td>\n",
              "      <td>0.138319</td>\n",
              "      <td>-0.030679</td>\n",
              "      <td>0.096610</td>\n",
              "      <td>-0.083076</td>\n",
              "      <td>-0.131427</td>\n",
              "      <td>-0.195810</td>\n",
              "      <td>0.106360</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.847172</td>\n",
              "      <td>-0.681674</td>\n",
              "      <td>-0.336422</td>\n",
              "      <td>-0.882588</td>\n",
              "      <td>0.168823</td>\n",
              "      <td>-0.308693</td>\n",
              "      <td>-0.611161</td>\n",
              "      <td>-0.684296</td>\n",
              "      <td>-0.670760</td>\n",
              "      <td>-0.665353</td>\n",
              "      <td>-0.726710</td>\n",
              "      <td>-0.883257</td>\n",
              "      <td>-0.684296</td>\n",
              "      <td>-0.865447</td>\n",
              "      <td>-0.718123</td>\n",
              "      <td>-0.069968</td>\n",
              "      <td>-0.891804</td>\n",
              "      <td>-0.072024</td>\n",
              "      <td>-0.263580</td>\n",
              "      <td>-0.555497</td>\n",
              "      <td>-0.780061</td>\n",
              "      <td>-0.792545</td>\n",
              "      <td>-0.773168</td>\n",
              "      <td>-0.811447</td>\n",
              "      <td>-0.872369</td>\n",
              "      <td>-0.780061</td>\n",
              "      <td>-0.937495</td>\n",
              "      <td>-0.771929</td>\n",
              "      <td>-0.268916</td>\n",
              "      <td>-0.903797</td>\n",
              "      <td>0.161745</td>\n",
              "      <td>-0.316548</td>\n",
              "      <td>-0.625132</td>\n",
              "      <td>0.016774</td>\n",
              "      <td>0.018471</td>\n",
              "      <td>0.009239</td>\n",
              "      <td>-0.005184</td>\n",
              "      <td>-0.485936</td>\n",
              "      <td>0.050310</td>\n",
              "      <td>-0.052888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.101996</td>\n",
              "      <td>0.099974</td>\n",
              "      <td>0.089927</td>\n",
              "      <td>0.441481</td>\n",
              "      <td>0.367558</td>\n",
              "      <td>0.321641</td>\n",
              "      <td>0.418113</td>\n",
              "      <td>0.348005</td>\n",
              "      <td>0.272619</td>\n",
              "      <td>0.499259</td>\n",
              "      <td>0.275367</td>\n",
              "      <td>0.270346</td>\n",
              "      <td>0.307290</td>\n",
              "      <td>0.344642</td>\n",
              "      <td>0.273504</td>\n",
              "      <td>0.360966</td>\n",
              "      <td>0.247963</td>\n",
              "      <td>0.169930</td>\n",
              "      <td>0.139157</td>\n",
              "      <td>0.369250</td>\n",
              "      <td>0.281389</td>\n",
              "      <td>0.225390</td>\n",
              "      <td>0.382802</td>\n",
              "      <td>0.423485</td>\n",
              "      <td>0.370670</td>\n",
              "      <td>0.305378</td>\n",
              "      <td>0.215601</td>\n",
              "      <td>0.217885</td>\n",
              "      <td>0.243623</td>\n",
              "      <td>0.249931</td>\n",
              "      <td>0.204292</td>\n",
              "      <td>0.179513</td>\n",
              "      <td>0.205269</td>\n",
              "      <td>0.249038</td>\n",
              "      <td>0.203791</td>\n",
              "      <td>0.216885</td>\n",
              "      <td>0.239661</td>\n",
              "      <td>0.371085</td>\n",
              "      <td>0.342575</td>\n",
              "      <td>0.397366</td>\n",
              "      <td>...</td>\n",
              "      <td>0.225938</td>\n",
              "      <td>0.362278</td>\n",
              "      <td>0.655646</td>\n",
              "      <td>0.183024</td>\n",
              "      <td>0.256948</td>\n",
              "      <td>0.356966</td>\n",
              "      <td>0.342497</td>\n",
              "      <td>0.331079</td>\n",
              "      <td>0.334390</td>\n",
              "      <td>0.339232</td>\n",
              "      <td>0.285558</td>\n",
              "      <td>0.177879</td>\n",
              "      <td>0.331079</td>\n",
              "      <td>0.198988</td>\n",
              "      <td>0.312846</td>\n",
              "      <td>0.601676</td>\n",
              "      <td>0.157287</td>\n",
              "      <td>0.279288</td>\n",
              "      <td>0.336229</td>\n",
              "      <td>0.332831</td>\n",
              "      <td>0.269771</td>\n",
              "      <td>0.260130</td>\n",
              "      <td>0.281655</td>\n",
              "      <td>0.241814</td>\n",
              "      <td>0.190186</td>\n",
              "      <td>0.269771</td>\n",
              "      <td>0.135854</td>\n",
              "      <td>0.281252</td>\n",
              "      <td>0.619626</td>\n",
              "      <td>0.137176</td>\n",
              "      <td>0.237319</td>\n",
              "      <td>0.313899</td>\n",
              "      <td>0.302581</td>\n",
              "      <td>0.331326</td>\n",
              "      <td>0.443540</td>\n",
              "      <td>0.601208</td>\n",
              "      <td>0.477218</td>\n",
              "      <td>0.509278</td>\n",
              "      <td>0.300866</td>\n",
              "      <td>0.276196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.930369</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.909707</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976927</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.982511</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.999911</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.993559</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.999989</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.958535</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976580</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.987874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.032037</td>\n",
              "      <td>-0.011209</td>\n",
              "      <td>-0.028448</td>\n",
              "      <td>-0.992140</td>\n",
              "      <td>-0.983570</td>\n",
              "      <td>-0.984661</td>\n",
              "      <td>-0.992902</td>\n",
              "      <td>-0.984131</td>\n",
              "      <td>-0.986661</td>\n",
              "      <td>-0.795613</td>\n",
              "      <td>-0.740709</td>\n",
              "      <td>-0.706783</td>\n",
              "      <td>0.292317</td>\n",
              "      <td>0.120478</td>\n",
              "      <td>0.261302</td>\n",
              "      <td>-0.987058</td>\n",
              "      <td>-0.999933</td>\n",
              "      <td>-0.999802</td>\n",
              "      <td>-0.999775</td>\n",
              "      <td>-0.993827</td>\n",
              "      <td>-0.987326</td>\n",
              "      <td>-0.988590</td>\n",
              "      <td>-0.670651</td>\n",
              "      <td>-0.586260</td>\n",
              "      <td>-0.529911</td>\n",
              "      <td>-0.369168</td>\n",
              "      <td>-0.046810</td>\n",
              "      <td>-0.152010</td>\n",
              "      <td>-0.127687</td>\n",
              "      <td>-0.207400</td>\n",
              "      <td>-0.184645</td>\n",
              "      <td>0.066981</td>\n",
              "      <td>-0.090277</td>\n",
              "      <td>-0.052123</td>\n",
              "      <td>-0.177603</td>\n",
              "      <td>-0.036752</td>\n",
              "      <td>-0.249794</td>\n",
              "      <td>-0.387478</td>\n",
              "      <td>-0.412106</td>\n",
              "      <td>-0.156395</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999854</td>\n",
              "      <td>-0.988945</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.968254</td>\n",
              "      <td>-0.006938</td>\n",
              "      <td>-0.597505</td>\n",
              "      <td>-0.876586</td>\n",
              "      <td>-0.982611</td>\n",
              "      <td>-0.978072</td>\n",
              "      <td>-0.979060</td>\n",
              "      <td>-0.980935</td>\n",
              "      <td>-0.994082</td>\n",
              "      <td>-0.982611</td>\n",
              "      <td>-0.999668</td>\n",
              "      <td>-0.985550</td>\n",
              "      <td>-0.668778</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.260172</td>\n",
              "      <td>-0.510679</td>\n",
              "      <td>-0.798767</td>\n",
              "      <td>-0.992586</td>\n",
              "      <td>-0.993150</td>\n",
              "      <td>-0.992383</td>\n",
              "      <td>-0.993862</td>\n",
              "      <td>-0.993884</td>\n",
              "      <td>-0.992586</td>\n",
              "      <td>-0.999950</td>\n",
              "      <td>-0.991683</td>\n",
              "      <td>-0.955696</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.020312</td>\n",
              "      <td>-0.548129</td>\n",
              "      <td>-0.843966</td>\n",
              "      <td>-0.108225</td>\n",
              "      <td>-0.261002</td>\n",
              "      <td>-0.470267</td>\n",
              "      <td>-0.373565</td>\n",
              "      <td>-0.810953</td>\n",
              "      <td>-0.047752</td>\n",
              "      <td>-0.140560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.038975</td>\n",
              "      <td>-0.002921</td>\n",
              "      <td>-0.019602</td>\n",
              "      <td>-0.914202</td>\n",
              "      <td>-0.827970</td>\n",
              "      <td>-0.827696</td>\n",
              "      <td>-0.924421</td>\n",
              "      <td>-0.838559</td>\n",
              "      <td>-0.852735</td>\n",
              "      <td>-0.717007</td>\n",
              "      <td>-0.622414</td>\n",
              "      <td>-0.608987</td>\n",
              "      <td>0.770743</td>\n",
              "      <td>0.613644</td>\n",
              "      <td>0.588534</td>\n",
              "      <td>-0.854684</td>\n",
              "      <td>-0.994916</td>\n",
              "      <td>-0.985107</td>\n",
              "      <td>-0.985091</td>\n",
              "      <td>-0.932825</td>\n",
              "      <td>-0.883919</td>\n",
              "      <td>-0.886424</td>\n",
              "      <td>-0.280990</td>\n",
              "      <td>-0.156747</td>\n",
              "      <td>-0.178075</td>\n",
              "      <td>-0.148095</td>\n",
              "      <td>0.087958</td>\n",
              "      <td>0.003232</td>\n",
              "      <td>0.040350</td>\n",
              "      <td>-0.036530</td>\n",
              "      <td>-0.043815</td>\n",
              "      <td>0.180801</td>\n",
              "      <td>0.048125</td>\n",
              "      <td>0.150606</td>\n",
              "      <td>-0.055702</td>\n",
              "      <td>0.109040</td>\n",
              "      <td>-0.087323</td>\n",
              "      <td>-0.167763</td>\n",
              "      <td>-0.192983</td>\n",
              "      <td>0.147958</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994521</td>\n",
              "      <td>-0.923667</td>\n",
              "      <td>-0.553895</td>\n",
              "      <td>-0.904762</td>\n",
              "      <td>0.158930</td>\n",
              "      <td>-0.356897</td>\n",
              "      <td>-0.721541</td>\n",
              "      <td>-0.840812</td>\n",
              "      <td>-0.774059</td>\n",
              "      <td>-0.797541</td>\n",
              "      <td>-0.806613</td>\n",
              "      <td>-0.956554</td>\n",
              "      <td>-0.840812</td>\n",
              "      <td>-0.973637</td>\n",
              "      <td>-0.884941</td>\n",
              "      <td>-0.057985</td>\n",
              "      <td>-0.948718</td>\n",
              "      <td>-0.084244</td>\n",
              "      <td>-0.320436</td>\n",
              "      <td>-0.646993</td>\n",
              "      <td>-0.927167</td>\n",
              "      <td>-0.920251</td>\n",
              "      <td>-0.915701</td>\n",
              "      <td>-0.928603</td>\n",
              "      <td>-0.969071</td>\n",
              "      <td>-0.927167</td>\n",
              "      <td>-0.996601</td>\n",
              "      <td>-0.925470</td>\n",
              "      <td>-0.339246</td>\n",
              "      <td>-0.904762</td>\n",
              "      <td>0.170819</td>\n",
              "      <td>-0.353980</td>\n",
              "      <td>-0.710071</td>\n",
              "      <td>0.017627</td>\n",
              "      <td>0.029079</td>\n",
              "      <td>0.001515</td>\n",
              "      <td>-0.005503</td>\n",
              "      <td>-0.706619</td>\n",
              "      <td>0.176777</td>\n",
              "      <td>0.004583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.044000</td>\n",
              "      <td>0.004303</td>\n",
              "      <td>-0.011676</td>\n",
              "      <td>-0.246026</td>\n",
              "      <td>-0.313069</td>\n",
              "      <td>-0.450478</td>\n",
              "      <td>-0.294903</td>\n",
              "      <td>-0.362671</td>\n",
              "      <td>-0.540521</td>\n",
              "      <td>0.054178</td>\n",
              "      <td>-0.284695</td>\n",
              "      <td>-0.280810</td>\n",
              "      <td>0.834147</td>\n",
              "      <td>0.707537</td>\n",
              "      <td>0.672058</td>\n",
              "      <td>-0.345716</td>\n",
              "      <td>-0.726293</td>\n",
              "      <td>-0.837480</td>\n",
              "      <td>-0.900593</td>\n",
              "      <td>-0.390880</td>\n",
              "      <td>-0.524758</td>\n",
              "      <td>-0.642569</td>\n",
              "      <td>0.028784</td>\n",
              "      <td>0.199298</td>\n",
              "      <td>0.112374</td>\n",
              "      <td>0.122806</td>\n",
              "      <td>0.267300</td>\n",
              "      <td>0.136935</td>\n",
              "      <td>0.198503</td>\n",
              "      <td>0.173327</td>\n",
              "      <td>0.112126</td>\n",
              "      <td>0.288838</td>\n",
              "      <td>0.187093</td>\n",
              "      <td>0.331843</td>\n",
              "      <td>0.103646</td>\n",
              "      <td>0.243988</td>\n",
              "      <td>0.078482</td>\n",
              "      <td>0.072191</td>\n",
              "      <td>0.007006</td>\n",
              "      <td>0.389240</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.746165</td>\n",
              "      <td>-0.391131</td>\n",
              "      <td>0.334381</td>\n",
              "      <td>-0.873016</td>\n",
              "      <td>0.356940</td>\n",
              "      <td>-0.077546</td>\n",
              "      <td>-0.441418</td>\n",
              "      <td>-0.437430</td>\n",
              "      <td>-0.422576</td>\n",
              "      <td>-0.397253</td>\n",
              "      <td>-0.543877</td>\n",
              "      <td>-0.835667</td>\n",
              "      <td>-0.437430</td>\n",
              "      <td>-0.788136</td>\n",
              "      <td>-0.491117</td>\n",
              "      <td>0.506547</td>\n",
              "      <td>-0.846154</td>\n",
              "      <td>0.119520</td>\n",
              "      <td>-0.070230</td>\n",
              "      <td>-0.408554</td>\n",
              "      <td>-0.620259</td>\n",
              "      <td>-0.651780</td>\n",
              "      <td>-0.615832</td>\n",
              "      <td>-0.696083</td>\n",
              "      <td>-0.813202</td>\n",
              "      <td>-0.620259</td>\n",
              "      <td>-0.925023</td>\n",
              "      <td>-0.613134</td>\n",
              "      <td>0.326093</td>\n",
              "      <td>-0.873016</td>\n",
              "      <td>0.316240</td>\n",
              "      <td>-0.137462</td>\n",
              "      <td>-0.503837</td>\n",
              "      <td>0.167695</td>\n",
              "      <td>0.314876</td>\n",
              "      <td>0.496871</td>\n",
              "      <td>0.352690</td>\n",
              "      <td>-0.488765</td>\n",
              "      <td>0.246834</td>\n",
              "      <td>0.109507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.945956</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960341</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977852</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999755</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.992901</td>\n",
              "      <td>0.915411</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.969236</td>\n",
              "      <td>0.884134</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.975905</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.959305</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.968254</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.938491</td>\n",
              "      <td>0.911653</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998702</td>\n",
              "      <td>0.991288</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.482229</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 561 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tBodyAcc-Mean-1                  ...  tZAxisAcc-AngleWRTGravity-1    \n",
              "count                      7767.000000  ...                      7767.000000\n",
              "mean                          0.038759  ...                        -0.052888\n",
              "std                           0.101996  ...                         0.276196\n",
              "min                          -1.000000  ...                        -0.987874\n",
              "25%                           0.032037  ...                        -0.140560\n",
              "50%                           0.038975  ...                         0.004583\n",
              "75%                           0.044000  ...                         0.109507\n",
              "max                           1.000000  ...                         1.000000\n",
              "\n",
              "[8 rows x 561 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdCvFAPtOEXH",
        "colab_type": "code",
        "outputId": "5b78672d-025b-4c57-f75d-2b012bbf76a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7767, 561)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95tnUfuFNyiR",
        "colab_type": "text"
      },
      "source": [
        "#Ajout de la variable subject id"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xTBajAVV5nI",
        "colab_type": "code",
        "outputId": "7b8da42b-92d2-4118-f02a-2b5940c38419",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "subject_id_train = pd.read_csv('Train/subject_id_train.txt',header=None)\n",
        "subject_id_train.columns = ['subject_id_train']\n",
        "subject_id_train.head(5)"
      ],
      "execution_count": 0,
      "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>subject_id_train</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   subject_id_train\n",
              "0                 1\n",
              "1                 1\n",
              "2                 1\n",
              "3                 1\n",
              "4                 1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0Lul1_vUdM5",
        "colab_type": "code",
        "outputId": "408ce0bd-c22d-4840-b091-d9d57cc20d4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "y_train = pd.read_csv('Train/y_train.txt',header=None)\n",
        "y_train.columns = ['y_train']\n",
        "y_train.head(5)"
      ],
      "execution_count": 0,
      "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>y_train</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   y_train\n",
              "0        5\n",
              "1        5\n",
              "2        5\n",
