Activity_Recognition_Smartphones.ipynb

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        "## **Human Activity Recognition with Smartphones | End To End Machine Learning Projects**\n",
        "\n",
        "### This project is about classifying the human activities based on the smartphone data. The dataset is downloaded from kaggle. Data consist of various features and activitives. Activities are as follow:\n",
        "\n",
        "*   Walking\n",
        "*   Sitting\n",
        "*   Standing\n",
        "*   laying\n",
        "*   Walking upstairs\n",
        "*   Walking downstairs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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        "# Importing required libraries\n",
        "\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.feature_selection import RFE\n",
        "import joblib\n"
      ]
    },
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      "source": [
        "# reading the dataset\n",
        "\n",
        "train_data = pd.read_csv(\"/content/train.csv\")\n",
        "test_data = pd.read_csv(\"/content/train.csv\")"
      ],
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      "execution_count": 2,
      "outputs": []
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        "# displaying the first few rows of the train_data\n",
        "\n",
        "train_data.head()"
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              "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
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              "\n",
              "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
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              "            '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-a86ff276-103c-4dcf-859e-7992e9700f60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of rows and columns in the trainig data\n",
        "\n",
        "train_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlTehkLTR6gp",
        "outputId": "2da84951-8eef-4c46-8aee-4444820a4c94"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7352, 563)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# duplicate values\n",
        "\n",
        "train_data.duplicated().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJnN__aoSP1R",
        "outputId": "5dda5d4c-a5ff-4ee6-e5b8-d96f08a33d7a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# duplicate values in columns\n",
        "\n",
        "duplicate_col = train_data.columns[train_data.T.duplicated()].tolist()"
      ],
      "metadata": {
        "id": "siI8ZDj9SeDz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "caByCNulT3Wt",
        "outputId": "1561d2e6-8fb5-4dd5-9809-3c452ae879a8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['tBodyAccMag-sma()',\n",
              " 'tGravityAccMag-mean()',\n",
              " 'tGravityAccMag-std()',\n",
              " 'tGravityAccMag-mad()',\n",
              " 'tGravityAccMag-max()',\n",
              " 'tGravityAccMag-min()',\n",
              " 'tGravityAccMag-sma()',\n",
              " 'tGravityAccMag-energy()',\n",
              " 'tGravityAccMag-iqr()',\n",
              " 'tGravityAccMag-entropy()',\n",
              " 'tGravityAccMag-arCoeff()1',\n",
              " 'tGravityAccMag-arCoeff()2',\n",
              " 'tGravityAccMag-arCoeff()3',\n",
              " 'tGravityAccMag-arCoeff()4',\n",
              " 'tBodyAccJerkMag-sma()',\n",
              " 'tBodyGyroMag-sma()',\n",
              " 'tBodyGyroJerkMag-sma()',\n",
              " 'fBodyAccMag-sma()',\n",
              " 'fBodyBodyAccJerkMag-sma()',\n",
              " 'fBodyBodyGyroMag-sma()',\n",
              " 'fBodyBodyGyroJerkMag-sma()']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(duplicate_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYIQ2HweT5ow",
        "outputId": "948b428c-94f5-4f5f-acb0-ffb845d7c4d0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = train_data.drop(duplicate_col, axis=1)"
      ],
      "metadata": {
        "id": "0RjozhqST70L"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiFZ8xpHUKNH",
        "outputId": "01eeef2f-3b5c-4fe6-ea82-9bba49693552"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7352, 542)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# missing values\n",
        "\n",
        "train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jmJArM4VNFH",
        "outputId": "cb981161-d71b-411a-c25f-9ed4b27eeaa1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tBodyAcc-mean()-X       0\n",
              "tBodyAcc-mean()-Y       0\n",
              "tBodyAcc-mean()-Z       0\n",
              "tBodyAcc-std()-X        0\n",
              "tBodyAcc-std()-Y        0\n",
              "                       ..\n",
              "angle(X,gravityMean)    0\n",
              "angle(Y,gravityMean)    0\n",
              "angle(Z,gravityMean)    0\n",
              "subject                 0\n",
              "Activity                0\n",
              "Length: 542, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the distribution of targeted variable which is activity\n",
        "\n",
        "train_data['Activity']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8J7DTlNVXJA",
