Human_Activity_recognition.ipynb

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        "<a href=\"https://colab.research.google.com/github/OyewoleRasheed/Rawsheedlearns/blob/main/Human_Activity_recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
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        "id": "JGqxzXZzLiis"
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      "outputs": []
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      "source": [
        "#read train and test dataset\n",
        "train= pd.read_csv(\"/train-1.csv\")\n",
        "test = pd.read_csv(\"/test.csv\")"
      ]
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      "source": [
        "train.shape"
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              "(3523, 563)"
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          "execution_count": 6
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      "source": [
        "train[\"DATA\"] = \"Train\"\n",
        "test[\"DATA\"] = \"Test\"\n",
        "combined_d = pd.concat([train, test], axis =0).reset_index(drop= True)"
      ],
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      "execution_count": 25,
      "outputs": []
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        "combined_d.head()"
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            "text/plain": [
              "   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></th>\n",
              "      <th>tBodyAcc-mean()-X</th>\n",
              "      <th>tBodyAcc-mean()-Y</th>\n",
              "      <th>tBodyAcc-mean()-Z</th>\n",
              "      <th>tBodyAcc-std()-X</th>\n",
              "      <th>tBodyAcc-std()-Y</th>\n",
              "      <th>tBodyAcc-std()-Z</th>\n",
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              "      <th>angle(tBodyAccMean,gravity)</th>\n",
              "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
              "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
              "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
              "      <th>angle(X,gravityMean)</th>\n",
              "      <th>angle(Y,gravityMean)</th>\n",
              "      <th>angle(Z,gravityMean)</th>\n",
              "      <th>subject</th>\n",
              "      <th>Activity</th>\n",
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              "      <th>1</th>\n",
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              "      <td>-0.978944</td>\n",
              "      <td>-0.996520</td>\n",
              "      <td>-0.963668</td>\n",
              "      <td>-0.977469</td>\n",
              "      <td>-0.938692</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.118559</td>\n",
              "      <td>0.177899</td>\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-53e0d0d4-35d5-4d04-8e92-a3bd4bcee65a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "combined_d"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " combined_d[\"subject\"] = \"#\" + combined_d[\"subject\"].astype(str)"
      ],
      "metadata": {
        "id": "dfk4AlC-Z9Gf"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_d.dtypes.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UkHBXOpxgCI7",
        "outputId": "3f250307-6b73-4dfd-f501-45bf41da40a5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float64    561\n",
              "object       3\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def basic_details(df):\n",
        "  a = pd.DataFrame()\n",
        "  a[\"Missing Value\"] = df.isnull().sum()\n",
        "  a[\"Unique Value\"] = df.nunique()\n",
        "  a[\"dtypes\"] = df.dtypes\n",
        "  return a"
      ],
      "metadata": {
        "id": "RwdWoHMAgaen"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "basic_details(combined_d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "3yrdmqWHhh9F",
        "outputId": "7d9399ed-59aa-452b-c2f3-3163297c3911"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Missing Value  Unique Value   dtypes\n",
              "tBodyAcc-mean()-X                 0         10292  float64\n",
              "tBodyAcc-mean()-Y                 0         10299  float64\n",
              "tBodyAcc-mean()-Z                 0         10293  float64\n",
              "tBodyAcc-std()-X                  0         10295  float64\n",
              "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",
              "\n",
              "[564 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b04b3aef-6b30-45cd-8dae-62a7b2891a34\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>subject</th>\n",
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              "      <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",
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              "      <td>2</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>564 rows × 3 columns</p>\n",
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              "    </button>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "    .colab-df-buttons div {\n",
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              "      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-b04b3aef-6b30-45cd-8dae-62a7b2891a34 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-b04b3aef-6b30-45cd-8dae-62a7b2891a34');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b006da90-c6f9-4388-8f17-d73702c24bae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"basic_details(combined_d)\",\n  \"rows\": 564,\n  \"fields\": [\n    {\n      \"column\": \"Missing Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unique Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2025,\n        \"min\": 2,\n        \"max\": 10299,\n        \"num_unique_values\": 238,\n        \"samples\": [\n          10171\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dtypes\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"object\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "activity = combined_d['Activity']\n",
        "label_counts= activity.value_counts()\n",
        "plt.figure(figsize = (12,8))\n",
        "plt.bar(label_counts.index, label_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "zK7b8fwzhmVo",
        "outputId": "979242d9-b0f8-4638-f927-44b30806eb13"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 6 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data= combined_d[\"DATA\"]\n",
        "Subject = combined_d[\"subject\"]\n",
        "train = combined_d.copy()\n",
        "train = train.drop([\"DATA\",\"subject\",\"Activity\"], axis =1)"
      ],
      "metadata": {
        "id": "UWoUH4cmiLjn"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "slc = StandardScaler()\n",
        "train = slc.fit_transform(train)\n"
      ],
      "metadata": {
        "id": "_xRij2jQjrOP"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca= PCA(n_components= 0.9, random_state= 42)\n",
        "train= pca.fit_transform(train)"
      ],
      "metadata": {
        "id": "4hw4Pv3akMgg"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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 = 42)\n"
      ],
      "metadata": {
        "id": "rI1x9IhFke_f"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_folds=10\n",
        "seed =0\n",
        "scoring= 'accuracy'\n",
        "results ={}\n",
        "accuracy ={}"
      ],
      "metadata": {
        "id": "-0I_NQzAlQZr"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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\" )"
      ],
      "metadata": {
        "id": "aZo_a_FLlhsK"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val = cross_val_score(model, X_train, y_train, cv= 10, scoring= scoring)\n",
        "results[\"GSCV\"] = (cross_val.mean(), cross_val.std())"
      ],
      "metadata": {
        "id": "Fg8bEQfAuXhr"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)\n",
        "y_predict = model.predict(X_test)"
      ],
      "metadata": {
        "id": "i4WYbL2xvl2f"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy[\"GSCV\"] = accuracy_score(y_test, y_predict)\n",
        "print(classification_report(y_test, y_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85BCv8IGv6Zu",
        "outputId": "44b8f1fc-ac3b-4df1-fce6-ea224ede4bd7"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    precision    recall  f1-score   support\n",
            "\n",
            "            LAYING       1.00      1.00      1.00       406\n",
            "           SITTING       0.90      0.85      0.88       377\n",
            "          STANDING       0.85      0.90      0.88       354\n",
            "           WALKING       0.98      0.99      0.99       369\n",
            "WALKING_DOWNSTAIRS       0.99      0.96      0.98       284\n",
            "  WALKING_UPSTAIRS       0.97      0.99      0.98       270\n",
            "\n",
            "          accuracy                           0.95      2060\n",
            "         macro avg       0.95      0.95      0.95      2060\n",
            "      weighted avg       0.95      0.95      0.95      2060\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_predict)\n",
        "sns.heatmap(cm, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "ZQSlvxfawPiT",
        "outputId": "6923bd5c-5870-41cd-db89-50799850e794"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jfh2cbnSxYON"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}