Geolife_Outlierdetection_TMD.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OmidGhadami95/Geolife-OutlierDetection-TMD/blob/main/Geolife_Outlierdetection_TMD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCSG7JvWutEo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b948baba-a1c7-4b38-e848-da1009e0739a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice:\n",
        "- Use IQR or z-score not both\n",
        "- Tune z-score hyperparameter (threshold)\n",
        "- This part must perform before calculating aggregated features\n"
      ],
      "metadata": {
        "id": "qYz3JJfI0Fkj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "XWe0xrQt82Bn",
        "outputId": "74c1e884-e8b0-4e00-da93-4a64f23fd7c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pyproj in /usr/local/lib/python3.7/dist-packages (3.2.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from pyproj) (2022.9.24)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install pyproj\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import pyproj\n",
        "import numpy as np\n",
        "import os\n",
        "from multiprocessing.pool import Pool\n",
        "import time\n",
        "from IPython.display import display, HTML\n",
        "import glob\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Loading Data from csv to dataframes**"
      ],
      "metadata": {
        "id": "vKVlKC4RuApn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_label(label):\n",
        "    return label.lstrip(',').rstrip(',').replace(',,', ',')\n",
        "\n",
        "INPUT_FOLDER = '/content/drive/MyDrive/processed_data/020.csv'\n",
        "headers_metadf = ['trajectory_id', 'start_time', 'end_time', 'v_ave', 'v_med', 'v_max', 'a_ave', 'a_med', 'a_max', 'labels']\n",
        "\n",
        "df = pd.read_csv(INPUT_FOLDER,index_col=0)\n",
        "df_labeled = df.dropna(subset=['acceleration','velocity','v_ave','v_med','v_max', 'a_ave', 'a_med', 'a_max', 'labels']) #!!!!!MODIFIED!!!!!\n",
        "df_labeled.loc[:,'labels'] = df_labeled['labels'].apply(lambda x: clean_label(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtpERYS-9AZA",
        "outputId": "dcf1501b-c67c-439b-d6d5-bfcb875e9798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_column(ilocs[0], value, pi)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_labeled.head()"
      ],
      "metadata": {
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          "height": 444
        },
        "id": "hrnJa3XN-r16",
        "outputId": "51208595-f0c9-4106-bb62-ac8f07ce04f7"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "         lat        long  altitude             datetime        timedelta  \\\n",
              "0  39.981590  116.327578         0  2011-11-25 01:44:22  0 days 00:00:01   \n",
              "1  39.981557  116.327582         0  2011-11-25 01:44:23  0 days 00:00:56   \n",
              "2  39.981382  116.327678         0  2011-11-25 01:45:19  0 days 00:00:01   \n",
              "3  39.980920  116.327830         0  2011-11-25 01:45:20  0 days 00:00:01   \n",
              "4  39.980923  116.327820         0  2011-11-25 01:45:21  0 days 00:00:01   \n",
              "\n",
              "    distance   velocity  acceleration   trajectory_id  subfolder labels  \\\n",
              "0   3.712078   3.712078     -3.335068  20111125014422         20   bike   \n",
              "1  21.112585   0.377010      0.937420  20111125014422         20   bike   \n",
              "2  52.872515  52.872515    -51.941601  20111125014422         20   bike   \n",
              "3   0.930914   0.930914      7.179167  20111125014422         20   bike   \n",
              "4   8.110082   8.110082     -4.270712  20111125014422         20   bike   \n",
              "\n",
              "     v_ave     v_med      v_max     a_ave     a_med      a_max  \n",
              "0  3.48697  3.187125  52.872515 -0.089624 -0.042987  13.862124  \n",
              "1  3.48697  3.187125  52.872515 -0.089624 -0.042987  13.862124  \n",
              "2  3.48697  3.187125  52.872515 -0.089624 -0.042987  13.862124  \n",
              "3  3.48697  3.187125  52.872515 -0.089624 -0.042987  13.862124  \n",
              "4  3.48697  3.187125  52.872515 -0.089624 -0.042987  13.862124  "
            ],
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              "      <th></th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>altitude</th>\n",
              "      <th>datetime</th>\n",
              "      <th>timedelta</th>\n",
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              "      <th>velocity</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>trajectory_id</th>\n",
              "      <th>subfolder</th>\n",
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              "      <th>v_ave</th>\n",
              "      <th>v_med</th>\n",
              "      <th>v_max</th>\n",
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              "      <th>a_med</th>\n",
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              "      <th>0</th>\n",
              "      <td>39.981590</td>\n",
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              "      <td>2011-11-25 01:44:22</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.981557</td>\n",
              "      <td>116.327582</td>\n",
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              "      <td>2011-11-25 01:44:23</td>\n",
              "      <td>0 days 00:00:56</td>\n",
              "      <td>21.112585</td>\n",
              "      <td>0.377010</td>\n",
              "      <td>0.937420</td>\n",
              "      <td>20111125014422</td>\n",
              "      <td>20</td>\n",
              "      <td>bike</td>\n",
              "      <td>3.48697</td>\n",
              "      <td>3.187125</td>\n",
              "      <td>52.872515</td>\n",
              "      <td>-0.089624</td>\n",
              "      <td>-0.042987</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>39.981382</td>\n",
              "      <td>116.327678</td>\n",
              "      <td>0</td>\n",
              "      <td>2011-11-25 01:45:19</td>\n",
              "      <td>0 days 00:00:01</td>\n",
              "      <td>52.872515</td>\n",
              "      <td>52.872515</td>\n",
              "      <td>-51.941601</td>\n",
              "      <td>20111125014422</td>\n",
              "      <td>20</td>\n",
              "      <td>bike</td>\n",
              "      <td>3.48697</td>\n",
              "      <td>3.187125</td>\n",
              "      <td>52.872515</td>\n",
              "      <td>-0.089624</td>\n",
              "      <td>-0.042987</td>\n",
              "      <td>13.862124</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.980920</td>\n",
              "      <td>116.327830</td>\n",
              "      <td>0</td>\n",
              "      <td>2011-11-25 01:45:20</td>\n",
              "      <td>0 days 00:00:01</td>\n",
              "      <td>0.930914</td>\n",
              "      <td>0.930914</td>\n",
              "      <td>7.179167</td>\n",
              "      <td>20111125014422</td>\n",
              "      <td>20</td>\n",
              "      <td>bike</td>\n",
              "      <td>3.48697</td>\n",
              "      <td>3.187125</td>\n",
              "      <td>52.872515</td>\n",
              "      <td>-0.089624</td>\n",
              "      <td>-0.042987</td>\n",