        "outputId": "b90bd890-d902-4262-a03d-769db0531021"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0               STANDING\n",
              "1               STANDING\n",
              "2               STANDING\n",
              "3               STANDING\n",
              "4               STANDING\n",
              "              ...       \n",
              "7347    WALKING_UPSTAIRS\n",
              "7348    WALKING_UPSTAIRS\n",
              "7349    WALKING_UPSTAIRS\n",
              "7350    WALKING_UPSTAIRS\n",
              "7351    WALKING_UPSTAIRS\n",
              "Name: Activity, Length: 7352, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Activity' column to categorical data type\n",
        "\n",
        "train_data['Activity'] = train_data['Activity'].astype('category')"
      ],
      "metadata": {
        "id": "m3DR7c_-W6nb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=train_data, x='Activity')\n",
        "plt.xticks(rotation=35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Ab8cELy_V1kz",
        "outputId": "3824624f-2dab-49e6-c83e-8e7ed3c65d26"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assinging targeted value to the variable y\n",
        "\n",
        "X = train_data.drop('Activity', axis=1)\n",
        "y = train_data['Activity']"
      ],
      "metadata": {
        "id": "OPBhpHrtXh-F"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "gezysqsBqbom",
        "outputId": "5188cdb6-722c-4a14-9b36-a30c2a768b96"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  \\\n",
              "0              0.288585          -0.020294          -0.132905   \n",
              "1              0.278419          -0.016411          -0.123520   \n",
              "2              0.279653          -0.019467          -0.113462   \n",
              "3              0.279174          -0.026201          -0.123283   \n",
              "4              0.276629          -0.016570          -0.115362   \n",
              "...                 ...                ...                ...   \n",
              "7347           0.299665          -0.057193          -0.181233   \n",
              "7348           0.273853          -0.007749          -0.147468   \n",
              "7349           0.273387          -0.017011          -0.045022   \n",
              "7350           0.289654          -0.018843          -0.158281   \n",
              "7351           0.351503          -0.012423          -0.203867   \n",
              "\n",
              "      tBodyAcc-std()-X  tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  \\\n",
              "0            -0.995279         -0.983111         -0.913526         -0.995112   \n",
              "1            -0.998245         -0.975300         -0.960322         -0.998807   \n",
              "2            -0.995380         -0.967187         -0.978944         -0.996520   \n",
              "3            -0.996091         -0.983403         -0.990675         -0.997099   \n",
              "4            -0.998139         -0.980817         -0.990482         -0.998321   \n",
              "...                ...               ...               ...               ...   \n",
              "7347         -0.195387          0.039905          0.077078         -0.282301   \n",
              "7348         -0.235309          0.004816          0.059280         -0.322552   \n",
              "7349         -0.218218         -0.103822          0.274533         -0.304515   \n",
              "7350         -0.219139         -0.111412          0.268893         -0.310487   \n",
              "7351         -0.269270         -0.087212          0.177404         -0.377404   \n",
              "\n",
              "      tBodyAcc-mad()-Y  tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  \\\n",
              "0            -0.983185         -0.923527         -0.934724  ...   \n",
              "1            -0.974914         -0.957686         -0.943068  ...   \n",
              "2            -0.963668         -0.977469         -0.938692  ...   \n",
              "3            -0.982750         -0.989302         -0.938692  ...   \n",
              "4            -0.979672         -0.990441         -0.942469  ...   \n",
              "...                ...               ...               ...  ...   \n",
              "7347          0.043616          0.060410          0.210795  ...   \n",
              "7348         -0.029456          0.080585          0.117440  ...   \n",
              "7349         -0.098913          0.332584          0.043999  ...   \n",
              "7350         -0.068200          0.319473          0.101702  ...   \n",
              "7351         -0.038678          0.229430          0.269013  ...   \n",
              "\n",
              "      fBodyBodyGyroJerkMag-skewness()  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
              "0                           -0.298676                        -0.710304   \n",
              "1                           -0.595051                        -0.861499   \n",
              "2                           -0.390748                        -0.760104   \n",
              "3                           -0.117290                        -0.482845   \n",