              "3        5\n",
              "4        5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86x3sFvSU7jN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "activity_labels = open('activity_labels.txt').read().splitlines()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYVDDAlLVD5e",
        "colab_type": "code",
        "outputId": "09f15de7-7a7a-4883-eaa2-cc0f961d0550",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "activity_labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1 WALKING           ',\n",
              " '2 WALKING_UPSTAIRS  ',\n",
              " '3 WALKING_DOWNSTAIRS',\n",
              " '4 SITTING           ',\n",
              " '5 STANDING          ',\n",
              " '6 LAYING            ',\n",
              " '7 STAND_TO_SIT      ',\n",
              " '8 SIT_TO_STAND      ',\n",
              " '9 SIT_TO_LIE        ',\n",
              " '10 LIE_TO_SIT        ',\n",
              " '11 STAND_TO_LIE      ',\n",
              " '12 LIE_TO_STAND      ']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnKETeFTZcdb",
        "colab_type": "code",
        "outputId": "501ae845-0eb5-4812-ff63-1725720019b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "dictionary= {}\n",
        "for i in activity_labels:\n",
        "  sep=i.split(' ')\n",
        "  dictionary[int(sep[0])]= sep[1]\n",
        "\n",
        "print(dictionary)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{1: 'WALKING', 2: 'WALKING_UPSTAIRS', 3: 'WALKING_DOWNSTAIRS', 4: 'SITTING', 5: 'STANDING', 6: 'LAYING', 7: 'STAND_TO_SIT', 8: 'SIT_TO_STAND', 9: 'SIT_TO_LIE', 10: 'LIE_TO_SIT', 11: 'STAND_TO_LIE', 12: 'LIE_TO_STAND'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTlf7R40UiIp",
        "colab_type": "code",
        "outputId": "c7766261-d627-41dc-f08f-52834c96f004",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "activity_labels = pd.DataFrame(dictionary,index=[0])\n",
        "activity_labels"
      ],
      "execution_count": 0,
      "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>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WALKING</td>\n",
              "      <td>WALKING_UPSTAIRS</td>\n",
              "      <td>WALKING_DOWNSTAIRS</td>\n",
              "      <td>SITTING</td>\n",
              "      <td>STANDING</td>\n",
              "      <td>LAYING</td>\n",
              "      <td>STAND_TO_SIT</td>\n",
              "      <td>SIT_TO_STAND</td>\n",
              "      <td>SIT_TO_LIE</td>\n",
              "      <td>LIE_TO_SIT</td>\n",
              "      <td>STAND_TO_LIE</td>\n",
              "      <td>LIE_TO_STAND</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        1                 2   ...            11            12\n",
              "0  WALKING  WALKING_UPSTAIRS  ...  STAND_TO_LIE  LIE_TO_STAND\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34AqiqWdUyps",
        "colab_type": "code",
        "outputId": "02496e04-8239-4fb5-d6f0-8e00878bb393",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "training_set=  pd.concat([subject_id_train, X_train, y_train], axis=1)\n",
        "training_set.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7767, 563)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROYj0bhnegiv",
        "colab_type": "code",
        "outputId": "e34e771a-49bc-4c8c-c402-aaf0d21ae2be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "print(training_set)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      subject_id_train  ...  y_train\n",
            "0                    1  ...        5\n",
            "1                    1  ...        5\n",
            "2                    1  ...        5\n",
            "3                    1  ...        5\n",
            "4                    1  ...        5\n",
            "...                ...  ...      ...\n",
            "7762                30  ...        2\n",
            "7763                30  ...        2\n",
            "7764                30  ...        2\n",
            "7765                30  ...        2\n",
            "7766                30  ...        2\n",
            "\n",
            "[7767 rows x 563 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXC0jMoreiiR",
        "colab_type": "code",
        "outputId": "3b8fbdfe-405f-461b-f6ca-789f748396c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "X_test = pd.read_csv('Test/X_test.txt',sep=' ',header=None)\n",
        "X_test.columns = features\n",
        "subject_id_test = pd.read_csv('Test/subject_id_test.txt',header=None)\n",
        "subject_id_test.columns = ['subject_id_test']\n",
        "y_test = pd.read_csv('Test/y_test.txt',header=None)\n",
        "test_set = pd.concat([subject_id_test, X_test, y_test], axis=1)\n",
        "test_set.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3162, 563)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHJlsc8pfKyG",
        "colab_type": "code",
        "outputId": "a488f58b-2643-4546-e6eb-d27b3afa90d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "training_set.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "subject_id_train                     int64\n",
              "tBodyAcc-Mean-1                    float64\n",
              "tBodyAcc-Mean-2                    float64\n",
              "tBodyAcc-Mean-3                    float64\n",
              "tBodyAcc-STD-1                     float64\n",
              "                                    ...   \n",
              "tBodyGyroJerk-AngleWRTGravity-1    float64\n",
              "tXAxisAcc-AngleWRTGravity-1        float64\n",
              "tYAxisAcc-AngleWRTGravity-1        float64\n",
              "tZAxisAcc-AngleWRTGravity-1        float64\n",
              "y_train                              int64\n",
              "Length: 563, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DTU41NpHxV3q",
        "colab_type": "text"
      },
      "source": [
        "#Correlation Study"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxM5ULiHIlUK",
        "colab_type": "code",
        "outputId": "b39192dc-ae08-4920-a359-b833adf937c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "plt.matshow(training_set.corr())\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mn5pgOx67/qYBBhoX9NG3dwGx0CDZuR6v6ZSBSDTxhirRmRbJaAWrBM0Jn/5DCcJYvHsP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\nzs5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fIrcIxddOLPJEaz9tFZNqj76yWhA9HdLVITf4q2yp6mj8B+x2DH/TvhawHpaT7oC1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OP+bA93jp/hSn+NQ90dXD++Qs5K6GY+O0s1JJppr0WQmRTih2eP8SfNOd5dXeGbfcmLc4pG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\nxoSf6aJfsMJYkrPcbzFca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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2T8FYCI1YUVe",
        "colab_type": "code",
        "outputId": "a31383ee-5501-46e2-dea5-94fbfb95c762",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "training_set.corr()"
      ],
      "execution_count": 0,
      "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>subject_id_train</th>\n",
              "      <th>tBodyAcc-Mean-1</th>\n",
              "      <th>tBodyAcc-Mean-2</th>\n",
              "      <th>tBodyAcc-Mean-3</th>\n",
              "      <th>tBodyAcc-STD-1</th>\n",
              "      <th>tBodyAcc-STD-2</th>\n",
              "      <th>tBodyAcc-STD-3</th>\n",
              "      <th>tBodyAcc-Mad-1</th>\n",
              "      <th>tBodyAcc-Mad-2</th>\n",
              "      <th>tBodyAcc-Mad-3</th>\n",
              "      <th>tBodyAcc-Max-1</th>\n",
              "      <th>tBodyAcc-Max-2</th>\n",
              "      <th>tBodyAcc-Max-3</th>\n",
              "      <th>tBodyAcc-Min-1</th>\n",
              "      <th>tBodyAcc-Min-2</th>\n",
              "      <th>tBodyAcc-Min-3</th>\n",
              "      <th>tBodyAcc-SMA-1</th>\n",
              "      <th>tBodyAcc-Energy-1</th>\n",
              "      <th>tBodyAcc-Energy-2</th>\n",
              "      <th>tBodyAcc-Energy-3</th>\n",
              "      <th>tBodyAcc-IQR-1</th>\n",
              "      <th>tBodyAcc-IQR-2</th>\n",
              "      <th>tBodyAcc-IQR-3</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-2</th>\n",
              "      <th>tBodyAcc-ARCoeff-3</th>\n",
              "      <th>tBodyAcc-ARCoeff-4</th>\n",
              "      <th>tBodyAcc-ARCoeff-5</th>\n",
              "      <th>tBodyAcc-ARCoeff-6</th>\n",
              "      <th>tBodyAcc-ARCoeff-7</th>\n",
              "      <th>tBodyAcc-ARCoeff-8</th>\n",
              "      <th>tBodyAcc-ARCoeff-9</th>\n",
              "      <th>tBodyAcc-ARCoeff-10</th>\n",
              "      <th>tBodyAcc-ARCoeff-11</th>\n",
              "      <th>tBodyAcc-ARCoeff-12</th>\n",
              "      <th>tBodyAcc-Correlation-1</th>\n",
              "      <th>tBodyAcc-Correlation-2</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyAccJerkMag-IQR-1</th>\n",
              "      <th>fBodyAccJerkMag-ropy-1</th>\n",
              "      <th>fBodyAccJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyAccJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyAccJerkMag-Skewness-1</th>\n",
              "      <th>fBodyAccJerkMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroMag-Mean-1</th>\n",
              "      <th>fBodyGyroMag-STD-1</th>\n",
              "      <th>fBodyGyroMag-Mad-1</th>\n",
              "      <th>fBodyGyroMag-Max-1</th>\n",
              "      <th>fBodyGyroMag-Min-1</th>\n",
              "      <th>fBodyGyroMag-SMA-1</th>\n",
              "      <th>fBodyGyroMag-Energy-1</th>\n",
              "      <th>fBodyGyroMag-IQR-1</th>\n",
              "      <th>fBodyGyroMag-ropy-1</th>\n",
              "      <th>fBodyGyroMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroMag-Skewness-1</th>\n",
              "      <th>fBodyGyroMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mean-1</th>\n",
              "      <th>fBodyGyroJerkMag-STD-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mad-1</th>\n",
              "      <th>fBodyGyroJerkMag-Max-1</th>\n",
              "      <th>fBodyGyroJerkMag-Min-1</th>\n",
              "      <th>fBodyGyroJerkMag-SMA-1</th>\n",
              "      <th>fBodyGyroJerkMag-Energy-1</th>\n",
              "      <th>fBodyGyroJerkMag-IQR-1</th>\n",
              "      <th>fBodyGyroJerkMag-ropy-1</th>\n",
              "      <th>fBodyGyroJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroJerkMag-Skewness-1</th>\n",
              "      <th>fBodyGyroJerkMag-Kurtosis-1</th>\n",
              "      <th>tBodyAcc-AngleWRTGravity-1</th>\n",
              "      <th>tBodyAccJerk-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyro-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyroJerk-AngleWRTGravity-1</th>\n",
              "      <th>tXAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tYAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tZAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>y_train</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>subject_id_train</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000838</td>\n",
              "      <td>-0.003036</td>\n",
              "      <td>-0.002953</td>\n",
              "      <td>-0.064116</td>\n",
              "      <td>-0.115576</td>\n",
              "      <td>-0.046601</td>\n",
              "      <td>-0.062688</td>\n",
              "      <td>-0.114454</td>\n",
              "      <td>-0.049274</td>\n",
              "      <td>-0.056991</td>\n",
              "      <td>-0.091976</td>\n",
              "      <td>-0.026608</td>\n",
              "      <td>0.082757</td>\n",
              "      <td>0.110389</td>\n",
              "      <td>0.038758</td>\n",
              "      <td>-0.075865</td>\n",
              "      <td>-0.048037</td>\n",
              "      <td>-0.104864</td>\n",
              "      <td>-0.017306</td>\n",
              "      <td>-0.063699</td>\n",
              "      <td>-0.098734</td>\n",
              "      <td>-0.055862</td>\n",
              "      <td>-0.055185</td>\n",
              "      <td>-0.111494</td>\n",
              "      <td>-0.056790</td>\n",
              "      <td>0.065052</td>\n",
              "      <td>-0.093889</td>\n",
              "      <td>0.032712</td>\n",
              "      <td>0.058134</td>\n",
              "      <td>0.062591</td>\n",
              "      <td>-0.085957</td>\n",
              "      <td>0.005515</td>\n",
              "      <td>0.065186</td>\n",
              "      <td>0.078279</td>\n",
              "      <td>-0.081913</td>\n",
              "      <td>-0.020183</td>\n",
              "      <td>0.151044</td>\n",
              "      <td>0.016019</td>\n",
              "      <td>-0.009348</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.104059</td>\n",
              "      <td>-0.097523</td>\n",
              "      <td>-0.007757</td>\n",
              "      <td>0.041957</td>\n",
              "      <td>-0.028882</td>\n",
              "      <td>-0.014668</td>\n",
              "      <td>-0.108901</td>\n",
              "      <td>-0.063855</td>\n",
              "      <td>-0.078536</td>\n",
              "      <td>-0.064380</td>\n",
              "      <td>-0.089172</td>\n",
              "      <td>-0.108901</td>\n",
              "      <td>-0.066998</td>\n",
              "      <td>-0.119701</td>\n",
              "      <td>-0.105092</td>\n",
              "      <td>0.003598</td>\n",
              "      <td>-0.105444</td>\n",
              "      <td>0.025966</td>\n",
              "      <td>0.006801</td>\n",
              "      <td>-0.133610</td>\n",
              "      <td>-0.127751</td>\n",
              "      <td>-0.129216</td>\n",
              "      <td>-0.128214</td>\n",
              "      <td>-0.101078</td>\n",
              "      <td>-0.133610</td>\n",
              "      <td>-0.102551</td>\n",
              "      <td>-0.131764</td>\n",
              "      <td>-0.114640</td>\n",
              "      <td>0.028972</td>\n",
              "      <td>-0.000843</td>\n",
              "      <td>-0.051817</td>\n",
              "      <td>-0.047904</td>\n",
              "      <td>-0.004855</td>\n",
              "      <td>0.006730</td>\n",
              "      <td>-0.004858</td>\n",
              "      <td>0.010756</td>\n",
              "      <td>0.027059</td>\n",
              "      <td>-0.008410</td>\n",
              "      <td>-0.101147</td>\n",
              "      <td>0.029684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tBodyAcc-Mean-1</th>\n",
              "      <td>-0.000838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.635967</td>\n",
              "      <td>-0.630025</td>\n",
              "      <td>0.023420</td>\n",
              "      <td>-0.020698</td>\n",
              "      <td>-0.045023</td>\n",
              "      <td>0.030579</td>\n",
              "      <td>-0.024288</td>\n",
              "      <td>-0.049180</td>\n",
              "      <td>0.147468</td>\n",
              "      <td>-0.222029</td>\n",
              "      <td>-0.203667</td>\n",
              "      <td>0.210084</td>\n",
              "      <td>-0.124608</td>\n",
              "      <td>-0.113029</td>\n",
              "      <td>-0.006647</td>\n",
              "      <td>0.078382</td>\n",
              "      <td>-0.072502</td>\n",
              "      <td>-0.091122</td>\n",
              "      <td>0.050009</td>\n",
              "      <td>-0.043055</td>\n",
              "      <td>-0.054066</td>\n",
              "      <td>0.374775</td>\n",
              "      <td>-0.195464</td>\n",
              "      <td>-0.231440</td>\n",
              "      <td>-0.016685</td>\n",
              "      <td>0.022886</td>\n",
              "      <td>-0.030188</td>\n",
              "      <td>0.026998</td>\n",
              "      <td>-0.007270</td>\n",
              "      <td>0.019343</td>\n",
              "      <td>-0.025465</td>\n",
              "      <td>0.008048</td>\n",
              "      <td>0.002290</td>\n",
              "      <td>0.014189</td>\n",
              "      <td>-0.027051</td>\n",
              "      <td>0.013201</td>\n",
              "      <td>-0.031998</td>\n",
              "      <td>-0.034969</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007318</td>\n",
              "      <td>-0.012121</td>\n",
              "      <td>0.009170</td>\n",
              "      <td>0.016356</td>\n",
              "      <td>-0.010540</td>\n",
              "      <td>-0.006424</td>\n",
              "      <td>-0.000181</td>\n",
              "      <td>0.009456</td>\n",
              "      <td>-0.002786</td>\n",
              "      <td>0.017191</td>\n",
              "      <td>0.050183</td>\n",
              "      <td>-0.000181</td>\n",
              "      <td>0.002681</td>\n",
              "      <td>-0.010316</td>\n",
              "      <td>-0.006773</td>\n",
              "      <td>0.001087</td>\n",
              "      <td>-0.002537</td>\n",
              "      <td>0.028968</td>\n",
              "      <td>0.029060</td>\n",
              "      <td>-0.007503</td>\n",
              "      <td>-0.008213</td>\n",
              "      <td>-0.007274</td>\n",
              "      <td>-0.009156</td>\n",
              "      <td>-0.014433</td>\n",
              "      <td>-0.007503</td>\n",
              "      <td>-0.001019</td>\n",
              "      <td>-0.009012</td>\n",
              "      <td>-0.008748</td>\n",
              "      <td>0.015058</td>\n",
              "      <td>0.008851</td>\n",
              "      <td>-0.019793</td>\n",
              "      <td>-0.017768</td>\n",
              "      <td>-0.132641</td>\n",
              "      <td>0.026053</td>\n",
              "      <td>-0.053429</td>\n",
              "      <td>0.030064</td>\n",
              "      <td>0.124145</td>\n",
              "      <td>-0.098704</td>\n",
              "      <td>-0.079874</td>\n",
              "      <td>0.066650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tBodyAcc-Mean-2</th>\n",
              "      <td>-0.003036</td>\n",
              "      <td>-0.635967</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.472460</td>\n",
              "      <td>-0.022666</td>\n",
              "      <td>0.043462</td>\n",
              "      <td>0.065096</td>\n",
              "      <td>-0.024190</td>\n",
              "      <td>0.052732</td>\n",
              "      <td>0.075577</td>\n",
              "      <td>-0.112580</td>\n",
              "      <td>0.327296</td>\n",
              "      <td>0.160337</td>\n",
              "      <td>-0.131505</td>\n",
              "      <td>0.188338</td>\n",
              "      <td>0.072607</td>\n",
              "      <td>0.046573</td>\n",
              "      <td>-0.035778</td>\n",
              "      <td>0.153614</td>\n",
              "      <td>0.127853</td>\n",
              "      <td>-0.028300</td>\n",
              "      <td>0.078385</td>\n",
              "      <td>0.092584</td>\n",
              "      <td>-0.230948</td>\n",
              "      <td>0.354477</td>\n",
              "      <td>0.185017</td>\n",
              "      <td>0.024076</td>\n",
              "      <td>-0.028366</td>\n",
              "      <td>0.010639</td>\n",
              "      <td>-0.027725</td>\n",
              "      <td>0.007375</td>\n",
              "      <td>-0.016053</td>\n",
              "      <td>0.000677</td>\n",
              "      <td>-0.010268</td>\n",
              "      <td>-0.002010</td>\n",
              "      <td>-0.008055</td>\n",
              "      <td>-0.008620</td>\n",
              "      <td>0.004855</td>\n",
              "      <td>-0.004960</td>\n",
              "      <td>0.010058</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009809</td>\n",
              "      <td>0.001918</td>\n",
              "      <td>-0.015911</td>\n",
              "      <td>-0.001615</td>\n",
              "      <td>-0.007657</td>\n",
              "      <td>-0.010974</td>\n",
              "      <td>-0.001457</td>\n",
              "      <td>0.004730</td>\n",
              "      <td>0.000467</td>\n",
