completed PP1

IT20151010 - PP1/Compnent3.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.feature_selection import RFE\n",
+        "from imblearn.over_sampling import SMOTE\n",
+        "\n",
+        "# Load the CSV data\n",
+        "df = pd.read_csv('kidney_disease_data.csv')\n",
+        "\n",
+        "# Convert boolean values to integer\n",
+        "for col in df.columns:\n",
+        "    if df[col].dtype == 'bool':\n",
+        "        df[col] = df[col].astype(int)\n",
+        "\n",
+        "# Convert 'Disease' column to binary, 'Yes' to 1 and 'No' to 0\n",
+        "df['Disease'] = df['Disease'].map({'Yes': 1, 'No': 0})\n",
+        "\n",
+        "# Check class balance\n",
+        "print(df['Disease'].value_counts())\n",
+        "\n",
+        "# Split the data into inputs and target\n",
+        "X = df.drop(['Patient_ID', 'Disease'], axis=1)\n",
+        "y = df['Disease']\n",
+        "\n",
+        "# Handle class imbalance\n",
+        "smote = SMOTE()\n",
+        "X, y = smote.fit_resample(X, y)\n",
+        "\n",
+        "# Normalize the data\n",
+        "sc = StandardScaler()\n",
+        "X = sc.fit_transform(X)\n",
+        "\n",
+        "# Feature selection with RFE\n",
+        "model = LogisticRegression()\n",
+        "rfe = RFE(estimator=model, n_features_to_select=5)  # Select top 5 features\n",
+        "X_rfe = rfe.fit_transform(X, y)\n",
+        "\n",
+        "# Split the data into train and test sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X_rfe, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Define classifiers\n",
+        "classifiers = {\n",
+        "    \"Logistic Regression\": LogisticRegression(),\n",
+        "    \"Random Forest\": RandomForestClassifier(),\n",
+        "    \"SVM\": SVC()\n",
+        "}\n",
+        "\n",
+        "# Define hyperparameters for tuning\n",
+        "param_grids = {\n",
+        "    \"Logistic Regression\": [\n",
+        "        {'penalty': ['l1', 'l2'], 'C': [0.1, 1.0, 10], 'solver': ['liblinear']},\n",
+        "        {'penalty': ['l2', None], 'C': [0.1, 1.0, 10], 'solver': ['newton-cg', 'lbfgs']},\n",
+        "        {'penalty': ['elasticnet'], 'C': [0.1, 1.0, 10], 'l1_ratio': [0.1, 0.5, 0.9], 'solver': ['saga']}\n",
+        "    ],\n",
+        "    \"Random Forest\": {\n",
+        "        'n_estimators': [10, 50, 100, 200],\n",
+        "        'max_depth': [None, 10, 20, 30],\n",
+        "        'min_samples_split': [2, 5, 10],\n",
+        "        'min_samples_leaf': [1, 2, 4],\n",
+        "        'bootstrap': [True, False]\n",
+        "    },\n",
+        "    \"SVM\": [\n",
+        "        {'C': [1, 10, 100], 'kernel': ['linear']},\n",
+        "        {'C': [1, 10, 100], 'gamma': [0.1, 0.01, 0.001], 'kernel': ['rbf']}\n",
+        "    ]\n",
+        "}\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TLHzLJI-byBu",
+        "outputId": "8a4d1794-0977-43b2-ad6c-b3a41840e50c"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0    60\n",
+            "1    50\n",
+            "Name: Disease, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0ScZk77zblAy",
+        "outputId": "35a3921a-0508-4c68-ddb0-ec8dd0cf782a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Logistic Regression\n",
+            "Best Hyperparameters::\n",
+            "{'C': 0.1, 'l1_ratio': 0.9, 'penalty': 'elasticnet', 'solver': 'saga'}\n",
+            "Model Accuracy:  0.5416666666666666\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.57      0.33      0.42        12\n",
+            "           1       0.53      0.75      0.62        12\n",
+            "\n",
+            "    accuracy                           0.54        24\n",
+            "   macro avg       0.55      0.54      0.52        24\n",
+            "weighted avg       0.55      0.54      0.52        24\n",
+            "\n",
+            "ROC AUC Score:  0.5416666666666667\n",
+            "Cross-Validation Score:  0.4999999999999999\n",
+            "\n",
+            " Random Forest\n",
+            "Best Hyperparameters::\n",