              "4                           -0.351471                        -0.699205   \n",
              "...                               ...                              ...   \n",
              "7347                        -0.588433                        -0.880324   \n",
              "7348                        -0.390738                        -0.680744   \n",
              "7349                         0.025145                        -0.304029   \n",
              "7350                         0.063907                        -0.344314   \n",
              "7351                        -0.387052                        -0.740738   \n",
              "\n",
              "      angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
              "0                       -0.112754                              0.030400   \n",
              "1                        0.053477                             -0.007435   \n",
              "2                       -0.118559                              0.177899   \n",
              "3                       -0.036788                             -0.012892   \n",
              "4                        0.123320                              0.122542   \n",
              "...                           ...                                   ...   \n",
              "7347                    -0.190437                              0.829718   \n",
              "7348                     0.064907                              0.875679   \n",
              "7349                     0.052806                             -0.266724   \n",
              "7350                    -0.101360                              0.700740   \n",
              "7351                    -0.280088                             -0.007739   \n",
              "\n",
              "      angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
              "0                            -0.464761                             -0.018446   \n",
              "1                            -0.732626                              0.703511   \n",
              "2                             0.100699                              0.808529   \n",
              "3                             0.640011                             -0.485366   \n",
              "4                             0.693578                             -0.615971   \n",
              "...                                ...                                   ...   \n",
              "7347                          0.206972                             -0.425619   \n",
              "7348                         -0.879033                              0.400219   \n",
              "7349                          0.864404                              0.701169   \n",
              "7350                          0.936674                             -0.589479   \n",
              "7351                         -0.056088                             -0.616956   \n",
              "\n",
              "      angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  \\\n",
              "0                -0.841247              0.179941             -0.058627   \n",
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              "...                    ...                   ...                   ...   \n",
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              "7350             -0.785181              0.246432              0.025339   \n",
              "7351             -0.783267              0.246809              0.036695   \n",
              "\n",
              "      subject  \n",
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              "7347       30  \n",
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              "7351       30  \n",
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              "      <td>0.036695</td>\n",
              "      <td>30</td>\n",
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              "  }\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-5ae51cb3-1a27-424f-bb5a-4fa42d448bd2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3K6ZfyaHqikQ",
        "outputId": "d319f483-16b5-4c10-c92c-86249baaab09"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0               STANDING\n",
              "1               STANDING\n",
              "2               STANDING\n",
              "3               STANDING\n",
              "4               STANDING\n",
              "              ...       \n",
              "7347    WALKING_UPSTAIRS\n",
              "7348    WALKING_UPSTAIRS\n",
              "7349    WALKING_UPSTAIRS\n",
              "7350    WALKING_UPSTAIRS\n",
              "7351    WALKING_UPSTAIRS\n",
              "Name: Activity, Length: 7352, dtype: category\n",
              "Categories (6, object): ['LAYING', 'SITTING', 'STANDING', 'WALKING', 'WALKING_DOWNSTAIRS',\n",
              "                         'WALKING_UPSTAIRS']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting y into numerical values\n",
        "\n",
        "le = LabelEncoder()  #creating instanace\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "A-rchXgwqnxE"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBBKfI1zrGN3",