              "      <td>0.007334</td>\n",
              "      <td>0.003919</td>\n",
              "      <td>-0.001457</td>\n",
              "      <td>-0.001345</td>\n",
              "      <td>-0.007289</td>\n",
              "      <td>0.004474</td>\n",
              "      <td>-0.010061</td>\n",
              "      <td>-0.008987</td>\n",
              "      <td>0.015737</td>\n",
              "      <td>0.014689</td>\n",
              "      <td>-0.007398</td>\n",
              "      <td>-0.007943</td>\n",
              "      <td>-0.008755</td>\n",
              "      <td>-0.006519</td>\n",
              "      <td>-0.001904</td>\n",
              "      <td>-0.007398</td>\n",
              "      <td>-0.011481</td>\n",
              "      <td>-0.007751</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>-0.023044</td>\n",
              "      <td>-0.018214</td>\n",
              "      <td>0.013575</td>\n",
              "      <td>0.010830</td>\n",
              "      <td>0.047590</td>\n",
              "      <td>0.021639</td>\n",
              "      <td>0.072296</td>\n",
              "      <td>0.029428</td>\n",
              "      <td>-0.088592</td>\n",
              "      <td>0.080304</td>\n",
              "      <td>0.043551</td>\n",
              "      <td>0.074336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tBodyAcc-Mean-3</th>\n",
              "      <td>-0.002953</td>\n",
              "      <td>-0.630025</td>\n",
              "      <td>0.472460</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013337</td>\n",
              "      <td>0.046654</td>\n",
              "      <td>0.069783</td>\n",
              "      <td>-0.014879</td>\n",
              "      <td>0.055531</td>\n",
              "      <td>0.080151</td>\n",
              "      <td>-0.099253</td>\n",
              "      <td>0.184070</td>\n",
              "      <td>0.308372</td>\n",
              "      <td>-0.136107</td>\n",
              "      <td>0.080113</td>\n",
              "      <td>0.182002</td>\n",
              "      <td>0.056521</td>\n",
              "      <td>-0.028870</td>\n",
              "      <td>0.125207</td>\n",
              "      <td>0.190495</td>\n",
              "      <td>-0.019589</td>\n",
              "      <td>0.081418</td>\n",
              "      <td>0.094278</td>\n",
              "      <td>-0.221589</td>\n",
              "      <td>0.160639</td>\n",
              "      <td>0.410747</td>\n",
              "      <td>0.012369</td>\n",
              "      <td>-0.013185</td>\n",
              "      <td>0.006911</td>\n",
              "      <td>-0.029251</td>\n",
              "      <td>-0.010866</td>\n",
              "      <td>0.006335</td>\n",
              "      <td>-0.008547</td>\n",
              "      <td>-0.005414</td>\n",
              "      <td>-0.010364</td>\n",
              "      <td>-0.000925</td>\n",
              "      <td>0.002402</td>\n",
              "      <td>-0.008124</td>\n",
              "      <td>0.024650</td>\n",
              "      <td>0.037572</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002762</td>\n",
              "      <td>0.009041</td>\n",
              "      <td>-0.025112</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>0.007872</td>\n",
              "      <td>0.006054</td>\n",
              "      <td>0.001600</td>\n",
              "      <td>0.006621</td>\n",
              "      <td>0.008569</td>\n",
              "      <td>0.002258</td>\n",
              "      <td>-0.032673</td>\n",
              "      <td>0.001600</td>\n",
              "      <td>-0.001161</td>\n",
              "      <td>-0.000810</td>\n",
              "      <td>0.011061</td>\n",
              "      <td>0.006200</td>\n",
              "      <td>-0.013349</td>\n",
              "      <td>-0.013731</td>\n",
              "      <td>-0.019403</td>\n",
              "      <td>-0.002056</td>\n",
              "      <td>-0.000501</td>\n",
              "      <td>-0.002035</td>\n",
              "      <td>0.001943</td>\n",
              "      <td>0.001461</td>\n",
              "      <td>-0.002056</td>\n",
              "      <td>-0.010508</td>\n",
              "      <td>-0.000100</td>\n",
              "      <td>0.009391</td>\n",
              "      <td>-0.026346</td>\n",
              "      <td>-0.009945</td>\n",
              "      <td>0.024812</td>\n",
              "      <td>0.022980</td>\n",
              "      <td>0.027744</td>\n",
              "      <td>0.001779</td>\n",
              "      <td>0.016080</td>\n",
              "      <td>-0.018802</td>\n",
              "      <td>-0.079965</td>\n",
              "      <td>0.042147</td>\n",
              "      <td>0.073170</td>\n",
              "      <td>0.054117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tBodyAcc-STD-1</th>\n",
              "      <td>-0.064116</td>\n",
              "      <td>0.023420</td>\n",
              "      <td>-0.022666</td>\n",
              "      <td>-0.013337</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.879795</td>\n",
              "      <td>0.804476</td>\n",
              "      <td>0.998320</td>\n",
              "      <td>0.858936</td>\n",
              "      <td>0.784682</td>\n",
              "      <td>0.968917</td>\n",
              "      <td>0.816745</td>\n",
              "      <td>0.784391</td>\n",
              "      <td>-0.938419</td>\n",
              "      <td>-0.885168</td>\n",
              "      <td>-0.809226</td>\n",
              "      <td>0.917400</td>\n",
              "      <td>0.919145</td>\n",
              "      <td>0.558108</td>\n",
              "      <td>0.456402</td>\n",
              "      <td>0.978415</td>\n",
              "      <td>0.796303</td>\n",
              "      <td>0.726591</td>\n",
              "      <td>0.737025</td>\n",
              "      <td>0.771924</td>\n",
              "      <td>0.711828</td>\n",
              "      <td>-0.679008</td>\n",
              "      <td>0.527704</td>\n",
              "      <td>-0.349012</td>\n",
              "      <td>0.317335</td>\n",
              "      <td>-0.508749</td>\n",
              "      <td>0.506261</td>\n",
              "      <td>-0.168927</td>\n",
              "      <td>0.012755</td>\n",
              "      <td>-0.468168</td>\n",
              "      <td>0.492779</td>\n",
              "      <td>-0.238584</td>\n",
              "      <td>0.137935</td>\n",
              "      <td>-0.412195</td>\n",
              "      <td>-0.051251</td>\n",
              "      <td>...</td>\n",
              "      <td>0.935460</td>\n",
              "      <td>0.942407</td>\n",
              "      <td>-0.041144</td>\n",
              "      <td>-0.395677</td>\n",
              "      <td>0.445963</td>\n",
              "      <td>0.401736</td>\n",
              "      <td>0.870484</td>\n",
              "      <td>0.803534</td>\n",
              "      <td>0.844981</td>\n",
              "      <td>0.748382</td>\n",
              "      <td>0.534098</td>\n",
              "      <td>0.870484</td>\n",
              "      <td>0.687109</td>\n",
              "      <td>0.849111</td>\n",
              "      <td>0.882212</td>\n",
              "      <td>0.299717</td>\n",
              "      <td>0.180104</td>\n",
              "      <td>-0.219218</td>\n",
              "      <td>-0.217782</td>\n",
              "      <td>0.824012</td>\n",
              "      <td>0.779580</td>\n",
              "      <td>0.797815</td>\n",
              "      <td>0.750880</td>\n",
              "      <td>0.680245</td>\n",
              "      <td>0.824012</td>\n",
              "      <td>0.547268</td>\n",
              "      <td>0.812315</td>\n",
              "      <td>0.901140</td>\n",
              "      <td>0.109143</td>\n",
              "      <td>-0.065772</td>\n",
              "      <td>0.138397</td>\n",
              "      <td>0.105513</td>\n",
              "      <td>-0.022767</td>\n",
              "      <td>-0.007824</td>\n",
              "      <td>0.014518</td>\n",
              "      <td>-0.022963</td>\n",
              "      <td>-0.339444</td>\n",
              "      <td>0.422214</td>\n",
              "      <td>0.363769</td>\n",
              "      <td>-0.505562</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",
              "      <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>tBodyGyroJerk-AngleWRTGravity-1</th>\n",
              "      <td>0.010756</td>\n",
              "      <td>0.030064</td>\n",
              "      <td>0.029428</td>\n",
              "      <td>-0.018802</td>\n",
              "      <td>-0.022963</td>\n",
              "      <td>-0.005665</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>-0.022722</td>\n",
              "      <td>-0.001583</td>\n",
              "      <td>0.000626</td>\n",
              "      <td>-0.025747</td>\n",
              "      <td>0.003953</td>\n",
              "      <td>0.000434</td>\n",
              "      <td>0.020979</td>\n",
              "      <td>0.027398</td>\n",
              "      <td>0.018431</td>\n",
              "      <td>-0.008810</td>\n",
              "      <td>-0.032002</td>\n",
              "      <td>0.020201</td>\n",
              "      <td>0.009166</td>\n",
              "      <td>-0.020935</td>\n",
              "      <td>0.006039</td>\n",
              "      <td>-0.002834</td>\n",
              "      <td>0.015381</td>\n",
              "      <td>-0.003489</td>\n",
              "      <td>-0.016367</td>\n",
              "      <td>0.006869</td>\n",
              "      <td>-0.013923</td>\n",
              "      <td>0.021557</td>\n",
              "      <td>-0.023442</td>\n",
              "      <td>-0.004974</td>\n",
              "      <td>-0.005306</td>\n",
              "      <td>-0.002539</td>\n",
              "      <td>0.006801</td>\n",
              "      <td>-0.010405</td>\n",
              "      <td>-0.009006</td>\n",
              "      <td>0.007983</td>\n",
              "      <td>-0.001097</td>\n",
              "      <td>0.029917</td>\n",
              "      <td>-0.005865</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.034857</td>\n",
              "      <td>-0.020321</td>\n",
              "      <td>-0.003704</td>\n",
              "      <td>-0.004984</td>\n",
              "      <td>-0.004211</td>\n",
              "      <td>-0.002188</td>\n",
              "      <td>-0.023094</td>\n",
              "      <td>-0.011424</td>\n",
              "      <td>-0.013094</td>\n",
              "      <td>-0.011793</td>\n",
              "      <td>-0.024053</td>\n",
              "      <td>-0.023094</td>\n",
              "      <td>-0.013901</td>\n",
              "      <td>-0.020806</td>\n",
              "      <td>-0.014310</td>\n",
              "      <td>-0.008105</td>\n",
              "      <td>-0.022748</td>\n",
              "      <td>0.006711</td>\n",
              "      <td>0.003592</td>\n",
              "      <td>-0.033487</td>\n",
              "      <td>-0.038613</td>\n",
              "      <td>-0.036489</td>\n",
              "      <td>-0.038131</td>\n",
              "      <td>-0.028895</td>\n",
              "      <td>-0.033487</td>\n",
              "      <td>-0.039430</td>\n",
              "      <td>-0.032921</td>\n",
              "      <td>-0.015181</td>\n",
              "      <td>-0.006245</td>\n",
              "      <td>-0.002348</td>\n",
              "      <td>-0.010313</td>\n",
              "      <td>-0.009147</td>\n",
              "      <td>-0.017277</td>\n",
              "      <td>0.025000</td>\n",
              "      <td>-0.115076</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026446</td>\n",
              "      <td>-0.007795</td>\n",
              "      <td>-0.005032</td>\n",
              "      <td>0.019467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tXAxisAcc-AngleWRTGravity-1</th>\n",
              "      <td>0.027059</td>\n",
              "      <td>0.124145</td>\n",
              "      <td>-0.088592</td>\n",
              "      <td>-0.079965</td>\n",
              "      <td>-0.339444</td>\n",
              "      <td>-0.328960</td>\n",
              "      <td>-0.298680</td>\n",
              "      <td>-0.332691</td>\n",
              "      <td>-0.317816</td>\n",
              "      <td>-0.291831</td>\n",
              "      <td>-0.344166</td>\n",
              "      <td>-0.342359</td>\n",
              "      <td>-0.323885</td>\n",
              "      <td>0.350471</td>\n",
              "      <td>0.318541</td>\n",
              "      <td>0.280032</td>\n",
              "      <td>-0.297719</td>\n",
              "      <td>-0.248842</td>\n",
              "      <td>-0.149769</td>\n",
              "      <td>-0.109284</td>\n",
              "      <td>-0.313506</td>\n",
              "      <td>-0.290680</td>\n",
              "      <td>-0.270456</td>\n",
              "      <td>-0.131357</td>\n",
              "      <td>-0.394736</td>\n",
              "      <td>-0.349104</td>\n",
              "      <td>0.106994</td>\n",
              "      <td>-0.173752</td>\n",
              "      <td>0.035788</td>\n",
              "      <td>-0.127160</td>\n",
              "      <td>0.333894</td>\n",
              "      <td>-0.266854</td>\n",
              "      <td>0.069459</td>\n",
              "      <td>0.060514</td>\n",
              "      <td>0.294777</td>\n",
              "      <td>-0.237767</td>\n",
              "      <td>0.097735</td>\n",
              "      <td>0.021851</td>\n",
              "      <td>0.230080</td>\n",
              "      <td>-0.142498</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.370240</td>\n",
              "      <td>-0.386448</td>\n",
              "      <td>0.031292</td>\n",
              "      <td>0.191048</td>\n",
              "      <td>-0.205709</td>\n",
              "      <td>-0.184892</td>\n",
              "      <td>-0.336628</td>\n",
              "      <td>-0.302647</td>\n",
              "      <td>-0.325840</td>\n",
              "      <td>-0.276190</td>\n",
              "      <td>-0.182440</td>\n",
              "      <td>-0.336628</td>\n",
              "      <td>-0.244738</td>\n",
              "      <td>-0.337737</td>\n",
              "      <td>-0.363346</td>\n",
              "      <td>-0.149039</td>\n",
              "      <td>-0.003762</td>\n",
              "      <td>0.154572</td>\n",
              "      <td>0.172314</td>\n",
              "      <td>-0.326611</td>\n",
              "      <td>-0.322538</td>\n",
              "      <td>-0.323760</td>\n",
              "      <td>-0.318330</td>\n",
              "      <td>-0.266175</td>\n",
              "      <td>-0.326611</td>\n",
              "      <td>-0.216254</td>\n",
              "      <td>-0.323039</td>\n",
              "      <td>-0.361971</td>\n",
              "      <td>-0.032807</td>\n",
              "      <td>0.087845</td>\n",
              "      <td>-0.079524</td>\n",
              "      <td>-0.071166</td>\n",
              "      <td>0.023298</td>\n",
              "      <td>0.037000</td>\n",
              "      <td>-0.022708</td>\n",
              "      <td>0.026446</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.783715</td>\n",
              "      <td>-0.645989</td>\n",
              "      <td>0.514504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tYAxisAcc-AngleWRTGravity-1</th>\n",
              "      <td>-0.008410</td>\n",
              "      <td>-0.098704</td>\n",
              "      <td>0.080304</td>\n",
              "      <td>0.042147</td>\n",
              "      <td>0.422214</td>\n",
              "      <td>0.423689</td>\n",
              "      <td>0.385133</td>\n",
              "      <td>0.413780</td>\n",
              "      <td>0.411093</td>\n",
              "      <td>0.372595</td>\n",
              "      <td>0.431880</td>\n",
              "      <td>0.416223</td>\n",
              "      <td>0.392948</td>\n",
              "      <td>-0.429049</td>\n",
              "      <td>-0.419712</td>\n",
              "      <td>-0.377390</td>\n",
              "      <td>0.370058</td>\n",
              "      <td>0.279130</td>\n",
              "      <td>0.174657</td>\n",
              "      <td>0.134914</td>\n",
              "      <td>0.391378</td>\n",
              "      <td>0.377924</td>\n",
              "      <td>0.336302</td>\n",
              "      <td>0.281647</td>\n",
              "      <td>0.489362</td>\n",
              "      <td>0.405405</td>\n",
              "      <td>-0.245100</td>\n",
              "      <td>0.245025</td>\n",
              "      <td>-0.083158</td>\n",
              "      <td>0.139303</td>\n",
              "      <td>-0.414335</td>\n",
              "      <td>0.298226</td>\n",
              "      <td>-0.092533</td>\n",
              "      <td>-0.050715</td>\n",
              "      <td>-0.342291</td>\n",
              "      <td>0.285017</td>\n",
              "      <td>-0.105196</td>\n",
              "      <td>0.009988</td>\n",
              "      <td>-0.132198</td>\n",
              "      <td>0.091249</td>\n",
              "      <td>...</td>\n",
              "      <td>0.456476</td>\n",
              "      <td>0.479097</td>\n",
              "      <td>-0.013240</td>\n",
              "      <td>-0.193201</td>\n",
              "      <td>0.230648</td>\n",
              "      <td>0.214782</td>\n",
              "      <td>0.432839</td>\n",
              "      <td>0.394962</td>\n",
              "      <td>0.426577</td>\n",
              "      <td>0.360746</td>\n",
              "      <td>0.231394</td>\n",
              "      <td>0.432839</td>\n",
              "      <td>0.324614</td>\n",
              "      <td>0.442198</td>\n",
              "      <td>0.470146</td>\n",
              "      <td>0.170579</td>\n",
              "      <td>-0.011993</td>\n",
              "      <td>-0.160648</td>\n",
              "      <td>-0.185461</td>\n",
              "      <td>0.405294</td>\n",
              "      <td>0.384988</td>\n",
              "      <td>0.392706</td>\n",
              "      <td>0.375721</td>\n",
              "      <td>0.330829</td>\n",
              "      <td>0.405294</td>\n",
              "      <td>0.250612</td>\n",
              "      <td>0.402849</td>\n",
              "      <td>0.462013</td>\n",
              "      <td>0.053413</td>\n",
              "      <td>-0.085890</td>\n",
              "      <td>0.072599</td>\n",
              "      <td>0.062872</td>\n",
              "      <td>-0.019241</td>\n",
              "      <td>-0.042355</td>\n",
              "      <td>0.003139</td>\n",
              "      <td>-0.007795</td>\n",
              "      <td>-0.783715</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.591166</td>\n",
              "      <td>-0.573815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tZAxisAcc-AngleWRTGravity-1</th>\n",
              "      <td>-0.101147</td>\n",
              "      <td>-0.079874</td>\n",
              "      <td>0.043551</td>\n",
              "      <td>0.073170</td>\n",
              "      <td>0.363769</td>\n",
              "      <td>0.369203</td>\n",
              "      <td>0.414432</td>\n",
              "      <td>0.357139</td>\n",
              "      <td>0.357991</td>\n",
              "      <td>0.398343</td>\n",
              "      <td>0.370933</td>\n",
              "      <td>0.373113</td>\n",
              "      <td>0.384640</td>\n",
              "      <td>-0.368727</td>\n",
              "      <td>-0.365694</td>\n",
              "      <td>-0.423243</td>\n",
              "      <td>0.346593</td>\n",
              "      <td>0.246745</td>\n",
              "      <td>0.171501</td>\n",
              "      <td>0.195912</td>\n",
              "      <td>0.339442</td>\n",
              "      <td>0.331056</td>\n",
              "      <td>0.351554</td>\n",
              "      <td>0.209083</td>\n",
              "      <td>0.384950</td>\n",
              "      <td>0.407312</td>\n",