+            "{'bootstrap': True, 'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 10}\n",
+            "Model Accuracy:  0.4583333333333333\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.43      0.25      0.32        12\n",
+            "           1       0.47      0.67      0.55        12\n",
+            "\n",
+            "    accuracy                           0.46        24\n",
+            "   macro avg       0.45      0.46      0.43        24\n",
+            "weighted avg       0.45      0.46      0.43        24\n",
+            "\n",
+            "ROC AUC Score:  0.4583333333333333\n",
+            "Cross-Validation Score:  0.4999999999999999\n",
+            "\n",
+            " SVM\n",
+            "Best Hyperparameters::\n",
+            "{'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
+            "Model Accuracy:  0.625\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.64      0.58      0.61        12\n",
+            "           1       0.62      0.67      0.64        12\n",
+            "\n",
+            "    accuracy                           0.62        24\n",
+            "   macro avg       0.63      0.62      0.62        24\n",
+            "weighted avg       0.63      0.62      0.62        24\n",
+            "\n",
+            "ROC AUC Score:  0.6249999999999999\n",
+            "Cross-Validation Score:  0.4999999999999999\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import classification_report, roc_auc_score\n",
+        "from sklearn.model_selection import learning_curve\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Continuation from the previous code...\n",
+        "\n",
+        "for classifier in classifiers.keys():\n",
+        "    grid = GridSearchCV(classifiers[classifier], param_grids[classifier], cv=5)  # 5-fold cross-validation\n",
+        "    grid.fit(X_train, y_train)\n",
+        "\n",
+        "    # Predict on test data with best parameters\n",
+        "    y_pred = grid.predict(X_test)\n",
+        "\n",
+        "    # Measure the accuracy of the model\n",
+        "    accuracy = accuracy_score(y_test, y_pred)\n",
+        "\n",
+        "    # Print Classification Report\n",
+        "    print(\"\\n\", classifier)\n",
+        "    print(\"Best Hyperparameters::\\n{}\".format(grid.best_params_))\n",
+        "    print(\"Model Accuracy: \", accuracy)\n",
+        "    print(\"\\nClassification Report:\")\n",
+        "    print(classification_report(y_test, y_pred))\n",
+        "\n",
+        "    # Compute and print ROC AUC score\n",
+        "    roc_auc = roc_auc_score(y_test, y_pred)\n",
+        "    print(\"ROC AUC Score: \", roc_auc)\n",
+        "\n",
+        "    # Compute and print cross-validation score\n",
+        "    cv_score = cross_val_score(model, X, y, cv=5)  # 5-fold cross-validation\n",
+        "    print(\"Cross-Validation Score: \", cv_score.mean())\n",
+        "\n",
+        "\n",
+        "    \n"
+      ]
+    }
+  ]
+}
\ No newline at end of file

IT20151010 - PP1/kidney_disease_data.csv

+Patient_ID,Change_in_Urination,Swelling,Fatigue_Weakness,Skin_Rash_Itching,Shortness_of_Breath,Metallic_Taste_in_Mouth,Feeling_Cold,Dizziness_Trouble_Concentrating,Pain_in_Back_or_Sides,Nausea_Vomiting,Disease
+P1,True,False,True,False,True,False,True,False,True,False,Yes
+P2,False,True,True,True,False,True,False,True,False,True,No
+P3,True,False,True,True,True,False,True,False,True,False,Yes
+P4,False,True,False,True,False,True,False,True,False,True,No
+P5,False,False,True,False,False,False,True,False,True,False,Yes
+P6,True,False,False,True,True,True,False,True,False,True,No
+P7,False,True,True,False,True,False,True,False,True,False,Yes
+P8,True,False,True,True,False,True,False,True,False,True,No
+P9,False,True,False,True,True,False,True,False,True,False,Yes
+P10,False,False,False,False,False,True,False,True,False,True,No
+P11,False,True,True,False,False,True,False,True,False,True,Yes
+P12,True,True,False,False,False,True,False,False,False,True,No
+P13,True,True,False,True,True,False,False,False,False,False,Yes