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              "      <td>2011-11-25 01:45:21</td>\n",
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              "      <td>-4.270712</td>\n",
              "      <td>20111125014422</td>\n",
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              "      <td>bike</td>\n",
              "      <td>3.48697</td>\n",
              "      <td>3.187125</td>\n",
              "      <td>52.872515</td>\n",
              "      <td>-0.089624</td>\n",
              "      <td>-0.042987</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "before_outlier = len(df_labeled.index.unique())"
      ],
      "metadata": {
        "id": "ROwj4CBCTFdR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_labeled.labels.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZgXSKyUhCSA",
        "outputId": "4412f5e0-7cd3-4c46-ac06-ac5133100f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['bike', 'walk', 'bus', 'subway', 'train', 'car'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bike = df_labeled.loc[df_labeled.labels=='bike']"
      ],
      "metadata": {
        "id": "fIygFhKE-wQ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_walk = df_labeled.loc[df_labeled.labels=='walk']"
      ],
      "metadata": {
        "id": "VJjOyCV_GElM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_bus = df_labeled.loc[df_labeled.labels=='bus']"
      ],
      "metadata": {
        "id": "bC55UPbkGEes"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_subway = df_labeled.loc[df_labeled.labels=='subway']"
      ],
      "metadata": {
        "id": "FvfgOMxkGEUy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_labeled.loc[df_labeled.labels=='train']"
      ],
      "metadata": {
        "id": "G0ZoqIwkGEOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_car = df_labeled.loc[df_labeled.labels=='car']"
      ],
      "metadata": {
        "id": "t7dnBd9Ig9a-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **How many samples in each class does it have?**"
      ],
      "metadata": {
        "id": "jNpdnw_pu831"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "class_list = [\"walk\", \"bike\", \"bus\", \"car\", \"train\", \"subway\"]\n",
        "y = np.array([len(df_walk), len(df_bike), len(df_bus), len(df_car), len(df_subway), len(df_train)])\n",
        "\n",
        "plt.pie(y, labels=class_list)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "trlvxjt_G7wf",
        "outputId": "3ae06a95-4f64-4685-b3f4-3454af337a81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Wnp7OHYpIBNirlLoznfvtDl03gcxotgy7ddfuI78wYmgrIgd96qJEfIumyPGLpvgYVqM2z3neqp5UraYI9D/Q9n6rrU9p3bpJpXXrPn6vLdC3fveAo96vKZu0t77/mJ0teX1JWrsarcSW3VbbNqWs2v6ollFASfp/yqxy5RqOJgBcakgyOeSo1raX1xXkn9SV7bcPlJG3necfmd+mmr6yQr385Vetsj5tdHrNm5fY13/QrjemDdr1xsfvNReUflhTNnFvbdnEpj0loWRroMhSKr7VSmzdYbVtaVLJDwuVtXcoWCPo5KzDQboUACJSBDwKDMe+b/Ij4A5gulKqRkSmA3cqpU5JfWSKiCwHBgI/VUr9WkT+B3hOKfWUiDwJ1CmlLhKRi4AxSqkbROQvwAigEPiZUupXqe9PVkrNS9VyMTBBKXXVweo1AeBit+2qHfr1YYMVIl0+yFrzpM/js+Skx2f5mLjZeufC5636w3cxTexftC4pbNk9ePiOpYOH77D7JSlQjUVDa2vKJiVqSyfI3uLPlST9hYMUrXutxAfVVtuWOiuxDWXtLkW1jsKZN4i7OhHL6cAOpVQFgIgEsQPgYCZjLzxSBLwpIlFgKTALeAoYxr+Gz88C/pT6+iKl1G4R6QO8LiJPYAfPDSJyrVKqDZgDfO9QxeoKgJ2a2vWUcW1to4cmkq/uyAuc0JPPvzPSN/HauT5K9qnd31pkrZgdU2MCFiO6ux8BKW7cMbq4ccfo0JbnALDE3xrvN2prTdmkurrSo/z7ik4cbPkCoxSgrN2brbatH1iJLc1W8qO+WHuHgjrgzcos2t7F7WLAXSJyB7BQKbW0k/z9q1KqCWgSkZew18tYCswTkQnAu8AAERmCvU7hFanPXSEiZ6W+HgGMVUqtEJEXgTNEZC2Qp5SKHapxXQFgJrjMkltraovnDhnUq3009JXSX37Jf8qvvqisWW+r189bbFHawDTpxc0+n0rmD4hvmDAgvgE22u8l/AUNdf3HvV8zcFK8vv/Ygua+k8Yqn38YgLKa41Zix2YrsaXOSmz3qeTuMmgbBfTp1Q/XdV0KAKXUeyJyLPAl4DYReQFI8K//rzqeSXW8C6+UUttFpD/22cQS7JGI52DfMGwQkVOAfwNOVErtE5FF7fb7G2ABsA54sLN6dQXAek3tes4JzS0T+yeTq+v9/l6vxKxEfEsmyXFLJvkYvFttnfO89f6UjWqyL01DZQPJlpLy2jVTy2v/NQt6S36/XbWlE6pryyY1xvuNKmntM2oyIgMAlLKSKlm70Ups/chKbGmxEjuLUI3DQWVixqltXdlIRIYCu5VSD4lIPTAXqAamAc8AZ3f4yFdF5CfYlwCnAOHU+yuAecCp2HMRPJ56gf34tS518B9Fu7ULlVKvisgI4Fjsy4tDMmcAHhCurWsLHzYwrfv8sFRG/ORc/4i8hGo+4zX1yleXW/37tjIxrY0ABa17yod+uKJ86IcrPn6vsc+gLbVlE7fVlh3d1lA8ojRRcMwUCo/9eLiusprqrMT2zVZiS9xKbPerZH0ZtI2m56NQk8CmLm47CfhPEbGwe7z+APss5bci8iNgUYft1wAvYd8E/JFSakfq/aXAF5RSG0RkM3bI7h/s9Szw/dRp/nrssGjvUWCqUqqus2J19QMQoAFn3uxxpeNGDl/f7PNldI27o7aqtRc+n6wZ9RHTJXun5igkuadk5Ps1Ayd9tHvAeBqLBg+yfPmjEfn4D5xSVkIld1Vbia07rbatrVZyZzFq3whQXbk+Wj//kYU5s5SdiCwE7lFKvdDptloCACASfAM4Rk/j3vNgsGTZ3aUDZmajreImVf/NxdbqU1erUMBiZDba7Cjpy99XHxyzoWbg5Lq6/kfmNfUZOFz5Ap9aj0JZjTVWYvtWq23LHiu5w6+S9eWQGMUnR60+Of+RhV87VHupa/ZvKaXu706dIvJ06nO9fjKWquE1YLVS6htd+ozGAPgTcK6exr0nCcnpoRE7Evb1YXYopWauVW+c/5KVHLiH6b25aZgObYGiut2lR71fUzapsT44pm9LQf8Q4ivvuJ1SyTaV3LnJatu6y0psTSjVFL36Dw/856H2LSIh7Lv+R3d4P6CUSqT1B0kjncNBzY3ALPKD/1t7Gjb9X7Bf9gJARJZNkGnLJvgYVKe2Vf7Dev/Y99UEn+JTB1025CUaBwzauWr6oJ2rPn6vqbBsR23pxC01ZUc3N5SM7N+WVzRGxF8igSFH+gJDjkytYn9fF3ZfBYwRkbewr/2bgTrsVbCPPFDHHQARqQamA8XYNwlfBmZiP3X4auoRYcboPAM4A/ibnsa9KTVUuFGJpPeOYDcEkqr1S6+rlWcts0qKWpikq46DUYi1t3jYppqyoz/cXTohubdo2MBkoLDisl+cWn2oz7U/A0g9posCRyulNqW+X9q+4w4wWylV2yEANmD3GHxLRB4FnlJKPZShHxXQewbwMmDhzoEjjlSoVJ8vNu577eniokMOEsqkhF/yn5ohM5+a4WPsdrV+zvPJnWM+4FhxyA1hQflK9m4bU7J325hRm58F2Dl+3drqHuzqtf0Hf8qnOu7w6e7Fm5RSb6W+XgWEetBut+g7+CLxeuxeU0YWLaitm4JSe3XXAfDPYTJuwYWBWXOu8ieemSaL2/xdftSWTct7+LmPV27q0HFnCvAmB+5a3dLu6yRZ+AOt+6+vmc46y4KW1X9Gc/OqzrfMnn2FEnzwC/7Z5/8wMOqus3xvftSfFco+AJygqwHQwMFHPB60445uugNgieb2PenWXbuPRClHLiv26lG+Yy7/QWDGZZf6d756pCyyRPu4kS4tvKKUqgVeEZG3gY5PDJ4FAqmOO1V8uuOONvpuAgJEguWYgUFafGPo4C4PFdbJn1Rtp61SK89+xepb0kyvuzN3UwNQNn7dWtfOYak3AAAiwbXYj0qMLFqfl7fx68MGj+rOUGHdRn+g/jnn+eQHR27nWLHvmmfan8evW9ux776r6L4EAHhedwFelBoq/JruOrpj4xAZe+MFgZMvvNqv/na8LGn1836Gm4xmeP/aOeEMYBbmXoAWrxYWvDN3yKC0D+DJpunvWau/86K1b3Adx0l675orYNj4dWs/SOM+P+aUHoJOCADBHmppZgnW4KTDh70V9/un6q6jt0r3qI8ueMFad8J6Nc6vGJyGXS4fv25tl8ZOiMgFwDXYobEGezTef2CPJ6gFzldKfZSaBHQMMBrYopQ6Lw119or+AACIBO8FrtRdhhctLOq78vrDBrpmumufpRKff1Ot/MZSq7BfE70JtmvGr1t7V2cbichE4ElgZmrOv1LsIKhXSikRmQuMV0rNTwXAl4GTMt3Ft6uccA8A4BHdBXjVGY37phdalmvGZVg+CTw3zTdj7rzA1Gsv8r+/dgRLFPRkWa/HO98EsCfseEwpVQOglNqNPSHocyISA66FT8yT8JRTDn5wTgCsALboLsKrLq2Pu3LK682DZMzN3w6cXDnf7/vrDFnaEuCfXfzo6+PXrd3ci6b/G7hPKTUJe1LO9r3+Gg/8ET2cEQCRuAIe012GV10QbzghoNRW3XVkSnO+FD/8Wf+s71wbGHv7Ob4120tZpg69PmWnc+m18yLwDREpA3vQD3bPv/1zCFb2rOrscEYA2P6guwCv2j9UWHcd2fDWGN/kq74XmPm9y/31SyfI4qSwo8MmjcDDXd2fUuod4MfAYhFZDdwNRIDHRGQVUJOm0jPCGTcB94sEl2FPfWxkmROGCuvgs1Tys6vVynOXWHnBfRwj8OD4dWu/q7uubHHSGQDAvboL8KrUUOG3ddeRbZZP/C8c4zvhkisDx86f669eMU7+R3dN2eS0AHgCczNQmwW1dVOdMlRYh23lsnPOX999o/Mt3cNZARCJJ7HvoBoa2EOFWxw1VDjLfqa7gGxzVgDYfgN49q+QbrfuqnXsUOEM20HXn/27hvMCwJ4p6H91l+FVqVWFc2qQUJrcFauMuXbY78E4LwBsP8M5M8J4zm27aofiqMdDGbcN6NZ8/m7hzACIxDcAv9Vdhlfl4lDhXrolVhlr1l2EDs4MANvNmHsB2txaU5uNCTecYD3d6/nnKs4NgEj8Q+BO3WV41QnNLRODyeRbnW+Z826MVcY8e7np3ACw3QlkZEIGo3Ph2jrtE1Zk2Co8eOe/PWcHQCTeiN2v2tAgNVTYzUu5L4hVxrx0s/NTnB0Att8C7+ouwqsurY/v0l1DhjwTq4z9XXcRujk/AOzegVfrLsOrLog3zHDhUOEG7HH6nuf8AACIxJ8Dfq+7DC9y6VDh62KVMbeFWo/kRgDY5mEWEdHi8rr4cZKa8soFFgO/0F2EU+ROAETiu4HLdJfhRS4aKtwEzPX6jb/20hIAIhJKrYnW8f3fiMiE1Ne979QTiT+OmTlIC5cMFb4xVhnboLsIJ8noGYBSaq5SKt138C8DzPVblrlgqPCrwD26i3CadAZAQEQeFpG1IvK4iPQVkUUi8ok550VkoIgsF5EKESkXkSdE5PXU6zOdtmKPFrwQsNJYu9EFOTxUuA44P1YZM78zHaQzAMYB9yulxmPPw35pxw1EZBD2ems3KaWi2KP+7lFKHQecjT0XQOci8ReBG9NUt9FFOTpU2AK+FauMZXodwZyUzgDYqpTav5b6Q0DHpafzgBeAHyql9i8I+m/AfSLyFvAU0E9EujYIJRK/HTOVeNbl4FDhm2KVsWd1F+FU6QyAjr8UHf93Arvv9Wkd2p+hlJqaeg1T3bvRNAdY3f1SjZ5KDRV+XXcdXfQkcLvuIpwsnQFwuIjsn9L7W8DLHb6vgIuAo0TkutR7fwcu37+BiHRvLTd7rMCZOHzudbe5paa2r+4aumAtUGke+R1aOgNgPXCZiKwFBgA/77iBUioJnAecKiKXAlcA00VkjYi8C3y/