              "      <td>-0.173616</td>\n",
              "      <td>0.196602</td>\n",
              "      <td>-0.056025</td>\n",
              "      <td>0.130522</td>\n",
              "      <td>-0.295569</td>\n",
              "      <td>0.272034</td>\n",
              "      <td>-0.074236</td>\n",
              "      <td>-0.046438</td>\n",
              "      <td>-0.342410</td>\n",
              "      <td>0.277845</td>\n",
              "      <td>-0.123258</td>\n",
              "      <td>0.075289</td>\n",
              "      <td>-0.198880</td>\n",
              "      <td>0.208666</td>\n",
              "      <td>...</td>\n",
              "      <td>0.404939</td>\n",
              "      <td>0.426265</td>\n",
              "      <td>-0.032968</td>\n",
              "      <td>-0.171914</td>\n",
              "      <td>0.215170</td>\n",
              "      <td>0.195042</td>\n",
              "      <td>0.402568</td>\n",
              "      <td>0.358360</td>\n",
              "      <td>0.386974</td>\n",
              "      <td>0.330357</td>\n",
              "      <td>0.245382</td>\n",
              "      <td>0.402568</td>\n",
              "      <td>0.311498</td>\n",
              "      <td>0.402434</td>\n",
              "      <td>0.415424</td>\n",
              "      <td>0.155718</td>\n",
              "      <td>0.059432</td>\n",
              "      <td>-0.189305</td>\n",
              "      <td>-0.202644</td>\n",
              "      <td>0.392419</td>\n",
              "      <td>0.369543</td>\n",
              "      <td>0.379017</td>\n",
              "      <td>0.357036</td>\n",
              "      <td>0.320719</td>\n",
              "      <td>0.392419</td>\n",
              "      <td>0.259851</td>\n",
              "      <td>0.388261</td>\n",
              "      <td>0.426638</td>\n",
              "      <td>0.022146</td>\n",
              "      <td>-0.053637</td>\n",
              "      <td>0.056305</td>\n",
              "      <td>0.050418</td>\n",
              "      <td>-0.013344</td>\n",
              "      <td>-0.034435</td>\n",
              "      <td>-0.009774</td>\n",
              "      <td>-0.005032</td>\n",
              "      <td>-0.645989</td>\n",
              "      <td>0.591166</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.461137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y_train</th>\n",
              "      <td>0.029684</td>\n",
              "      <td>0.066650</td>\n",
              "      <td>0.074336</td>\n",
              "      <td>0.054117</td>\n",
              "      <td>-0.505562</td>\n",
              "      <td>-0.398140</td>\n",
              "      <td>-0.363235</td>\n",
              "      <td>-0.495875</td>\n",
              "      <td>-0.365423</td>\n",
              "      <td>-0.330773</td>\n",
              "      <td>-0.536458</td>\n",
              "      <td>-0.375650</td>\n",
              "      <td>-0.396103</td>\n",
              "      <td>0.498377</td>\n",
              "      <td>0.501543</td>\n",
              "      <td>0.442883</td>\n",
              "      <td>-0.316597</td>\n",
              "      <td>-0.244653</td>\n",
              "      <td>0.077922</td>\n",
              "      <td>0.091986</td>\n",
              "      <td>-0.469319</td>\n",
              "      <td>-0.291545</td>\n",
              "      <td>-0.254308</td>\n",
              "      <td>-0.410280</td>\n",
              "      <td>-0.479789</td>\n",
              "      <td>-0.432473</td>\n",
              "      <td>0.369846</td>\n",
              "      <td>-0.325289</td>\n",
              "      <td>0.115362</td>\n",
              "      <td>-0.148856</td>\n",
              "      <td>0.282056</td>\n",
              "      <td>-0.295358</td>\n",
              "      <td>0.005264</td>\n",
              "      <td>0.055591</td>\n",
              "      <td>0.305135</td>\n",
              "      <td>-0.325482</td>\n",
              "      <td>0.061924</td>\n",
              "      <td>0.026408</td>\n",
              "      <td>0.203135</td>\n",
              "      <td>-0.051360</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.591316</td>\n",
              "      <td>-0.600905</td>\n",
              "      <td>-0.027319</td>\n",
              "      <td>0.181489</td>\n",
              "      <td>-0.341956</td>\n",
              "      <td>-0.330313</td>\n",
              "      <td>-0.498798</td>\n",
              "      <td>-0.366678</td>\n",
              "      <td>-0.439149</td>\n",
              "      <td>-0.316033</td>\n",
              "      <td>-0.250963</td>\n",
              "      <td>-0.498798</td>\n",
              "      <td>-0.301514</td>\n",
              "      <td>-0.548975</td>\n",
              "      <td>-0.543803</td>\n",
              "      <td>-0.361245</td>\n",
              "      <td>-0.292898</td>\n",
              "      <td>0.279230</td>\n",
              "      <td>0.266285</td>\n",
              "      <td>-0.539801</td>\n",
              "      <td>-0.530550</td>\n",
              "      <td>-0.529374</td>\n",
              "      <td>-0.532524</td>\n",
              "      <td>-0.435238</td>\n",
              "      <td>-0.539801</td>\n",
              "      <td>-0.363203</td>\n",
              "      <td>-0.531681</td>\n",
              "      <td>-0.562271</td>\n",
              "      <td>-0.142525</td>\n",
              "      <td>-0.042809</td>\n",
              "      <td>-0.145731</td>\n",
              "      <td>-0.140537</td>\n",
              "      <td>0.064530</td>\n",
              "      <td>0.113462</td>\n",
              "      <td>0.022811</td>\n",
              "      <td>0.019467</td>\n",
              "      <td>0.514504</td>\n",
              "      <td>-0.573815</td>\n",
              "      <td>-0.461137</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>563 rows × 563 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 subject_id_train  ...   y_train\n",
              "subject_id_train                         1.000000  ...  0.029684\n",
              "tBodyAcc-Mean-1                         -0.000838  ...  0.066650\n",
              "tBodyAcc-Mean-2                         -0.003036  ...  0.074336\n",
              "tBodyAcc-Mean-3                         -0.002953  ...  0.054117\n",
              "tBodyAcc-STD-1                          -0.064116  ... -0.505562\n",
              "...                                           ...  ...       ...\n",
              "tBodyGyroJerk-AngleWRTGravity-1          0.010756  ...  0.019467\n",
              "tXAxisAcc-AngleWRTGravity-1              0.027059  ...  0.514504\n",
              "tYAxisAcc-AngleWRTGravity-1             -0.008410  ... -0.573815\n",
              "tZAxisAcc-AngleWRTGravity-1             -0.101147  ... -0.461137\n",
              "y_train                                  0.029684  ...  1.000000\n",
              "\n",
              "[563 rows x 563 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IJQelXrrfsm",
        "colab_type": "text"
      },
      "source": [
        "#Importation des librairies Machine Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnVAFhEqfsGo",
        "colab_type": "code",
        "outputId": "3d2c9784-49e0-4183-839b-de0515cda878",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "import numpy as np\n",
        "from numpy import pi\n",
        "\n",
        "from matplotlib.colors import rgb2hex\n",
        "from matplotlib.cm import get_cmap\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "from plotly.offline import init_notebook_mode, iplot\n",
        "import plotly.graph_objs as go\n",
        "init_notebook_mode(connected=True)\n",
        "\n",
        "from collections import Counter\n",
        "from cycler import cycler\n",
        "\n",
        "from scipy.stats import norm, skew, probplot\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFaAXnrTgUxs",
        "colab_type": "code",
        "outputId": "f26877b8-c9fb-49a9-ef87-149874a11cbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 0,
      "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>tBodyAcc-Mean-1</th>\n",
              "      <th>tBodyAcc-Mean-2</th>\n",
              "      <th>tBodyAcc-Mean-3</th>\n",
              "      <th>tBodyAcc-STD-1</th>\n",
              "      <th>tBodyAcc-STD-2</th>\n",
              "      <th>tBodyAcc-STD-3</th>\n",
              "      <th>tBodyAcc-Mad-1</th>\n",
              "      <th>tBodyAcc-Mad-2</th>\n",
              "      <th>tBodyAcc-Mad-3</th>\n",
              "      <th>tBodyAcc-Max-1</th>\n",
              "      <th>tBodyAcc-Max-2</th>\n",
              "      <th>tBodyAcc-Max-3</th>\n",
              "      <th>tBodyAcc-Min-1</th>\n",
              "      <th>tBodyAcc-Min-2</th>\n",
              "      <th>tBodyAcc-Min-3</th>\n",
              "      <th>tBodyAcc-SMA-1</th>\n",
              "      <th>tBodyAcc-Energy-1</th>\n",
              "      <th>tBodyAcc-Energy-2</th>\n",
              "      <th>tBodyAcc-Energy-3</th>\n",
              "      <th>tBodyAcc-IQR-1</th>\n",
              "      <th>tBodyAcc-IQR-2</th>\n",
              "      <th>tBodyAcc-IQR-3</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-2</th>\n",
              "      <th>tBodyAcc-ARCoeff-3</th>\n",
              "      <th>tBodyAcc-ARCoeff-4</th>\n",
              "      <th>tBodyAcc-ARCoeff-5</th>\n",
              "      <th>tBodyAcc-ARCoeff-6</th>\n",
              "      <th>tBodyAcc-ARCoeff-7</th>\n",
              "      <th>tBodyAcc-ARCoeff-8</th>\n",
              "      <th>tBodyAcc-ARCoeff-9</th>\n",
              "      <th>tBodyAcc-ARCoeff-10</th>\n",
              "      <th>tBodyAcc-ARCoeff-11</th>\n",
              "      <th>tBodyAcc-ARCoeff-12</th>\n",
              "      <th>tBodyAcc-Correlation-1</th>\n",
              "      <th>tBodyAcc-Correlation-2</th>\n",
              "      <th>tBodyAcc-Correlation-3</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyAccJerkMag-Energy-1</th>\n",
              "      <th>fBodyAccJerkMag-IQR-1</th>\n",
              "      <th>fBodyAccJerkMag-ropy-1</th>\n",
              "      <th>fBodyAccJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyAccJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyAccJerkMag-Skewness-1</th>\n",
              "      <th>fBodyAccJerkMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroMag-Mean-1</th>\n",
              "      <th>fBodyGyroMag-STD-1</th>\n",
              "      <th>fBodyGyroMag-Mad-1</th>\n",
              "      <th>fBodyGyroMag-Max-1</th>\n",
              "      <th>fBodyGyroMag-Min-1</th>\n",
              "      <th>fBodyGyroMag-SMA-1</th>\n",
              "      <th>fBodyGyroMag-Energy-1</th>\n",
              "      <th>fBodyGyroMag-IQR-1</th>\n",
              "      <th>fBodyGyroMag-ropy-1</th>\n",
              "      <th>fBodyGyroMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroMag-Skewness-1</th>\n",
              "      <th>fBodyGyroMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mean-1</th>\n",
              "      <th>fBodyGyroJerkMag-STD-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mad-1</th>\n",
              "      <th>fBodyGyroJerkMag-Max-1</th>\n",
              "      <th>fBodyGyroJerkMag-Min-1</th>\n",
              "      <th>fBodyGyroJerkMag-SMA-1</th>\n",
              "      <th>fBodyGyroJerkMag-Energy-1</th>\n",
              "      <th>fBodyGyroJerkMag-IQR-1</th>\n",
              "      <th>fBodyGyroJerkMag-ropy-1</th>\n",
              "      <th>fBodyGyroJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroJerkMag-Skewness-1</th>\n",
              "      <th>fBodyGyroJerkMag-Kurtosis-1</th>\n",
              "      <th>tBodyAcc-AngleWRTGravity-1</th>\n",
              "      <th>tBodyAccJerk-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyro-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyroJerk-AngleWRTGravity-1</th>\n",
              "      <th>tXAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tYAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tZAxisAcc-AngleWRTGravity-1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.043580</td>\n",
              "      <td>-0.005970</td>\n",
              "      <td>-0.035054</td>\n",
              "      <td>-0.995381</td>\n",
              "      <td>-0.988366</td>\n",
              "      <td>-0.937382</td>\n",
              "      <td>-0.995007</td>\n",
              "      <td>-0.988816</td>\n",
              "      <td>-0.953325</td>\n",
              "      <td>-0.794796</td>\n",
              "      <td>-0.744893</td>\n",
              "      <td>-0.648447</td>\n",
              "      <td>0.841796</td>\n",
              "      <td>0.708440</td>\n",
              "      <td>0.651716</td>\n",
              "      <td>-0.975752</td>\n",
              "      <td>-0.999950</td>\n",
              "      <td>-0.999888</td>\n",
              "      <td>-0.998014</td>\n",
              "      <td>-0.993999</td>\n",
              "      <td>-0.991980</td>\n",
              "      <td>-0.970970</td>\n",
              "      <td>-0.547095</td>\n",
              "      <td>-0.700974</td>\n",
              "      <td>-0.622697</td>\n",
              "      <td>0.921884</td>\n",
              "      <td>-0.719483</td>\n",
              "      <td>0.342168</td>\n",
              "      <td>-0.161318</td>\n",
              "      <td>0.266049</td>\n",
              "      <td>-0.274351</td>\n",
              "      <td>0.267205</td>\n",
              "      <td>-0.020958</td>\n",
              "      <td>0.382610</td>\n",
              "      <td>-0.501748</td>\n",
              "      <td>0.512463</td>\n",
              "      <td>-0.206337</td>\n",
              "      <td>0.376778</td>\n",
              "      <td>0.435172</td>\n",
              "      <td>0.660199</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999918</td>\n",
              "      <td>-0.991736</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.349260</td>\n",
              "      <td>-0.517127</td>\n",
              "      <td>-0.801006</td>\n",
              "      <td>-0.980135</td>\n",
              "      <td>-0.961301</td>\n",
              "      <td>-0.974129</td>\n",
              "      <td>-0.956013</td>\n",
              "      <td>-0.989894</td>\n",
              "      <td>-0.980135</td>\n",
              "      <td>-0.999240</td>\n",
              "      <td>-0.992673</td>\n",
              "      <td>-0.701291</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.132480</td>\n",
              "      <td>0.565697</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>-0.991994</td>\n",
              "      <td>-0.990877</td>\n",
              "      <td>-0.990169</td>\n",
              "      <td>-0.992521</td>\n",
              "      <td>-0.991044</td>\n",
              "      <td>-0.991994</td>\n",
              "      <td>-0.999937</td>\n",
              "      <td>-0.990537</td>\n",
              "      <td>-0.871306</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.012236</td>\n",
              "      <td>-0.314848</td>\n",
              "      <td>-0.713308</td>\n",
              "      <td>-0.112754</td>\n",
              "      <td>0.030400</td>\n",
              "      <td>-0.464761</td>\n",
              "      <td>-0.018446</td>\n",
              "      <td>-0.841559</td>\n",
              "      <td>0.179913</td>\n",
              "      <td>-0.051718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.039480</td>\n",
              "      <td>-0.002131</td>\n",
              "      <td>-0.029067</td>\n",
              "      <td>-0.998348</td>\n",
              "      <td>-0.982945</td>\n",
              "      <td>-0.971273</td>\n",
              "      <td>-0.998702</td>\n",
              "      <td>-0.983315</td>\n",
              "      <td>-0.974000</td>\n",
              "      <td>-0.802537</td>\n",
              "      <td>-0.736338</td>\n",
              "      <td>-0.712415</td>\n",
              "      <td>0.838758</td>\n",
              "      <td>0.708440</td>\n",
              "      <td>0.659340</td>\n",
              "      <td>-0.987427</td>\n",
              "      <td>-0.999993</td>\n",
              "      <td>-0.999826</td>\n",
              "      <td>-0.999411</td>\n",
              "      <td>-0.998918</td>\n",
              "      <td>-0.985482</td>\n",
              "      <td>-0.973481</td>\n",
              "      <td>-0.781973</td>\n",
              "      <td>-0.534604</td>\n",
              "      <td>-0.593165</td>\n",
              "      <td>0.607435</td>\n",
              "      <td>-0.266783</td>\n",
              "      <td>0.275882</td>\n",
              "      <td>0.200417</td>\n",
              "      <td>0.131266</td>\n",
              "      <td>-0.149017</td>\n",
              "      <td>0.292436</td>\n",
              "      <td>-0.192986</td>\n",
              "      <td>0.217496</td>\n",
              "      <td>-0.089175</td>\n",
              "      <td>0.059909</td>\n",
              "      <td>-0.236609</td>\n",
              "      <td>-0.012696</td>\n",
              "      <td>-0.072711</td>\n",
              "      <td>0.578649</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999867</td>\n",
              "      <td>-0.991506</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.841270</td>\n",
              "      <td>0.533688</td>\n",
              "      <td>-0.625993</td>\n",
              "      <td>-0.898311</td>\n",
              "      <td>-0.988296</td>\n",
              "      <td>-0.983313</td>\n",
              "      <td>-0.982951</td>\n",
              "      <td>-0.987406</td>\n",
              "      <td>-0.992134</td>\n",
              "      <td>-0.988296</td>\n",
              "      <td>-0.999811</td>\n",
              "      <td>-0.993996</td>\n",
              "      <td>-0.720683</td>\n",
              "      <td>-0.948718</td>\n",
              "      <td>-0.268979</td>\n",
              "      <td>-0.364219</td>\n",
              "      <td>-0.723724</td>\n",
              "      <td>-0.995857</td>\n",
              "      <td>-0.996580</td>\n",
              "      <td>-0.995671</td>\n",
              "      <td>-0.996939</td>\n",
              "      <td>-0.994436</td>\n",
              "      <td>-0.995857</td>\n",
              "      <td>-0.999981</td>\n",
              "      <td>-0.994623</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.202804</td>\n",
              "      <td>-0.603199</td>\n",
              "      <td>-0.860677</td>\n",
              "      <td>0.053477</td>\n",
              "      <td>-0.007435</td>\n",
              "      <td>-0.732626</td>\n",
              "      <td>0.703511</td>\n",
              "      <td>-0.845092</td>\n",
              "      <td>0.180261</td>\n",
              "      <td>-0.047436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.039978</td>\n",
              "      <td>-0.005153</td>\n",
              "      <td>-0.022651</td>\n",
              "      <td>-0.995482</td>\n",
              "      <td>-0.977314</td>\n",
              "      <td>-0.984760</td>\n",
              "      <td>-0.996415</td>\n",
              "      <td>-0.975835</td>\n",
              "      <td>-0.985973</td>\n",
              "      <td>-0.798477</td>\n",
              "      <td>-0.736338</td>\n",
              "      <td>-0.712415</td>\n",
              "      <td>0.834002</td>\n",
              "      <td>0.705008</td>\n",
              "      <td>0.674551</td>\n",
              "      <td>-0.988528</td>\n",
              "      <td>-0.999972</td>\n",
              "      <td>-0.999719</td>\n",
              "      <td>-0.999803</td>\n",
              "      <td>-0.996898</td>\n",
              "      <td>-0.976781</td>\n",
              "      <td>-0.986754</td>\n",
              "      <td>-0.688176</td>\n",
              "      <td>-0.520514</td>\n",
              "      <td>-0.593165</td>\n",
              "      <td>0.272262</td>\n",
              "      <td>-0.056424</td>\n",
              "      <td>0.322283</td>\n",
              "      <td>-0.273292</td>\n",
              "      <td>0.037180</td>\n",
