DISEASE_PREDICTION.ipynb

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              "              Disease  itching  skin_rash  nodal_skin_eruptions  \\\n",
              "0    Fungal infection        1          1                     1   \n",
              "1    Fungal infection        0          1                     1   \n",
              "2    Fungal infection        1          0                     1   \n",
              "3    Fungal infection        1          1                     0   \n",
              "4    Fungal infection        1          1                     1   \n",
              "..                ...      ...        ...                   ...   \n",
              "299          Impetigo        0          0                     0   \n",
              "300          Impetigo        0          1                     0   \n",
              "301          Impetigo        0          1                     0   \n",
              "302          Impetigo        0          1                     0   \n",
              "303          Impetigo        0          1                     0   \n",
              "\n",
              "     dischromic _patches  continuous_sneezing  shivering  chills  \\\n",
              "0                      1                    0          0       0   \n",
              "1                      1                    0          0       0   \n",
              "2                      1                    0          0       0   \n",
              "3                      1                    0          0       0   \n",
              "4                      0                    0          0       0   \n",
              "..                   ...                  ...        ...     ...   \n",
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              "301                    0                    0          0       0   \n",
              "302                    0                    0          0       0   \n",
              "303                    0                    0          0       0   \n",
              "\n",
              "     watering_from_eyes  stomach_pain  ...  bladder_discomfort  \\\n",
              "0                     0             0  ...                   0   \n",
              "1                     0             0  ...                   0   \n",
              "2                     0             0  ...                   0   \n",
              "3                     0             0  ...                   0   \n",
              "4                     0             0  ...                   0   \n",
              "..                  ...           ...  ...                 ...   \n",
              "299                   0             0  ...                   0   \n",
              "300                   0             0  ...                   0   \n",
              "301                   0             0  ...                   0   \n",
              "302                   0             0  ...                   0   \n",
              "303                   0             0  ...                   0   \n",
              "\n",
              "     foul_smell_of urine  continuous_feel_of_urine  skin_peeling  \\\n",
              "0                      0                         0             0   \n",
              "1                      0                         0             0   \n",
              "2                      0                         0             0   \n",
              "3                      0                         0             0   \n",
              "4                      0                         0             0   \n",
              "..                   ...                       ...           ...   \n",
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              "300                    0                         0             0   \n",
              "301                    0                         0             0   \n",
              "302                    0                         0             0   \n",
              "303                    0                         0             0   \n",
              "\n",
              "     silver_like_dusting  small_dents_in_nails  inflammatory_nails  blister  \\\n",
              "0                      0                     0                   0        0   \n",
              "1                      0                     0                   0        0   \n",
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              "301                    0                     0                   0        0   \n",
              "302                    0                     0                   0        1   \n",
              "303                    0                     0                   0        1   \n",
              "\n",
              "     red_sore_around_nose  yellow_crust_ooze  \n",
              "0                       0                  0  \n",
              "1                       0                  0  \n",
              "2                       0                  0  \n",
              "3                       0                  0  \n",
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              "..                    ...                ...  \n",
              "299                     1                  1  \n",
              "300                     1                  1  \n",
              "301                     1                  1  \n",
              "302                     0                  1  \n",
              "303                     1                  0  \n",
              "\n",
              "[304 rows x 123 columns]"
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              "      <th></th>\n",
              "      <th>Disease</th>\n",
              "      <th>itching</th>\n",
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              "      <th>dischromic _patches</th>\n",
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              "      <th>silver_like_dusting</th>\n",
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              "      <th>inflammatory_nails</th>\n",
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              "      <th>300</th>\n",
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              "      <td>Impetigo</td>\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "df=pd.read_csv(\"/content/dis-sym-processed-categorical.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95882b14-bf26-4c36-8d21-3e4f1ea52a0e",
      "metadata": {
        "id": "95882b14-bf26-4c36-8d21-3e4f1ea52a0e",
        "outputId": "ea097d8f-9e09-4542-f919-bf37bcbcdf0c"
      },
      "outputs": [
        {
          "name": "stdin",
          "output_type": "stream",
          "text": [
            "enter symptom: itching\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n"
          ]
        },
        {
          "name": "stdin",
          "output_type": "stream",
          "text": [
            "enter 'no' to stop writing symptoms: no\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "for these symptoms: ['itching']\n",
            "u may have --- Chicken pox --- with probability ---10.0 %\n",
            "u may have --- Chronic cholestasis --- with probability ---14.2857 %\n",
            "u may have --- Drug Reaction --- with probability ---20.0 %\n",
            "u may have --- Fungal infection --- with probability ---25.0 %\n",
            "u may have --- Hepatitis B --- with probability ---10.0 %\n",
            "u may have --- Jaundice --- with probability ---12.5 %\n"
          ]
        }
      ],
      "source": [
        "# finding disease prediction using basic probabilty\n",
        "\n",
        "filtered_df=[]\n",
        "sym=[]\n",
        "c=0\n",
        "while True:\n",
        "    x=input(\"enter symptom:\")\n",
        "    sym.append(x)\n",
        "    c+=1\n",
        "    print(c)\n",
        "    if(c==1):\n",
        "        filtered_df = df[df[x] == 1]\n",
        "    else:\n",
        "        filtered_df = filtered_df[filtered_df[x] == 1]\n",
        "    # Count the number of '1' values in each row\n",
        "    row_counts = (filtered_df == 1).sum(axis=1)\n",
        "\n",
        "    # Display the DataFrame with row counts\n",
        "    filtered_df['Row_Counts'] = row_counts\n",
        "    filtered_df['prob'] = round((c / row_counts) * 100, 4)\n",
        "    if(filtered_df.empty):\n",
        "        print(sym)\n",
        "        break\n",
        "    y=input(\"enter 'no' to stop writing symptoms:\")\n",
        "    if(y=='no'):\n",
        "        # Select the row with the highest 'prob' value for each unique 'Disease'\n",
        "        selected_rows = filtered_df.loc[filtered_df.groupby('Disease')['prob'].idxmin()]\n",
        "\n",
        "        cols_to_print = ['Disease', 'prob']\n",
        "\n",
        "        print(\"for these symptoms:\",sym)\n",
        "        # Iterate through the DataFrame and print selected columns\n",
        "        for _, row in selected_rows.iterrows():\n",
        "            print(\"u may have ---\",\" --- with probability ---\".join(str(row[col]) for col in cols_to_print),\"%\")\n",
        "        break\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d3812bd-6de3-4972-a6f6-ada66267744a",
      "metadata": {
        "id": "2d3812bd-6de3-4972-a6f6-ada66267744a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "ed611eac-c5f3-4cf0-ad58-330fd47dbafb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed611eac-c5f3-4cf0-ad58-330fd47dbafb",
        "outputId": "8995a27a-a916-47db-d8d0-888817682f2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 1.0000\n",
            "Test Accuracy: 1.0000\n",
            "Cross-Validation Scores: [0.96721311 1.         1.         0.98360656 1.        ]\n",
            "Mean CV Score: 0.9902\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "X = df.drop(['Disease'],axis=1)\n",
        "y = df['Disease']\n",
        "\n",
        "# Train Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "# rfmodel = RandomForestClassifier(max_depth=5, max_features='log2', max_leaf_nodes=3, n_estimators=100)\n",
        "rfmodel = RandomForestClassifier()\n",
        "\n",
        "rfmodel.fit(X_train, y_train)\n",
        "y_pred = rfmodel.predict(X_test)\n",
        "\n",
        "# Train Accuracy vs. Test Accuracy:\n",
        "# Compare the performance metrics (e.g., accuracy, precision, recall) on the training set and the test set.\n",
        "# Significant discrepancies where the training performance is much higher than the test performance can indicate overfitting.\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Predict on training and test sets\n",
        "y_train_pred = rfmodel.predict(X_train)\n",
        "y_test_pred = rfmodel.predict(X_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "\n",
        "\n",
        "# Cross-Validation Scores: Use cross-validation to assess the model's performance on different subsets of the data.\n",
        "# If the performance is significantly better on the training folds compared to the validation folds, it may suggest overfitting.\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Perform cross-validation\n",
        "cv_scores = cross_val_score(rfmodel, X, y, cv=5)  # 5-fold cross-validation\n",
        "\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Mean CV Score: {cv_scores.mean():.4f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "74516958-97c2-4a0b-8317-63e89b72ddd9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "74516958-97c2-4a0b-8317-63e89b72ddd9",
        "outputId": "565bd5a5-61b8-45b1-ec2b-45a8d24cb1a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature importances:\n",
            "                   Feature  Importance\n",
            "26             high_fever    0.026424\n",
            "28                fatigue    0.024496\n",
            "0                 itching    0.022102\n",
            "60             mild_fever    0.021705\n",
            "16       loss_of_appetite    0.020028\n",
            "..                    ...         ...\n",
            "83           bloody_stool    0.001547\n",
            "82    pain_in_anal_region    0.001517\n",
            "90            weight_gain    0.000829\n",
            "91   cold_hands_and_feets    0.000786\n",
            "109    pus_filled_pimples    0.000690\n",
            "\n",
            "[122 rows x 2 columns]\n",
            "Important features:\n",
            " 26               high_fever\n",
            "28                  fatigue\n",
            "0                   itching\n",
            "60               mild_fever\n",
            "16         loss_of_appetite\n",
            "              ...          \n",
            "19      burning_micturition\n",
            "96      swollen_extremeties\n",
            "61      swelled_lymph_nodes\n",
            "39              dehydration\n",
            "73    distention_of_abdomen\n",
            "Name: Feature, Length: 86, dtype: object\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of important features: 86\n"
          ]
        }
      ],
      "source": [
        "# Inspect Feature Importances: If some features have very high importance while others have negligible importance,\n",
        "# it might indicate that the model is overly reliant on specific features, which can lead to overfitting.\n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'rfmodel' is your trained Random Forest model\n",
        "importances = rfmodel.feature_importances_\n",
        "\n",
        "# Assuming 'X' is your DataFrame containing features\n",
        "feature_names = X.columns\n",
        "\n",
        "# Create a DataFrame of feature importances\n",
        "importance_df = pd.DataFrame({\n",
        "    'Feature': feature_names,\n",
        "    'Importance': importances\n",
        "})\n",
        "\n",
        "# Sort the DataFrame by importance in descending order\n",
