eeg_final.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vTRCDdxw-mRl"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhUUuB-9-YPU",
        "outputId": "04a8bac2-6360-4b31-e9c4-43717a047ece"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "LEkecWUa-ym2",
        "outputId": "2cf03f64-b5a8-447a-976d-ec78709b7c15"
      },
      "outputs": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>no.</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>IQ</th>\n",
              "      <th>AB.A.delta.a.FP1</th>\n",
              "      <th>AB.A.delta.b.FP2</th>\n",
              "      <th>AB.A.delta.c.F7</th>\n",
              "      <th>AB.A.delta.d.F3</th>\n",
              "      <th>AB.A.delta.e.Fz</th>\n",
              "      <th>AB.A.delta.f.F4</th>\n",
              "      <th>...</th>\n",
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              "      <th>COH.F.gamma.r.O1.s.O2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
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              "      <td>932.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "      <td>945.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>473.000000</td>\n",
              "      <td>30.594804</td>\n",
              "      <td>13.438710</td>\n",
              "      <td>101.580472</td>\n",
              "      <td>20.182936</td>\n",
              "      <td>21.177584</td>\n",
              "      <td>17.749553</td>\n",
              "      <td>18.901698</td>\n",
              "      <td>20.447079</td>\n",
              "      <td>19.490329</td>\n",
              "      <td>...</td>\n",
              "      <td>75.889633</td>\n",
              "      <td>55.049113</td>\n",
              "      <td>56.959796</td>\n",
              "      <td>60.739169</td>\n",
              "      <td>69.829254</td>\n",
              "      <td>47.862489</td>\n",
              "      <td>66.832798</td>\n",
              "      <td>39.301406</td>\n",
              "      <td>66.153213</td>\n",
              "      <td>57.056207</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>272.942302</td>\n",
              "      <td>11.781592</td>\n",
              "      <td>2.550461</td>\n",
              "      <td>17.022414</td>\n",
              "      <td>11.282022</td>\n",
              "      <td>12.230662</td>\n",
              "      <td>10.003598</td>\n",
              "      <td>9.079482</td>\n",
              "      <td>9.742912</td>\n",
              "      <td>9.252500</td>\n",
              "      <td>...</td>\n",
              "      <td>15.861641</td>\n",
              "      <td>19.541956</td>\n",
              "      <td>18.326785</td>\n",
              "      <td>18.052887</td>\n",
              "      <td>17.725321</td>\n",
              "      <td>19.685722</td>\n",
              "      <td>17.028701</td>\n",
              "      <td>20.790933</td>\n",
              "      <td>18.088548</td>\n",
              "      <td>19.600107</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>3.272260</td>\n",
              "      <td>3.244199</td>\n",
              "      <td>3.050507</td>\n",
              "      <td>3.412618</td>\n",
              "      <td>5.066593</td>\n",
              "      <td>4.048931</td>\n",
              "      <td>...</td>\n",
              "      <td>12.611954</td>\n",
              "      <td>0.519048</td>\n",
              "      <td>6.926792</td>\n",
              "      <td>0.708008</td>\n",
              "      <td>2.421748</td>\n",
              "      <td>0.036664</td>\n",
              "      <td>1.032207</td>\n",
              "      <td>1.228502</td>\n",
              "      <td>0.363268</td>\n",
              "      <td>3.988805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>237.000000</td>\n",
              "      <td>21.730000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>12.784872</td>\n",
              "      <td>13.019269</td>\n",
              "      <td>11.134327</td>\n",
              "      <td>12.460586</td>\n",
              "      <td>13.548645</td>\n",
              "      <td>12.637717</td>\n",
              "      <td>...</td>\n",
              "      <td>68.014443</td>\n",
              "      <td>41.607506</td>\n",
              "      <td>43.682444</td>\n",
              "      <td>48.374883</td>\n",
              "      <td>58.937785</td>\n",
              "      <td>32.581046</td>\n",
              "      <td>55.872070</td>\n",
              "      <td>22.049743</td>\n",
              "      <td>54.710605</td>\n",
              "      <td>43.955229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>473.000000</td>\n",
              "      <td>26.150000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>17.065286</td>\n",