+P14,True,False,True,True,True,True,True,True,True,True,No
+P15,True,False,False,False,False,True,True,False,True,False,No
+P16,False,False,False,False,True,False,True,True,False,False,No
+P17,False,False,True,True,True,False,False,False,True,False,Yes
+P18,False,True,True,False,True,True,True,True,False,False,No
+P19,False,False,True,False,False,False,True,False,False,True,Yes
+P20,False,False,True,True,True,True,True,False,False,False,Yes
+P21,False,False,False,False,True,False,False,True,False,False,No
+P22,False,True,True,True,True,True,True,False,False,True,Yes
+P23,True,True,True,True,True,False,True,False,True,False,No
+P24,True,True,True,True,True,False,False,True,True,True,No
+P25,True,True,True,False,True,True,False,True,False,True,No
+P26,True,False,True,True,False,False,True,False,False,False,No
+P27,True,True,False,False,False,False,True,False,False,False,No
+P28,False,True,True,False,False,True,False,True,False,False,No
+P29,False,True,True,True,True,True,False,False,True,True,Yes
+P30,False,True,True,False,False,False,False,True,False,False,No
+P31,False,False,True,True,True,True,True,True,False,False,Yes
+P32,True,True,True,False,False,True,True,False,False,False,No
+P33,True,True,True,False,True,True,True,True,False,True,No
+P34,False,True,False,False,False,False,True,True,False,False,Yes
+P35,True,False,False,True,False,False,True,False,True,True,Yes
+P36,True,False,False,False,False,False,True,True,True,True,No
+P37,False,False,False,False,False,False,False,True,True,True,Yes
+P38,False,False,False,True,False,False,True,False,False,False,No
+P39,False,False,False,True,False,True,True,True,False,False,No
+P40,False,True,False,False,True,False,True,False,False,True,Yes
+P41,True,False,True,False,True,True,True,False,True,True,Yes
+P42,True,True,False,True,True,False,False,True,False,False,No
+P43,False,True,True,False,True,False,False,True,True,False,Yes
+P44,False,True,True,True,False,False,True,False,True,True,Yes
+P45,True,True,True,False,True,False,True,True,True,True,No
+P46,False,True,True,True,False,True,True,False,True,False,No
+P47,False,True,False,False,False,False,False,True,True,True,Yes
+P48,True,True,True,False,False,False,False,False,True,False,No
+P49,True,False,False,False,True,True,True,False,True,True,No
+P50,True,False,False,False,True,False,False,True,True,False,No
+P51,True,False,False,True,False,False,False,False,True,False,Yes
+P52,True,False,False,False,True,False,False,True,False,True,Yes
+P53,True,True,True,True,False,True,False,True,True,False,No
+P54,True,False,False,False,True,True,False,True,True,False,No
+P55,True,True,True,False,False,False,True,False,True,True,No
+P56,True,False,False,False,False,True,True,False,True,False,Yes
+P57,False,True,False,True,True,True,True,True,False,False,Yes
+P58,False,False,False,False,True,True,True,False,True,False,Yes
+P59,False,False,False,False,True,True,False,False,True,True,Yes
+P60,True,True,False,True,True,False,True,True,False,True,Yes
+P61,True,False,True,False,False,True,True,False,False,True,Yes
+P62,False,True,False,False,True,False,True,False,True,True,Yes
+P63,True,False,False,True,False,False,True,True,False,True,No
+P64,True,True,True,True,False,True,False,True,False,False,No
+P65,False,False,True,False,False,False,False,False,True,True,Yes
+P66,False,True,False,False,True,False,True,False,True,True,No
+P67,False,False,True,False,True,False,True,False,False,True,No
+P68,False,False,True,False,False,False,True,False,False,False,No
+P69,False,False,False,True,False,False,False,True,True,True,No
+P70,True,False,True,False,False,True,True,True,False,False,No
+P71,False,True,True,True,True,True,True,True,True,True,No
+P72,False,True,True,True,False,False,False,True,True,False,Yes
+P73,True,True,False,True,True,True,False,False,False,False,Yes
+P74,True,True,False,False,False,True,False,True,True,True,No
+P75,False,True,True,False,False,False,False,True,True,False,No
+P76,False,False,True,True,False,False,True,False,True,False,No
+P77,True,False,True,True,True,False,True,False,True,False,No