              "      <td>-0.133612</td>\n",
              "      <td>0.332487</td>\n",
              "      <td>-0.240491</td>\n",
              "      <td>0.348733</td>\n",
              "      <td>-0.195409</td>\n",
              "      <td>0.229436</td>\n",
              "      <td>-0.316816</td>\n",
              "      <td>-0.123889</td>\n",
              "      <td>-0.181137</td>\n",
              "      <td>0.608219</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999845</td>\n",
              "      <td>-0.987029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.904762</td>\n",
              "      <td>0.661975</td>\n",
              "      <td>-0.725887</td>\n",
              "      <td>-0.926663</td>\n",
              "      <td>-0.989255</td>\n",
              "      <td>-0.986019</td>\n",
              "      <td>-0.984533</td>\n",
              "      <td>-0.991701</td>\n",
              "      <td>-0.995857</td>\n",
              "      <td>-0.989255</td>\n",
              "      <td>-0.999854</td>\n",
              "      <td>-0.993256</td>\n",
              "      <td>-0.736521</td>\n",
              "      <td>-0.794872</td>\n",
              "      <td>-0.212429</td>\n",
              "      <td>-0.564868</td>\n",
              "      <td>-0.874594</td>\n",
              "      <td>-0.995034</td>\n",
              "      <td>-0.995308</td>\n",
              "      <td>-0.994868</td>\n",
              "      <td>-0.996133</td>\n",
              "      <td>-0.995863</td>\n",
              "      <td>-0.995034</td>\n",
              "      <td>-0.999973</td>\n",
              "      <td>-0.993834</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>0.440079</td>\n",
              "      <td>-0.404427</td>\n",
              "      <td>-0.761847</td>\n",
              "      <td>-0.118559</td>\n",
              "      <td>0.177899</td>\n",
              "      <td>0.100699</td>\n",
              "      <td>0.808529</td>\n",
              "      <td>-0.849230</td>\n",
              "      <td>0.180610</td>\n",
              "      <td>-0.042271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.039785</td>\n",
              "      <td>-0.011809</td>\n",
              "      <td>-0.028916</td>\n",
              "      <td>-0.996194</td>\n",
              "      <td>-0.988569</td>\n",
              "      <td>-0.993256</td>\n",
              "      <td>-0.996994</td>\n",
              "      <td>-0.988526</td>\n",
              "      <td>-0.993135</td>\n",
              "      <td>-0.798477</td>\n",
              "      <td>-0.752778</td>\n",
              "      <td>-0.722186</td>\n",
              "      <td>0.834002</td>\n",
              "      <td>0.705008</td>\n",
              "      <td>0.673208</td>\n",
              "      <td>-0.990389</td>\n",
              "      <td>-0.999978</td>\n",
              "      <td>-0.999783</td>\n",
              "      <td>-0.999815</td>\n",
              "      <td>-0.996949</td>\n",
              "      <td>-0.989437</td>\n",
              "      <td>-0.992440</td>\n",
              "      <td>-0.715103</td>\n",
              "      <td>-0.860988</td>\n",
              "      <td>-0.916429</td>\n",
              "      <td>0.062816</td>\n",
              "      <td>0.082940</td>\n",
              "      <td>0.200566</td>\n",
              "      <td>-0.378262</td>\n",
              "      <td>0.090063</td>\n",
              "      <td>-0.209264</td>\n",
              "      <td>0.316530</td>\n",
              "      <td>-0.090862</td>\n",
              "      <td>0.396383</td>\n",
              "      <td>-0.353643</td>\n",
              "      <td>0.503754</td>\n",
              "      <td>-0.490389</td>\n",
              "      <td>-0.304759</td>\n",
              "      <td>-0.362708</td>\n",
              "      <td>0.506602</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999894</td>\n",
              "      <td>-0.988427</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.680038</td>\n",
              "      <td>-0.702305</td>\n",
              "      <td>-0.907781</td>\n",
              "      <td>-0.989413</td>\n",
              "      <td>-0.987827</td>\n",
              "      <td>-0.987057</td>\n",
              "      <td>-0.987801</td>\n",
              "      <td>-0.996334</td>\n",
              "      <td>-0.989413</td>\n",
              "      <td>-0.999876</td>\n",
              "      <td>-0.989153</td>\n",
              "      <td>-0.720891</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.043398</td>\n",
              "      <td>-0.257142</td>\n",
              "      <td>-0.516341</td>\n",
              "      <td>-0.995224</td>\n",
              "      <td>-0.995417</td>\n",
              "      <td>-0.995951</td>\n",
              "      <td>-0.995346</td>\n",
              "      <td>-0.995728</td>\n",
              "      <td>-0.995224</td>\n",
              "      <td>-0.999974</td>\n",
              "      <td>-0.995305</td>\n",
              "      <td>-0.955696</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.430891</td>\n",
              "      <td>-0.138373</td>\n",
              "      <td>-0.491604</td>\n",
              "      <td>-0.036788</td>\n",
              "      <td>-0.012892</td>\n",
              "      <td>0.640011</td>\n",
              "      <td>-0.485366</td>\n",
              "      <td>-0.848947</td>\n",
              "      <td>0.181907</td>\n",
              "      <td>-0.040826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.038758</td>\n",
              "      <td>-0.002289</td>\n",
              "      <td>-0.023863</td>\n",
              "      <td>-0.998241</td>\n",
              "      <td>-0.986774</td>\n",
              "      <td>-0.993115</td>\n",
              "      <td>-0.998216</td>\n",
              "      <td>-0.986479</td>\n",
              "      <td>-0.993825</td>\n",
              "      <td>-0.801982</td>\n",
              "      <td>-0.746505</td>\n",
              "      <td>-0.717858</td>\n",
              "      <td>0.838581</td>\n",
              "      <td>0.705854</td>\n",
              "      <td>0.673208</td>\n",
              "      <td>-0.995057</td>\n",
              "      <td>-0.999992</td>\n",
              "      <td>-0.999882</td>\n",
              "      <td>-0.999908</td>\n",
              "      <td>-0.997772</td>\n",
              "      <td>-0.987726</td>\n",
              "      <td>-0.995109</td>\n",
              "      <td>-0.836774</td>\n",
              "      <td>-0.589200</td>\n",
              "      <td>-0.773771</td>\n",
              "      <td>0.312105</td>\n",
              "      <td>-0.095254</td>\n",
              "      <td>0.194399</td>\n",
              "      <td>-0.007998</td>\n",
              "      <td>0.266740</td>\n",
              "      <td>-0.318965</td>\n",
              "      <td>0.409731</td>\n",
              "      <td>-0.224589</td>\n",
              "      <td>0.520354</td>\n",
              "      <td>-0.319167</td>\n",
              "      <td>0.234376</td>\n",
              "      <td>-0.102650</td>\n",
              "      <td>-0.154974</td>\n",
              "      <td>-0.189796</td>\n",
              "      <td>0.598515</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999941</td>\n",
              "      <td>-0.994542</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.560592</td>\n",
              "      <td>-0.529957</td>\n",
              "      <td>-0.857124</td>\n",
              "      <td>-0.991433</td>\n",
              "      <td>-0.989051</td>\n",
              "      <td>-0.987932</td>\n",
              "      <td>-0.992145</td>\n",
              "      <td>-0.998404</td>\n",
              "      <td>-0.991433</td>\n",
              "      <td>-0.999902</td>\n",
              "      <td>-0.989339</td>\n",
              "      <td>-0.763372</td>\n",
              "      <td>-0.897436</td>\n",
              "      <td>-0.270529</td>\n",
              "      <td>-0.539596</td>\n",
              "      <td>-0.833661</td>\n",
              "      <td>-0.995096</td>\n",
              "      <td>-0.995645</td>\n",
              "      <td>-0.995508</td>\n",
              "      <td>-0.995683</td>\n",
              "      <td>-0.997414</td>\n",
              "      <td>-0.995096</td>\n",
              "      <td>-0.999974</td>\n",
              "      <td>-0.995566</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.936508</td>\n",
              "      <td>0.137735</td>\n",
              "      <td>-0.366214</td>\n",
              "      <td>-0.702490</td>\n",
              "      <td>0.123320</td>\n",
              "      <td>0.122542</td>\n",
              "      <td>0.693578</td>\n",
              "      <td>-0.615971</td>\n",
              "      <td>-0.848164</td>\n",
              "      <td>0.185124</td>\n",
              "      <td>-0.037080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 561 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   tBodyAcc-Mean-1                  ...  tZAxisAcc-AngleWRTGravity-1    \n",
              "0                         0.043580  ...                        -0.051718\n",
              "1                         0.039480  ...                        -0.047436\n",
              "2                         0.039978  ...                        -0.042271\n",
              "3                         0.039785  ...                        -0.040826\n",
              "4                         0.038758  ...                        -0.037080\n",
              "\n",
              "[5 rows x 561 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GdrU6DLgWx6",
        "colab_type": "code",
        "outputId": "dd667e8b-5f15-4a3e-d946-3b425d8e2ea4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7767, 561)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yzXGEJXgYjL",
        "colab_type": "code",
        "outputId": "ecce068f-fc45-497e-a5c2-af14a000c48e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "X_train.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['tBodyAcc-Mean-1                ', 'tBodyAcc-Mean-2                ',\n",
              "       'tBodyAcc-Mean-3                ', 'tBodyAcc-STD-1                 ',\n",
              "       'tBodyAcc-STD-2                 ', 'tBodyAcc-STD-3                 ',\n",
              "       'tBodyAcc-Mad-1                 ', 'tBodyAcc-Mad-2                 ',\n",
              "       'tBodyAcc-Mad-3                 ', 'tBodyAcc-Max-1                 ',\n",
              "       ...\n",
              "       'fBodyGyroJerkMag-MeanFreq-1    ', 'fBodyGyroJerkMag-Skewness-1    ',\n",
              "       'fBodyGyroJerkMag-Kurtosis-1    ', 'tBodyAcc-AngleWRTGravity-1     ',\n",
              "       'tBodyAccJerk-AngleWRTGravity-1 ', 'tBodyGyro-AngleWRTGravity-1    ',\n",
              "       'tBodyGyroJerk-AngleWRTGravity-1', 'tXAxisAcc-AngleWRTGravity-1    ',\n",
              "       'tYAxisAcc-AngleWRTGravity-1    ', 'tZAxisAcc-AngleWRTGravity-1    '],\n",
              "      dtype='object', length=561)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u99BCE3LgaV-",
        "colab_type": "code",
        "outputId": "2a387a46-e512-41cb-d44c-9afdf4d45391",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "X_train.describe()"
      ],
      "execution_count": 0,
      "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>tBodyAcc-Mean-1</th>\n",
              "      <th>tBodyAcc-Mean-2</th>\n",
              "      <th>tBodyAcc-Mean-3</th>\n",
              "      <th>tBodyAcc-STD-1</th>\n",
              "      <th>tBodyAcc-STD-2</th>\n",
              "      <th>tBodyAcc-STD-3</th>\n",
              "      <th>tBodyAcc-Mad-1</th>\n",
              "      <th>tBodyAcc-Mad-2</th>\n",
              "      <th>tBodyAcc-Mad-3</th>\n",
              "      <th>tBodyAcc-Max-1</th>\n",
              "      <th>tBodyAcc-Max-2</th>\n",
              "      <th>tBodyAcc-Max-3</th>\n",
              "      <th>tBodyAcc-Min-1</th>\n",
              "      <th>tBodyAcc-Min-2</th>\n",
              "      <th>tBodyAcc-Min-3</th>\n",
              "      <th>tBodyAcc-SMA-1</th>\n",
              "      <th>tBodyAcc-Energy-1</th>\n",
              "      <th>tBodyAcc-Energy-2</th>\n",
              "      <th>tBodyAcc-Energy-3</th>\n",
              "      <th>tBodyAcc-IQR-1</th>\n",
              "      <th>tBodyAcc-IQR-2</th>\n",
              "      <th>tBodyAcc-IQR-3</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ropy-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-1</th>\n",
              "      <th>tBodyAcc-ARCoeff-2</th>\n",
              "      <th>tBodyAcc-ARCoeff-3</th>\n",
              "      <th>tBodyAcc-ARCoeff-4</th>\n",
              "      <th>tBodyAcc-ARCoeff-5</th>\n",
              "      <th>tBodyAcc-ARCoeff-6</th>\n",
              "      <th>tBodyAcc-ARCoeff-7</th>\n",
              "      <th>tBodyAcc-ARCoeff-8</th>\n",
              "      <th>tBodyAcc-ARCoeff-9</th>\n",
              "      <th>tBodyAcc-ARCoeff-10</th>\n",
              "      <th>tBodyAcc-ARCoeff-11</th>\n",
              "      <th>tBodyAcc-ARCoeff-12</th>\n",
              "      <th>tBodyAcc-Correlation-1</th>\n",
              "      <th>tBodyAcc-Correlation-2</th>\n",
              "      <th>tBodyAcc-Correlation-3</th>\n",
              "      <th>...</th>\n",
              "      <th>fBodyAccJerkMag-Energy-1</th>\n",
              "      <th>fBodyAccJerkMag-IQR-1</th>\n",
              "      <th>fBodyAccJerkMag-ropy-1</th>\n",
              "      <th>fBodyAccJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyAccJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyAccJerkMag-Skewness-1</th>\n",
              "      <th>fBodyAccJerkMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroMag-Mean-1</th>\n",
              "      <th>fBodyGyroMag-STD-1</th>\n",
              "      <th>fBodyGyroMag-Mad-1</th>\n",
              "      <th>fBodyGyroMag-Max-1</th>\n",
              "      <th>fBodyGyroMag-Min-1</th>\n",
              "      <th>fBodyGyroMag-SMA-1</th>\n",
              "      <th>fBodyGyroMag-Energy-1</th>\n",
              "      <th>fBodyGyroMag-IQR-1</th>\n",
              "      <th>fBodyGyroMag-ropy-1</th>\n",
              "      <th>fBodyGyroMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroMag-Skewness-1</th>\n",
              "      <th>fBodyGyroMag-Kurtosis-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mean-1</th>\n",
              "      <th>fBodyGyroJerkMag-STD-1</th>\n",
              "      <th>fBodyGyroJerkMag-Mad-1</th>\n",
              "      <th>fBodyGyroJerkMag-Max-1</th>\n",
              "      <th>fBodyGyroJerkMag-Min-1</th>\n",
              "      <th>fBodyGyroJerkMag-SMA-1</th>\n",
              "      <th>fBodyGyroJerkMag-Energy-1</th>\n",
              "      <th>fBodyGyroJerkMag-IQR-1</th>\n",
              "      <th>fBodyGyroJerkMag-ropy-1</th>\n",
              "      <th>fBodyGyroJerkMag-MaxInds-1</th>\n",
              "      <th>fBodyGyroJerkMag-MeanFreq-1</th>\n",
              "      <th>fBodyGyroJerkMag-Skewness-1</th>\n",
              "      <th>fBodyGyroJerkMag-Kurtosis-1</th>\n",
              "      <th>tBodyAcc-AngleWRTGravity-1</th>\n",
              "      <th>tBodyAccJerk-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyro-AngleWRTGravity-1</th>\n",
              "      <th>tBodyGyroJerk-AngleWRTGravity-1</th>\n",
              "      <th>tXAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tYAxisAcc-AngleWRTGravity-1</th>\n",
              "      <th>tZAxisAcc-AngleWRTGravity-1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "      <td>7767.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.038759</td>\n",
              "      <td>-0.000647</td>\n",
              "      <td>-0.018155</td>\n",
              "      <td>-0.599017</td>\n",
              "      <td>-0.634424</td>\n",
              "      <td>-0.691270</td>\n",
              "      <td>-0.623886</td>\n",
              "      <td>-0.657884</td>\n",
              "      <td>-0.740154</td>\n",
              "      <td>-0.360200</td>\n",
              "      <td>-0.492537</td>\n",
              "      <td>-0.472255</td>\n",
              "      <td>0.559253</td>\n",
              "      <td>0.401466</td>\n",
              "      <td>0.443246</td>\n",
              "      <td>-0.649103</td>\n",
              "      <td>-0.825839</td>\n",
              "      <td>-0.895025</td>\n",
              "      <td>-0.926409</td>\n",
              "      <td>-0.676961</td>\n",
              "      <td>-0.737241</td>\n",
              "      <td>-0.794020</td>\n",
              "      <td>-0.298958</td>\n",
              "      <td>-0.181455</td>\n",
              "      <td>-0.195345</td>\n",
              "      <td>-0.125491</td>\n",
              "      <td>0.114611</td>\n",
              "      <td>-0.013326</td>\n",
              "      <td>0.035385</td>\n",
              "      <td>-0.018562</td>\n",
              "      <td>-0.030709</td>\n",
              "      <td>0.173692</td>\n",
              "      <td>0.047870</td>\n",
              "      <td>0.138319</td>\n",
              "      <td>-0.030679</td>\n",
              "      <td>0.096610</td>\n",
              "      <td>-0.083076</td>\n",
              "      <td>-0.131427</td>\n",
              "      <td>-0.195810</td>\n",
              "      <td>0.106360</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.847172</td>\n",
              "      <td>-0.681674</td>\n",
              "      <td>-0.336422</td>\n",
              "      <td>-0.882588</td>\n",
              "      <td>0.168823</td>\n",
              "      <td>-0.308693</td>\n",
              "      <td>-0.611161</td>\n",
              "      <td>-0.684296</td>\n",
              "      <td>-0.670760</td>\n",
              "      <td>-0.665353</td>\n",
              "      <td>-0.726710</td>\n",
              "      <td>-0.883257</td>\n",
              "      <td>-0.684296</td>\n",
              "      <td>-0.865447</td>\n",
              "      <td>-0.718123</td>\n",
              "      <td>-0.069968</td>\n",
              "      <td>-0.891804</td>\n",
              "      <td>-0.072024</td>\n",
              "      <td>-0.263580</td>\n",
              "      <td>-0.555497</td>\n",
              "      <td>-0.780061</td>\n",
              "      <td>-0.792545</td>\n",
              "      <td>-0.773168</td>\n",
              "      <td>-0.811447</td>\n",
              "      <td>-0.872369</td>\n",
              "      <td>-0.780061</td>\n",
              "      <td>-0.937495</td>\n",
              "      <td>-0.771929</td>\n",
              "      <td>-0.268916</td>\n",
              "      <td>-0.903797</td>\n",
              "      <td>0.161745</td>\n",
              "      <td>-0.316548</td>\n",
              "      <td>-0.625132</td>\n",
              "      <td>0.016774</td>\n",
              "      <td>0.018471</td>\n",
              "      <td>0.009239</td>\n",
              "      <td>-0.005184</td>\n",
              "      <td>-0.485936</td>\n",
              "      <td>0.050310</td>\n",
              "      <td>-0.052888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.101996</td>\n",
              "      <td>0.099974</td>\n",
              "      <td>0.089927</td>\n",
              "      <td>0.441481</td>\n",
              "      <td>0.367558</td>\n",
              "      <td>0.321641</td>\n",
              "      <td>0.418113</td>\n",
              "      <td>0.348005</td>\n",
              "      <td>0.272619</td>\n",
              "      <td>0.499259</td>\n",
              "      <td>0.275367</td>\n",
              "      <td>0.270346</td>\n",
              "      <td>0.307290</td>\n",
              "      <td>0.344642</td>\n",
              "      <td>0.273504</td>\n",
              "      <td>0.360966</td>\n",
              "      <td>0.247963</td>\n",
              "      <td>0.169930</td>\n",
              "      <td>0.139157</td>\n",
              "      <td>0.369250</td>\n",
              "      <td>0.281389</td>\n",
              "      <td>0.225390</td>\n",
              "      <td>0.382802</td>\n",