        "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Display the sorted DataFrame\n",
        "print(\"Feature importances:\\n\", importance_df)\n",
        "\n",
        "# Define a threshold or percentage to filter features\n",
        "# Example: keep features with importance above a certain threshold\n",
        "threshold = 0.005 # Example threshold\n",
        "important_features = importance_df[importance_df['Importance'] > threshold]['Feature']\n",
        "\n",
        "# Example: keep top N features\n",
        "# top_n = 20\n",
        "# important_features = importance_df.head(top_n)['Feature']\n",
        "\n",
        "print(\"Important features:\\n\", important_features)\n",
        "\n",
        "# Filter the original DataFrame to keep only the important features, excluding 'Disease'\n",
        "# Ensure 'Disease' column remains in the DataFrame\n",
        "if 'Disease' in X.columns:\n",
        "    important_features = [feature for feature in important_features if feature != 'Disease']\n",
        "\n",
        "filtered_X = X[important_features]\n",
        "\n",
        "# Optionally, train the model again with the reduced set of features\n",
        "# rfmodel.fit(filtered_X, y)\n",
        "# y_pred = rfmodel.predict(filtered_X)\n",
        "# print(\"New model performance:\\n\", accuracy_score(y, y_pred))\n",
        "\n",
        "# Visualize feature importances after removal\n",
        "plt.figure(figsize=(12, 8))\n",
        "plt.barh(importance_df['Feature'], importance_df['Importance'], color='skyblue')\n",
        "plt.xlabel('Importance')\n",
        "plt.title('Feature Importances')\n",
        "plt.gca().invert_yaxis()  # Highest importance at the top\n",
        "plt.show()\n",
        "\n",
        "print(f\"Number of important features: {len(important_features)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "99de77e4-ac0c-40cb-a799-084e6860f678",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99de77e4-ac0c-40cb-a799-084e6860f678",
        "outputId": "4fae8218-b49e-4e19-eb15-44a1315d1d3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns to drop: ['shivering', 'watering_from_eyes', 'internal_itching', 'muscle_wasting', 'patches_in_throat', 'blurred_and_distorted_vision', 'obesity', 'excessive_hunger', 'visual_disturbances', 'weakness_of_one_body_side', 'history_of_alcohol_consumption', 'fluid_overload', 'pain_during_bowel_movements', 'pain_in_anal_region', 'bloody_stool', 'bruising', 'swollen_legs', 'swollen_blood_vessels', 'prominent_veins_on_calf', 'weight_gain', 'cold_hands_and_feets', 'mood_swings', 'puffy_face_and_eyes', 'enlarged_thyroid', 'abnormal_menstruation', 'drying_and_tingling_lips', 'pus_filled_pimples', 'blackheads', 'scurring', 'foul_smell_of urine', 'continuous_feel_of_urine', 'skin_peeling', 'silver_like_dusting', 'small_dents_in_nails', 'red_sore_around_nose', 'yellow_crust_ooze']\n"
          ]
        }
      ],
      "source": [
        "# List of all columns in the DataFrame\n",
        "a = list(df.columns)\n",
        "\n",
        "# List of columns to keep\n",
        "important_features = list(important_features)  # Ensure it is a list\n",
        "important_features.append('Disease')\n",
        "\n",
        "to_drop=list()\n",
        "\n",
        "for i in a:\n",
        "    if i not in important_features:\n",
        "        to_drop.append(i)\n",
        "\n",
        "print(\"Columns to drop:\", to_drop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "f0812e68-3338-46a2-8fe4-ba9f692fcaec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "f0812e68-3338-46a2-8fe4-ba9f692fcaec",
        "outputId": "edd509cb-d33b-466b-e332-081134cee8d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Disease  itching  skin_rash  nodal_skin_eruptions  \\\n",
              "0    Fungal infection        1          1                     1   \n",
              "1    Fungal infection        0          1                     1   \n",
              "2    Fungal infection        1          0                     1   \n",
              "3    Fungal infection        1          1                     0   \n",
              "4    Fungal infection        1          1                     1   \n",
              "..                ...      ...        ...                   ...   \n",
              "299          Impetigo        0          0                     0   \n",
              "300          Impetigo        0          1                     0   \n",
              "301          Impetigo        0          1                     0   \n",
              "302          Impetigo        0          1                     0   \n",
              "303          Impetigo        0          1                     0   \n",
              "\n",
              "     dischromic _patches  continuous_sneezing  chills  stomach_pain  acidity  \\\n",
              "0                      1                    0       0             0        0   \n",
              "1                      1                    0       0             0        0   \n",
              "2                      1                    0       0             0        0   \n",
              "3                      1                    0       0             0        0   \n",
              "4                      0                    0       0             0        0   \n",
              "..                   ...                  ...     ...           ...      ...   \n",
              "299                    0                    0       0             0        0   \n",
              "300                    0                    0       0             0        0   \n",
              "301                    0                    0       0             0        0   \n",
              "302                    0                    0       0             0        0   \n",
              "303                    0                    0       0             0        0   \n",
              "\n",
              "     ulcers_on_tongue  ...  knee_pain  hip_joint_pain  swelling_joints  \\\n",
              "0                   0  ...          0               0                0   \n",
              "1                   0  ...          0               0                0   \n",
              "2                   0  ...          0               0                0   \n",
              "3                   0  ...          0               0                0   \n",
              "4                   0  ...          0               0                0   \n",
              "..                ...  ...        ...             ...              ...   \n",
              "299                 0  ...          0               0                0   \n",
              "300                 0  ...          0               0                0   \n",
              "301                 0  ...          0               0                0   \n",
              "302                 0  ...          0               0                0   \n",
              "303                 0  ...          0               0                0   \n",
              "\n",
              "     painful_walking  movement_stiffness  spinning_movements  unsteadiness  \\\n",
              "0                  0                   0                   0             0   \n",
              "1                  0                   0                   0             0   \n",
              "2                  0                   0                   0             0   \n",
              "3                  0                   0                   0             0   \n",
              "4                  0                   0                   0             0   \n",
              "..               ...                 ...                 ...           ...   \n",
              "299                0                   0                   0             0   \n",
              "300                0                   0                   0             0   \n",
              "301                0                   0                   0             0   \n",
              "302                0                   0                   0             0   \n",
              "303                0                   0                   0             0   \n",
              "\n",
              "     bladder_discomfort  inflammatory_nails  blister  \n",
              "0                     0                   0        0  \n",
              "1                     0                   0        0  \n",
              "2                     0                   0        0  \n",
              "3                     0                   0        0  \n",
              "4                     0                   0        0  \n",
              "..                  ...                 ...      ...  \n",
              "299                   0                   0        1  \n",
              "300                   0                   0        1  \n",
              "301                   0                   0        0  \n",
              "302                   0                   0        1  \n",
              "303                   0                   0        1  \n",
              "\n",
              "[304 rows x 87 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-88d21f18-6ca5-4a44-b092-d56e85465e7e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Disease</th>\n",
              "      <th>itching</th>\n",
              "      <th>skin_rash</th>\n",
              "      <th>nodal_skin_eruptions</th>\n",
              "      <th>dischromic _patches</th>\n",
              "      <th>continuous_sneezing</th>\n",
              "      <th>chills</th>\n",
              "      <th>stomach_pain</th>\n",
              "      <th>acidity</th>\n",
              "      <th>ulcers_on_tongue</th>\n",
              "      <th>...</th>\n",
              "      <th>knee_pain</th>\n",
              "      <th>hip_joint_pain</th>\n",
              "      <th>swelling_joints</th>\n",
              "      <th>painful_walking</th>\n",
              "      <th>movement_stiffness</th>\n",
              "      <th>spinning_movements</th>\n",
              "      <th>unsteadiness</th>\n",
              "      <th>bladder_discomfort</th>\n",
              "      <th>inflammatory_nails</th>\n",
              "      <th>blister</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fungal infection</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fungal infection</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fungal infection</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fungal infection</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fungal infection</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>Impetigo</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>Impetigo</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>Impetigo</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>Impetigo</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>Impetigo</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>304 rows × 87 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88d21f18-6ca5-4a44-b092-d56e85465e7e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-88d21f18-6ca5-4a44-b092-d56e85465e7e 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-88d21f18-6ca5-4a44-b092-d56e85465e7e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-71c16581-c2e2-45dc-b524-c9389afc2ea9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-71c16581-c2e2-45dc-b524-c9389afc2ea9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\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-71c16581-c2e2-45dc-b524-c9389afc2ea9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ad8d9fcf-a99d-4b56-aa9b-79599c3e5335\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_reduced')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ad8d9fcf-a99d-4b56-aa9b-79599c3e5335 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_reduced');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_reduced"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df_reduced = df.drop(columns=to_drop)\n",
        "df_reduced"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd099cb6-1052-49c5-93a8-635c5af592d0",
      "metadata": {
        "id": "bd099cb6-1052-49c5-93a8-635c5af592d0"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "004b4c8e-058e-45d7-8db3-cde257c675a5",
      "metadata": {
        "id": "004b4c8e-058e-45d7-8db3-cde257c675a5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "ab8e5d10-29b3-4b24-ae24-e7978cd56ecd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ab8e5d10-29b3-4b24-ae24-e7978cd56ecd",
        "outputId": "1486dc71-3e79-44b1-eb2f-efb97440d8d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.96721311 0.91803279 0.93442623 0.95081967 0.95      ]\n",
            "Cross-validated accuracy: 0.9440983606557378\n",
            "Confusion Matrix:\n",
            "[[2 0 0 ... 0 0 0]\n",
            " [0 1 0 ... 0 0 0]\n",
            " [0 0 2 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 1 0 0]\n",
            " [0 0 0 ... 0 3 0]\n",
            " [0 0 0 ... 0 0 2]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "X = df_reduced.drop(['Disease'],axis=1)\n",
        "y = df_reduced['Disease']\n",
        "\n",
        "# Train Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "# rfmodel = RandomForestClassifier(max_depth=5, max_features='log2', max_leaf_nodes=3, n_estimators=100)\n",
        "rfmodel = RandomForestClassifier()\n",
        "\n",
        "rfmodel.fit(X_train, y_train)\n",
        "y_pred = rfmodel.predict(X_test)\n",
        "\n",
        "# # accuracy\n",
        "# accuracy = accuracy_score(y_test, y_pred)\n",
        "# print(\"Accuracy:\", accuracy)\n",
        "\n",
        "\n",
        "# Perform cross-validation\n",
        "cv_scores = cross_val_score(rfmodel, X, y, cv=5)\n",
        "print(cv_scores)\n",