261G4tXAOdhnGEYWzGhuOTqYTDr5zGsPcFasMtaguxCnc9bCIL0RCf47ZkbhrHHwqsIJ4KuxytjTugvJBbnTE7Azkfh9wI90l+EVDh0qrLBP+83B30XuCQCASPwm4Ke6y/AKBw4VvjxWGTM9RbvBPZcA7UWC9wJX6i7D7ZKQnB4asSMhMkJ3LcD1scpYle4ico27zgD2i8TnYUZ8ZZyDhgrfaA7+nnFnANguxcOzvWaLA4YKR2KVsds62+hgA9a8zr0BEIkrYC7wa92luFmhUn1Ob9z3joamFXBtrDJ2i4a2XcO9AQAQiVtE4pcA1/PpnolGmtxQWzcly0OFm4FzYpWx7k4bf6ABa9UiMhBARKaLyKLU17NF5K3U600RKUnzz+AI7g6A/SLxKuwOSC26S3GjoGX1PyF7Q4V3AafGKmM9mc670wFr7VwDXKaUmgrMwp5MxHW8EQAAkfgjwOeAWt2luNGtNbVHolSmF9d8D5gRq4wt7+HnOxuw1t4rwN0icgXQXynlyp6m3gkAgEj8FeBEwMwKk2ZDE8kh41rbXs1gE0uBE2OVsY292MeBBqwl+NdxUPjxN5Sqwr6H1Ad4RUSO6kW7juWtAACIxP8JzACe0V2K2/w4c0OFfwN8PlYZ293L/RxowFo1MC313tn7NxSRMUqpmFLqDuB1wASAa0TitUAFcBXmvkDaZGCocB3w9Vhl7OJYZSwd/04HGrB2C/AzEVnJJ5eknycib4vIGqANl/7BcGdPwO6IBKcCf8SlCZ9tKwoL3r54yKCj07CrRcB3YpWxbWnYl3EQ3jwDaC8Sfwv7FND0F0iDNAwVbgMWAJ8zB3/mmTOA9iLBs4FfAmW6S8llfyvuu3JBeY+GCm/Anr8vV2YcynnmDKC9SPwJ7GfFv8LMOtxjX97b7aHCLdhTd001B392mTOAg4kEpwP3ASfoLiUXPRAsWXZP6YCZXdj0r8DVvXy8Z/SQCYBDiQQFex7DnwDlmqvJKUlITguN+CApMvwgm6wDrjRLdOtlLgEOJRJXROK/xb4suA/IxUUxtEgNFT7QX/U9wHxgsjn49TNnAN0RCQ4HrgUuxu4hZhxCs0jT8SOHNyp7sE0DcD9wV6wy5rSZhDzLBEBPRIKHAVdjDyZx5SixdLl5YOkzfy4pfh34WRp68hlpZgKgNyLBAdjrGlwJlGquxmk2AcMAt5YAAAGaSURBVPcCDxCJ5/qqwq5lAiAdIsG+wNexVyqaDYjegrRJAv/A7rv/JJF4spPtDc1MAKRbJDgKqEy9QnqLyZo3gd8DfyQS/1B3MUbXmQDIFPsR4inYZwVfAYJa60m/rcDDwENE4jqmBDPSwARANkSCAezJJypSr/F6C+qRVmAZ9in+P4DXicRNb8kcZwJAh0hwGPbsRJ/DvmcwUm9BB6SwV17ef8AvJRLfp7ckI91MADiB/TRhSofXRKAgSxXUA+8AMeDt1GsNkXhdlto3NDEB4FT2ZcNRwBhg8EFehwF52D06pd1rvyT2HIi72r12tvt6I/A2kbgZdutRJgDcyL4BKYBKrY9gGAdkAsAwPMwMBjIMDzMBYBgeZgLAMDzMBIBheJgJAMPwMBMAhuFhJgAMw8NMABiGh5kAMAwPMwFgGB5mAsAwPMwEgGF4mAkAw/AwEwCG4WEmAAzDw0wAGIaHmQAwDA8zAWAYHmYCwDA8zASAYXiYCQDD8DATAIbhYSYADMPDTAAYhoeZADAMDzMBYBgeZgLAMDzs/wH5Gj2A6/kaNgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Outlier detecting and removing for all classes on acceleration and velocity columns**"
      ],
      "metadata": {
        "id": "66Q6t_NCVtZC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Main Functions**"
      ],
      "metadata": {
        "id": "72t6yOMtl7_z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# IQR (Interquartile range)\n",
        "# this function delete records which are higher than Q3+1.5*IQR in a specific column (Upper bound)\n",
        "\n",
        "def IQRmethod(df, column):\n",
        "  print('your dataframe has', len(df[column]), 'records')\n",
        "  Q1 = np.percentile(df[column], 25, interpolation = 'midpoint')\n",
        "  Q3 = np.percentile(df[column], 75, interpolation = 'midpoint')\n",
        "  IQR = Q3 - Q1\n",
        "  if column == 'velocity':\n",
        "    df = df[df[column] < (Q3+1.5*IQR)]\n",
        "  elif column == 'acceleration':\n",
        "    df = df[df[column] < (Q3+1.5*IQR)]\n",
        "    df = df[df[column] > (Q1-1.5*IQR)]\n",
        "  print('your dataframe has', len(df[column]), 'records after omit upper baound for' , column)\n",
        "  return df"
      ],
      "metadata": {
        "id": "MSBb675UlzY3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# z-score\n",
        "#this function delete records which are higher than +4 z-score in a specific column (threshold)\n",
        "\n",
        "def ZS(df, column, threshold):\n",
        "  print('your dataframe has', len(df[column]), 'records')\n",
        "  s = sum(df[column])\n",
        "  mean = s / len(df[column])\n",
        "  d = [(each - mean)**2 for each in df[column]]\n",
        "  sum_d = sum(d)\n",
        "  standard_deviation = (sum_d / (len(df[column]) - 1)) ** 0.5\n",
        "  z_score = [(each - mean)/standard_deviation for each in df[column]]\n",
        "  se = pd.Series(z_score)\n",
        "  #df['z_score'] = se.values\n",
        "  df.loc[:,'z_score'] = se    ##################################################????????????????????????????????????????????\n",
        "  if column == 'velocity':\n",
        "    df.drop(df[df['z_score'] >= threshold].index, inplace = True)\n",
        "  elif column == 'acceleration':\n",
        "    df.drop(df[df['z_score'] >= threshold].index, inplace = True)\n",
        "    df.drop(df[df['z_score'] <= -threshold].index, inplace = True)\n",
        "  df = df.drop(['z_score'], axis=1)\n",
        "  print('your dataframe has', len(df[column]), 'records after omit upper baound for' , column)\n",
        "  return df"
      ],
      "metadata": {
        "id": "RY3Nfhm4mcLA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1- walk**"
      ],
      "metadata": {
        "id": "peFJBjfQoAV8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1-A) box plot before omit outliers**"
      ],
      "metadata": {
        "id": "NPEVgrMOpvHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_walk['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "KM2TzIGNDVwA",
        "outputId": "b34395a9-be46-4415-ae77-504ec6c8e2cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc439717b90>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Lv6TXzOzn06Jfl/SS6nBelCw9XGtms9L3W2EsdTcvGWPNxWOSOtK7Ia6VdKRkqaImmVmbkqW7T7v7uyW7HpN0s5lNN7PLlXyw+N0JV+zuFf0naZmSTw1/JGlNpdurQP8/ruRXpz5Je9N/y5SsnT4l6YeSnpR0YXRfJzmu6yQ9nr6+In3T5CV9U9L06P5NcAzXSOpN5+ZfJDXX67xI6pb0A0kvSvpHSdPraV4kPaxk/XpIyW8nXxxrLiSZkrujfiTpe0ru/ggfw1nGkley1lvIgI0lx69Jx/KKpPbJtMWfIgNAED6EA4AgBDAABCGAASAIAQwAQQhgAAhCAKNumNmxMs/7CzO7Pn19m5nNmtqeAeXhNjTUDTM75u5z3mMd+5Tcd3qufEU66ljj2Q8BKsPM/krSa+7+lXS7S9IxJTfqf1bJHyNsdfd1mfNM0t8oeayhS7rb3R9J9/2ZpD9U8rCh7e5+h5ltkfS4kucsXCLpaTN7S8kfPHzY3W9Lz/1jSYvc/fZKjhsoYAkCkR5RErQFn1XyhLOFSh7pd42kj5nZJzLn/W6672olz1H4kpl90MzalTzU5lfc/WolIV3k7vdIel3SJ939k5L+SdJvp8/6kKTPS/raFI4PGBdXwAjj7s+b2QfM7BJJ85Q8dHyxpN+Q9Hx62BwlgfydklM/Lulhdx9R8sCXZyX9kqRfk/SAp3+r7+7Z59Nm2z9mZt+WdKOZvSypyd2/N3UjBMZHACPaN5U8cObnlFwRXybpL919U5Xav1/Snyt5DsMDVWoTkMQSBOI9ouTJbJ9REsb/JukL6fOXZWYtZvaBzDk7JP2eJd9tN0/JN2N8V8nXxny+cJeDmV14hvbelvT+woYnj7OcL+kPlDyEBagaroARyt2/b2bvl3TQk0cSvmFmV0n6j+SzNh1T8qFa6XN9t0paKukFJR/Crfbk8ZQ9ZnaNpF4zOylpm5Kr21Kb0+NeT9eBpWQt+Bp3P1SZUQJnxm1oOO9Z8u3QX3b3p6L7gvMLSxA4b1nytfavSvo/whcRuAIGgCBcAQNAEAIYAIIQwAAQhAAGgCAEMAAE+X/bcRqOMPiBuwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#acceleration\n",
        "\n",
        "sns.boxplot(df_walk['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "N7Ud_KQ7pQIC",
        "outputId": "02f0b295-46a8-4460-d9ff-b501ff7576d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc43918a2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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KpwYOJF+9vUzS9yuWf+MF5vx96vdfMbOtkt4jqV3FrzTP5IOSfuTuryS3d0j6gqS/TW7vSv49KOlTF1gWMCMCGPPKzFapGKw3uftPzexZSYckzfbtukna6+5/dBFzRlK/PyHpdnc/bGafVfFaAhdjNPl3Qhw/uEicgsB8+1lJp5Pw/ZCKr1bfJemjyZXcZGbvTebuVfHVpZLxKyU9L+m3zKwjGVtiZh+oWMds5pS8W9IJM2uT9Mep8TeS+yq9LClXWrakP5H0r7PYbmDOCGDMtz2SWs3smIpXqnpe0kkVT0PsMrPDevsUwVZJV5rZi8n477j7SUmflfQNMzui4qmFslfPs5mTslnSDyQdkJS+iPlTkr6U/LHtfallv6XilbN2mtlRSZOSHn0njQAuhKuhAUAQXgEDQBACGACCEMAAEIQABoAgBDAABCGAASAIAQwAQf4fs2YBUHHKb88AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1-B) IQR**"
      ],
      "metadata": {
        "id": "VVzBOqiIp6ZH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_walk = IQRmethod(df_walk,'velocity')\n",
        "df_walk = IQRmethod(df_walk,'acceleration')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sp0dUyzJePrD",
        "outputId": "809a0a99-df8e-43a4-ecde-818e4da55e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 52098 records\n",
            "your dataframe has 48503 records after omit upper baound for velocity\n",