              "      <td>0.423485</td>\n",
              "      <td>0.370670</td>\n",
              "      <td>0.305378</td>\n",
              "      <td>0.215601</td>\n",
              "      <td>0.217885</td>\n",
              "      <td>0.243623</td>\n",
              "      <td>0.249931</td>\n",
              "      <td>0.204292</td>\n",
              "      <td>0.179513</td>\n",
              "      <td>0.205269</td>\n",
              "      <td>0.249038</td>\n",
              "      <td>0.203791</td>\n",
              "      <td>0.216885</td>\n",
              "      <td>0.239661</td>\n",
              "      <td>0.371085</td>\n",
              "      <td>0.342575</td>\n",
              "      <td>0.397366</td>\n",
              "      <td>...</td>\n",
              "      <td>0.225938</td>\n",
              "      <td>0.362278</td>\n",
              "      <td>0.655646</td>\n",
              "      <td>0.183024</td>\n",
              "      <td>0.256948</td>\n",
              "      <td>0.356966</td>\n",
              "      <td>0.342497</td>\n",
              "      <td>0.331079</td>\n",
              "      <td>0.334390</td>\n",
              "      <td>0.339232</td>\n",
              "      <td>0.285558</td>\n",
              "      <td>0.177879</td>\n",
              "      <td>0.331079</td>\n",
              "      <td>0.198988</td>\n",
              "      <td>0.312846</td>\n",
              "      <td>0.601676</td>\n",
              "      <td>0.157287</td>\n",
              "      <td>0.279288</td>\n",
              "      <td>0.336229</td>\n",
              "      <td>0.332831</td>\n",
              "      <td>0.269771</td>\n",
              "      <td>0.260130</td>\n",
              "      <td>0.281655</td>\n",
              "      <td>0.241814</td>\n",
              "      <td>0.190186</td>\n",
              "      <td>0.269771</td>\n",
              "      <td>0.135854</td>\n",
              "      <td>0.281252</td>\n",
              "      <td>0.619626</td>\n",
              "      <td>0.137176</td>\n",
              "      <td>0.237319</td>\n",
              "      <td>0.313899</td>\n",
              "      <td>0.302581</td>\n",
              "      <td>0.331326</td>\n",
              "      <td>0.443540</td>\n",
              "      <td>0.601208</td>\n",
              "      <td>0.477218</td>\n",
              "      <td>0.509278</td>\n",
              "      <td>0.300866</td>\n",
              "      <td>0.276196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.930369</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.909707</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976927</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.982511</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.999911</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.993559</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.999989</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.958535</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976580</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.987874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.032037</td>\n",
              "      <td>-0.011209</td>\n",
              "      <td>-0.028448</td>\n",
              "      <td>-0.992140</td>\n",
              "      <td>-0.983570</td>\n",
              "      <td>-0.984661</td>\n",
              "      <td>-0.992902</td>\n",
              "      <td>-0.984131</td>\n",
              "      <td>-0.986661</td>\n",
              "      <td>-0.795613</td>\n",
              "      <td>-0.740709</td>\n",
              "      <td>-0.706783</td>\n",
              "      <td>0.292317</td>\n",
              "      <td>0.120478</td>\n",
              "      <td>0.261302</td>\n",
              "      <td>-0.987058</td>\n",
              "      <td>-0.999933</td>\n",
              "      <td>-0.999802</td>\n",
              "      <td>-0.999775</td>\n",
              "      <td>-0.993827</td>\n",
              "      <td>-0.987326</td>\n",
              "      <td>-0.988590</td>\n",
              "      <td>-0.670651</td>\n",
              "      <td>-0.586260</td>\n",
              "      <td>-0.529911</td>\n",
              "      <td>-0.369168</td>\n",
              "      <td>-0.046810</td>\n",
              "      <td>-0.152010</td>\n",
              "      <td>-0.127687</td>\n",
              "      <td>-0.207400</td>\n",
              "      <td>-0.184645</td>\n",
              "      <td>0.066981</td>\n",
              "      <td>-0.090277</td>\n",
              "      <td>-0.052123</td>\n",
              "      <td>-0.177603</td>\n",
              "      <td>-0.036752</td>\n",
              "      <td>-0.249794</td>\n",
              "      <td>-0.387478</td>\n",
              "      <td>-0.412106</td>\n",
              "      <td>-0.156395</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.999854</td>\n",
              "      <td>-0.988945</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.968254</td>\n",
              "      <td>-0.006938</td>\n",
              "      <td>-0.597505</td>\n",
              "      <td>-0.876586</td>\n",
              "      <td>-0.982611</td>\n",
              "      <td>-0.978072</td>\n",
              "      <td>-0.979060</td>\n",
              "      <td>-0.980935</td>\n",
              "      <td>-0.994082</td>\n",
              "      <td>-0.982611</td>\n",
              "      <td>-0.999668</td>\n",
              "      <td>-0.985550</td>\n",
              "      <td>-0.668778</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.260172</td>\n",
              "      <td>-0.510679</td>\n",
              "      <td>-0.798767</td>\n",
              "      <td>-0.992586</td>\n",
              "      <td>-0.993150</td>\n",
              "      <td>-0.992383</td>\n",
              "      <td>-0.993862</td>\n",
              "      <td>-0.993884</td>\n",
              "      <td>-0.992586</td>\n",
              "      <td>-0.999950</td>\n",
              "      <td>-0.991683</td>\n",
              "      <td>-0.955696</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.020312</td>\n",
              "      <td>-0.548129</td>\n",
              "      <td>-0.843966</td>\n",
              "      <td>-0.108225</td>\n",
              "      <td>-0.261002</td>\n",
              "      <td>-0.470267</td>\n",
              "      <td>-0.373565</td>\n",
              "      <td>-0.810953</td>\n",
              "      <td>-0.047752</td>\n",
              "      <td>-0.140560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.038975</td>\n",
              "      <td>-0.002921</td>\n",
              "      <td>-0.019602</td>\n",
              "      <td>-0.914202</td>\n",
              "      <td>-0.827970</td>\n",
              "      <td>-0.827696</td>\n",
              "      <td>-0.924421</td>\n",
              "      <td>-0.838559</td>\n",
              "      <td>-0.852735</td>\n",
              "      <td>-0.717007</td>\n",
              "      <td>-0.622414</td>\n",
              "      <td>-0.608987</td>\n",
              "      <td>0.770743</td>\n",
              "      <td>0.613644</td>\n",
              "      <td>0.588534</td>\n",
              "      <td>-0.854684</td>\n",
              "      <td>-0.994916</td>\n",
              "      <td>-0.985107</td>\n",
              "      <td>-0.985091</td>\n",
              "      <td>-0.932825</td>\n",
              "      <td>-0.883919</td>\n",
              "      <td>-0.886424</td>\n",
              "      <td>-0.280990</td>\n",
              "      <td>-0.156747</td>\n",
              "      <td>-0.178075</td>\n",
              "      <td>-0.148095</td>\n",
              "      <td>0.087958</td>\n",
              "      <td>0.003232</td>\n",
              "      <td>0.040350</td>\n",
              "      <td>-0.036530</td>\n",
              "      <td>-0.043815</td>\n",
              "      <td>0.180801</td>\n",
              "      <td>0.048125</td>\n",
              "      <td>0.150606</td>\n",
              "      <td>-0.055702</td>\n",
              "      <td>0.109040</td>\n",
              "      <td>-0.087323</td>\n",
              "      <td>-0.167763</td>\n",
              "      <td>-0.192983</td>\n",
              "      <td>0.147958</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994521</td>\n",
              "      <td>-0.923667</td>\n",
              "      <td>-0.553895</td>\n",
              "      <td>-0.904762</td>\n",
              "      <td>0.158930</td>\n",
              "      <td>-0.356897</td>\n",
              "      <td>-0.721541</td>\n",
              "      <td>-0.840812</td>\n",
              "      <td>-0.774059</td>\n",
              "      <td>-0.797541</td>\n",
              "      <td>-0.806613</td>\n",
              "      <td>-0.956554</td>\n",
              "      <td>-0.840812</td>\n",
              "      <td>-0.973637</td>\n",
              "      <td>-0.884941</td>\n",
              "      <td>-0.057985</td>\n",
              "      <td>-0.948718</td>\n",
              "      <td>-0.084244</td>\n",
              "      <td>-0.320436</td>\n",
              "      <td>-0.646993</td>\n",
              "      <td>-0.927167</td>\n",
              "      <td>-0.920251</td>\n",
              "      <td>-0.915701</td>\n",
              "      <td>-0.928603</td>\n",
              "      <td>-0.969071</td>\n",
              "      <td>-0.927167</td>\n",
              "      <td>-0.996601</td>\n",
              "      <td>-0.925470</td>\n",
              "      <td>-0.339246</td>\n",
              "      <td>-0.904762</td>\n",
              "      <td>0.170819</td>\n",
              "      <td>-0.353980</td>\n",
              "      <td>-0.710071</td>\n",
              "      <td>0.017627</td>\n",
              "      <td>0.029079</td>\n",
              "      <td>0.001515</td>\n",
              "      <td>-0.005503</td>\n",
              "      <td>-0.706619</td>\n",
              "      <td>0.176777</td>\n",
              "      <td>0.004583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.044000</td>\n",
              "      <td>0.004303</td>\n",
              "      <td>-0.011676</td>\n",
              "      <td>-0.246026</td>\n",
              "      <td>-0.313069</td>\n",
              "      <td>-0.450478</td>\n",
              "      <td>-0.294903</td>\n",
              "      <td>-0.362671</td>\n",
              "      <td>-0.540521</td>\n",
              "      <td>0.054178</td>\n",
              "      <td>-0.284695</td>\n",
              "      <td>-0.280810</td>\n",
              "      <td>0.834147</td>\n",
              "      <td>0.707537</td>\n",
              "      <td>0.672058</td>\n",
              "      <td>-0.345716</td>\n",
              "      <td>-0.726293</td>\n",
              "      <td>-0.837480</td>\n",
              "      <td>-0.900593</td>\n",
              "      <td>-0.390880</td>\n",
              "      <td>-0.524758</td>\n",
              "      <td>-0.642569</td>\n",
              "      <td>0.028784</td>\n",
              "      <td>0.199298</td>\n",
              "      <td>0.112374</td>\n",
              "      <td>0.122806</td>\n",
              "      <td>0.267300</td>\n",
              "      <td>0.136935</td>\n",
              "      <td>0.198503</td>\n",
              "      <td>0.173327</td>\n",
              "      <td>0.112126</td>\n",
              "      <td>0.288838</td>\n",
              "      <td>0.187093</td>\n",
              "      <td>0.331843</td>\n",
              "      <td>0.103646</td>\n",
              "      <td>0.243988</td>\n",
              "      <td>0.078482</td>\n",
              "      <td>0.072191</td>\n",
              "      <td>0.007006</td>\n",
              "      <td>0.389240</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.746165</td>\n",
              "      <td>-0.391131</td>\n",
              "      <td>0.334381</td>\n",
              "      <td>-0.873016</td>\n",
              "      <td>0.356940</td>\n",
              "      <td>-0.077546</td>\n",
              "      <td>-0.441418</td>\n",
              "      <td>-0.437430</td>\n",
              "      <td>-0.422576</td>\n",
              "      <td>-0.397253</td>\n",
              "      <td>-0.543877</td>\n",
              "      <td>-0.835667</td>\n",
              "      <td>-0.437430</td>\n",
              "      <td>-0.788136</td>\n",
              "      <td>-0.491117</td>\n",
              "      <td>0.506547</td>\n",
              "      <td>-0.846154</td>\n",
              "      <td>0.119520</td>\n",
              "      <td>-0.070230</td>\n",
              "      <td>-0.408554</td>\n",
              "      <td>-0.620259</td>\n",
              "      <td>-0.651780</td>\n",
              "      <td>-0.615832</td>\n",
              "      <td>-0.696083</td>\n",
              "      <td>-0.813202</td>\n",
              "      <td>-0.620259</td>\n",
              "      <td>-0.925023</td>\n",
              "      <td>-0.613134</td>\n",
              "      <td>0.326093</td>\n",
              "      <td>-0.873016</td>\n",
              "      <td>0.316240</td>\n",
              "      <td>-0.137462</td>\n",
              "      <td>-0.503837</td>\n",
              "      <td>0.167695</td>\n",
              "      <td>0.314876</td>\n",
              "      <td>0.496871</td>\n",
              "      <td>0.352690</td>\n",
              "      <td>-0.488765</td>\n",
              "      <td>0.246834</td>\n",
              "      <td>0.109507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.945956</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960341</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977852</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999755</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.992901</td>\n",
              "      <td>0.915411</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.969236</td>\n",
              "      <td>0.884134</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.975905</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.959305</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.968254</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.938491</td>\n",
              "      <td>0.911653</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998702</td>\n",
              "      <td>0.991288</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.482229</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 561 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tBodyAcc-Mean-1                  ...  tZAxisAcc-AngleWRTGravity-1    \n",
              "count                      7767.000000  ...                      7767.000000\n",
              "mean                          0.038759  ...                        -0.052888\n",
              "std                           0.101996  ...                         0.276196\n",
              "min                          -1.000000  ...                        -0.987874\n",
              "25%                           0.032037  ...                        -0.140560\n",
              "50%                           0.038975  ...                         0.004583\n",
              "75%                           0.044000  ...                         0.109507\n",
              "max                           1.000000  ...                         1.000000\n",
              "\n",
              "[8 rows x 561 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAbd1kbGgcPN",
        "colab_type": "code",
        "outputId": "e5436444-ee03-45e5-d7aa-40945fd5e399",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7767 entries, 0 to 7766\n",
            "Columns: 561 entries, tBodyAcc-Mean-1                 to tZAxisAcc-AngleWRTGravity-1    \n",
            "dtypes: float64(561)\n",
            "memory usage: 33.2 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5QHtoQqgeIt",
        "colab_type": "code",
        "outputId": "57e4326d-6e7a-4d48-847f-ca1296c18440",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print('Training data: %d instances, %d features' % X_train.shape)\n",
        "print('Test data:     %d instances, %d features' % X_test.shape)\n",
        "print(\"Unique target labels:\", np.unique(y_train['y_train']))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data: 7767 instances, 561 features\n",
            "Test data:     3162 instances, 561 features\n",
            "Unique target labels: [ 1  2  3  4  5  6  7  8  9 10 11 12]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59e8edHIuPx7",
        "colab_type": "text"
      },
      "source": [
        "#Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dB10L9uWgioI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-LawDIGl0By",
        "colab_type": "code",
        "outputId": "6f94e705-f27e-4fd3-b837-3d7a4cfe54f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "LR = LogisticRegression(solver='newton-cg').fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "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",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVS1aesEl1t7",
        "colab_type": "code",
        "outputId": "aabf9ab8-2c56-4c36-8c84-481e14c4c925",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "yhat = LR.predict(X_test)\n",
        "yhat"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 5, 5, ..., 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dtNjl2ol3Yg",
        "colab_type": "code",
        "outputId": "9fef94c6-54ab-48b2-db49-41267bc1f03b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "yhat_prob = LR.predict_proba(X_test)\n",
        "yhat_prob "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.06966687e-06, 1.38511669e-06, 1.53594199e-06, ...,\n",
              "        1.52261361e-06, 7.31895647e-06, 8.99360331e-07],\n",
              "       [2.14452987e-06, 7.17867441e-07, 3.38729448e-08, ...,\n",
              "        9.85241567e-07, 4.99370355e-06, 1.89679867e-06],\n",
              "       [5.11781060e-06, 3.12058649e-08, 1.19136129e-07, ...,\n",
              "        7.40630805e-08, 5.56644936e-06, 4.88827887e-08],\n",
              "       ...,\n",
              "       [2.09359525e-03, 9.95322490e-01, 1.79123800e-03, ...,\n",
              "        1.48563479e-06, 1.78314893e-04, 1.55336921e-04],\n",
              "       [7.34612174e-03, 9.87874186e-01, 3.29205744e-03, ...,\n",
              "        1.70044571e-06, 3.80553616e-04, 5.44537971e-04],\n",
              "       [1.01378265e-01, 8.81341095e-01, 1.16341050e-02, ...,\n",
              "        3.51717932e-06, 1.19277295e-03, 2.54956218e-03]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjYelFSZl4TM",
        "colab_type": "code",
        "outputId": "ea5aa84c-2ddd-4ec4-b4c5-68a0d5ddc79e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "from sklearn.metrics import jaccard_similarity_score\n",
        "jaccard_similarity_score(y_test, yhat)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning:\n",
            "\n",
            "jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9459203036053131"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FXk4gGkqH_t",
        "colab_type": "code",
        "outputId": "ca5a8fc8-262e-423e-cee6-35af5911d049",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score,recall_score,f1_score\n",
        "cnf_matrix = confusion_matrix(y_test, yhat)\n",
        "acc=accuracy_score(y_test,yhat)\n",
        "recall=recall_score(y_test,yhat,average='weighted')\n",
        "f1=f1_score(y_test,yhat,average='weighted')\n",
        "print(yhat)\n",
        "print(cnf_matrix)\n",
        "print(acc)\n",
        "print(recall)\n",
        "print(f1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5 5 5 ... 2 2 2]\n",
            "[[493   0   3   0   0   0   0   0   0   0   0   0]\n",
            " [ 26 442   1   0   0   0   1   0   0   0   1   0]\n",
            " [  3  12 405   0   0   0   0   0   0   0   0   0]\n",
            " [  0   4   0 448  55   1   0   0   0   0   0   0]\n",
            " [  2   0   0  12 541   0   1   0   0   0   0   0]\n",
            " [  0   0   0   0   0 545   0   0   0   0   0   0]\n",
            " [  0   1   0   2   2   0  16   0   1   0   1   0]\n",
