Human_Activity_Recognition_using_Smartphone_Data_with_Machine_Learning.ipynb

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      "source": [
        "#####\n",
        "#####\n",
        "#####   @author BERKAY SULEYMAN DIK -> berkod-ai.com\n",
        "#####\n",
        "#####"
      ],
      "metadata": {
        "id": "PcGzyhZgr_f5"
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      "metadata": {
        "id": "fH9EF50WmOBB"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reading the data"
      ],
      "metadata": {
        "id": "MybjjDEhopho"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"train-1.csv\")\n",
        "test = pd.read_csv(\"test.csv\")"
      ],
      "metadata": {
        "id": "HSLu2k2vmnvD"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####To Combine both the  data frames"
      ],
      "metadata": {
        "id": "SLFAGBuSow2I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Data'] = 'Train'\n",
        "test['Data'] = 'Test'\n",
        "both = pd.concat([train, test], axis=0).reset_index(drop=True)\n",
        "both['subject'] = '#' + both['subject'].astype(str)"
      ],
      "metadata": {
        "id": "pgkEIjLjmqwA"
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      "execution_count": 4,
      "outputs": []
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        "train.shape, test.shape"
      ],
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      "source": [
        "both.head()"
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              "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
              "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
              "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
              "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
              "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
              "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
              "\n",
              "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
              "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
              "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
              "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
              "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
              "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
              "\n",
              "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  angle(tBodyAccMean,gravity)  \\\n",
              "0         -0.923527         -0.934724  ...                    -0.112754   \n",
              "1         -0.957686         -0.943068  ...                     0.053477   \n",
              "2         -0.977469         -0.938692  ...                    -0.118559   \n",
              "3         -0.989302         -0.938692  ...                    -0.036788   \n",
              "4         -0.990441         -0.942469  ...                     0.123320   \n",
              "\n",
              "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
              "0                              0.030400                         -0.464761   \n",
              "1                             -0.007435                         -0.732626   \n",
              "2                              0.177899                          0.100699   \n",
              "3                             -0.012892                          0.640011   \n",
              "4                              0.122542                          0.693578   \n",
              "\n",
              "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
              "0                             -0.018446             -0.841247   \n",
              "1                              0.703511             -0.844788   \n",
              "2                              0.808529             -0.848933   \n",
              "3                             -0.485366             -0.848649   \n",
              "4                             -0.615971             -0.847865   \n",
              "\n",
              "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity   Data  \n",
              "0              0.179941             -0.058627       #1  STANDING  Train  \n",
              "1              0.180289             -0.054317       #1  STANDING  Train  \n",
              "2              0.180637             -0.049118       #1  STANDING  Train  \n",
              "3              0.181935             -0.047663       #1  STANDING  Train  \n",
              "4              0.185151             -0.043892       #1  STANDING  Train  \n",
              "\n",
              "[5 rows x 564 columns]"
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              "      <th>tBodyAcc-mean()-X</th>\n",
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              "float64    561\n",
              "object       3\n",
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        "def basic_details(df):\n",
        "    b = pd.DataFrame()\n",
        "    b['Missing value'] = df.isnull().sum()\n",
        "    b['N unique value'] = df.nunique()\n",
        "    b['dtype'] = df.dtypes\n",
        "    return b\n",
        "basic_details(both)"
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              "                      Missing value  N unique value    dtype\n",
              "tBodyAcc-mean()-X                 0           10292  float64\n",
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              "tBodyAcc-std()-Y                  0           10297  float64\n",
              "...                             ...             ...      ...\n",
              "angle(Y,gravityMean)              0           10299  float64\n",
              "angle(Z,gravityMean)              0           10299  float64\n",
              "subject                           0              30   object\n",
              "Activity                          0               6   object\n",
              "Data                              0               2   object\n",
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              "    <tr>\n",
              "      <th>tBodyAcc-std()-X</th>\n",
              "      <td>0</td>\n",
              "      <td>10295</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tBodyAcc-std()-Y</th>\n",
              "      <td>0</td>\n",
              "      <td>10297</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>angle(Y,gravityMean)</th>\n",
              "      <td>0</td>\n",
              "      <td>10299</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>angle(Z,gravityMean)</th>\n",
              "      <td>0</td>\n",
              "      <td>10299</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subject</th>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Activity</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>564 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f874e7b5-13d7-4046-a22e-2b9a77bd2485')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f874e7b5-13d7-4046-a22e-2b9a77bd2485 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f874e7b5-13d7-4046-a22e-2b9a77bd2485');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "activity = both['Activity']\n",
        "label_counts = activity.value_counts()\n",
        "\n",
        "plt.figure(figsize= (12, 8))\n",
        "plt.bar(label_counts.index, label_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "oPxdHMkcmqlZ",
        "outputId": "150b6cb4-54b8-455a-a9cd-fddaa8c2ab17"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 6 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data = both['Data']\n",
        "Subject = both['subject']\n",
        "train = both.copy()\n",
        "train = train.drop(['Data','subject','Activity'], axis =1)"
      ],
      "metadata": {
        "id": "Actn409_oc7S"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## To Scale the data"
      ],
      "metadata": {
        "id": "u2DphXoIo3Uv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Standard Scaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "slc = StandardScaler()\n",
        "train = slc.fit_transform(train)\n",
        "\n",
        "# dimensionality reduction\n",
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=0.9, random_state=0)\n",
        "train = pca.fit_transform(train)"
      ],
      "metadata": {
        "id": "uUYETxmVoc3Q"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the  data into training and testing\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(train, activity, test_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "K2Xetjh5oc1a"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Test options and evaluation metric\n",
        "num_folds = 10\n",
        "seed = 0\n",
        "scoring = 'accuracy'\n",
        "results = {}\n",
        "accuracy = {}"
      ],
      "metadata": {
        "id": "jx2FcGnKoczk"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Activity Recognition Algorithm"
      ],
      "metadata": {
        "id": "hzDvr9VWrdcS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finalizing the model and comparing the test, predict results\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "model = KNeighborsClassifier(algorithm= 'auto', n_neighbors= 8, p= 1, weights= 'distance')\n",
        "\n",
        "_ = cross_val_score(model, X_train, y_train, cv=10, scoring=scoring)\n",
        "results[\"GScv\"] = (_.mean(), _.std())\n",
        "\n",
        "model.fit(X_train, y_train) \n",
        "y_predict = model.predict(X_test)\n",
        "\n",
        "accuracy[\"GScv\"] = accuracy_score(y_test, y_predict)\n",
        "\n",
        "print(classification_report(y_test, y_predict))\n",
        "\n",
        "cm= confusion_matrix(y_test, y_predict)\n",
        "sns.heatmap(cm, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "u9K3IJqUocxJ",
        "outputId": "04486d67-608d-4de6-cae4-c42af9e78f4b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       377\n",
            "           SITTING       0.92      0.87      0.90       364\n",
            "          STANDING       0.89      0.93      0.91       390\n",
            "           WALKING       0.96      0.99      0.97       335\n",
            "WALKING_DOWNSTAIRS       0.99      0.95      0.97       278\n",
            "  WALKING_UPSTAIRS       0.98      0.98      0.98       316\n",
            "\n",
            "          accuracy                           0.95      2060\n",
            "         macro avg       0.96      0.95      0.95      2060\n",
            "      weighted avg       0.95      0.95      0.95      2060\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ba6857af0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}