            "your dataframe has 48503 records\n",
            "your dataframe has 43342 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1-C) z-score**"
      ],
      "metadata": {
        "id": "WOkfb5G6reo0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_walk = ZS(df_walk,'velocity',3)\n",
        "df_walk = ZS(df_walk,'acceleration',3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Wef9c1wrdoz",
        "outputId": "9f518739-930b-4a5e-b11e-545ee19f9eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 43342 records\n",
            "your dataframe has 43148 records after omit upper baound for velocity\n",
            "your dataframe has 43148 records\n",
            "your dataframe has 43148 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1-D) box plot after omit outliers**\n"
      ],
      "metadata": {
        "id": "fDmN6JI1r6nT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_walk['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "XzfJYgAJsPP6",
        "outputId": "9699325c-1ea0-4d29-d533-8ff386830b44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc43911b2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#acceleration\n",
        "\n",
        "sns.boxplot(df_walk['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "RUJlzCOGsSic",
        "outputId": "fd8a6781-dd33-4500-b2ae-55e1bb371172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc43adb4750>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2) bike**"
      ],
      "metadata": {
        "id": "DFu6ORBdsyKJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2-A) box plot before omit outliers**"
      ],
      "metadata": {
        "id": "tILHGb58s7IV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_bike['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Fuvu-bE2s8xa",
        "outputId": "97c7c5ce-c074-48b8-a17f-aff905439a0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc439016f50>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#acceleration\n",
        "\n",
        "sns.boxplot(df_bike['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "8jqoJwvWs8u2",
        "outputId": "28d95727-ab9c-4ad3-c339-2df1a35f3335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4390d9c10>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2-B) IQR**"
      ],
      "metadata": {
        "id": "qnw3HBU5tHoV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_bike = IQRmethod(df_bike,'velocity')\n",
        "df_bike = IQRmethod(df_bike,'acceleration')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaCqaKuXs8sP",
        "outputId": "3c052e91-a6ce-43c7-a191-dc5074af2993"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 63622 records\n",
            "your dataframe has 60915 records after omit upper baound for velocity\n",
            "your dataframe has 60915 records\n",
            "your dataframe has 54355 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2-C) z-score**"
      ],
      "metadata": {
        "id": "Dt1B51cQtMWx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_bike = ZS(df_bike,'velocity',3)\n",
        "df_bike = ZS(df_bike,'acceleration',3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJke5Utxs8od",
        "outputId": "88e44992-8bf6-4c51-e7b3-f9ed7a5e24d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 54355 records\n",
            "your dataframe has 54355 records after omit upper baound for velocity\n",
            "your dataframe has 54355 records\n",
            "your dataframe has 54355 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2-D) box plot after omit outliers**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "tL91CY8vtckX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_bike['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "XB_UhvLYthbJ",
        "outputId": "fccffa05-2b64-443a-eeed-a7d2a095d522"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc43906b290>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#acceleration\n",
        "\n",
        "sns.boxplot(df_bike['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "rGUNqNbbthVn",
        "outputId": "d27ad63f-987c-47ef-9eea-524c5696625c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc439054850>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3) bus**"
      ],
      "metadata": {
        "id": "NYdIPrumtpNr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3-A) box plot before omit outliers**"
      ],
      "metadata": {
        "id": "qT_WuZzqdmYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_bus['velocity'])"
      ],
      "metadata": {
        "id": "Vz2_HbUJtohy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "b900b0d8-abcd-4e4e-d309-96ecdfa9bf2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438c52450>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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P7pT0c0o+QipJa5UE8j8XFP2opCfd/ZqSh9L8k6SfkPQzkp5w97fS+uf9aK67XzGzk5J2mNmYpCZ3Hy3dCIH5EcCI9vdKHpzyw0ruiDdJ+mN3/+sKtf+4kudxnJb0RIXaBCSxBIF4Tyl5AtouJWH8j5J+LX1mssys1czeV1Tma5I+kX6H3E1KvjHiXyWdkPSp3LsczGzdLO29Kek9uY30MY8bJP2qpCdLOTBgIdwBI5S7f8PM3iNpIv2Gh9fMbLOkf0lea9MVJS+qFT7v9iuSPiLpP5W8CPfp9LGNx9Mnhw2b2duSjiq5uy30WHreN9N1YClZC77T3b9dnlECs+NtaKh76bcwf8bdX4juC+oLSxCoW+nXvb8i6SrhiwjcAQNAEO6AASAIAQwAQQhgAAhCAANAEAIYAIL8P3p6rS9nIvygAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acceleration\n",
        "\n",
        "sns.boxplot(df_bus['acceleration'])"
      ],
      "metadata": {
        "id": "q7uhHJ4YtofH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "2045ab5e-fd64-4f04-d528-a378b913ce43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc43906f710>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3-B) IQR**"
      ],
      "metadata": {
        "id": "n_-p97vPeKSP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_bus = IQRmethod(df_bus,'velocity')\n",
        "df_bus = IQRmethod(df_bus,'acceleration')"
      ],
      "metadata": {
        "id": "HpplXp4Gtwgl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d627f135-02f2-4fc7-900a-820ce6441b54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 16639 records\n",
            "your dataframe has 16583 records after omit upper baound for velocity\n",
            "your dataframe has 16583 records\n",
            "your dataframe has 14804 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3-C) z-score**"
      ],
      "metadata": {
        "id": "2XKJn0AoenFf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_bus = ZS(df_bus,'velocity',4)\n",
        "df_bus = ZS(df_bus,'acceleration',4)"
      ],
      "metadata": {
        "id": "PQbYg2nItwd-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "860ea938-d49f-4d02-c06b-ca90971eb930"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 14804 records\n",
            "your dataframe has 14804 records after omit upper baound for velocity\n",
            "your dataframe has 14804 records\n",
            "your dataframe has 14804 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3-D) box plot after omit outliers**"
      ],
      "metadata": {
        "id": "BNKDXZGVel5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_bus['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "8ldWHaNnegZx",
        "outputId": "f957068b-789c-489d-b324-138692ff8911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc439738e10>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#acceleration\n",
        "\n",
        "sns.boxplot(df_bus['acceleration'])"
      ],