            " [  0   0   0   0   0   0   0  10   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  22   0   9   1]\n",
            " [  0   0   0   0   0   0   0   0   0  20   0   5]\n",
            " [  1   1   0   2   0   0   0   0  12   1  32   0]\n",
            " [  0   1   0   0   0   0   0   0   0   5   4  17]]\n",
            "0.9459203036053131\n",
            "0.9459203036053131\n",
            "0.9455422736131213\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mp7rPMTiqK9I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import itertools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Su06ixpvqNi0",
        "colab_type": "code",
        "outputId": "3ef26789-0919-4c29-9a88-78bb8c526e49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "cnf_matrix = confusion_matrix(y_test, yhat)\n",
        "np.set_printoptions(precision=12)\n",
        "\n",
        "plt.figure()\n",
        "print(cnf_matrix)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[493   0   3   0   0   0   0   0   0   0   0   0]\n",
            " [ 26 442   1   0   0   0   1   0   0   0   1   0]\n",
            " [  3  12 405   0   0   0   0   0   0   0   0   0]\n",
            " [  0   4   0 448  55   1   0   0   0   0   0   0]\n",
            " [  2   0   0  12 541   0   1   0   0   0   0   0]\n",
            " [  0   0   0   0   0 545   0   0   0   0   0   0]\n",
            " [  0   1   0   2   2   0  16   0   1   0   1   0]\n",
            " [  0   0   0   0   0   0   0  10   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  22   0   9   1]\n",
            " [  0   0   0   0   0   0   0   0   0  20   0   5]\n",
            " [  1   1   0   2   0   0   0   0  12   1  32   0]\n",
            " [  0   1   0   0   0   0   0   0   0   5   4  17]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oc-RY8w1qRNS",
        "colab_type": "code",
        "outputId": "4b7ea2bd-e153-4698-ed59-ff502711a469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "print (classification_report(y_test, yhat))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.94      0.99      0.97       496\n",
            "           2       0.96      0.94      0.95       471\n",
            "           3       0.99      0.96      0.98       420\n",
            "           4       0.97      0.88      0.92       508\n",
            "           5       0.90      0.97      0.94       556\n",
            "           6       1.00      1.00      1.00       545\n",
            "           7       0.89      0.70      0.78        23\n",
            "           8       1.00      1.00      1.00        10\n",
            "           9       0.63      0.69      0.66        32\n",
            "          10       0.77      0.80      0.78        25\n",
            "          11       0.68      0.65      0.67        49\n",
            "          12       0.74      0.63      0.68        27\n",
            "\n",
            "    accuracy                           0.95      3162\n",
            "   macro avg       0.87      0.85      0.86      3162\n",
            "weighted avg       0.95      0.95      0.95      3162\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpiewE7iqTX1",
        "colab_type": "code",
        "outputId": "81d913ae-1352-4684-8805-33fe82dd3361",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from sklearn.metrics import log_loss\n",
        "print (\"LogLoss: : %.2f\" % log_loss(y_test, yhat_prob))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogLoss: : 0.16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7mxpFPgqUxW",
        "colab_type": "code",
        "outputId": "52143af7-25fc-4443-9687-bef616cd64fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "LR2=LogisticRegression(solver='newton-cg').fit(X_train,y_train)\n",
        "yhat_prob2 = LR2.predict_proba(X_test)\n",
        "log_loss(y_test, yhat_prob2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "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",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.15962089059488713"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dxgGaVJqV-8",
        "colab_type": "code",
        "outputId": "60ce44a0-1717-4b65-e2a7-30e8e4683132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "LR3 = LogisticRegression(solver='sag',random_state = 0).fit(X_train,y_train)\n",
        "yhat_prob3 = LR3.predict_proba(X_test)\n",
        "print (\"LogLoss: : %.2f\" % log_loss(y_test, yhat_prob3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "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",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "LogLoss: : 0.16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning:\n",
            "\n",
            "The max_iter was reached which means the coef_ did not converge\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gn2u6-AFsETR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yhat2=LR2.predict(X_test)\n",
        "yhat3=LR3.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oIQ7QAttPPm",
        "colab_type": "code",
        "outputId": "eaba7fa2-7951-4479-b215-61f5bb0431b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "yhat2.dtype"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgE7tgXDt3su",
        "colab_type": "code",
        "outputId": "3ddc1306-fe9a-4a86-e817-91ed5caed072",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "y_test.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xy6Sm3_OqZou",
        "colab_type": "code",
        "outputId": "5d78f8b6-a449-40f8-d0ad-c66569ff7362",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "cnf_matrix = confusion_matrix(y_test, yhat2)\n",
        "np.set_printoptions(precision=12)\n",
        "\n",
        "plt.figure()\n",
        "print(cnf_matrix)\n",
        "print(accuracy_score(y_test,yhat2))\n",
        "print(recall_score(y_test,yhat2,average='weighted'))\n",
        "print(f1_score(y_test,yhat2,average='weighted'))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[493   0   3   0   0   0   0   0   0   0   0   0]\n",
            " [ 26 442   1   0   0   0   1   0   0   0   1   0]\n",
            " [  3  12 405   0   0   0   0   0   0   0   0   0]\n",
            " [  0   4   0 448  55   1   0   0   0   0   0   0]\n",
            " [  2   0   0  12 541   0   1   0   0   0   0   0]\n",
            " [  0   0   0   0   0 545   0   0   0   0   0   0]\n",
            " [  0   1   0   2   2   0  16   0   1   0   1   0]\n",
            " [  0   0   0   0   0   0   0  10   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  22   0   9   1]\n",
            " [  0   0   0   0   0   0   0   0   0  20   0   5]\n",
            " [  1   1   0   2   0   0   0   0  12   1  32   0]\n",
            " [  0   1   0   0   0   0   0   0   0   5   4  17]]\n",
            "0.9459203036053131\n",
            "0.9459203036053131\n",
            "0.9455422736131213\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcil9NmMr469",
        "colab_type": "code",
        "outputId": "095c3c5e-b4ae-40e7-fbdd-47ccdcc81dde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "\n",
        "cnf_matrix = confusion_matrix(y_test, yhat3)\n",
        "np.set_printoptions(precision=12)\n",
        "\n",
        "plt.figure()\n",
        "print(cnf_matrix)\n",
        "print(accuracy_score(y_test,yhat3))\n",
        "print(recall_score(y_test,yhat3,average='weighted'))\n",
        "print(f1_score(y_test,yhat3,average='weighted'))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[492   1   3   0   0   0   0   0   0   0   0   0]\n",
            " [ 26 441   2   0   0   0   1   0   0   0   1   0]\n",
            " [  3  13 404   0   0   0   0   0   0   0   0   0]\n",
            " [  0   4   0 447  56   1   0   0   0   0   0   0]\n",
            " [  2   0   0  11 542   0   1   0   0   0   0   0]\n",
            " [  0   1   0   0   0 544   0   0   0   0   0   0]\n",
            " [  0   1   0   2   2   0  16   0   1   0   1   0]\n",
            " [  0   0   0   0   0   0   0  10   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  22   0   9   1]\n",
            " [  0   0   0   0   0   0   0   0   0  19   0   6]\n",
            " [  1   2   0   2   0   0   0   0  12   0  32   0]\n",
            " [  0   1   0   0   0   0   0   0   0   4   4  18]]\n",
            "0.9446552814674257\n",
            "0.9446552814674257\n",
            "0.9442888563275245\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ruEVfsgfuhj0",
        "colab_type": "text"
      },
      "source": [
        "#Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5QVpxzcr8Ys",
        "colab_type": "code",
        "outputId": "65123450-ca87-4048-dcfe-dc1e1d8023a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "randomForest = RandomForestClassifier(n_estimators=600, max_depth=300, max_features='sqrt')\n",
        "randomForest.fit(X_train, y_train)\n",
        "\n",
        "rfc_predict = randomForest.predict(X_test)\n",
        "from sklearn import metrics\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, rfc_predict)) # 0.90\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "print(confusion_matrix(y_test,rfc_predict))\n",
        "print(classification_report(y_test,rfc_predict))\n",
        "print(accuracy_score(y_test, rfc_predict))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: DataConversionWarning:\n",
            "\n",
            "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",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9098671726755219\n",
            "[[482   4  10   0   0   0   0   0   0   0   0   0]\n",
            " [ 39 424   8   0   0   0   0   0   0   0   0   0]\n",
            " [ 20  46 354   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0 452  55   0   1   0   0   0   0   0]\n",
            " [  0   0   0  43 513   0   0   0   0   0   0   0]\n",
            " [  0   1   0   0   0 544   0   0   0   0   0   0]\n",
            " [  0   1   0   2   0   0  18   1   0   0   1   0]\n",
            " [  0   0   0   0   0   0   1   9   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  24   0   8   0]\n",
            " [  0   0   0   0   0   0   0   0   0  15   1   9]\n",
            " [  3   0   0   1   0   0   3   0  13   1  28   0]\n",
            " [  0   1   0   0   0   0   0   0   0   9   3  14]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.89      0.97      0.93       496\n",
            "           2       0.89      0.90      0.89       471\n",
            "           3       0.95      0.84      0.89       420\n",
            "           4       0.91      0.89      0.90       508\n",
            "           5       0.90      0.92      0.91       556\n",
            "           6       1.00      1.00      1.00       545\n",
            "           7       0.78      0.78      0.78        23\n",
            "           8       0.90      0.90      0.90        10\n",
            "           9       0.65      0.75      0.70        32\n",
            "          10       0.60      0.60      0.60        25\n",
            "          11       0.68      0.57      0.62        49\n",
            "          12       0.61      0.52      0.56        27\n",
            "\n",
            "    accuracy                           0.91      3162\n",
            "   macro avg       0.81      0.80      0.81      3162\n",
            "weighted avg       0.91      0.91      0.91      3162\n",
            "\n",
            "0.9098671726755219\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfwUUVisONPJ",
        "colab_type": "code",
        "outputId": "47563d5b-2290-489c-a1aa-b76af7c57925",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(randomForest.feature_importances_) #We use Random Forest to select important features"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.001297928086 0.001314910842 0.000530842323 0.006896057354\n",
            " 0.000596475522 0.000427532766 0.004934577127 0.000547174404\n",
            " 0.000389550017 0.009192333604 0.000691235252 0.000431574878\n",
            " 0.000763898804 0.000626563085 0.000339929096 0.000937298007\n",
            " 0.005872789231 0.001051302356 0.000433576769 0.001449729501\n",
            " 0.000508988921 0.000324625885 0.001823562464 0.000801410264\n",
            " 0.000565582276 0.000409737286 0.000216508597 0.000210200831\n",
            " 0.000354683887 0.000225239019 0.000186755037 0.000172292201\n",
            " 0.000229882293 0.000238262013 0.000194498781 0.000173002892\n",
            " 0.000293784076 0.005272471819 0.001183126034 0.002229949001\n",
            " 0.025745718015 0.02535106669  0.008036780825 0.001881453792\n",
            " 0.001667279752 0.000887687498 0.00208137804  0.001376435187\n",
            " 0.000694968643 0.027589973354 0.025588530911 0.010856420653\n",
            " 0.022886240313 0.024026629083 0.008960499314 0.003688256415\n",
            " 0.02638706645  0.016909849928 0.006978668566 0.00121127179\n",
            " 0.001045117256 0.000420096642 0.002409231382 0.007130199976\n",
            " 0.000386450695 0.00522414416  0.004218157434 0.003085893948\n",
            " 0.002809332554 0.007301481174 0.006547396504 0.005451369345\n",
            " 0.003172332885 0.007023451818 0.009323903003 0.006155323311\n",
            " 0.004096561906 0.002648432002 0.000961917405 0.001832528105\n",
            " 0.000226248638 0.000249242484 0.000218256361 0.004577456342\n",
            " 0.001065375292 0.000444774278 0.006881717126 0.003764855095\n",
            " 0.000921700458 0.002876944422 0.000368534672 0.000584442996\n",
            " 0.000629429096 0.000264736981 0.000296653622 0.007679103113\n",
            " 0.006841123589 0.002121399028 0.000449518542 0.006094595425\n",
            " 0.007136396259 0.002549985666 0.002782157016 0.004830367994\n",
            " 0.000944961055 0.000213803306 0.000188838671 0.000239378352\n",
            " 0.000299673646 0.000202429496 0.000143121355 0.000180349876\n",
            " 0.000263488551 0.000264524361 0.000254667272 0.000264241837\n",
            " 0.000336005083 0.000874620159 0.000416644342 0.000391973521\n",
            " 0.000832761403 0.000459237706 0.000840517078 0.002397133866\n",
            " 0.001378311573 0.000796205463 0.002662749515 0.001732852225\n",
            " 0.001034062486 0.002696079868 0.000336700787 0.000612861216\n",
            " 0.002435935215 0.000330558852 0.000611751267 0.000671387644\n",
            " 0.001660932    0.000607926756 0.001012735816 0.002805156565\n",
            " 0.003614808965 0.001231529683 0.001882506816 0.000346589643\n",
            " 0.000551251098 0.00098961208  0.000189593998 0.000220665758\n",
            " 0.000378826721 0.00041086713  0.000314789986 0.000221658663\n",
            " 0.000354328774 0.000174005979 0.000170880169 0.000199783614\n",
            " 0.000425245801 0.000807540499 0.000690005651 0.00166144521\n",
            " 0.00036069917  0.000201880055 0.000350874217 0.00148446565\n",
            " 0.000368360761 0.001192955802 0.001930266206 0.00033452059\n",
            " 0.007399874734 0.000636907232 0.000350997314 0.000395522565\n",
            " 0.000976374404 0.000312660807 0.0002388486   0.001383372512\n",
            " 0.001244563639 0.000434323394 0.001400680492 0.004995162001\n",
            " 0.002732071432 0.005836498945 0.0010083232   0.000297100139\n",
            " 0.002232741013 0.001574746598 0.00029515956  0.000581808477\n",
            " 0.00023995662  0.000355914504 0.000177637567 0.000169773738\n",
            " 0.000269181885 0.000189879546 0.000235037621 0.000252089013\n",
            " 0.000375859136 0.000782758984 0.000604384001 0.000507920074\n",
            " 0.002524453788 0.008090434973 0.006328056866 0.001996015868\n",
            " 0.000339975721 0.002115620411 0.001898626692 0.000647134496\n",
            " 0.000763048361 0.005269956664 0.001884695917 0.000270661298\n",
            " 0.000225502706 0.001159609748 0.006618744033 0.00608855118\n",
            " 0.002645152608 0.000312130968 0.001676809731 0.002144780592\n",
            " 0.000780216124 0.000721393849 0.004796244039 0.001639144872\n",
            " 0.000212990817 0.000254452097 0.004240115243 0.000651184468\n",
            " 0.002889699864 0.000237524585 0.000245108639 0.005111701928\n",
            " 0.004294582185 0.002738942711 0.004861413836 0.000215631306\n",
            " 0.000311053959 0.000186436214 0.000217613563 0.000878721063\n",
            " 0.000304207394 0.00030154525  0.000318738417 0.000409968116\n",
            " 0.000587717044 0.000442044395 0.000313628795 0.000358013647\n",
            " 0.000350676059 0.000240541188 0.000247048828 0.000243991477\n",
            " 0.002994568675 0.000295407271 0.000279709594 0.000256634126\n",
            " 0.000224450077 0.001846017396 0.000385638568 0.000407910562\n",
            " 0.000290659805 0.000273909896 0.000249364645 0.000213047023\n",
            " 0.000233552248 0.006768183304 0.000583690824 0.00042469775\n",
            " 0.004868838353 0.00049054406  0.000678012648 0.007387756918\n",
            " 0.000671698846 0.000298168722 0.002946297175 0.000924382512\n",
            " 0.000462064674 0.000183480853 0.000177078443 0.000167594072\n",
            " 0.002908187085 0.004765735117 0.000403447255 0.000461132238\n",
            " 0.000592027875 0.000217566624 0.000241333905 0.001632187394\n",
            " 0.000223377706 0.000258942291 0.00150002096  0.00081413536\n",
            " 0.000428591769 0.000929714263 0.000730662832 0.002248253909\n",
            " 0.002158128539 0.00135022182  0.001232411852 0.000995938664\n",
            " 0.000470640752 0.000361106722 0.005839511613 0.001637732252\n",
            " 0.000282590552 0.000241756584 0.000247718564 0.000237091499\n",
            " 0.000200042268 0.000184037961 0.005284031356 0.000297323611\n",
            " 0.000391219525 0.00018930219  0.006873714024 0.000549332304\n",
            " 0.001198575274 0.000625585567 0.000956683254 0.000283945436\n",
            " 0.000233621391 0.000227310672 0.000214533535 0.000181464392\n",
            " 0.000512369569 0.000606044476 0.00022987578  0.000222697407\n",
            " 0.000496052636 0.000248141741 0.000807165426 0.000288270698\n",
            " 0.000988230927 0.000273717018 0.000218852669 0.000216313316\n",
            " 0.000193435498 0.000180468466 0.000533270227 0.00085794815\n",
            " 0.000264600207 0.000202801371 0.000438555556 0.000294991757\n",
            " 0.00448116524  0.000685485248 0.000310623236 0.004448385238\n",
            " 0.002142458059 0.000520368627 0.002241135021 0.000728557754\n",
            " 0.00090582891  0.004683281924 0.001439224343 0.000347942586\n",
            " 0.000164538806 0.000137595508 0.00018014433  0.002520836897\n",
            " 0.004576549908 0.001365392655 0.000417661687 0.001342126674\n",
            " 0.000821728894 0.000331434143 0.004554169499 0.001066870357\n",
            " 0.000657434701 0.001987844168 0.000278775754 0.001462955004\n",
            " 0.000273140512 0.000268759931 0.000363503693 0.00024255101\n",
            " 0.00024823206  0.000257465949 0.000277336273 0.000213350048\n",
            " 0.000220474886 0.010874164824 0.003168334171 0.000888322342\n",
            " 0.000219253257 0.000267118575 0.000216557672 0.00020811693\n",
            " 0.00017652186  0.006702634551 0.000838695423 0.000704814097\n",
            " 0.000190853512 0.003528555472 0.000655552952 0.000706798534\n",
            " 0.000354803387 0.000704044958 0.000272611051 0.00023275453\n",
            " 0.000512226762 0.000227420345 0.000175569415 0.00123726758\n",
            " 0.001155952506 0.000208785929 0.000174096372 0.002168694907\n",
            " 0.000292702895 0.000666809875 0.000365699572 0.001010386305\n",
            " 0.000472549177 0.000321987907 0.000220590123 0.000216456031\n",
            " 0.000180163458 0.000405783934 0.00136710882  0.000258500281\n",
            " 0.000234727547 0.000987635726 0.000432816728 0.001848770871\n",
            " 0.001181751154 0.000476808049 0.002658880573 0.000721351034\n",
            " 0.001061324946 0.002274761259 0.000724439676 0.000719074931\n",
            " 0.002028965077 0.000985449349 0.001020736696 0.00023732794\n",
            " 0.000140069664 0.000234830704 0.002340347948 0.002367411612\n",