        "mean_cv_accuracy = cv_scores.mean()\n",
        "print(\"Cross-validated accuracy:\", mean_cv_accuracy)\n",
        "\n",
        "# import joblib\n",
        "#Save the model to a joblib file\n",
        "# joblib.dump(rfmodel, 'pred-dis.joblib')\n",
        "\n",
        "# import pickle\n",
        "# with open('pred-dis.pkl', 'wb') as file:\n",
        "#     pickle.dump(rfmodel, file)\n",
        "\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Compute and print the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "5433bd72-ed34-4696-b3d1-cbd086d4dffe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5433bd72-ed34-4696-b3d1-cbd086d4dffe",
        "outputId": "bdfaa8de-8f64-4ca9-9b59-0e94ae2bdeb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 1.0000\n",
            "Test Accuracy: 0.9672\n"
          ]
        }
      ],
      "source": [
        "# Train Accuracy vs. Test Accuracy:\n",
        "# Compare the performance metrics (e.g., accuracy, precision, recall) on the training set and the test set.\n",
        "# Significant discrepancies where the training performance is much higher than the test performance can indicate overfitting.\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Predict on training and test sets\n",
        "y_train_pred = rfmodel.predict(X_train)\n",
        "y_test_pred = rfmodel.predict(X_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "print(f\"Train Accuracy: {train_accuracy:.4f}\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "a5805284-65cc-4267-98fe-05f70ed858b9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5805284-65cc-4267-98fe-05f70ed858b9",
        "outputId": "f676c826-0f9a-4cb7-de39-69c14f964631"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Scores: [0.96721311 0.93442623 0.96721311 0.93442623 0.96666667]\n",
            "Mean CV Score: 0.9540\n"
          ]
        }
      ],
      "source": [
        "# Cross-Validation Scores: Use cross-validation to assess the model's performance on different subsets of the data.\n",
        "# If the performance is significantly better on the training folds compared to the validation folds, it may suggest overfitting.\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Perform cross-validation\n",
        "cv_scores = cross_val_score(rfmodel, X, y, cv=5)  # 5-fold cross-validation\n",
        "\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Mean CV Score: {cv_scores.mean():.4f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "16d0fbda-aee5-4925-b962-8181adcfde0d",
      "metadata": {
        "id": "16d0fbda-aee5-4925-b962-8181adcfde0d"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "02d7333f-1a0e-42bd-9cd7-14d577bf0d86",
      "metadata": {
        "id": "02d7333f-1a0e-42bd-9cd7-14d577bf0d86"
      },
      "outputs": [],
      "source": [
        "# Hyperparameters and Their Justifications\n",
        "\n",
        "\n",
        "# n_estimators (Number of Trees)\n",
        "\n",
        "# Recommended Value: 100–500\n",
        "# Reasoning: A larger number of trees generally improves the model's performance by reducing variance and avoiding overfitting.\n",
        "#             With 122 features, having a moderate to large number of trees helps ensure that the ensemble of trees captures complex interactions between symptoms.\n",
        "#             However, very large numbers can lead to increased computation time with diminishing returns. Start with 100 and increase if necessary.\n",
        "\n",
        "\n",
        "# max_depth (Maximum Depth of Trees)\n",
        "\n",
        "# Recommended Value: 10–30\n",
        "# Reasoning: Given the potentially high number of symptoms (features), you might start with deeper trees to capture complex patterns.\n",
        "#            However, deeper trees can overfit, especially if the dataset is not very large. Setting a maximum depth helps control this.\n",
        "#            A depth of 10-30 provides a good balance between capturing detail and preventing overfitting.\n",
        "\n",
        "\n",
        "# min_samples_split (Minimum Samples Required to Split an Internal Node)\n",
        "\n",
        "# Recommended Value: 2–10\n",
        "# Reasoning: With 122 symptoms, a higher min_samples_split value can prevent the model from creating splits based on very few samples,\n",
        "#            which might help in reducing overfitting.\n",
        "#            A starting value of 2 (which allows splits on nodes with just one sample) can be increased if the model shows signs of overfitting.\n",
        "\n",
        "\n",
        "# min_samples_leaf (Minimum Samples Required at a Leaf Node)\n",
        "\n",
        "# Recommended Value: 1–10\n",
        "# Reasoning: Setting a minimum number of samples per leaf node helps to avoid creating leaves that capture noise.\n",
        "#            For a dataset with 122 symptoms, starting with 1 and\n",
        "#            increasing if needed can help in generalizing better by preventing very specific splits.\n",
        "\n",
        "\n",
        "# max_features (Number of Features to Consider for Each Split)\n",
        "\n",
        "# Recommended Value: \"sqrt\" or \"log2\"\n",
        "# Reasoning: With 122 features, setting max_features to \"sqrt\" (which is the square root of the number of features, approximately 11)\n",
        "#               or \"log2\" (which is about 7) helps to ensure each tree considers a subset of features, which promotes diversity among trees\n",
        "#               and reduces overfitting.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "87db813d-8ef1-4beb-b7a7-547321a02e9d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87db813d-8ef1-4beb-b7a7-547321a02e9d",
        "outputId": "e1379b13-032c-4f39-d590-8eb6a6727f0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n",
            "Best Parameters:\n",
            " {'max_depth': None, 'max_features': 'log2', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
            "Accuracy Score:\n",
            " 0.9672131147540983\n",
            "Classification Report:\n",
            "                                          precision    recall  f1-score   support\n",
            "\n",
            "(vertigo) Paroymsal  Positional Vertigo       1.00      1.00      1.00         2\n",
            "                                   AIDS       1.00      1.00      1.00         1\n",
            "                                   Acne       0.67      0.67      0.67         3\n",
            "                    Alcoholic hepatitis       1.00      1.00      1.00         1\n",
            "                                Allergy       0.75      1.00      0.86         3\n",
            "                       Bronchial Asthma       1.00      1.00      1.00         3\n",
            "                   Cervical spondylosis       1.00      1.00      1.00         1\n",
            "                            Chicken pox       1.00      1.00      1.00         3\n",
            "                    Chronic cholestasis       1.00      1.00      1.00         2\n",
            "                                 Dengue       1.00      1.00      1.00         3\n",
            "                              Diabetes        1.00      1.00      1.00         2\n",
            "           Dimorphic hemmorhoids(piles)       1.00      1.00      1.00         2\n",
            "                          Drug Reaction       1.00      1.00      1.00         1\n",
            "                            Hepatitis B       1.00      1.00      1.00         2\n",
            "                            Hepatitis C       1.00      1.00      1.00         1\n",
            "                            Hepatitis D       1.00      1.00      1.00         1\n",
            "                            Hepatitis E       1.00      1.00      1.00         2\n",
            "                        Hyperthyroidism       1.00      1.00      1.00         2\n",
            "                           Hypoglycemia       1.00      1.00      1.00         3\n",
            "                         Hypothyroidism       1.00      1.00      1.00         2\n",
            "                               Impetigo       0.00      0.00      0.00         1\n",
            "                               Jaundice       1.00      1.00      1.00         1\n",
            "                                Malaria       1.00      1.00      1.00         2\n",
            "                               Migraine       1.00      1.00      1.00         4\n",
            "           Paralysis (brain hemorrhage)       1.00      1.00      1.00         1\n",
            "                    Peptic ulcer diseae       1.00      1.00      1.00         1\n",
            "                              Pneumonia       1.00      1.00      1.00         2\n",
            "                              Psoriasis       1.00      1.00      1.00         1\n",
            "                           Tuberculosis       1.00      1.00      1.00         2\n",
            "                Urinary tract infection       1.00      1.00      1.00         1\n",
            "                         Varicose veins       1.00      1.00      1.00         3\n",
            "                            hepatitis A       1.00      1.00      1.00         2\n",
            "\n",
            "                               accuracy                           0.97        61\n",
            "                              macro avg       0.95      0.96      0.95        61\n",
            "                           weighted avg       0.95      0.97      0.96        61\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'n_estimators': [100,  300,  500],\n",
        "    'max_depth': [10, 20, 30, None],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 3, 6],\n",
        "    'max_features': ['sqrt','log2']\n",
        "}\n",
        "\n",
        "# Initialize the Random Forest model\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "# Set up GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid,\n",
        "                           cv=5, n_jobs=-1, verbose=2,\n",
        "                           scoring='accuracy')\n",
        "\n",
        "# Fit the GridSearchCV\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Get the best parameters and model\n",
        "best_params = grid_search.best_params_\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "# Display the best parameters\n",
        "print(\"Best Parameters:\\n\", best_params)\n",
        "\n",
        "# Evaluate the model\n",
        "y_pred = best_model.predict(X_test)\n",
        "print(\"Accuracy Score:\\n\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "ee7019c8-97a8-4f6a-b123-80cd01b15b70",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee7019c8-97a8-4f6a-b123-80cd01b15b70",
        "outputId": "d7fdc6ab-f4e1-4578-9278-9b24c32a03b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 0,\n",
              " 0]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "a=list(df_reduced.iloc[0])[1:]\n",
        "a"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clxhOmzw1ew_",
        "outputId": "17f9ffdb-5ab2-4e74-9bb4-cb85e6366998"
      },
      "id": "clxhOmzw1ew_",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = best_model.predict([a])\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfd7yzb30-6C",
        "outputId": "19f60702-be6c-49fc-928b-fdeb9e190282"
      },
      "id": "cfd7yzb30-6C",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Fungal infection'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "from joblib import dump , load\n",
        "\n",
        "\n",
        "probabilities = best_model.predict_proba([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "\n",
        "])\n",
        "disease_probabilities = dict(zip(best_model.classes_, probabilities[0]))\n",
        "\n",
        "top_n = 5  # Number of top probabilities to display\n",
        "sorted_probabilities = sorted(disease_probabilities.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
        "\n",
        "# disease_df = pd.read_csv('models/disease_description.csv')\n",
        "# precaution_df = pd.read_csv('models/disease_precaution.csv')\n",
        "\n",
        "\n",
        "count = 0\n",
        "print(\"top 5 diseases you may have with corresponding probability:\")\n",
        "print('_______________________________________________________________________________')\n",
        "for disease, probability in sorted_probabilities:\n",
        "    if probability > 0:\n",
        "        print(f\"Probability of having '{disease}': {int(probability*100)}%\")\n",
        "\n",
        "        # #Filter the DataFrame to retrieve the description of the predicted disease\n",
        "        # predicted_disease_description = disease_df[disease_df['Disease'] == disease]['Symptom_Description']\n",
        "        # predicted_disease_precautions = precaution_df[precaution_df['Disease'] == disease]\n",
        "\n",
        "        # #Print the description of the disease\n",
        "        # if not predicted_disease_description.empty:\n",
        "        #     print(\"DESCRIPTION: \",predicted_disease_description.values[0])\n",
        "        # else:\n",
        "        #     print(\"Description not found for the predicted disease.\")\n",
        "\n",
        "        # print('\\n')\n",
        "        # if not predicted_disease_precautions.empty:\n",
        "        #     print('PRECAUTIONS:')\n",
        "        #     for column in ['Symptom_precaution_0', 'Symptom_precaution_1', 'Symptom_precaution_2', 'Symptom_precaution_3']:\n",
        "        #         precaution = predicted_disease_precautions[column].values[0]\n",
        "        #         print(precaution)\n",
        "        # else:\n",
        "        #     print(\"Precautions not found for the predicted disease.\")\n",
        "\n",