      "metadata": {
        "id": "DnIb_Kr4txSw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "84cc118a-2b4e-4cac-dcce-db18455222c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4428936d0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAM8UlEQVR4nO3df4xldX3G8edhF1wRq+Da1S7GdR0rrm5/0K1BbMxSiWmIESyS0hQraku3P8ZNmpqQtLpZY9La/lM6/bGhxkJDwhIUFQ3WgkJNVNBZyjLAgl4R090gLGBQuxVX+PSPc0Yvw87MHfbe88zcfb8SsnfunNzvZ07uvDlz7twzrioBALp3XHoAADhWEWAACCHAABBCgAEghAADQMjqpWy8du3a2rBhw4hGAYDxtGfPnkeq6sVz719SgDds2KDp6enhTQUAxwDb3znS/ZyCAIAQAgwAIQQYAEIIMACEEGAACCHAABBCgAEghAADQAgBBoAQAgwAIQQYAEIIMACEEGAACCHAABBCgAEghAADQAgBBoAQAgwAIQQYAEKW9DfhgPlMTU2p1+tFZzhw4IAkaf369Z2uOzExocnJyU7XxHggwBiKXq+nO+7apydPPCU2w6pDj0uSvvtEd0/rVYce62wtjB8CjKF58sRT9H+nnRNb/7n33iBJnc4wuybwbHAOGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCxjLAU1NTmpqaSo8BYATG6ft7dXqAUej1eukRAIzIOH1/j+URMACsBAQYAEIIMACEEGAACCHAABBCgAEghAADQAgBBoAQAgwAIQQYAEIIMACEEGAACCHAABBCgAEghAADQAgBBoAQAgwAIQQYAEIIMACEEGAACCHAABBCgAEghAADQAgBBoAQAgwAIQQYAEIIMACEEGAACCHAABBCgAEghAADQAgBBoAQAgwAIQQYAEIIMACEEGAACCHAABBCgAEgZHUXi+zcuVM333xzF0s9zeTkpKampjpfF8Do3HPPPTp8+LC2bt3a6brbtm3ThRdeONTH7OQIOBFfSZqZmYmsC2B0Dh8+HFl3165dQ3/MkQd4586do15iQZOTk9H1AQzPRRddFF1/9+7dQ328kZ+CSB39zpqZmdH27dujMxwLer2ejvtxpcfo3HE/+r56vR/wHOvI/v37o+vv2rVrqKchFj0Ctn2J7Wnb0wcPHhzawgBwrFv0CLiqLpd0uSRt2bJlRR7iXHbZZekRxt727du15/6H0mN07qk1P6eJjet4jnWk6xfeRm3k54DPOuusUS+xoM2bN0fXBzA8p556anT9bdu2DfXxRh7gHTt2jHqJBfFraMD4uOqqq6Lrr8hfQ0sdBXP0C4yf448/PrLusI9+pY7eiLFjx45Oj4RnX5HmvBwwfjZt2iRpPL6/eSsyAIQQYAAIIcAAEEKAASCEAANACAEGgBACDAAhBBgAQggwAIQQYAAIIcAAEEKAASCEAANACAEGgBACDAAhBBgAQggwAIQQYAAIIcAAEEKAASCEAANACAEGgBACDAAhBBgAQggwAIQQYAAIIcAAEEKAASCEAANACAEGgBACDAAhBBgAQggwAIQQYAAIIcAAEEKAASCEAANACAEGgJDV6QFGYWJiIj0CgBEZp+/vsQzw5ORkegQAIzJO39+cggCAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0AIAQaAEAIMACEEGABCCDAAhBBgAAghwAAQQoABIIQAA0DI6vQAGB+rDj2m5957Q3D9RyWp0xlWHXpM0rrO1sN4IcAYiomJifQIOnDgJ5Kk9eu7DOK6ZfG1Y2UiwBiKycnJ9AjAisM5YAAIIcAAEEKAASCEAANACAEGgBACDAAhBBgAQggwAIQQYAAIIcAAEEKAASCEAANACAEGgBACDAAhBBgAQggwAIQQYAAIIcAAEEKAASCEAANAiKtq8I3tg5K+M7pxnmGtpEc6XG+YmD2D2XNW8vyjnv3lVfXiuXcuKcBdsz1dVVvSczwbzJ7B7Dkref7U7JyCAIAQAgwAIcs9wJenBzgKzJ7B7Dkref7I7Mv6HDAAjLPlfgQMAGOLAANAyLIKsO0LbN9t+ynb8/5KiO0HbM/YvsP2dJczzmcJs/+W7fts92xf2uWM87F9iu0bbX+z/ffkebZ7st3nd9i+vus558yy4H60/Rzb17Sfv832hu6nPLIBZr/Y9sG+ff0HiTmPxPbHbD9s+655Pm/b/9B+bXfaPr3rGeczwOxbbT/et98/OPKhqmrZ/CfpNZJeLekWSVsW2O4BSWvT8y51dkmrJH1L0kZJJ0jaK2nTMpj9byVd2t6+VNJH5tnuh+lZB92Pkv5E0q729oWSrknPvYTZL5b0j+lZ55n/TZJOl3TXPJ8/R9LnJFnSGZJuS8+8hNm3SvpslzMtqyPgqtpXVfel53g2Bpz99ZJ6VXV/Vf1Y0m5J545+ukWdK+nK9vaVks4LzjKIQfZj/9f0cUlvtu0OZ5zPcn0ODKSqviTpsQU2OVfSv1fjVkkvtP3SbqZb2ACzd25ZBXgJStJ/2t5j+5L0MEuwXtL/9H28v70vbV1VPdje/q6kdfNst8b2tO1bbScjPch+/Ok2VfUTSY9LelEn0y1s0OfA+e2P8B+3/bJuRhuK5focH9QbbO+1/Tnbrx31YqtHvcBctm+S9JIjfOovq+rTAz7Mb1TVAds/L+lG2/e2/3cbqSHNHrHQ7P0fVFXZnu93E1/e7veNkr5oe6aqvjXsWaHPSLq6qp6w/UdqjuR/MzzTseB2Nc/xH9o+R9KnJL1qlAt2HuCqOnsIj3Gg/fdh259U82PdyAM8hNkPSOo/mjm1vW/kFprd9kO2X1pVD7Y/Lj48z2PM7vf7bd8i6VfVnM/s2iD7cXab/bZXS3qBpEe7GW9Bi85eVf1zflTNOfqVIvYcP1pV9f2+2zfY/mfba6tqZBfpWXGnIGw/z/bzZ29LeoukI76quQx9XdKrbL/C9glqXhyK/jZB63pJ72pvv0vSM47mbZ9s+znt7bWS3ijpns4mfLpB9mP/1/QOSV+s9pWWsEVnn3PO9G2S9nU439G6XtLvt78NcYakx/tOby1rtl8y+zqB7der6eNo/6edfmVyzquQb1dzzugJSQ9J+nx7/y9IuqG9vVHNK8d7Jd2t5sf/FTF7+/E5kr6h5shxucz+IklfkPRNSTdJOqW9f4ukj7a3z5Q00+73GUnvDc/8jP0o6UOS3tbeXiPpWkk9SV+TtDG9n5cw+1+3z+29km6WdFp65r7Zr5b0oKTD7fP9vZK2SdrWft6S/qn92ma0wG8zLcPZ/6xvv98q6cxRz8RbkQEgZMWdggCAcUGAASCEAANACAEGgBACDAAhBBgrhu1bFrrS3BIf6zzbm/o+/pDto36TELAUBBhjy/aqBT59nqSfBriqPlhVN41+KuBnCDCGzvan2gsl3T17saT2Gri3txc6+UJ730m2/83NtZ3vtH1+e/9bbH+13f5a2ycdYY0jbuPmWtEfsX27pAts/6Htr7frfsL2ibbPVPMOs79rr/v6SttX2H5H+xhvtv3f7Vwf63sH4AO2d7Zrztg+rZMdirFFgDEK76mqX1PzTrr32V4n6V8lnV9Vvyzpgna7D6h5q+rmqvolNRf4WSvprySdXVWnS5qW9Of9Dz7ANo9W1elVtVvSdVX16+26+9S8g+8rat4y+/6q+pXqu6CQ7TWSrpD0O1W1Wc31Uv6477Efadf8F0l/cbQ7Cse2zi/Gg2PC+2y/vb39MkmXSPpSVX1bkqpq9pqsZ6u5FoLa+79n+61qTg18uX1b/gmSvjrn8c9YZJtr+m6/zvaHJb1Q0kmSPr/I7K+W9O2q+kb78ZWS/lTS37cfX9f+u0fSby/yWMCCCDCGyvZWNWF9Q1Udaq+adoekQX9ct6Qbq+p3j2Kb/+27fYWk86pqr+2L1fzVg6PxRPvvk+L7B0eJUxAYthdI+l4b39PUHK2ukfQm26+Qmr9B1257o5qjS7X3n6zmIihvtD3R3vc82784Z41Btpn1fEkP2j5e0u/13f+D9nNz3Sdpw+xjS3qnpP8a4OsGlowAY9j+Q9Jq2/sk/Y2aWB5UcxriOtt79bNTBB+WdLLtu9r7z6qqg2r+JtrVtu9Uc2rhaUfPg2zT5wOSbpP0ZUn39t2/W9L72xfbXtn32D+S9G5J19qekfSUpF3PZkcAi+FqaAAQwhEwAIQQYAAIIcAAEEKAASCEAANACAEGgBACDAAh/w8KSKs9uRb5kgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4- subway**"
      ],
      "metadata": {
        "id": "UBLjYhHmfBrj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4-A) box plot before omit outliers**"
      ],
      "metadata": {
        "id": "lin0lKkufO74"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_subway['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "emwo25FogLOc",
        "outputId": "678c7663-5c02-42d9-8f1d-aa20e3de5e82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4576e6c90>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKSUlEQVR4nO3df4xlZ13H8c+3u6hbi5GmZYERWXCaaBNl0RU1aRASYoBoUEOKGhOCifgHTrbGBBv/gRCNRsOPOiHGqqXEaK1GK4TUH6QS4Q+D7kq1xRa8ISU49MfWEulmVxvaxz/uWTJut+3uMvd+p3Nfr2Qz956ZO89zNmfeOfPce8/UGCMALN8l3RMAWFUCDNBEgAGaCDBAEwEGaLL/Qr74iiuuGIcOHVrQVAD2puPHjz88xrjy7O0XFOBDhw7l2LFjOzcrgBVQVV8413ZLEABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzS5oL8Jt2ybm5uZzWZLHXNraytJsra2ttRx4Xytr69nY2OjexrsgF0d4NlsljvvviePX3r50sbcd+q/kyQP/O+u/q9hRe079Uj3FNhBu74yj196eU5/5xuWNt6Be29PkqWOCefrzPHJ3mANGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigyVICvLm5mc3NzWUMBbCjFtmv/Qv5rmeZzWbLGAZgxy2yX5YgAJoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGa7F/GIFtbWzl9+nSOHj16QY+bzWa55LGxoFnBs88l//OVzGaPXvDPEhdvNpvlwIEDC/nez3gGXFVvq6pjVXXsxIkTC5kEwCp6xjPgMcaNSW5MkiNHjlzU6eja2lqS5IYbbrigxx09ejTHP//gxQwJe9IT3/QtWX/ZwQv+WeLiLfK3DWvAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigyf5lDLK+vr6MYQB23CL7tZQAb2xsLGMYgB23yH5ZggBoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAk/3dE3gm+049kgP33r7E8f4rSZY6JpyvfaceSXKwexrskF0d4PX19aWPubX11STJ2pqDnN3oYMvPBYuxqwO8sbHRPQWAhbEGDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmhSY4zz/+KqE0m+cJFjXZHk4Yt87F5g/+2//V9dLxljXHn2xgsK8Nejqo6NMY4sZbBdyP7bf/u/uvv/VCxBADQRYIAmywzwjUscazey/6vN/vMkS1sDBuD/swQB0ESAAZosPMBV9bqq+mxVzarq+kWPtxtV1X1VdVdV3VlVx7rns2hVdVNVPVRVd2/bdnlVfayq/mP6+LzOOS7SU+z/u6pqazoG7qyqN3TOcZGq6sVV9fGq+veq+kxVHZ22r8wxcL4WGuCq2pfkA0len+TqJD9dVVcvcsxd7DVjjMMr8lrIm5O87qxt1ye5Y4xxVZI7pvt71c158v4nyfumY+DwGOP2Jc9pmb6a5JfHGFcn+cEkb59+7lfpGDgviz4DfmWS2Rjj82OMx5L8aZI3LnhMmo0xPpHkkbM2vzHJh6bbH0ry40ud1BI9xf6vjDHG/WOMf5luP5rkniRrWaFj4HwtOsBrSb647f5/TttWzUjyd1V1vKre1j2ZJgfHGPdPtx9IcrBzMk1+sar+bVqiWIlfv6vqUJJXJPlUHANP4km45bhmjPG9mS/FvL2qXtU9oU5j/trHVXv94+8m+Y4kh5Pcn+Q9vdNZvKq6LMlfJLlujPGV7Z9b0WPgSRYd4K0kL952/9umbStljLE1fXwoyW2ZL82smger6oVJMn18qHk+SzXGeHCM8fgY44kkv589fgxU1XMyj+8fjzH+ctq80sfAuSw6wP+c5KqqemlVfUOSn0rykQWPuatU1TdX1XPP3E7yI0nufvpH7UkfSfKW6fZbkny4cS5LdyY8k5/IHj4GqqqS/GGSe8YY7932qZU+Bs5l4e+Em15u8/4k+5LcNMb49YUOuMtU1csyP+tNkv1J/mSv/x9U1S1JXp35JQgfTPLOJH+V5M+SfHvmlzS9doyxJ5+oeor9f3Xmyw8jyX1JfmHbeuieUlXXJPlkkruSPDFt/tXM14FX4hg4X96KDNDEk3AATQQYoIkAAzQRYIAmAgzQRIB51qiqkxf5uHdX1Wun29dV1aU7OzO4OF6GxrNGVZ0cY1z2dX6P+5IcGWOs8p9IZ5fY3z0BVldV/WaSL44xPjDdf1eSk0kqybVJvjHJbWOMd571uEryW5lfW2Mk+bUxxq3T534lyc9m/gaAvx5jXF9VNyf5aJIXTf8+XlUPJ/mjJN8zxrhueuzPJ7l6jPFLi9xvOMMSBJ1uzTy0Z1yb5ESSqzK/VsLhJN93josX/eT0uZcneW2S366qF1bV6zO/5OEPjDFennmkv2aM8TtJvpT5tZlfk/m7sn5sum5Bkrw1yU07uH/wtJwB02aM8emqen5VvSjJlUm+nOS7M79exqenL7ss8yB/YttDr0lyyxjj8cwv8PIPSb4/yQ8n+eAY49T0/Z/2ba5jjJNV9fdJfrSq7knynDHGXTu3h/D0BJhuf57kTUlekPkZ8UuS/MYY4/eWNP4fZH6dgnuTfHBJY0ISSxD0uzXzq+S9KfMY/22Sn5uuJZuqWquq55/1mE8meXNV7auqK5O8Ksk/JflYkreeeZVDVV1+jvEeTfLcM3fGGJ/K/JKpP5Pklp3cMXgmzoBpNcb4zHS5zq3p6mD3V9V3JfnH+XNtOZn5k2rbrx17W5IfSvKvmT8J944xxgNJ/qaqDic5VlWPJbk987Pb7W6cvu5L0zpwMl8LPjzG+PJi9hLOzcvQWHlV9dHM/2DmHd1zYbVYgmBlVdW3VtXnkpwWXzo4AwZo4gwYoIkAAzQRYIAmAgzQRIABmvwffDcqpu2nKSYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acceleration\n",