            " 0.00062702671  0.000777720042 0.000446425142 0.000307753649\n",
            " 0.000370439582 0.001401849623 0.000312232877 0.00036711675\n",
            " 0.002290280158 0.001542991576 0.004905544781 0.002936891259\n",
            " 0.000440259692 0.00039919472  0.000579720819 0.000455539516\n",
            " 0.000682968957 0.000663430905 0.000819147851 0.00074248683\n",
            " 0.002033274724 0.001564967296 0.000457243669 0.000364713343\n",
            " 0.000496395424 0.000502743645 0.000381872582 0.000275414224\n",
            " 0.002197816052 0.000517262934 0.000544501867 0.000449933219\n",
            " 0.002303446899 0.00042746854  0.000961564101 0.000271233903\n",
            " 0.00037503692  0.000362604949 0.000240850919 0.000199951012\n",
            " 0.00018496219  0.000186484105 0.000736044138 0.00045529376\n",
            " 0.000211007217 0.000204100399 0.000865151363 0.000388448475\n",
            " 0.001193340236 0.000327583467 0.00053994516  0.000392251041\n",
            " 0.00027447051  0.000307519003 0.000288748339 0.000275265686\n",
            " 0.000689506646 0.000486599594 0.00023739152  0.000251069039\n",
            " 0.000779564583 0.000380838543 0.003940240864 0.006461354981\n",
            " 0.005625081366 0.002618293717 0.000173491813 0.006079428597\n",
            " 0.008093309461 0.000936586113 0.000963720363 0.00109226749\n",
            " 0.001697926813 0.000444426447 0.000405727661 0.00099656288\n",
            " 0.001207421549 0.000620955206 0.00132373106  0.000165865753\n",
            " 0.000584292612 0.000833200322 0.000862599409 0.000500525815\n",
            " 0.000384680616 0.000240755851 0.000428387272 0.000368519042\n",
            " 0.000250017757 0.000284802709 0.000296165111 0.000263534706\n",
            " 0.000147562178 0.000302665814 0.000289635967 0.000250510877\n",
            " 0.000278574579 0.000599489965 0.001340780768 0.000291142736\n",
            " 0.000284375283 0.000244554718 0.000507560442 0.000297102079\n",
            " 0.000565642797 0.000168931873 0.000241263803 0.0003585636\n",
            " 0.000193844142 0.000156390232 0.000181386765 0.000242414314\n",
            " 0.000369049933 0.000411796814 0.000204006362 0.000246703195\n",
            " 0.000681824461 0.000558736638 0.027238895306 0.024833351825\n",
            " 0.012177069479]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSS6fvTGurFN",
        "colab_type": "text"
      },
      "source": [
        "#Feature Importance according to the Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAjykU2L2Bse",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_importances = pd.DataFrame(randomForest.feature_importances_,\n",
        "                                   index = X_train.columns,\n",
        "                                    columns=['importance']).sort_values('importance', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5icFJjql2T-j",
        "colab_type": "code",
        "outputId": "f15132ab-c7c9-4e1d-8b71-0ebabb28a6bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "source": [
        "print(feature_importances[0:30])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                 importance\n",
            "tGravityAcc-Max-1                  0.027590\n",
            "tXAxisAcc-AngleWRTGravity-1        0.027239\n",
            "tGravityAcc-Energy-1               0.026387\n",
            "tGravityAcc-Mean-1                 0.025746\n",
            "tGravityAcc-Max-2                  0.025589\n",
            "tGravityAcc-Mean-2                 0.025351\n",
            "tYAxisAcc-AngleWRTGravity-1        0.024833\n",
            "tGravityAcc-Min-2                  0.024027\n",
            "tGravityAcc-Min-1                  0.022886\n",
            "tGravityAcc-Energy-2               0.016910\n",
            "tZAxisAcc-AngleWRTGravity-1        0.012177\n",
            "fBodyAccJerk-BandsEnergyOld-1      0.010874\n",
            "tGravityAcc-Max-3                  0.010856\n",
            "tGravityAcc-ARCoeff-10             0.009324\n",
            "tBodyAcc-Max-1                     0.009192\n",
            "tGravityAcc-Min-3                  0.008960\n",
            "fBodyAccMag-Energy-1               0.008093\n",
            "tBodyAccMag-STD-1                  0.008090\n",
            "tGravityAcc-Mean-3                 0.008037\n",
            "tBodyAccJerk-SMA-1                 0.007679\n",
            "tBodyGyroJerk-Mad-3                0.007400\n",
            "fBodyAcc-Mad-1                     0.007388\n",
            "tGravityAcc-ARCoeff-5              0.007301\n",
            "tBodyAccJerk-IQR-2                 0.007136\n",
            "tGravityAcc-ropy-1                 0.007130\n",
            "tGravityAcc-ARCoeff-9              0.007023\n",
            "tGravityAcc-Energy-3               0.006979\n",
            "tBodyAcc-STD-1                     0.006896\n",
            "tBodyAccJerk-Mad-1                 0.006882\n",
            "fBodyAcc-BandsEnergyOld-13         0.006874\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4twhvnjGk5g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_train=X_train[feature_importances.index[0:300]]\n",
        "#new_y=y_train[feature_importances.index[0:300]]\n",
        "new_test= X_test[feature_importances.index[0:300]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5wgb0M1By0c",
        "colab_type": "code",
        "outputId": "d7b9f82f-ce24-4c58-b41c-1a991dedf3c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "feature_importances.iloc[2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "importance    0.026387\n",
              "Name: tGravityAcc-Energy-1           , dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-tD9v0evDGC",
        "colab_type": "text"
      },
      "source": [
        "#Logistic Regression and Random Forest with important features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkNDg01CLSJm",
        "colab_type": "code",
        "outputId": "f0d186e0-680f-4d2d-8cb3-900b59a802fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "source": [
        "LR4 = LogisticRegression(solver='newton-cg').fit(new_train,y_train)\n",
        "                       \n",
        "yhat4=LR4.predict(new_test)\n",
        "cnf_matrix = confusion_matrix(y_test, yhat4)\n",
        "np.set_printoptions(precision=12)\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "print(cnf_matrix)\n",
        "print(accuracy_score(y_test,yhat2))\n",
        "print(recall_score(y_test,yhat2,average='weighted'))\n",
        "print(f1_score(y_test,yhat2,average='weighted'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "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",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[[496   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [ 17 451   1   0   0   0   2   0   0   0   0   0]\n",
            " [  5   9 406   0   0   0   0   0   0   0   0   0]\n",
            " [  0   1   0 450  55   0   0   1   0   1   0   0]\n",
            " [  0   0   0  19 536   0   1   0   0   0   0   0]\n",
            " [  0   0   0   0   0 545   0   0   0   0   0   0]\n",
            " [  0   1   0   1   2   0  17   0   1   0   1   0]\n",
            " [  0   0   0   0   0   0   0  10   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  25   0   7   0]\n",
            " [  0   0   0   0   0   0   0   0   0  18   0   7]\n",
            " [  2   0   0   1   0   0   0   0  11   1  34   0]\n",
            " [  0   1   0   0   0   0   0   0   0   6   3  17]]\n",
            "0.9459203036053131\n",
            "0.9459203036053131\n",
            "0.9455422736131213\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNQV_LnVWH54",
        "colab_type": "code",
        "outputId": "d925172e-8bd6-47de-d7e1-ff2ce83e03e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        }
      },
      "source": [
        "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "size = 10000\n",
        "np.random.seed(seed=10)\n",
        "\n",
        "  \n",
        "rf = RandomForestRegressor(n_estimators=100, max_features=400)\n",
        "rf.fit(X_train, y_train)\n",
        "rf_predict= rf.predict(X_test)\n",
        "rf_predict = list(map(int, rf_predict))\n",
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, rf_predict)) # 0.90\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "print(confusion_matrix(y_test,rf_predict))\n",
        "print(classification_report(y_test,rf_predict))\n",
        "print(accuracy_score(y_test, rf_predict))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: DataConversionWarning:\n",
            "\n",
            "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",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6347248576850095\n",
            "[[480  16   0   0   0   0   0   0   0   0   0   0]\n",
            " [238 232   1   0   0   0   0   0   0   0   0   0]\n",
            " [ 10 347  63   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0 502   4   0   2   0   0   0   0   0]\n",
            " [  0   0   0 430 124   1   1   0   0   0   0   0]\n",
            " [  0   0   0   0   2 542   0   1   0   0   0   0]\n",
            " [  0   0   0   0   3   3  12   5   0   0   0   0]\n",
            " [  0   0   0   0   0   1   3   6   0   0   0   0]\n",
            " [  0   0   0   0   0   0   1   1  28   2   0   0]\n",
            " [  0   0   0   0   0   0   0   1   2  18   4   0]\n",
            " [  0   0   0   3   2   3   3   0  15  23   0   0]\n",
            " [  0   0   0   1   0   0   0   2   2  12  10   0]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.66      0.97      0.78       496\n",
            "           2       0.39      0.49      0.44       471\n",
            "           3       0.98      0.15      0.26       420\n",
            "           4       0.54      0.99      0.70       508\n",
            "           5       0.92      0.22      0.36       556\n",
            "           6       0.99      0.99      0.99       545\n",
            "           7       0.55      0.52      0.53        23\n",
            "           8       0.38      0.60      0.46        10\n",
            "           9       0.60      0.88      0.71        32\n",
            "          10       0.33      0.72      0.45        25\n",
            "          11       0.00      0.00      0.00        49\n",
            "          12       0.00      0.00      0.00        27\n",
            "\n",
            "    accuracy                           0.63      3162\n",
            "   macro avg       0.53      0.54      0.47      3162\n",
            "weighted avg       0.72      0.63      0.58      3162\n",
            "\n",
            "0.6347248576850095\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEbYeAilvLuL",
        "colab_type": "text"
      },
      "source": [
        "#Import libraries for deep learning with and without important features\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OeDJXLspZvof",
        "colab_type": "code",
        "outputId": "d9a71b96-e033-4ad5-8a7f-a6ac43bf1f29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "from google.colab import files\n",
        "import numpy as np\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import io\n",
        "import keras.preprocessing.text\n",
        "import tensorflow.keras.backend as K\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix as cm\n",
        "from sklearn.metrics import multilabel_confusion_matrix as mcm\n",
        "tf.keras.backend.clear_session()\n",
        "np.set_printoptions(precision=3, suppress=True)\n",
        "pd.options.display.max_colwidth = 1000"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zXhVB3EhHMx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "                              tf.keras.layers.Dense(64, activation='relu', input_shape=[len(new_train.columns)]),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(13, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syKn04xahnoa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',loss=tf.keras.losses.sparse_categorical_crossentropy,metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JR7MtUfqiMHU",
        "colab_type": "code",
        "outputId": "688ccef0-5e0e-46d6-a3e9-55b027e07dcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "history=model.fit(x=new_train, y=y_train, epochs=10, batch_size=8, validation_split=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 6990 samples, validate on 777 samples\n",
            "Epoch 1/10\n",
            "6990/6990 [==============================] - 2s 272us/sample - loss: 0.4885 - acc: 0.8067 - val_loss: 0.1616 - val_acc: 0.9344\n",
            "Epoch 2/10\n",
            "6990/6990 [==============================] - 2s 245us/sample - loss: 0.2003 - acc: 0.9147 - val_loss: 0.1240 - val_acc: 0.9498\n",
            "Epoch 3/10\n",
            "6990/6990 [==============================] - 2s 241us/sample - loss: 0.1557 - acc: 0.9353 - val_loss: 0.1762 - val_acc: 0.9189\n",
            "Epoch 4/10\n",
            "6990/6990 [==============================] - 2s 243us/sample - loss: 0.1321 - acc: 0.9461 - val_loss: 0.2416 - val_acc: 0.9060\n",
            "Epoch 5/10\n",
            "6990/6990 [==============================] - 2s 237us/sample - loss: 0.1236 - acc: 0.9482 - val_loss: 0.0950 - val_acc: 0.9614\n",
            "Epoch 6/10\n",
            "6990/6990 [==============================] - 2s 240us/sample - loss: 0.1150 - acc: 0.9546 - val_loss: 0.0927 - val_acc: 0.9653\n",
            "Epoch 7/10\n",
            "6990/6990 [==============================] - 2s 244us/sample - loss: 0.1117 - acc: 0.9561 - val_loss: 0.0947 - val_acc: 0.9678\n",
            "Epoch 8/10\n",
            "6990/6990 [==============================] - 2s 230us/sample - loss: 0.1000 - acc: 0.9598 - val_loss: 0.1361 - val_acc: 0.9459\n",
            "Epoch 9/10\n",
            "6990/6990 [==============================] - 2s 239us/sample - loss: 0.0896 - acc: 0.9644 - val_loss: 0.1184 - val_acc: 0.9434\n",
            "Epoch 10/10\n",
            "6990/6990 [==============================] - 2s 239us/sample - loss: 0.0974 - acc: 0.9617 - val_loss: 0.0748 - val_acc: 0.9704\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYJb2B7xi1dx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "125ae4e4-61cf-465a-f3c1-a04e6c83f1e8",
        "id": "dxk_KaY-wnII",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "model3 = tf.keras.Sequential([\n",
        "                              tf.keras.layers.Dense(64, activation='relu', input_shape=[len(X_train.columns)]),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(13, activation='sigmoid')\n",
        "])\n",
        "model3.compile(optimizer='adam',loss=tf.keras.losses.sparse_categorical_crossentropy,metrics=['accuracy'])\n",
        "history3=model3.fit(x=X_train, y=y_train, epochs=10, batch_size=8, validation_split=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 6990 samples, validate on 777 samples\n",
            "Epoch 1/10\n",
            "6990/6990 [==============================] - 2s 288us/sample - loss: 0.5317 - acc: 0.7910 - val_loss: 0.2128 - val_acc: 0.9305\n",
            "Epoch 2/10\n",
            "6990/6990 [==============================] - 2s 261us/sample - loss: 0.2169 - acc: 0.9159 - val_loss: 0.1363 - val_acc: 0.9524\n",
            "Epoch 3/10\n",
            "6990/6990 [==============================] - 2s 282us/sample - loss: 0.1736 - acc: 0.9292 - val_loss: 0.1860 - val_acc: 0.9266\n",
            "Epoch 4/10\n",
            "6990/6990 [==============================] - 2s 286us/sample - loss: 0.1415 - acc: 0.9418 - val_loss: 0.0878 - val_acc: 0.9678\n",
            "Epoch 5/10\n",
            "6990/6990 [==============================] - 2s 300us/sample - loss: 0.1310 - acc: 0.9474 - val_loss: 0.1251 - val_acc: 0.9485\n",
            "Epoch 6/10\n",
            "6990/6990 [==============================] - 2s 301us/sample - loss: 0.1074 - acc: 0.9577 - val_loss: 0.2053 - val_acc: 0.9112\n",
            "Epoch 7/10\n",
            "6990/6990 [==============================] - 2s 297us/sample - loss: 0.1056 - acc: 0.9548 - val_loss: 0.0909 - val_acc: 0.9717\n",
            "Epoch 8/10\n",
            "6990/6990 [==============================] - 2s 287us/sample - loss: 0.1071 - acc: 0.9565 - val_loss: 0.1396 - val_acc: 0.9395\n",
            "Epoch 9/10\n",
            "6990/6990 [==============================] - 2s 263us/sample - loss: 0.0922 - acc: 0.9635 - val_loss: 0.1937 - val_acc: 0.9241\n",
            "Epoch 10/10\n",
            "6990/6990 [==============================] - 2s 267us/sample - loss: 0.0820 - acc: 0.9677 - val_loss: 0.0734 - val_acc: 0.9743\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "P-QIj7BzvFFm",
        "colab": {}
      },
      "source": [
        "model2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(32, activation='relu', input_shape=[len(new_train.columns)]),\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.1),\n",
        "    tf.keras.layers.Dense(13, activation='softmax')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "EhNq-tWPvGjk",
        "colab": {}
      },
      "source": [
        "model2.compile(optimizer='adam',loss=tf.keras.losses.sparse_categorical_crossentropy,metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "046f8c78-69b0-4e46-b741-cd87b05b2888",
        "id": "vfWEG-UPvIUh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "history2=model2.fit(x=new_train, y=y_train, epochs=10, batch_size=4, validation_split=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 6990 samples, validate on 777 samples\n",
            "Epoch 1/10\n",
            "6990/6990 [==============================] - 3s 467us/sample - loss: 0.5803 - acc: 0.7690 - val_loss: 0.2550 - val_acc: 0.9048\n",
            "Epoch 2/10\n",
            "6990/6990 [==============================] - 3s 450us/sample - loss: 0.2705 - acc: 0.8907 - val_loss: 0.1945 - val_acc: 0.9163\n",
            "Epoch 3/10\n",
            "6990/6990 [==============================] - 3s 447us/sample - loss: 0.2032 - acc: 0.9176 - val_loss: 0.1754 - val_acc: 0.9228\n",
            "Epoch 4/10\n",
            "6990/6990 [==============================] - 3s 450us/sample - loss: 0.1703 - acc: 0.9305 - val_loss: 0.1736 - val_acc: 0.9228\n",
            "Epoch 5/10\n",
            "6990/6990 [==============================] - 3s 446us/sample - loss: 0.1505 - acc: 0.9392 - val_loss: 0.1693 - val_acc: 0.9215\n",
            "Epoch 6/10\n",
            "6990/6990 [==============================] - 3s 454us/sample - loss: 0.1268 - acc: 0.9482 - val_loss: 0.1525 - val_acc: 0.9498\n",
            "Epoch 7/10\n",
            "6990/6990 [==============================] - 3s 439us/sample - loss: 0.1271 - acc: 0.9479 - val_loss: 0.1923 - val_acc: 0.9215\n",
            "Epoch 8/10\n",
            "6990/6990 [==============================] - 3s 446us/sample - loss: 0.1207 - acc: 0.9515 - val_loss: 0.1096 - val_acc: 0.9524\n",
            "Epoch 9/10\n",
            "6990/6990 [==============================] - 3s 449us/sample - loss: 0.1155 - acc: 0.9512 - val_loss: 0.2677 - val_acc: 0.8867\n",
            "Epoch 10/10\n",
            "6990/6990 [==============================] - 3s 452us/sample - loss: 0.1175 - acc: 0.9534 - val_loss: 0.1537 - val_acc: 0.9279\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zp1iP9kov7P8",
        "colab_type": "text"
      },
      "source": [
        "#Plot of accuracy through model training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50dmsIFKxYU2",
        "colab_type": "code",
        "outputId": "f672f4a4-e5df-4f3f-ecb6-c03b166edfa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "source": [
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JIgL8Z1UZP31mISLw5BUTmDwic+8HLH3edYw78yEbT8kY06p2TP8VoXZsgRev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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cnwa5Zzj3CeSeCRknHhFKPp+yr6bpUDPV4X6PevZVN+Hz++fqiRBG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\ndWWgyJZA/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hN-0aT8EgI4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('my_model.h5') \n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}