        "#         print('\\n')\n",
        "\n",
        "        if(probability*100)<50:\n",
        "            print('since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation')\n",
        "        count += 1\n",
        "        print('_______________________________________________________________________________')\n",
        "        if count == top_n:\n",
        "            break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82IJ4gWs1mbc",
        "outputId": "120bdffc-fe32-4370-e574-01577081bd51"
      },
      "id": "82IJ4gWs1mbc",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "top 5 diseases you may have with corresponding probability:\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Allergy': 64%\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'AIDS': 5%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Acne': 5%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Common Cold': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Dimorphic hemmorhoids(piles)': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_reduced[df_reduced['Disease']==\"Allergy\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "7AH0YVqt2VoE",
        "outputId": "a16e8535-c4b6-4f9e-fb3a-dbd37354fe5e"
      },
      "id": "7AH0YVqt2VoE",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Disease  itching  skin_rash  nodal_skin_eruptions  dischromic _patches  \\\n",
              "5  Allergy        0          0                     0                    0   \n",
              "6  Allergy        0          0                     0                    0   \n",
              "7  Allergy        0          0                     0                    0   \n",
              "8  Allergy        0          0                     0                    0   \n",
              "9  Allergy        0          0                     0                    0   \n",
              "\n",
              "   continuous_sneezing  chills  stomach_pain  acidity  ulcers_on_tongue  ...  \\\n",
              "5                    1       1             0        0                 0  ...   \n",
              "6                    0       1             0        0                 0  ...   \n",
              "7                    1       1             0        0                 0  ...   \n",
              "8                    1       0             0        0                 0  ...   \n",
              "9                    1       1             0        0                 0  ...   \n",
              "\n",
              "   knee_pain  hip_joint_pain  swelling_joints  painful_walking  \\\n",
              "5          0               0                0                0   \n",
              "6          0               0                0                0   \n",
              "7          0               0                0                0   \n",
              "8          0               0                0                0   \n",
              "9          0               0                0                0   \n",
              "\n",
              "   movement_stiffness  spinning_movements  unsteadiness  bladder_discomfort  \\\n",
              "5                   0                   0             0                   0   \n",
              "6                   0                   0             0                   0   \n",
              "7                   0                   0             0                   0   \n",
              "8                   0                   0             0                   0   \n",
              "9                   0                   0             0                   0   \n",
              "\n",
              "   inflammatory_nails  blister  \n",
              "5                   0        0  \n",
              "6                   0        0  \n",
              "7                   0        0  \n",
              "8                   0        0  \n",
              "9                   0        0  \n",
              "\n",
              "[5 rows x 87 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d2876536-5023-4249-97d3-7f88c8054650\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Disease</th>\n",
              "      <th>itching</th>\n",
              "      <th>skin_rash</th>\n",
              "      <th>nodal_skin_eruptions</th>\n",
              "      <th>dischromic _patches</th>\n",
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              "      <th>ulcers_on_tongue</th>\n",
              "      <th>...</th>\n",
              "      <th>knee_pain</th>\n",
              "      <th>hip_joint_pain</th>\n",
              "      <th>swelling_joints</th>\n",
              "      <th>painful_walking</th>\n",
              "      <th>movement_stiffness</th>\n",
              "      <th>spinning_movements</th>\n",
              "      <th>unsteadiness</th>\n",
              "      <th>bladder_discomfort</th>\n",
              "      <th>inflammatory_nails</th>\n",
              "      <th>blister</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Allergy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Allergy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Allergy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Allergy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>Allergy</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 87 columns</p>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "joblib.dump(best_model, 'pred-dis.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuF8rkSI_H22",
        "outputId": "67c171c9-3c8c-4cb9-a5ac-bcdf12ea7936"
      },
      "id": "zuF8rkSI_H22",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pred-dis.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kjYe-bIPCYT9"
      },
      "id": "kjYe-bIPCYT9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "350UdAP8IXSk"
      },
      "id": "350UdAP8IXSk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Initialize the SVM model with default parameters\n",
        "svm_model = svm.SVC(probability=True)\n",
        "\n",
        "# Fit the model on the training data\n",
        "svm_model.fit(X_train, y_train)\n",
        "\n",
        "# Predict on the test set\n",
        "y_pred = svm_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Accuracy Score:\\n\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lwC4vVyIX58",
        "outputId": "8e86f1bb-86eb-400a-c7d0-18a8a8d14d1b"
      },
      "id": "0lwC4vVyIX58",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:\n",
            " 0.9508196721311475\n",
            "Classification Report:\n",
            "                                          precision    recall  f1-score   support\n",
            "\n",
            "(vertigo) Paroymsal  Positional Vertigo       1.00      1.00      1.00         2\n",
            "                                   AIDS       0.50      1.00      0.67         1\n",
            "                                   Acne       0.00      0.00      0.00         3\n",
            "                    Alcoholic hepatitis       1.00      1.00      1.00         1\n",
            "                                Allergy       1.00      1.00      1.00         3\n",
            "                       Bronchial Asthma       1.00      1.00      1.00         3\n",
            "                   Cervical spondylosis       1.00      1.00      1.00         1\n",
            "                            Chicken pox       1.00      1.00      1.00         3\n",
            "                    Chronic cholestasis       1.00      1.00      1.00         2\n",
            "                                 Dengue       1.00      1.00      1.00         3\n",
            "                              Diabetes        1.00      1.00      1.00         2\n",
            "           Dimorphic hemmorhoids(piles)       1.00      1.00      1.00         2\n",
            "                          Drug Reaction       1.00      1.00      1.00         1\n",
            "                       Fungal infection       0.00      0.00      0.00         0\n",
            "                            Hepatitis B       1.00      1.00      1.00         2\n",
            "                            Hepatitis C       1.00      1.00      1.00         1\n",
            "                            Hepatitis D       1.00      1.00      1.00         1\n",
            "                            Hepatitis E       1.00      1.00      1.00         2\n",
            "                        Hyperthyroidism       1.00      1.00      1.00         2\n",
            "                           Hypoglycemia       1.00      1.00      1.00         3\n",
            "                         Hypothyroidism       1.00      1.00      1.00         2\n",
            "                               Impetigo       1.00      1.00      1.00         1\n",
            "                               Jaundice       1.00      1.00      1.00         1\n",
            "                                Malaria       1.00      1.00      1.00         2\n",
            "                               Migraine       1.00      1.00      1.00         4\n",
            "           Paralysis (brain hemorrhage)       1.00      1.00      1.00         1\n",
            "                    Peptic ulcer diseae       1.00      1.00      1.00         1\n",
            "                              Pneumonia       1.00      1.00      1.00         2\n",
            "                              Psoriasis       1.00      1.00      1.00         1\n",
            "                           Tuberculosis       1.00      1.00      1.00         2\n",
            "                Urinary tract infection       1.00      1.00      1.00         1\n",
            "                         Varicose veins       1.00      1.00      1.00         3\n",
            "                            hepatitis A       1.00      1.00      1.00         2\n",
            "\n",
            "                               accuracy                           0.95        61\n",
            "                              macro avg       0.92      0.94      0.93        61\n",
            "                           weighted avg       0.94      0.95      0.95        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "from joblib import dump , load\n",
        "\n",
        "\n",
        "probabilities = svm_model.predict_proba([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "\n",
        "])\n",
        "disease_probabilities = dict(zip(svm_model.classes_, probabilities[0]))\n",
        "\n",
        "top_n = 5  # Number of top probabilities to display\n",
        "sorted_probabilities = sorted(disease_probabilities.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
        "\n",
        "# disease_df = pd.read_csv('models/disease_description.csv')\n",
        "# precaution_df = pd.read_csv('models/disease_precaution.csv')\n",
        "\n",
        "\n",
        "count = 0\n",
        "print(\"top 5 diseases you may have with corresponding probability:\")\n",
        "print('_______________________________________________________________________________')\n",
        "for disease, probability in sorted_probabilities:\n",
        "    if probability > 0:\n",
        "        print(f\"Probability of having '{disease}': {int(probability*100)}%\")\n",
        "\n",
        "        # #Filter the DataFrame to retrieve the description of the predicted disease\n",
        "        # predicted_disease_description = disease_df[disease_df['Disease'] == disease]['Symptom_Description']\n",
        "        # predicted_disease_precautions = precaution_df[precaution_df['Disease'] == disease]\n",
        "\n",
        "        # #Print the description of the disease\n",
        "        # if not predicted_disease_description.empty:\n",
        "        #     print(\"DESCRIPTION: \",predicted_disease_description.values[0])\n",
        "        # else:\n",
        "        #     print(\"Description not found for the predicted disease.\")\n",
        "\n",
        "        # print('\\n')\n",
        "        # if not predicted_disease_precautions.empty:\n",
        "        #     print('PRECAUTIONS:')\n",
        "        #     for column in ['Symptom_precaution_0', 'Symptom_precaution_1', 'Symptom_precaution_2', 'Symptom_precaution_3']:\n",
        "        #         precaution = predicted_disease_precautions[column].values[0]\n",
        "        #         print(precaution)\n",
        "        # else:\n",
        "        #     print(\"Precautions not found for the predicted disease.\")\n",
        "\n",
        "#         print('\\n')\n",
        "\n",
        "        if(probability*100)<50:\n",
        "            print('since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation')\n",
        "        count += 1\n",
        "        print('_______________________________________________________________________________')\n",
        "        if count == top_n:\n",
        "            break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZuxA_-0IYHo",
        "outputId": "5a69008c-b384-43fb-8fab-236a61600709"
      },
      "id": "EZuxA_-0IYHo",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "top 5 diseases you may have with corresponding probability:\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Acne': 8%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Allergy': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Dimorphic hemmorhoids(piles)': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Psoriasis': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Urinary tract infection': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters and Their Justifications:\n",
        "\n",
        "\n",
        "# C (Regularization Parameter)\n",
        "\n",
        "# Recommended Value: 0.1 to 100\n",
        "# Reasoning: The parameter C controls the trade-off between achieving a low error on the training data\n",