        "\n",
        "sns.boxplot(df_subway['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "eq1exqQkgL7c",
        "outputId": "478c48eb-5a9b-46ef-a25f-bbcc6e65553f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438dd64d0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4-B) IQR**"
      ],
      "metadata": {
        "id": "_XnRpgcBfjV-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_subway = IQRmethod(df_subway,'velocity')\n",
        "df_subway = IQRmethod(df_subway,'acceleration')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gdvhx2hCi15i",
        "outputId": "a78d741f-6281-4f98-c253-615f0a28e5fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 575 records\n",
            "your dataframe has 575 records after omit upper baound for velocity\n",
            "your dataframe has 575 records\n",
            "your dataframe has 530 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4-C) z-score**"
      ],
      "metadata": {
        "id": "BSoFjviEfv3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_subway = ZS(df_subway,'velocity',3)\n",
        "df_subway = ZS(df_subway,'acceleration',3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9vE_OqZjWiR",
        "outputId": "236709d4-c891-4841-dc54-93cbe3f04d41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 530 records\n",
            "your dataframe has 530 records after omit upper baound for velocity\n",
            "your dataframe has 530 records\n",
            "your dataframe has 530 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4-D) box plot after omit outliers**"
      ],
      "metadata": {
        "id": "vlLtaJwOf3nq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_subway['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "F376Z0BIl5v-",
        "outputId": "f7e41d87-403f-44ed-c187-06c2a175a078"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438d25990>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acceleration\n",
        "\n",
        "sns.boxplot(df_subway['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "baiJR5eMl5v-",
        "outputId": "974bea39-5d4d-430f-d8f6-5ac4b938a752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438c79f50>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5- train**"
      ],
      "metadata": {
        "id": "fuytFlpFfKYY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5-A) box plot before omit outliers**"
      ],
      "metadata": {
        "id": "QPMVtGzCfSlc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_train['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "VGYtopRlgXRJ",
        "outputId": "f59a7d6c-52f1-4f79-cc45-7501d841d28c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438ba2c10>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acceleration\n",
        "\n",
        "sns.boxplot(df_train['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "6eCe15-hgXDL",
        "outputId": "2f11ffd8-38e3-4cf3-f8b8-2b295f555682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438b1c350>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5-B) IQR**"
      ],
      "metadata": {
        "id": "Ry9g-EhEfm3f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = IQRmethod(df_train,'velocity')\n",
        "df_train = IQRmethod(df_train,'acceleration')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqHUjecRi8Fv",
        "outputId": "0e9a45be-b93c-447d-9856-4c14ab7bbdec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 4065 records\n",
            "your dataframe has 4064 records after omit upper baound for velocity\n",
            "your dataframe has 4064 records\n",
            "your dataframe has 3794 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5-C) z-score**"
      ],
      "metadata": {
        "id": "-dI5LEPyfzcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = ZS(df_train,'velocity',3)\n",
        "df_train = ZS(df_train,'acceleration',3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sr4BRKvOjfA9",
        "outputId": "054e1e44-61c3-4dd6-911d-1de1e8a0eda8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 3794 records\n",
            "your dataframe has 3794 records after omit upper baound for velocity\n",
            "your dataframe has 3794 records\n",
            "your dataframe has 3794 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5-D) box plot after omit outliers**"
      ],
      "metadata": {
        "id": "7xqfcGtOf-KD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_train['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "cs4ktDvGmFT9",
        "outputId": "ea4dd217-1aed-4314-bdb4-2091031e360b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438a8d150>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acceleration\n",
        "\n",
        "sns.boxplot(df_train['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "fu2EAZhamFT9",
        "outputId": "6268be55-63e3-4fac-e407-f4a785aa7d2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438a11110>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANnklEQVR4nO3df2ycB33H8c8ncRLHDSttgjLmJTjBYRVa9qPNUAFpohCtIVqTQqk2hLZ069YRTSYNaiqklElFqRijahqsCtRVNN4/bVVWQZCioibAKgFlxKwhZWnJtQkdVimpgwqqY2epv/xxj8MlteOzfXdf++79kiyfn3t8931853cfP7174ogQAKDx5mUPAACtigADQBICDABJCDAAJCHAAJCkbSorL1u2LLq6uuo0CgA0p/7+/lci4i0XLp9SgLu6unTo0KHaTQUALcD2T8dbziEIAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJFP6N+GASr29vSqVStljSJIGBgYkSZ2dncmTSN3d3erp6ckeA3MAAca0lUolPf3MUb3ecXn2KJo/9Kok6ecjuU/p+UOnUu8fcwsBxoy83nG5Tl+xMXsMLX52vySlzzI2B1ANjgEDQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAklkb4N7eXvX29maPAaCJzLautGUPMJFSqZQ9AoAmM9u6Mmv3gAGg2RFgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEjS1og7GRwc1G233aYTJ05owYIFsi3b2rp1q+69915FRHmYtjYtWLBAW7du1eHDh7V69epGjAeghQwNDWnDhg2SpBUrVui6667TPffcI9vavn277rvvPo2MjKizs1OnTp3S7bffrrvvvlt79uxRd3d3TWdpyB5wX1+fjh8/rojQmTNnNDIyouHh4fPiK0lnz57V6dOntXv3bknSiRMnGjEegBby4osvanh4WMPDwzp27Ni53kSEdu/erZGREUnSwMCATp8+rbvuukuvvfaadu3aVfNZ6h7gwcFB7d+/f9zrKuM73vLR0VH19/fXbTYArWVoaOhcYMdUdmi8Jp09e1ZSeYewVCrVdJ66H4Lo6+s7twHTsWPHDq1du7aGE6FWSqWS5p0Z/z+irWre8K9UKv1a27Ztyx4F45hpQHft2qW9e/fWZhhVsQds+xbbh2wfOnny5JTv4MCBA9MabMzo6OiMvh8Axkz0V3e1an1YdNI94Ii4X9L9krRu3bopT79+/Xrt27dvGqOVLVmyRHv27Jn296N+tm3bpv4XXs4eY1YZbf8dda9eznN2lrr22mvfcAhiKrq6umo3jBpwDHjLli1qa5v+kY4777yzhtMAaGUrV66c0fffcccdNZqkrO4BXrp0qTZu3DjudbYvunzevHm66qqr6jYbgNbS0dGhRYsWnbesskPjNWlsB7Krq2tuvgxty5YtWrVqlWxr4cKFWrRokdrb23Xrrbeet8FtbW1avHixtm/fLqn2u/sAsHLlSrW3t6u9vV1r1qw515ux1wGPBbqzs1OLFy/Wzp07dckll9R871dq0Bsxli5dqgcffHDc6zZv3jzu8oMHD9ZzJAAtqqOjQ48//vh5yzZt2jTu5THXXHNNXWbhrcgAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0CStuwBJtLd3Z09AoAmM9u6MmsD3NPTkz0CgCYz27rCIQgASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJK0ZQ+AuW3+0CktfnZ/9hiaPzQoSemzzB86JWl56gyYOwgwpq27uzt7hHMGBs5Kkjo7s+O3fFb9XDC7EWBMW09PT/YIwJzGMWAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkjgiql/ZPinpp/Ubp6GWSXole4gGaZVtZTubT7Ns69si4i0XLpxSgJuJ7UMRsS57jkZolW1lO5tPs28rhyAAIAkBBoAkrRzg+7MHaKBW2Va2s/k09ba27DFgAMjWynvAAJCKAANAkpYJsO0bbf/Y9qjtCV/WYnuD7edsl2x/qpEz1orty20/YftY8fmyCdZ73fbTxce