        "outputId": "ee311e60-9dda-4741-fe8e-340ce3afd12c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, ..., 5, 5, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting dataset into Training and Test set\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n"
      ],
      "metadata": {
        "id": "OA_5LmrbrjTX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# traing logistic regression model, as this is the multi class classification problem\n",
        "\n",
        "model_R = LogisticRegression()  #creating instance of Logistic Regression\n",
        "model_R.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "i1itV99csuVS",
        "outputId": "a0454db4-65e3-4281-a85b-d4db41938f48"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1 = model_R.predict(X_test)\n",
        "accuracy_score(y_test, y_pred1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooyYoHdgtAXy",
        "outputId": "9aaece68-868b-4fa0-8316-6937727cb9a6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9809653297076818"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# training random forest classifier\n",
        "#create instance of random forest\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Jm0cYL55tqga",
        "outputId": "1cdc5980-7036-4cdc-8ec6-366e59844744"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred2 = rf.predict(X_test)\n",
        "accuracy_score(y_test, y_pred2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1DpfbCHuhAx",
        "outputId": "f8eec63c-fbbc-4bda-9a95-d53b2ee2049c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9836845683208701"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# feature selection (filter based k-best method)\n",
        "\n",
        "k = 200\n",
        "selector = SelectKBest(f_classif, k=k)\n",
        "X_train_selected = selector.fit_transform(X_train, y_train)\n",
        "X_test_selected = selector.transform(X_test)\n",
        "\n",
        "selected_indices = selector.get_support(indices = True)\n",
        "selected_features = X_train.columns[selected_indices]\n",
        "print (selected_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SSYUQ02vAly",
        "outputId": "e13cceb7-ee54-4427-f8d4-99e00569781a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z',\n",
            "       'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z',\n",
            "       'tBodyAcc-max()-X', 'tBodyAcc-max()-Y', 'tBodyAcc-max()-Z',\n",
            "       'tBodyAcc-min()-X',\n",
            "       ...\n",
            "       'fBodyBodyGyroMag-mad()', 'fBodyBodyGyroMag-max()',\n",
            "       'fBodyBodyGyroMag-iqr()', 'fBodyBodyGyroMag-entropy()',\n",
            "       'fBodyBodyGyroJerkMag-mean()', 'fBodyBodyGyroJerkMag-mad()',\n",
            "       'fBodyBodyGyroJerkMag-iqr()', 'fBodyBodyGyroJerkMag-entropy()',\n",
            "       'angle(X,gravityMean)', 'angle(Y,gravityMean)'],\n",
            "      dtype='object', length=200)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# feature selection (wrapper based rfe method)\n",
        "\n"
      ],
      "metadata": {
        "id": "7SXEfxqQ4C94"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "estimator = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "6JIsy39l4iWe"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k = 100\n",
        "rfe_selector = RFE(estimator, n_features_to_select=k)\n",
        "X_train_selected_rfe = rfe_selector.fit_transform(X_train_selected, y_train)\n",
        "X_test_selected_rfe = rfe_selector.transform(X_test_selected)\n",
        "\n",
        "selected_indices_rfe = rfe_selector.get_support(indices=True)\n",
        "selected_features_rfe = selected_features[selected_indices_rfe]\n",
        "print(selected_features_rfe)"
      ],
      "metadata": {
        "id": "3TlO4sgW4nyn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8c18369-c67d-41ce-e2d1-e1ca6e2a8917"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['tBodyAcc-std()-X', 'tBodyAcc-mad()-X', 'tBodyAcc-max()-X',\n",
            "       'tBodyAcc-energy()-X', 'tBodyAcc-entropy()-X', 'tGravityAcc-mean()-X',\n",
            "       'tGravityAcc-mean()-Y', 'tGravityAcc-max()-X', 'tGravityAcc-max()-Y',\n",
            "       'tGravityAcc-min()-X', 'tGravityAcc-min()-Y', 'tGravityAcc-energy()-X',\n",
            "       'tGravityAcc-energy()-Y', 'tBodyAccJerk-std()-X',\n",
            "       'tBodyAccJerk-std()-Y', 'tBodyAccJerk-std()-Z', 'tBodyAccJerk-mad()-X',\n",
            "       'tBodyAccJerk-mad()-Y', 'tBodyAccJerk-mad()-Z', 'tBodyAccJerk-max()-X',\n",
            "       'tBodyAccJerk-max()-Z', 'tBodyAccJerk-sma()', 'tBodyAccJerk-energy()-X',\n",
            "       'tBodyAccJerk-energy()-Y', 'tBodyAccJerk-iqr()-X',\n",
            "       'tBodyAccJerk-iqr()-Y', 'tBodyAccJerk-entropy()-X', 'tBodyGyro-std()-X',\n",
            "       'tBodyGyro-std()-Z', 'tBodyGyro-mad()-X', 'tBodyGyro-mad()-Z',\n",
            "       'tBodyGyro-max()-X', 'tBodyGyro-min()-X', 'tBodyGyro-sma()',\n",
            "       'tBodyGyro-iqr()-X', 'tBodyGyro-iqr()-Y', 'tBodyGyro-iqr()-Z',\n",
            "       'tBodyGyroJerk-std()-X', 'tBodyGyroJerk-std()-Z',\n",
            "       'tBodyGyroJerk-mad()-X', 'tBodyGyroJerk-mad()-Z',\n",
            "       'tBodyGyroJerk-min()-X', 'tBodyGyroJerk-sma()', 'tBodyGyroJerk-iqr()-X',\n",