              "      <td>17.838251</td>\n",
              "      <td>15.541469</td>\n",
              "      <td>16.733004</td>\n",
              "      <td>18.065276</td>\n",
              "      <td>17.432568</td>\n",
              "      <td>...</td>\n",
              "      <td>78.647712</td>\n",
              "      <td>55.158858</td>\n",
              "      <td>56.657348</td>\n",
              "      <td>61.257972</td>\n",
              "      <td>72.298636</td>\n",
              "      <td>45.719426</td>\n",
              "      <td>68.238375</td>\n",
              "      <td>36.549938</td>\n",
              "      <td>67.988937</td>\n",
              "      <td>57.515871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>709.000000</td>\n",
              "      <td>35.450000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>24.492760</td>\n",
              "      <td>25.654394</td>\n",
              "      <td>21.623961</td>\n",
              "      <td>23.347900</td>\n",
              "      <td>25.573096</td>\n",
              "      <td>23.956748</td>\n",
              "      <td>...</td>\n",
              "      <td>87.398387</td>\n",
              "      <td>69.740640</td>\n",
              "      <td>70.649245</td>\n",
              "      <td>73.979100</td>\n",
              "      <td>83.066877</td>\n",
              "      <td>62.081839</td>\n",
              "      <td>79.192418</td>\n",
              "      <td>54.169209</td>\n",
              "      <td>79.527764</td>\n",
              "      <td>71.626382</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>945.000000</td>\n",
              "      <td>71.880000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>92.826192</td>\n",
              "      <td>101.515687</td>\n",
              "      <td>91.373456</td>\n",
              "      <td>82.544167</td>\n",
              "      <td>101.566662</td>\n",
              "      <td>77.197502</td>\n",
              "      <td>...</td>\n",
              "      <td>99.678649</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>99.307895</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>99.581629</td>\n",
              "      <td>98.720067</td>\n",
              "      <td>99.650154</td>\n",
              "      <td>98.413320</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>99.287092</td>\n",
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            ],
            "text/plain": [
              "              no.         age   education          IQ  AB.A.delta.a.FP1  \\\n",
              "count  945.000000  945.000000  930.000000  932.000000        945.000000   \n",
              "mean   473.000000   30.594804   13.438710  101.580472         20.182936   \n",
              "std    272.942302   11.781592    2.550461   17.022414         11.282022   \n",
              "min      1.000000   18.000000    0.000000   49.000000          3.272260   \n",
              "25%    237.000000   21.730000   12.000000   91.000000         12.784872   \n",
              "50%    473.000000   26.150000   13.000000  102.000000         17.065286   \n",
              "75%    709.000000   35.450000   16.000000  114.000000         24.492760   \n",
              "max    945.000000   71.880000   20.000000  145.000000         92.826192   \n",
              "\n",
              "       AB.A.delta.b.FP2  AB.A.delta.c.F7  AB.A.delta.d.F3  AB.A.delta.e.Fz  \\\n",
              "count        945.000000       945.000000       945.000000       945.000000   \n",
              "mean          21.177584        17.749553        18.901698        20.447079   \n",
              "std           12.230662        10.003598         9.079482         9.742912   \n",
              "min            3.244199         3.050507         3.412618         5.066593   \n",
              "25%           13.019269        11.134327        12.460586        13.548645   \n",
              "50%           17.838251        15.541469        16.733004        18.065276   \n",
              "75%           25.654394        21.623961        23.347900        25.573096   \n",
              "max          101.515687        91.373456        82.544167       101.566662   \n",
              "\n",
              "       AB.A.delta.f.F4  ...  COH.F.gamma.o.Pz.p.P4  COH.F.gamma.o.Pz.q.T6  \\\n",
              "count       945.000000  ...             945.000000             945.000000   \n",
              "mean         19.490329  ...              75.889633              55.049113   \n",
              "std           9.252500  ...              15.861641              19.541956   \n",
              "min           4.048931  ...              12.611954               0.519048   \n",