        "# and minimizing the model complexity (i.e., maximizing the margin between classes).\n",
        "# A higher C value allows the model to fit the training data more precisely,\n",
        "# which can reduce the margin but increase the risk of overfitting.\n",
        "# A lower C value creates a larger margin and may lead to underfitting.\n",
        "# For a dataset with 122 symptoms, starting with C values like 1, 10, and 100 provides a range to balance bias and variance.\n",
        "\n",
        "\n",
        "# kernel (Kernel Type)\n",
        "\n",
        "# Recommended Values: 'linear', 'rbf' (Radial Basis Function)\n",
        "# Reasoning:\n",
        "# Linear: Useful if the data is linearly separable.\n",
        "# Given 122 features, a linear kernel might work if the relationship between features and diseases is straightforward.\n",
        "# RBF: Handles non-linear data well.\n",
        "# It transforms the data into a higher-dimensional space, which can capture complex relationships between symptoms and diseases.\n",
        "# Given the high dimensionality of the feature space, starting with rbf is often beneficial to handle non-linearity.\n",
        "\n",
        "\n",
        "# gamma (Kernel Coefficient for RBF, Polynomial, and Sigmoid Kernels)\n",
        "\n",
        "# Recommended Values: 'scale', 'auto', or specific values like 0.001, 0.01, 0.1, 1\n",
        "# Reasoning: gamma determines how far the influence of a single training example reaches.\n",
        "# A high gamma value means the model will consider only the closest data points, leading to a complex\n",
        "# and potentially overfitted model. A low gamma value means the model considers points farther away, which can lead to underfitting.\n",
        "# The choice of 'scale' or 'auto' provides a good starting point, while specific values help fine-tune based on the complexity of the data.\n"
      ],
      "metadata": {
        "id": "Ud7coUQlIAna"
      },
      "id": "Ud7coUQlIAna",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Defining parameter range for SVM\n",
        "param_grid = {\n",
        "    'C': [0.1, 1, 10, 20, 100],\n",
        "    'gamma': [1, 0.1, 0.01, 0.001, 0.0001, 'auto'],\n",
        "    'kernel': ['rbf', 'linear', 'poly', 'sigmoid']\n",
        "}\n",
        "\n",
        "# Initialize the SVM model\n",
        "svm_model = svm.SVC(probability=True)\n",
        "\n",
        "# Set up the GridSearchCV with the SVM model and parameter grid\n",
        "grid = GridSearchCV(estimator=svm_model, param_grid=param_grid, refit=True, verbose=3, cv=5)\n",
        "\n",
        "# Fitting the model for grid search\n",
        "grid.fit(X_train, y_train)\n",
        "\n",
        "# Print best parameter after tuning\n",
        "print(\"Best Parameters:\\n\", grid.best_params_)\n",
        "\n",
        "# Print the best model after hyper-parameter tuning\n",
        "print(\"Best Estimator:\\n\", grid.best_estimator_)\n",
        "\n",
        "# Optionally, you can evaluate the best model on test data\n",
        "y_pred = grid.best_estimator_.predict(X_test)\n",
        "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_EpTlMDCYXf",
        "outputId": "2dd4d108-30d5-4353-8447-e2b0e515cd4f"
      },
      "id": "0_EpTlMDCYXf",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 120 candidates, totalling 600 fits\n",
            "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 3/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.917 total time=   0.1s\n",
            "[CV 5/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.938 total time=   0.1s\n",
            "[CV 1/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.918 total time=   0.1s\n",
            "[CV 3/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.938 total time=   0.1s\n",
            "[CV 5/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.958 total time=   0.1s\n",
            "[CV 1/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.020 total time=   0.1s\n",
            "[CV 2/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.082 total time=   0.1s\n",
            "[CV 4/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.104 total time=   0.1s\n",
            "[CV 5/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.042 total time=   0.1s\n",
            "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.917 total time=   0.1s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.938 total time=   0.1s\n",
            "[CV 1/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 3/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=0.917 total time=   0.1s\n",
            "[CV 5/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=0.938 total time=   0.1s\n",
            "[CV 1/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 3/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.917 total time=   0.1s\n",
            "[CV 5/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.938 total time=   0.1s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 3/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.917 total time=   0.1s\n",
            "[CV 5/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.938 total time=   0.1s\n",
            "[CV 1/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.122 total time=   0.3s\n",
            "[CV 4/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.125 total time=   0.2s\n",
            "[CV 5/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.021 total time=   0.2s\n",
            "[CV 1/5] END .....C=0.1, gamma=auto, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=0.1, gamma=auto, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=0.1, gamma=auto, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=0.1, gamma=auto, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=0.1, gamma=auto, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=0.1, gamma=auto, kernel=linear;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END ..C=0.1, gamma=auto, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 3/5] END ..C=0.1, gamma=auto, kernel=linear;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END ..C=0.1, gamma=auto, kernel=linear;, score=0.917 total time=   0.1s\n",
            "[CV 5/5] END ..C=0.1, gamma=auto, kernel=linear;, score=0.938 total time=   0.1s\n",
            "[CV 1/5] END ....C=0.1, gamma=auto, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=0.1, gamma=auto, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=0.1, gamma=auto, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=0.1, gamma=auto, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=0.1, gamma=auto, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=0.1, gamma=auto, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .C=0.1, gamma=auto, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .C=0.1, gamma=auto, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .C=0.1, gamma=auto, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .C=0.1, gamma=auto, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.939 total time=   0.1s\n",
            "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .......C=1, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .......C=1, gamma=1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .......C=1, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .......C=1, gamma=1, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END .......C=1, gamma=1, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .........C=1, gamma=1, kernel=poly;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END .........C=1, gamma=1, kernel=poly;, score=0.918 total time=   0.1s\n",
            "[CV 3/5] END .........C=1, gamma=1, kernel=poly;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END .........C=1, gamma=1, kernel=poly;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END .........C=1, gamma=1, kernel=poly;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.347 total time=   0.1s\n",
            "[CV 2/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.327 total time=   0.1s\n",
            "[CV 3/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.306 total time=   0.1s\n",
            "[CV 4/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.333 total time=   0.1s\n",
            "[CV 5/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.333 total time=   0.1s\n",
            "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .....C=1, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .....C=1, gamma=0.1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .....C=1, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .....C=1, gamma=0.1, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END .....C=1, gamma=0.1, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.408 total time=   0.1s\n",
            "[CV 2/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.510 total time=   0.1s\n",
            "[CV 3/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.551 total time=   0.1s\n",
            "[CV 4/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.500 total time=   0.1s\n",
            "[CV 5/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.750 total time=   0.1s\n",
            "[CV 1/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.898 total time=   0.1s\n",
            "[CV 2/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.878 total time=   0.1s\n",
            "[CV 3/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.857 total time=   0.1s\n",
            "[CV 4/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.938 total time=   0.1s\n",
            "[CV 5/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.896 total time=   0.1s\n",
            "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.204 total time=   0.1s\n",
            "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.245 total time=   0.1s\n",
            "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.224 total time=   0.1s\n",
            "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.229 total time=   0.1s\n",
            "[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.146 total time=   0.1s\n",
            "[CV 1/5] END ....C=1, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ....C=1, gamma=0.01, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ....C=1, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ....C=1, gamma=0.01, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ....C=1, gamma=0.01, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ......C=1, gamma=0.01, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ......C=1, gamma=0.01, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ......C=1, gamma=0.01, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ......C=1, gamma=0.01, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ......C=1, gamma=0.01, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ...C=1, gamma=0.01, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ...C=1, gamma=0.01, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ...C=1, gamma=0.01, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ...C=1, gamma=0.01, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ...C=1, gamma=0.01, kernel=sigmoid;, score=0.062 total time=   0.1s\n",
            "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ...C=1, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=1, gamma=0.001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=1, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=1, gamma=0.001, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ...C=1, gamma=0.001, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=1, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=1, gamma=0.0001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=1, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ..C=1, gamma=0.0001, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ..C=1, gamma=0.0001, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .......C=1, gamma=auto, kernel=rbf;, score=0.265 total time=   0.1s\n",
            "[CV 2/5] END .......C=1, gamma=auto, kernel=rbf;, score=0.306 total time=   0.1s\n",
            "[CV 3/5] END .......C=1, gamma=auto, kernel=rbf;, score=0.245 total time=   0.1s\n",
            "[CV 4/5] END .......C=1, gamma=auto, kernel=rbf;, score=0.312 total time=   0.1s\n",
            "[CV 5/5] END .......C=1, gamma=auto, kernel=rbf;, score=0.354 total time=   0.1s\n",
            "[CV 1/5] END ....C=1, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ....C=1, gamma=auto, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ....C=1, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ....C=1, gamma=auto, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ....C=1, gamma=auto, kernel=linear;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ......C=1, gamma=auto, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ......C=1, gamma=auto, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ......C=1, gamma=auto, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ......C=1, gamma=auto, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ......C=1, gamma=auto, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ...C=1, gamma=auto, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ...C=1, gamma=auto, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ...C=1, gamma=auto, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ...C=1, gamma=auto, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ...C=1, gamma=auto, kernel=sigmoid;, score=0.062 total time=   0.1s\n",
            "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.939 total time=   0.1s\n",