            "       'tBodyGyroJerk-iqr()-Z', 'tBodyAccMag-mean()', 'tBodyAccMag-std()',\n",
            "       'tBodyAccMag-mad()', 'tBodyAccMag-max()', 'tBodyAccMag-energy()',\n",
            "       'tBodyAccJerkMag-mean()', 'tBodyAccJerkMag-std()',\n",
            "       'tBodyAccJerkMag-mad()', 'tBodyAccJerkMag-energy()',\n",
            "       'tBodyAccJerkMag-iqr()', 'tBodyAccJerkMag-entropy()',\n",
            "       'tBodyGyroJerkMag-iqr()', 'fBodyAcc-mean()-X', 'fBodyAcc-std()-X',\n",
            "       'fBodyAcc-std()-Z', 'fBodyAcc-mad()-X', 'fBodyAcc-max()-X',\n",
            "       'fBodyAcc-max()-Y', 'fBodyAcc-energy()-X', 'fBodyAcc-bandsEnergy()-1,8',\n",
            "       'fBodyAcc-bandsEnergy()-1,16', 'fBodyAcc-bandsEnergy()-1,24',\n",
            "       'fBodyAcc-bandsEnergy()-1,8.1', 'fBodyAccJerk-mean()-Z',\n",
            "       'fBodyAccJerk-std()-X', 'fBodyAccJerk-std()-Y', 'fBodyAccJerk-std()-Z',\n",
            "       'fBodyAccJerk-mad()-Z', 'fBodyAccJerk-max()-Y', 'fBodyAccJerk-sma()',\n",
            "       'fBodyAccJerk-energy()-X', 'fBodyAccJerk-energy()-Y',\n",
            "       'fBodyAccJerk-bandsEnergy()-1,8', 'fBodyAccJerk-bandsEnergy()-1,16',\n",
            "       'fBodyAccJerk-bandsEnergy()-1,24', 'fBodyAccJerk-bandsEnergy()-1,24.1',\n",
            "       'fBodyGyro-mean()-X', 'fBodyGyro-mean()-Y', 'fBodyGyro-std()-X',\n",
            "       'fBodyGyro-std()-Y', 'fBodyGyro-mad()-X', 'fBodyGyro-mad()-Y',\n",
            "       'fBodyGyro-max()-X', 'fBodyGyro-max()-Z', 'fBodyGyro-entropy()-X',\n",
            "       'fBodyAccMag-mean()', 'fBodyAccMag-std()', 'fBodyAccMag-mad()',\n",
            "       'fBodyAccMag-max()', 'fBodyAccMag-energy()',\n",
            "       'fBodyBodyAccJerkMag-std()', 'fBodyBodyAccJerkMag-max()',\n",
            "       'fBodyBodyAccJerkMag-energy()', 'angle(X,gravityMean)',\n",
            "       'angle(Y,gravityMean)'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(selected_features_rfe))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzyQyu8WOOfA",
        "outputId": "9841c3a5-5f2f-46c7-c43a-8ff57bfea8b2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#training randomforestclassifier\n",
        "\n",
        "rf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "t9T5k-ggOc8P"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train_selected_rfe, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "7CpBzdC-OliA",
        "outputId": "9472faa1-e423-4d96-e1c3-56e7304d361f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rf = rf.predict(X_test_selected_rfe)"
      ],
      "metadata": {
        "id": "MpMBTaVPOyAT"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, y_pred_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPFBhywkO8SJ",
        "outputId": "16beaf79-182d-4fc3-a7f7-2c6ae05ee87a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9782460910944936"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(rf, \"model_rfe\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsDnltJWPJtz",
        "outputId": "ed3400f9-43fd-4bb2-9f9f-575d953fb9c0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_rfe']"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(selector, \"k_best_selector\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APap4H0LPoJ-",
        "outputId": "5dbcb1b7-caa4-4089-9278-6ab927da0f7b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['k_best_selector']"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(rfe_selector, \"rfe_selector\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TMM17cQPvit",
        "outputId": "de0e0f6a-3959-4097-a00f-7c6c1e11238f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rfe_selector']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = test_data.drop(\"Activity\", axis=1)"
      ],
      "metadata": {
        "id": "Mgb4pn_0P8ke"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicated_col = test_data.columns[test_data.T.duplicated()].to_list()"
      ],
      "metadata": {
        "id": "XrDHz4CFQJcz"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = test_data.drop(duplicated_col, axis=1)"
      ],
      "metadata": {
        "id": "khcX4U5XQiB_"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = joblib.load('model_rfe')"
      ],
      "metadata": {
        "id": "VA7j349ZQtoh"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "selector = joblib.load('k_best_selector')"
      ],
      "metadata": {
        "id": "Ras7oB2NQ4Qc"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfe_selector = joblib.load('rfe_selector')"
      ],
      "metadata": {
        "id": "YGply2BeRBWA"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "selector = selector.transform(test_data)"
      ],
      "metadata": {
        "id": "PGCtA9rORLk_"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_selected_rfe = rfe_selector.transform(selector)"
      ],
      "metadata": {
        "id": "9uEqKGJcSpyn"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test_selected_rfe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wN0aIuQ0S1aU",
        "outputId": "91449920-c986-4fa5-f7a8-c02cde13ff26"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, ..., 5, 5, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    }
  ]
}