              "25%          12.637717  ...              68.014443              41.607506   \n",
              "50%          17.432568  ...              78.647712              55.158858   \n",
              "75%          23.956748  ...              87.398387              69.740640   \n",
              "max          77.197502  ...              99.678649             100.000000   \n",
              "\n",
              "       COH.F.gamma.o.Pz.r.O1  COH.F.gamma.o.Pz.s.O2  COH.F.gamma.p.P4.q.T6  \\\n",
              "count             945.000000             945.000000             945.000000   \n",
              "mean               56.959796              60.739169              69.829254   \n",
              "std                18.326785              18.052887              17.725321   \n",
              "min                 6.926792               0.708008               2.421748   \n",
              "25%                43.682444              48.374883              58.937785   \n",
              "50%                56.657348              61.257972              72.298636   \n",
              "75%                70.649245              73.979100              83.066877   \n",
              "max                99.307895             100.000000              99.581629   \n",
              "\n",
              "       COH.F.gamma.p.P4.r.O1  COH.F.gamma.p.P4.s.O2  COH.F.gamma.q.T6.r.O1  \\\n",
              "count             945.000000             945.000000             945.000000   \n",
              "mean               47.862489              66.832798              39.301406   \n",
              "std                19.685722              17.028701              20.790933   \n",
              "min                 0.036664               1.032207               1.228502   \n",
              "25%                32.581046              55.872070              22.049743   \n",
              "50%                45.719426              68.238375              36.549938   \n",
              "75%                62.081839              79.192418              54.169209   \n",
              "max                98.720067              99.650154              98.413320   \n",
              "\n",
              "       COH.F.gamma.q.T6.s.O2  COH.F.gamma.r.O1.s.O2  \n",
              "count             945.000000             945.000000  \n",
              "mean               66.153213              57.056207  \n",
              "std                18.088548              19.600107  \n",
              "min                 0.363268               3.988805  \n",
              "25%                54.710605              43.955229  \n",
              "50%                67.988937              57.515871  \n",
              "75%                79.527764              71.626382  \n",
              "max               100.000000              99.287092  \n",
              "\n",
              "[8 rows x 1145 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "path=\"/content/eeg_demo3.csv\"\n",
        "# path2=\"/content/EEG.machinelearing_data_BRMH.csv\"\n",
        "df=pd.read_csv(\"/content/EEG.machinelearing_data_BRMH.csv\")\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "DNc7RKfSAW4b"
      },
      "outputs": [],
      "source": [
        "def func(path):\n",
        "  df=pd.read_csv(path)\n",
        "  df1 = df.filter(regex='^(?!.*COH)')\n",
        "  columns_to_remove = ['education', 'date', 'Unnamed: 122','no.','eeg.date']\n",
        "  df2 = df1.drop(columns=columns_to_remove, errors='ignore')\n",
        "  df3 = df2.dropna()\n",
        "  df3['sex'] = df3['sex'].replace({'M': 0, 'F': 1})\n",
        "  df3=df3.drop(['specific.disorder'],axis=1)\n",
        "  df3['sex'] = df3['sex'].replace({'M': 0, 'F': 1})\n",
        "\n",
        "  with open('/content/pca_model(1).pkl', 'rb') as file:\n",
        "        pca = pickle.load(file)\n",
        "\n",
        "  X = df3.drop(columns=['main.disorder'])  # Features\n",
        "  scaler = StandardScaler()\n",
        "  X = scaler.fit_transform(X)\n",
        "  pca_data = pca.transform(X)\n",
        "  with open('/content/rf_model(1).pkl', 'rb') as file:\n",
        "        rf1_model = pickle.load(file)\n",
        "\n",
        "  predicted_labels = rf1_model.predict(pca_data)\n",
        "  return predicted_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOy10090BAdb",
        "outputId": "a00438db-e1f2-4c35-d477-e10fa23cd2a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted labels: ['Mood disorder' 'Addictive disorder' 'Mood disorder']\n"
          ]
        }
      ],
      "source": [
        "\n",
        "predicted_labels=func(path)\n",
        "print(\"Predicted labels:\", predicted_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fwnm9zM9P4k3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}