            "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ......C=10, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ......C=10, gamma=1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ......C=10, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ......C=10, gamma=1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ......C=10, gamma=1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ........C=10, gamma=1, kernel=poly;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END ........C=10, gamma=1, kernel=poly;, score=0.918 total time=   0.1s\n",
            "[CV 3/5] END ........C=10, gamma=1, kernel=poly;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END ........C=10, gamma=1, kernel=poly;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END ........C=10, gamma=1, kernel=poly;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.592 total time=   0.1s\n",
            "[CV 2/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.571 total time=   0.1s\n",
            "[CV 3/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.490 total time=   0.1s\n",
            "[CV 4/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.583 total time=   0.1s\n",
            "[CV 5/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.583 total time=   0.1s\n",
            "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ....C=10, gamma=0.1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ....C=10, gamma=0.1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.837 total time=   0.1s\n",
            "[CV 2/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.776 total time=   0.1s\n",
            "[CV 3/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.776 total time=   0.1s\n",
            "[CV 4/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.854 total time=   0.1s\n",
            "[CV 5/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.833 total time=   0.1s\n",
            "[CV 1/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=10, gamma=0.01, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=10, gamma=0.01, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=10, gamma=0.01, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=10, gamma=0.01, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=10, gamma=0.01, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=10, gamma=0.01, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=10, gamma=0.01, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.918 total time=   0.1s\n",
            "[CV 2/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.878 total time=   0.1s\n",
            "[CV 3/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.878 total time=   0.1s\n",
            "[CV 4/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.938 total time=   0.1s\n",
            "[CV 5/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.917 total time=   0.1s\n",
            "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.224 total time=   0.1s\n",
            "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.245 total time=   0.1s\n",
            "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.224 total time=   0.1s\n",
            "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.250 total time=   0.1s\n",
            "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.188 total time=   0.1s\n",
            "[CV 1/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ..C=10, gamma=0.001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ..C=10, gamma=0.001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.062 total time=   0.1s\n",
            "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .C=10, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .C=10, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ......C=10, gamma=auto, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ......C=10, gamma=auto, kernel=rbf;, score=0.980 total time=   0.2s\n",
            "[CV 3/5] END ......C=10, gamma=auto, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ......C=10, gamma=auto, kernel=rbf;, score=0.979 total time=   0.2s\n",
            "[CV 5/5] END ......C=10, gamma=auto, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ...C=10, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=10, gamma=auto, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=10, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=10, gamma=auto, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=10, gamma=auto, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=10, gamma=auto, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=10, gamma=auto, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=10, gamma=auto, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=10, gamma=auto, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=10, gamma=auto, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=10, gamma=auto, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=10, gamma=auto, kernel=sigmoid;, score=0.918 total time=   0.1s\n",
            "[CV 3/5] END ..C=10, gamma=auto, kernel=sigmoid;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END ..C=10, gamma=auto, kernel=sigmoid;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END ..C=10, gamma=auto, kernel=sigmoid;, score=0.979 total time=   0.2s\n",
            "[CV 1/5] END .........C=20, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .........C=20, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 3/5] END .........C=20, gamma=1, kernel=rbf;, score=0.939 total time=   0.1s\n",
            "[CV 4/5] END .........C=20, gamma=1, kernel=rbf;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END .........C=20, gamma=1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ......C=20, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ......C=20, gamma=1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ......C=20, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ......C=20, gamma=1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ......C=20, gamma=1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ........C=20, gamma=1, kernel=poly;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END ........C=20, gamma=1, kernel=poly;, score=0.918 total time=   0.1s\n",
            "[CV 3/5] END ........C=20, gamma=1, kernel=poly;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END ........C=20, gamma=1, kernel=poly;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END ........C=20, gamma=1, kernel=poly;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .....C=20, gamma=1, kernel=sigmoid;, score=0.612 total time=   0.1s\n",
            "[CV 2/5] END .....C=20, gamma=1, kernel=sigmoid;, score=0.571 total time=   0.1s\n",
            "[CV 3/5] END .....C=20, gamma=1, kernel=sigmoid;, score=0.531 total time=   0.1s\n",
            "[CV 4/5] END .....C=20, gamma=1, kernel=sigmoid;, score=0.604 total time=   0.1s\n",
            "[CV 5/5] END .....C=20, gamma=1, kernel=sigmoid;, score=0.542 total time=   0.1s\n",
            "[CV 1/5] END .......C=20, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .......C=20, gamma=0.1, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .......C=20, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .......C=20, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .......C=20, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ....C=20, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ....C=20, gamma=0.1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ....C=20, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ....C=20, gamma=0.1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ....C=20, gamma=0.1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ......C=20, gamma=0.1, kernel=poly;, score=0.816 total time=   0.1s\n",
            "[CV 2/5] END ......C=20, gamma=0.1, kernel=poly;, score=0.878 total time=   0.1s\n",
            "[CV 3/5] END ......C=20, gamma=0.1, kernel=poly;, score=0.796 total time=   0.1s\n",
            "[CV 4/5] END ......C=20, gamma=0.1, kernel=poly;, score=0.875 total time=   0.1s\n",
            "[CV 5/5] END ......C=20, gamma=0.1, kernel=poly;, score=0.875 total time=   0.1s\n",
            "[CV 1/5] END ...C=20, gamma=0.1, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=20, gamma=0.1, kernel=sigmoid;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=20, gamma=0.1, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=20, gamma=0.1, kernel=sigmoid;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=20, gamma=0.1, kernel=sigmoid;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ......C=20, gamma=0.01, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ......C=20, gamma=0.01, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ......C=20, gamma=0.01, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ......C=20, gamma=0.01, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ......C=20, gamma=0.01, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ...C=20, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=20, gamma=0.01, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=20, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=20, gamma=0.01, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=20, gamma=0.01, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=20, gamma=0.01, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=20, gamma=0.01, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=20, gamma=0.01, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=20, gamma=0.01, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=20, gamma=0.01, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=20, gamma=0.01, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=20, gamma=0.01, kernel=sigmoid;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=20, gamma=0.01, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ..C=20, gamma=0.01, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ..C=20, gamma=0.01, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .....C=20, gamma=0.001, kernel=rbf;, score=0.551 total time=   0.1s\n",
            "[CV 2/5] END .....C=20, gamma=0.001, kernel=rbf;, score=0.510 total time=   0.1s\n",
            "[CV 3/5] END .....C=20, gamma=0.001, kernel=rbf;, score=0.592 total time=   0.1s\n",
            "[CV 4/5] END .....C=20, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.1s\n",
            "[CV 5/5] END .....C=20, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.1s\n",
            "[CV 1/5] END ..C=20, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=20, gamma=0.001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=20, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ..C=20, gamma=0.001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ..C=20, gamma=0.001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ....C=20, gamma=0.001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=20, gamma=0.001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=20, gamma=0.001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=20, gamma=0.001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=20, gamma=0.001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=20, gamma=0.001, kernel=sigmoid;, score=0.224 total time=   0.1s\n",
            "[CV 2/5] END .C=20, gamma=0.001, kernel=sigmoid;, score=0.245 total time=   0.1s\n",
            "[CV 3/5] END .C=20, gamma=0.001, kernel=sigmoid;, score=0.224 total time=   0.1s\n",
            "[CV 4/5] END .C=20, gamma=0.001, kernel=sigmoid;, score=0.250 total time=   0.1s\n",
            "[CV 5/5] END .C=20, gamma=0.001, kernel=sigmoid;, score=0.188 total time=   0.1s\n",
            "[CV 1/5] END ....C=20, gamma=0.0001, kernel=rbf;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=20, gamma=0.0001, kernel=rbf;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=20, gamma=0.0001, kernel=rbf;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=20, gamma=0.0001, kernel=rbf;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=20, gamma=0.0001, kernel=rbf;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=20, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .C=20, gamma=0.0001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .C=20, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .C=20, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .C=20, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ...C=20, gamma=0.0001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ...C=20, gamma=0.0001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ...C=20, gamma=0.0001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ...C=20, gamma=0.0001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ...C=20, gamma=0.0001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END C=20, gamma=0.0001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END C=20, gamma=0.0001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END C=20, gamma=0.0001, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END C=20, gamma=0.0001, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END C=20, gamma=0.0001, kernel=sigmoid;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ......C=20, gamma=auto, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ......C=20, gamma=auto, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ......C=20, gamma=auto, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ......C=20, gamma=auto, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ......C=20, gamma=auto, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ...C=20, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=20, gamma=auto, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=20, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=20, gamma=auto, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=20, gamma=auto, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=20, gamma=auto, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END .....C=20, gamma=auto, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END .....C=20, gamma=auto, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END .....C=20, gamma=auto, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END .....C=20, gamma=auto, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END ..C=20, gamma=auto, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=20, gamma=auto, kernel=sigmoid;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=20, gamma=auto, kernel=sigmoid;, score=0.959 total time=   0.2s\n",