+Rs85XZM9RrYX2X6kuP77trsaP+XMVbGdN9k+WfEY/kPGnDNl+8u2f2H7mQmut+0vFD+HH9m+stEz1kvLBFjSM5I+LOnJiVawPV/SfZI+KOmdkj5q+52NGa+mPiXpYESskXSw+Ho8pyPiT4qPTY0bb/qqfIxulvTLiOiWtFvS5xo75cxN4bn4SMVj+EBDh6ydvZI2XOT6D0paU3zcIumLDZipIVomwBFxNCKem2S1d0kqRcQLEXFG0sOSNtd/uprbLKmvuNwn6frEWWqtmseocvu/IukDtt3AGWuhWZ6Lk4qIJyWdusgqmyX9R5Q9JenNtt/amOnqq2UCXKVOSf9X8fXPimVzzfKIeKm4/HNJyydYr932IdtP2Z4rka7mMTq3TkSclfSqpKUNma52qn0u3lD8Wf4V2ysaM1rDNcvv5Ru0ZQ9QS7YPSPrdca7aGRFfa/Q89XSxba38IiLC9kSvNXxbRAzYXi3pm7aPRMTztZ4VdfN1SQ9FxIjtf1J5r//9yTNhCpoqwBGxfoY3MSCpci/i94tls87FttX2y7bfGhEvFX+q/WKC2xgoPr9g+9uS/lTSbA9wNY/R2Do/s90m6VJJg40Zr2Ym3c6IqNymByT9WwPmyjBnfi+nikMQ5/uBpDW2V9leKOmvJc2ZVwdU2CdpS3F5i6Q37P3bvsz2ouLyMknvlfS/DZtw+qp5jCq3/yOSvhlz7x1Hk27nBcdBN0k62sD5GmmfpL8tXg1xtaRXKw6xzW0R0RIfkj6k8rGjEUkvS/pGsfz3JO2vWG+jpJ+ovCe4M3vuaW7rUpVf/XBM0gFJlxfL10l6oLj8HklHJB0uPt+cPfcUtu8Nj5Gkz0jaVFxul/SopJKk/5a0OnvmOm3nZyX9uHgMvyXpiuyZp7mdD0l6SdL/F7+jN0v6uKSPF9db5VeEPF88V9dlz1yrD96KDABJOAQBAEkIMAAkIcAAkIQAA0ASAgwASQgw5gzb377YmeymeFvXV57cxvZnbM/0jTzAlBBgNK3ijGITuV7ls4xJkiLiXyLiQP2nAn6LAKPmbH/Vdn9x/uVbimUbbP/Q9mHbB4tlS2w/aPtIcUKZG4rlf2H7e8X6j9peMs59jLuO7RO2P2f7h5JutP2Ptn9Q3O9/2u6w/R6V3zn2+eI8um+3vdf2R4rb+IDt/ynm+nLFOwZP2L6zuM8jtq9oyA8UTYsAox7+PiKuUvmdd5+wvVzSv0u6ISL+WNKNxXqfVvltpWsj4o9UPiHQMkl3SFofEVdKOiTpk5U3XsU6gxFxZUQ8LOmxiPiz4n6PqvyOv++q/PbWHVE+j+7zFbfdrvL5af8qItaqfL6UrRW3/Upxn1+UdNtMf1BobU11Mh7MGp+w/aHi8gqVT6L9ZEQcl6SIGDv363qVz3GgYvkvbf+lyocGvlOcwnehpO9dcPtXT7LOIxWX/9D2LklvlrRE0jcmmf0PJB2PiJ8UX/dJ+mdJ9xZfP1Z87lf5BP/AtBFg1JTt96kc1ndHxFBxlrWnJVX757olPRERH53BOq9VXN4r6fqIOGz7Jknvq3KOiYwUn18Xvz+YIQ5BoNYuVfmfAxoqjpFerfLJcf7c9iqp/G/WFes+ofLepYrll0l6StJ7bXcXyy6x/Y4L7qOadca8SdJLthdI+ljF8l8X113oOUldY7ct6W8k/VcV2w1MGQFGrT0uqc32UUn/qnIsT6p8GOIx24f120MEuyRdZvuZYvk1EXFS0k2SHrL9I5UPLZy391zNOhU+Len7kr4j6dmK5Q9L2lH8z7a3V9z2sKS/k/So7SOSRiV9aTo/CGAynA0NAJKwBwwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJDkNwBepfwoVxEAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6- car**"
      ],
      "metadata": {
        "id": "roCw3ATchsva"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6-A) box plot before omit outliers**"
      ],
      "metadata": {
        "id": "3qjr57CRiFtC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_car['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "djZ47Y4ciemp",
        "outputId": "5ae8c80a-e9bc-40b6-9278-d6bdd15dd3fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc43896e050>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acceleration\n",
        "\n",
        "sns.boxplot(df_car['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "XYdGCiHxigAu",
        "outputId": "21c94490-598b-454f-b04f-ec8c489ac96d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438961050>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6-B) IQR**"
      ],
      "metadata": {
        "id": "EwxcznK6iThh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_car = IQRmethod(df_car,'velocity')\n",
        "df_car = IQRmethod(df_car,'acceleration')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfy_TUf7jIp9",
        "outputId": "89610b47-cd9e-40da-fbb1-16cd65a8cf8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 4211 records\n",
            "your dataframe has 4202 records after omit upper baound for velocity\n",
            "your dataframe has 4202 records\n",
            "your dataframe has 3961 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6-C) z-score**"
      ],
      "metadata": {
        "id": "sXSqgSEBiWoQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_car = ZS(df_car,'velocity',3)\n",
        "df_car = ZS(df_car,'acceleration',3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ir_CgKWCjlgp",
        "outputId": "b675fcd7-6bce-4f75-fcac-d7c54027011b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your dataframe has 3961 records\n",
            "your dataframe has 3961 records after omit upper baound for velocity\n",
            "your dataframe has 3961 records\n",
            "your dataframe has 3961 records after omit upper baound for acceleration\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6-D) box plot after omit outliers**"
      ],
      "metadata": {
        "id": "wwANCO1fiZCc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# velocity\n",
        "\n",
        "sns.boxplot(df_car['velocity'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "p1_6naCOmLXJ",
        "outputId": "c144eca9-5c53-4fd6-a99a-4bd9254eb52b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4388ce250>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acceleration\n",
        "\n",
        "sns.boxplot(df_car['acceleration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "dou1xb3pmLXK",
        "outputId": "5b8fcd54-37b8-481f-9e45-07865dc505f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc438836850>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualization after removing outliers**"
      ],
      "metadata": {
        "id": "htthdZpby01j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Min, Max, Mean and standard deviation for velocity and acceleration in each class**"
      ],
      "metadata": {
        "id": "HVTqyxEtzMYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "walk_velocity_max = df_walk.velocity.max()\n",
        "walk_acceleration_max = df_walk.acceleration.max()\n",
        "walk_acceleration_min = df_walk.acceleration.min()\n",
        "walk_acceleration_mean = df_walk.acceleration.mean()\n",
        "walk_velocity_mean = df_walk.velocity.mean()\n",
        "walk_acceleration_std = df_walk.acceleration.std()\n",
        "walk_velocity_std = df_walk.velocity.std()\n",
        "\n",
        "bike_velocity_max = df_bike.velocity.max()\n",
        "bike_acceleration_max = df_bike.acceleration.max()\n",
        "bike_acceleration_min = df_bike.acceleration.min()\n",
        "bike_acceleration_mean = df_bike.acceleration.mean()\n",
        "bike_velocity_mean = df_bike.velocity.mean()\n",
        "bike_acceleration_std = df_bike.acceleration.std()\n",
        "bike_velocity_std = df_bike.velocity.std()\n",
        "\n",
        "bus_velocity_max = df_bus.velocity.max()\n",
        "bus_acceleration_max = df_bus.acceleration.max()\n",
        "bus_acceleration_min = df_bus.acceleration.min()\n",
        "bus_acceleration_mean = df_bus.acceleration.mean()\n",
        "bus_velocity_mean = df_bus.velocity.mean()\n",
        "bus_acceleration_std = df_bus.acceleration.std()\n",
        "bus_velocity_std = df_bus.velocity.std()\n",
        "\n",
        "car_velocity_max = df_car.velocity.max()\n",
        "car_acceleration_max = df_car.acceleration.max()\n",
        "car_acceleration_min = df_car.acceleration.min()\n",
        "car_acceleration_mean = df_car.acceleration.mean()\n",
        "car_velocity_mean = df_car.velocity.mean()\n",
        "car_acceleration_std = df_car.acceleration.std()\n",
        "car_velocity_std = df_car.velocity.std()\n",
        "\n",
        "train_velocity_max = df_train.velocity.max()\n",
        "train_acceleration_max = df_train.acceleration.max()\n",
        "train_acceleration_min = df_train.acceleration.min()\n",
        "train_acceleration_mean = df_train.acceleration.mean()\n",
        "train_velocity_mean = df_train.velocity.mean()\n",
        "train_acceleration_std = df_train.acceleration.std()\n",
        "train_velocity_std = df_train.velocity.std()\n",
        "\n",
        "subway_velocity_max = df_subway.velocity.max()\n",
        "subway_acceleration_max = df_subway.acceleration.max()\n",