            "[CV 4/5] END ..C=20, gamma=auto, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ..C=20, gamma=auto, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.939 total time=   0.1s\n",
            "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=100, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .....C=100, gamma=1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .....C=100, gamma=1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .....C=100, gamma=1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .....C=100, gamma=1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .......C=100, gamma=1, kernel=poly;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END .......C=100, gamma=1, kernel=poly;, score=0.918 total time=   0.1s\n",
            "[CV 3/5] END .......C=100, gamma=1, kernel=poly;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END .......C=100, gamma=1, kernel=poly;, score=0.958 total time=   0.1s\n",
            "[CV 5/5] END .......C=100, gamma=1, kernel=poly;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.776 total time=   0.1s\n",
            "[CV 2/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.653 total time=   0.1s\n",
            "[CV 3/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.592 total time=   0.1s\n",
            "[CV 4/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.688 total time=   0.1s\n",
            "[CV 5/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.750 total time=   0.1s\n",
            "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.2s\n",
            "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.980 total time=   0.2s\n",
            "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.959 total time=   0.2s\n",
            "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ...C=100, gamma=0.1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=100, gamma=0.1, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.939 total time=   0.1s\n",
            "[CV 2/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.918 total time=   0.1s\n",
            "[CV 3/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.898 total time=   0.1s\n",
            "[CV 4/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.938 total time=   0.1s\n",
            "[CV 5/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.958 total time=   0.1s\n",
            "[CV 1/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ..C=100, gamma=0.01, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ..C=100, gamma=0.01, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.979 total time=   0.1s\n",
            "[CV 1/5] END .C=100, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .C=100, gamma=0.001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .C=100, gamma=0.001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .C=100, gamma=0.001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .C=100, gamma=0.001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.918 total time=   0.1s\n",
            "[CV 2/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.878 total time=   0.1s\n",
            "[CV 3/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.878 total time=   0.1s\n",
            "[CV 4/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.938 total time=   0.1s\n",
            "[CV 5/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.917 total time=   0.1s\n",
            "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.224 total time=   0.1s\n",
            "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.245 total time=   0.1s\n",
            "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.224 total time=   0.1s\n",
            "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.229 total time=   0.1s\n",
            "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.188 total time=   0.1s\n",
            "[CV 1/5] END C=100, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END C=100, gamma=0.0001, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END C=100, gamma=0.0001, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END C=100, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END C=100, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.021 total time=   0.1s\n",
            "[CV 1/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.041 total time=   0.1s\n",
            "[CV 2/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.122 total time=   0.1s\n",
            "[CV 4/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.125 total time=   0.1s\n",
            "[CV 5/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.062 total time=   0.1s\n",
            "[CV 1/5] END .....C=100, gamma=auto, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .....C=100, gamma=auto, kernel=rbf;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .....C=100, gamma=auto, kernel=rbf;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .....C=100, gamma=auto, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .....C=100, gamma=auto, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ..C=100, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END ..C=100, gamma=auto, kernel=linear;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END ..C=100, gamma=auto, kernel=linear;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END ..C=100, gamma=auto, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ..C=100, gamma=auto, kernel=linear;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ....C=100, gamma=auto, kernel=poly;, score=0.122 total time=   0.1s\n",
            "[CV 2/5] END ....C=100, gamma=auto, kernel=poly;, score=0.143 total time=   0.1s\n",
            "[CV 3/5] END ....C=100, gamma=auto, kernel=poly;, score=0.163 total time=   0.1s\n",
            "[CV 4/5] END ....C=100, gamma=auto, kernel=poly;, score=0.167 total time=   0.1s\n",
            "[CV 5/5] END ....C=100, gamma=auto, kernel=poly;, score=0.125 total time=   0.1s\n",
            "[CV 1/5] END .C=100, gamma=auto, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 2/5] END .C=100, gamma=auto, kernel=sigmoid;, score=0.980 total time=   0.1s\n",
            "[CV 3/5] END .C=100, gamma=auto, kernel=sigmoid;, score=0.959 total time=   0.1s\n",
            "[CV 4/5] END .C=100, gamma=auto, kernel=sigmoid;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .C=100, gamma=auto, kernel=sigmoid;, score=1.000 total time=   0.1s\n",
            "Best Parameters:\n",
            " {'C': 10, 'gamma': 1, 'kernel': 'linear'}\n",
            "Best Estimator:\n",
            " SVC(C=10, gamma=1, kernel='linear', probability=True)\n",
            "Test Accuracy: 0.8852459016393442\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "\n",
        "# Predict on the test set using the best model\n",
        "y_pred = grid.best_estimator_.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Accuracy Score:\\n\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFNjq7FpEC7T",
        "outputId": "8a670a72-05db-4231-b9f2-496b22e89e0b"
      },
      "id": "sFNjq7FpEC7T",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:\n",
            " 0.8852459016393442\n",
            "Classification Report:\n",
            "                                          precision    recall  f1-score   support\n",
            "\n",
            "(vertigo) Paroymsal  Positional Vertigo       1.00      1.00      1.00         2\n",
            "                                   AIDS       0.00      0.00      0.00         1\n",
            "                                   Acne       0.50      0.67      0.57         3\n",
            "                    Alcoholic hepatitis       1.00      1.00      1.00         1\n",
            "                                Allergy       0.38      1.00      0.55         3\n",
            "                       Bronchial Asthma       1.00      1.00      1.00         3\n",
            "                   Cervical spondylosis       1.00      1.00      1.00         1\n",
            "                            Chicken pox       1.00      1.00      1.00         3\n",
            "                    Chronic cholestasis       1.00      1.00      1.00         2\n",
            "                                 Dengue       1.00      1.00      1.00         3\n",
            "                              Diabetes        1.00      1.00      1.00         2\n",
            "           Dimorphic hemmorhoids(piles)       1.00      0.50      0.67         2\n",
            "                          Drug Reaction       1.00      1.00      1.00         1\n",
            "                            Hepatitis B       1.00      1.00      1.00         2\n",
            "                            Hepatitis C       1.00      1.00      1.00         1\n",
            "                            Hepatitis D       1.00      1.00      1.00         1\n",
            "                            Hepatitis E       1.00      1.00      1.00         2\n",
            "                        Hyperthyroidism       1.00      1.00      1.00         2\n",
            "                           Hypoglycemia       1.00      1.00      1.00         3\n",
            "                         Hypothyroidism       1.00      1.00      1.00         2\n",
            "                               Impetigo       0.00      0.00      0.00         1\n",
            "                               Jaundice       1.00      1.00      1.00         1\n",
            "                                Malaria       1.00      1.00      1.00         2\n",
            "                               Migraine       1.00      1.00      1.00         4\n",
            "           Paralysis (brain hemorrhage)       1.00      1.00      1.00         1\n",
            "                    Peptic ulcer diseae       1.00      1.00      1.00         1\n",
            "                              Pneumonia       1.00      1.00      1.00         2\n",
            "                              Psoriasis       0.00      0.00      0.00         1\n",
            "                           Tuberculosis       1.00      1.00      1.00         2\n",
            "                Urinary tract infection       0.00      0.00      0.00         1\n",
            "                         Varicose veins       1.00      0.67      0.80         3\n",
            "                            hepatitis A       1.00      1.00      1.00         2\n",
            "\n",
            "                               accuracy                           0.89        61\n",
            "                              macro avg       0.84      0.84      0.83        61\n",
            "                           weighted avg       0.88      0.89      0.87        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "from joblib import dump , load\n",
        "\n",
        "\n",
        "probabilities = grid.predict_proba([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "\n",
        "])\n",
        "disease_probabilities = dict(zip(grid.classes_, probabilities[0]))\n",
        "\n",
        "top_n = 5  # Number of top probabilities to display\n",
        "sorted_probabilities = sorted(disease_probabilities.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
        "\n",
        "# disease_df = pd.read_csv('models/disease_description.csv')\n",
        "# precaution_df = pd.read_csv('models/disease_precaution.csv')\n",
        "\n",
        "\n",
        "count = 0\n",
        "print(\"top 5 diseases you may have with corresponding probability:\")\n",
        "print('_______________________________________________________________________________')\n",
        "for disease, probability in sorted_probabilities:\n",
        "    if probability > 0:\n",
        "        print(f\"Probability of having '{disease}': {int(probability*100)}%\")\n",
        "\n",
        "        # #Filter the DataFrame to retrieve the description of the predicted disease\n",
        "        # predicted_disease_description = disease_df[disease_df['Disease'] == disease]['Symptom_Description']\n",
        "        # predicted_disease_precautions = precaution_df[precaution_df['Disease'] == disease]\n",
        "\n",
        "        # #Print the description of the disease\n",
        "        # if not predicted_disease_description.empty:\n",
        "        #     print(\"DESCRIPTION: \",predicted_disease_description.values[0])\n",
        "        # else:\n",
        "        #     print(\"Description not found for the predicted disease.\")\n",
        "\n",
        "        # print('\\n')\n",
        "        # if not predicted_disease_precautions.empty:\n",
        "        #     print('PRECAUTIONS:')\n",
        "        #     for column in ['Symptom_precaution_0', 'Symptom_precaution_1', 'Symptom_precaution_2', 'Symptom_precaution_3']:\n",
        "        #         precaution = predicted_disease_precautions[column].values[0]\n",
        "        #         print(precaution)\n",
        "        # else:\n",
        "        #     print(\"Precautions not found for the predicted disease.\")\n",
        "\n",
        "#         print('\\n')\n",
        "\n",
        "        if(probability*100)<50:\n",
        "            print('since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation')\n",
        "        count += 1\n",
        "        print('_______________________________________________________________________________')\n",
        "        if count == top_n:\n",
        "            break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2x6FPlwDpS6",
        "outputId": "62cf3756-2771-4c6b-c213-ec888df384f2"
      },