        "subway_acceleration_min = df_subway.acceleration.min()\n",
        "subway_acceleration_mean = df_subway.acceleration.mean()\n",
        "subway_velocity_mean = df_subway.velocity.mean()\n",
        "subway_acceleration_std = df_subway.acceleration.std()\n",
        "subway_velocity_std = df_subway.velocity.std()\n",
        "\n",
        "print('max velocity in walk dataframe :' , walk_velocity_max , '\\n' , 'max acceleration in walk dataframe :' , walk_acceleration_max , '\\n' ,\n",
        "       'min acceleration in walk dataframe :' , walk_acceleration_min)\n",
        "print('max velocity in bike dataframe :' , bike_velocity_max , '\\n' , 'max acceleration in bike dataframe :' , bike_acceleration_max , '\\n' ,\n",
        "       'min acceleration in walk dataframe :' , bike_acceleration_min)\n",
        "print('max velocity in bus dataframe :' , bus_velocity_max , '\\n' , 'max acceleration in bus dataframe :' , bus_acceleration_max , '\\n' ,\n",
        "       'min acceleration in walk dataframe :' , bus_acceleration_min)\n",
        "print('max velocity in car dataframe :' , car_velocity_max , '\\n' , 'max acceleration in car dataframe :' , car_acceleration_max , '\\n' ,\n",
        "       'min acceleration in walk dataframe :' , car_acceleration_min)\n",
        "print('max velocity in train dataframe :' , train_velocity_max , '\\n' , 'max acceleration in train dataframe :' , train_acceleration_max , '\\n' ,\n",
        "       'min acceleration in walk dataframe :' , train_acceleration_min)\n",
        "print('max velocity in subway dataframe :' , subway_velocity_max , '\\n' , 'max acceleration in subway dataframe :' , subway_acceleration_max , '\\n' ,\n",
        "       'min acceleration in walk dataframe :' , subway_acceleration_min)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RshKb8KASUpf",
        "outputId": "be58aefb-2b11-4b20-a86c-8969c1da4818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max velocity in walk dataframe : 3.438817456063122 \n",
            " max acceleration in walk dataframe : 1.1715463050514603 \n",
            " min acceleration in walk dataframe : -1.1328358089923452\n",
            "max velocity in bike dataframe : 6.775867444683232 \n",
            " max acceleration in bike dataframe : 1.90441560596544 \n",
            " min acceleration in walk dataframe : -1.8709322999078557\n",
            "max velocity in bus dataframe : 16.477411372594517 \n",
            " max acceleration in bus dataframe : 1.589891706813752 \n",
            " min acceleration in walk dataframe : -1.636691798496014\n",
            "max velocity in car dataframe : 29.651607230964803 \n",
            " max acceleration in car dataframe : 1.8583085837877515 \n",
            " min acceleration in walk dataframe : -1.9202864581849648\n",
            "max velocity in train dataframe : 36.70827801315609 \n",
            " max acceleration in train dataframe : 1.295547026663865 \n",
            " min acceleration in walk dataframe : -1.311856733868959\n",
            "max velocity in subway dataframe : 22.015673274119678 \n",
            " max acceleration in subway dataframe : 1.4504555791723297 \n",
            " min acceleration in walk dataframe : -1.5073652548512868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Velocity**"
      ],
      "metadata": {
        "id": "9L87xO4g0KQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "class_list = [\"walk\", \"bike\", \"bus\", \"car\", \"train\", \"subway\"]\n",
        "velocity_max_list = [walk_velocity_max, bike_velocity_max, bus_velocity_max, car_velocity_max, train_velocity_max, subway_velocity_max]\n",
        "velocity_mean_list = [walk_velocity_mean, bike_velocity_mean, bus_velocity_mean, car_velocity_mean, train_velocity_mean, subway_velocity_mean]\n",
        "velocity_std_list = [walk_velocity_std, bike_velocity_std, bus_velocity_std, car_velocity_std, train_velocity_std, subway_velocity_std]\n",
        "\n",
        "x = np.array(class_list)\n",
        "y = np.array(velocity_max_list)\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.title('Max velocity')\n",
        "plt.barh(x,y, color=\"hotpink\")\n",
        "\n",
        "x = np.array(class_list)\n",
        "y = np.array(velocity_mean_list)\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.title('Mean velocity')\n",
        "plt.barh(x,y)\n",
        "\n",
        "x = np.array(class_list)\n",
        "y = np.array(velocity_std_list)\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.title('std velocity')\n",
        "plt.barh(x,y)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "IwwCxzAr8BOd",
        "outputId": "51ef1d10-a1cc-49da-9034-f74add1858da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Acceleration**"
      ],
      "metadata": {
        "id": "El9e2W5T0cv5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "acceleration_max_list = [walk_acceleration_max, bike_acceleration_max, bus_acceleration_max, car_acceleration_max, train_acceleration_max, subway_acceleration_max]\n",
        "acceleration_min_list = [walk_acceleration_min, bike_acceleration_min, bus_acceleration_min, car_acceleration_min, train_acceleration_min, subway_acceleration_min]\n",
        "acceleration_mean_list = [walk_acceleration_mean, bike_acceleration_mean, bus_acceleration_mean, car_acceleration_mean, train_acceleration_mean, subway_acceleration_mean]\n",
        "acceleration_std_list = [walk_acceleration_std, bike_acceleration_std, bus_acceleration_std, car_acceleration_std, train_acceleration_std, subway_acceleration_std]\n",
        "\n",
        "x = np.array(class_list)\n",
        "y = np.array(acceleration_max_list)\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.barh(x,y, color ='green')\n",
        "\n",
        "x = np.array(class_list)\n",
        "y = np.array(acceleration_min_list)\n",
        "plt.plot()\n",
        "plt.barh(x,y)\n",
        "plt.title(\"Max & Min Acceleration\")\n",
        "\n",
        "x = np.array(class_list)\n",
        "y = np.array(acceleration_mean_list)\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.barh(x,y, color ='purple')\n",
        "plt.title('Mean Acceleration')\n",
        "\n",
        "x = np.array(class_list)\n",
        "y = np.array(acceleration_std_list)\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.barh(x,y, color ='gray')\n",
        "plt.title('std Acceleration')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "aCGIG_pBDtUX",
        "outputId": "5fd904df-0e7e-4373-c707-4084977bb1fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Merging all of dataframes after removing outliers**"
      ],
      "metadata": {
        "id": "keczFPCJP386"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import reduce\n",
        "\n",
        "data_frames = [df_walk, df_bike, df_train, df_subway, df_bus, df_car]\n",
        "\n",
        "df_merged = pd.concat([df_walk, df_bike, df_train, df_subway, df_bus, df_car])"
      ],
      "metadata": {
        "id": "kTyEvuQJtxJ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "after_outlier = len(df_merged.index.unique())\n",
        "print('Our dataframe decrease to' , after_outlier, 'from', before_outlier)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dL0SrpKuSybO",
        "outputId": "2ae24488-bb8b-420a-87f1-6fc436313518"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Our dataframe decrease to 21258 from 21991\n"
          ]
        }
      ]
    }
  ]
}