      "id": "m2x6FPlwDpS6",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "top 5 diseases you may have with corresponding probability:\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Psoriasis': 5%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Varicose veins': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Dimorphic hemmorhoids(piles)': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Urinary tract infection': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'AIDS': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PpRZhJTvQvYK"
      },
      "id": "PpRZhJTvQvYK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f7BwFyb2Qva0"
      },
      "id": "f7BwFyb2Qva0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Train a basic Logistic Regression model\n",
        "logistic_model = LogisticRegression(max_iter=10000)\n",
        "logistic_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = logistic_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Accuracy Score:\\n\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ym6BC50BQvej",
        "outputId": "70f61f41-4844-4808-d315-65983319fe10"
      },
      "id": "Ym6BC50BQvej",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:\n",
            " 0.9836065573770492\n",
            "Classification Report:\n",
            "                                          precision    recall  f1-score   support\n",
            "\n",
            "(vertigo) Paroymsal  Positional Vertigo       1.00      1.00      1.00         2\n",
            "                                   AIDS       1.00      1.00      1.00         1\n",
            "                                   Acne       1.00      0.67      0.80         3\n",
            "                    Alcoholic hepatitis       1.00      1.00      1.00         1\n",
            "                                Allergy       1.00      1.00      1.00         3\n",
            "                       Bronchial Asthma       1.00      1.00      1.00         3\n",
            "                   Cervical spondylosis       1.00      1.00      1.00         1\n",
            "                            Chicken pox       1.00      1.00      1.00         3\n",
            "                    Chronic cholestasis       1.00      1.00      1.00         2\n",
            "                                 Dengue       1.00      1.00      1.00         3\n",
            "                              Diabetes        1.00      1.00      1.00         2\n",
            "           Dimorphic hemmorhoids(piles)       0.67      1.00      0.80         2\n",
            "                          Drug Reaction       1.00      1.00      1.00         1\n",
            "                            Hepatitis B       1.00      1.00      1.00         2\n",
            "                            Hepatitis C       1.00      1.00      1.00         1\n",
            "                            Hepatitis D       1.00      1.00      1.00         1\n",
            "                            Hepatitis E       1.00      1.00      1.00         2\n",
            "                        Hyperthyroidism       1.00      1.00      1.00         2\n",
            "                           Hypoglycemia       1.00      1.00      1.00         3\n",
            "                         Hypothyroidism       1.00      1.00      1.00         2\n",
            "                               Impetigo       1.00      1.00      1.00         1\n",
            "                               Jaundice       1.00      1.00      1.00         1\n",
            "                                Malaria       1.00      1.00      1.00         2\n",
            "                               Migraine       1.00      1.00      1.00         4\n",
            "           Paralysis (brain hemorrhage)       1.00      1.00      1.00         1\n",
            "                    Peptic ulcer diseae       1.00      1.00      1.00         1\n",
            "                              Pneumonia       1.00      1.00      1.00         2\n",
            "                              Psoriasis       1.00      1.00      1.00         1\n",
            "                           Tuberculosis       1.00      1.00      1.00         2\n",
            "                Urinary tract infection       1.00      1.00      1.00         1\n",
            "                         Varicose veins       1.00      1.00      1.00         3\n",
            "                            hepatitis A       1.00      1.00      1.00         2\n",
            "\n",
            "                               accuracy                           0.98        61\n",
            "                              macro avg       0.99      0.99      0.99        61\n",
            "                           weighted avg       0.99      0.98      0.98        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "from joblib import dump , load\n",
        "\n",
        "\n",
        "probabilities = logistic_model.predict_proba([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "\n",
        "])\n",
        "disease_probabilities = dict(zip(logistic_model.classes_, probabilities[0]))\n",
        "\n",
        "top_n = 5  # Number of top probabilities to display\n",
        "sorted_probabilities = sorted(disease_probabilities.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
        "\n",
        "# disease_df = pd.read_csv('models/disease_description.csv')\n",
        "# precaution_df = pd.read_csv('models/disease_precaution.csv')\n",
        "\n",
        "\n",
        "count = 0\n",
        "print(\"top 5 diseases you may have with corresponding probability:\")\n",
        "print('_______________________________________________________________________________')\n",
        "for disease, probability in sorted_probabilities:\n",
        "    if probability > 0:\n",
        "        print(f\"Probability of having '{disease}': {int(probability*100)}%\")\n",
        "\n",
        "        # #Filter the DataFrame to retrieve the description of the predicted disease\n",
        "        # predicted_disease_description = disease_df[disease_df['Disease'] == disease]['Symptom_Description']\n",
        "        # predicted_disease_precautions = precaution_df[precaution_df['Disease'] == disease]\n",
        "\n",
        "        # #Print the description of the disease\n",
        "        # if not predicted_disease_description.empty:\n",
        "        #     print(\"DESCRIPTION: \",predicted_disease_description.values[0])\n",
        "        # else:\n",
        "        #     print(\"Description not found for the predicted disease.\")\n",
        "\n",
        "        # print('\\n')\n",
        "        # if not predicted_disease_precautions.empty:\n",
        "        #     print('PRECAUTIONS:')\n",
        "        #     for column in ['Symptom_precaution_0', 'Symptom_precaution_1', 'Symptom_precaution_2', 'Symptom_precaution_3']:\n",
        "        #         precaution = predicted_disease_precautions[column].values[0]\n",
        "        #         print(precaution)\n",
        "        # else:\n",
        "        #     print(\"Precautions not found for the predicted disease.\")\n",
        "\n",
        "#         print('\\n')\n",
        "\n",
        "        if(probability*100)<50:\n",
        "            print('since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation')\n",
        "        count += 1\n",
        "        print('_______________________________________________________________________________')\n",
        "        if count == top_n:\n",
        "            break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBJ3g2EzRPsG",
        "outputId": "748ba798-17c2-4b07-833b-fb25be94944e"
      },
      "id": "cBJ3g2EzRPsG",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "top 5 diseases you may have with corresponding probability:\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Allergy': 10%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Dimorphic hemmorhoids(piles)': 5%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Urinary tract infection': 5%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'Varicose veins': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n",
            "Probability of having 'AIDS': 4%\n",
            "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
            "_______________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'C': [0.1, 1, 10, 100],\n",
        "    'penalty': ['l1', 'l2', 'elasticnet', None],  # Regularization type\n",
        "    'solver': ['liblinear', 'saga', 'lbfgs', 'newton-cg']  # Solvers\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "grid_search = GridSearchCV(LogisticRegression(max_iter=10000), param_grid, cv=5, verbose=3, n_jobs=-1)\n",
        "\n",
        "# Fit GridSearchCV\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best parameters and estimator\n",
        "print(\"Best Parameters:\\n\", grid_search.best_params_)\n",
        "print(\"Best Estimator:\\n\", grid_search.best_estimator_)\n",
        "\n",
        "# Get the best model\n",
        "best_logistic_model = grid_search.best_estimator_\n",
        "\n",
        "# Make predictions with the best model\n",
        "y_pred_best = best_logistic_model.predict(X_test)\n",
        "\n",
        "# Evaluate the tuned model\n",
        "print(\"Accuracy Score after tuning:\\n\", accuracy_score(y_test, y_pred_best))\n",
        "print(\"Classification Report after tuning:\\n\", classification_report(y_test, y_pred_best))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jltYSNsLRhSi",
        "outputId": "43a9e727-a9de-448c-f12e-7258e0f478be"
      },
      "id": "jltYSNsLRhSi",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n",
            "Best Parameters:\n",
            " {'C': 0.1, 'penalty': None, 'solver': 'lbfgs'}\n",
            "Best Estimator:\n",
            " LogisticRegression(C=0.1, max_iter=10000, penalty=None)\n",
            "Accuracy Score after tuning:\n",
            " 0.9508196721311475\n",
            "Classification Report after tuning:\n",
            "                                          precision    recall  f1-score   support\n",
            "\n",
            "(vertigo) Paroymsal  Positional Vertigo       1.00      1.00      1.00         2\n",
            "                                   AIDS       1.00      1.00      1.00         1\n",
            "                                   Acne       0.50      0.67      0.57         3\n",
            "                    Alcoholic hepatitis       1.00      1.00      1.00         1\n",
            "                                Allergy       0.75      1.00      0.86         3\n",
            "                       Bronchial Asthma       1.00      1.00      1.00         3\n",
            "                   Cervical spondylosis       1.00      1.00      1.00         1\n",
            "                            Chicken pox       1.00      1.00      1.00         3\n",
            "                    Chronic cholestasis       1.00      1.00      1.00         2\n",
            "                                 Dengue       1.00      1.00      1.00         3\n",
            "                              Diabetes        1.00      1.00      1.00         2\n",
            "           Dimorphic hemmorhoids(piles)       1.00      1.00      1.00         2\n",
            "                          Drug Reaction       1.00      1.00      1.00         1\n",
            "                            Hepatitis B       1.00      1.00      1.00         2\n",
            "                            Hepatitis C       1.00      1.00      1.00         1\n",
            "                            Hepatitis D       1.00      1.00      1.00         1\n",
            "                            Hepatitis E       1.00      1.00      1.00         2\n",
            "                        Hyperthyroidism       1.00      1.00      1.00         2\n",
            "                           Hypoglycemia       1.00      1.00      1.00         3\n",
            "                         Hypothyroidism       1.00      1.00      1.00         2\n",
            "                               Impetigo       0.00      0.00      0.00         1\n",
            "                               Jaundice       1.00      1.00      1.00         1\n",
            "                                Malaria       1.00      1.00      1.00         2\n",
            "                               Migraine       1.00      1.00      1.00         4\n",
            "           Paralysis (brain hemorrhage)       1.00      1.00      1.00         1\n",
            "                    Peptic ulcer diseae       1.00      1.00      1.00         1\n",
            "                              Pneumonia       1.00      1.00      1.00         2\n",
            "                              Psoriasis       0.00      0.00      0.00         1\n",
            "                           Tuberculosis       1.00      1.00      1.00         2\n",
            "                Urinary tract infection       1.00      1.00      1.00         1\n",
            "                         Varicose veins       1.00      1.00      1.00         3\n",
            "                            hepatitis A       1.00      1.00      1.00         2\n",
            "\n",
            "                               accuracy                           0.95        61\n",
            "                              macro avg       0.91      0.93      0.92        61\n",
            "                           weighted avg       0.93      0.95      0.94        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1zXbb9XgWBoL"
      },
      "id": "1zXbb9XgWBoL",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
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        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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