+P78,False,False,False,False,False,False,True,False,True,False,Yes
+P79,True,False,False,False,True,False,False,False,False,True,No
+P80,True,True,True,True,True,True,False,True,False,False,Yes
+P81,True,True,True,False,False,True,True,True,True,True,No
+P82,True,True,True,False,False,True,False,True,True,False,Yes
+P83,True,False,True,False,False,False,True,False,True,True,Yes
+P84,False,True,True,True,False,False,True,True,True,True,Yes
+P85,False,False,True,True,True,True,True,False,True,False,Yes
+P86,True,True,False,False,False,True,False,True,False,False,No
+P87,True,False,True,False,False,True,True,True,True,True,No
+P88,True,False,True,True,False,True,False,True,True,False,Yes
+P89,True,True,True,False,False,False,True,False,False,True,Yes
+P90,False,True,True,True,False,False,False,True,False,False,Yes
+P91,True,True,True,True,True,True,False,False,True,True,No
+P92,True,True,False,False,False,True,True,False,False,False,No
+P93,False,True,False,True,False,False,False,True,False,False,No
+P94,False,False,False,True,True,False,False,False,True,True,No
+P95,False,False,False,True,False,False,True,False,True,True,Yes
+P96,False,False,True,False,True,True,False,True,False,True,No
+P97,False,True,True,True,False,True,True,True,True,True,No
+P98,False,True,True,False,True,False,False,True,False,False,Yes
+P99,False,True,False,False,True,True,False,False,False,False,Yes
+P100,True,True,True,True,True,True,False,False,True,True,No
+P101,False,False,True,True,False,True,True,True,True,True,Yes
+P102,True,True,True,True,False,True,False,False,True,True,Yes
+P103,False,True,True,True,False,False,False,True,True,False,No
+P104,False,True,False,True,True,False,False,False,False,True,No
+P105,True,True,True,False,True,True,False,False,False,False,No
+P106,True,False,False,False,True,True,True,False,True,False,No
+P107,True,True,False,True,True,True,True,True,False,False,No
+P108,False,False,True,False,True,False,True,False,True,False,Yes
+P109,False,True,False,False,False,False,False,True,False,False,Yes
+P110,True,False,False,False,True,True,True,False,False,False,Yes

IT20151010 - PP1/nephrotic_syndrome_dataset.csv

+Patient_ID,Age,Gender,Underlying_Cause,Proteinuria,Edema,Albumin_Level,Cholesterol_Level,Infection_Count,Nephrotic_Syndrome
+1,60,Female,Minimal Change Disease,Very High,Yes,Normal,Normal,1,Yes
+2,46,Female,Focal Segmental GS,High,Yes,Low,High,1,Yes
+3,56,Female,Focal Segmental GS,Low,No,Normal,Normal,1,Yes
+4,56,Female,Healthy,High,No,Normal,High,2,No
+5,21,Male,Minimal Change Disease,Moderate,Yes,Very Low,Normal,1,Yes
+6,54,Male,Membranous Nephropathy,Normal,No,Very Low,Normal,0,Yes
+7,49,Female,Minimal Change Disease,Low,Yes,Normal,Normal,2,Yes
+8,24,Male,Healthy,Very High,Yes,Normal,Normal,2,No
+9,75,Male,Focal Segmental GS,Moderate,Yes,Very Low,Normal,2,Yes
+10,67,Female,Healthy,Low,Yes,Normal,High,0,No
+11,69,Male,Focal Segmental GS,Very High,No,Very Low,Normal,1,Yes
+12,59,Male,Focal Segmental GS,Low,Yes,Normal,High,1,Yes
+13,52,Female,Minimal Change Disease,Moderate,No,Normal,Normal,1,Yes
+14,57,Female,Healthy,Moderate,Yes,Normal,High,1,No
+15,46,Female,Healthy,Low,No,Normal,High,1,No
+16,50,Female,Minimal Change Disease,Moderate,Yes,Very Low,Normal,1,Yes
+17,43,Male,Healthy,High,Yes,Very Low,Elevated,1,No
+18,24,Female,Minimal Change Disease,Very High,Yes,Very Low,High,2,Yes
+19,49,Female,Focal Segmental GS,High,No,Very Low,Elevated,2,Yes
+20,60,Male,Membranous Nephropathy,Normal,No,Very Low,High,3,Yes
+21,57,Male,Membranous Nephropathy,Very High,Yes,Very Low,Normal,3,Yes
+22,67,Male,Healthy,Very High,No,Very Low,Elevated,2,No
+23,34,Female,Minimal Change Disease,Very High,Yes,Normal,Normal,0,Yes
+24,25,Male,Minimal Change Disease,Normal,Yes,Low,Normal,0,Yes
+25,31,Male,Healthy,High,No,Very Low,High,2,No
+26,27,Male,Focal Segmental GS,Low,No,Low,High,0,Yes
+27,56,Female,Healthy,Normal,Yes,Normal,Normal,0,No
+28,19,Female,Minimal Change Disease,Low,No,Normal,Elevated,0,Yes
+29,55,Male,Minimal Change Disease,High,No,Very Low,Elevated,3,Yes
+30,62,Male,Minimal Change Disease,High,No,Low,High,3,Yes
+31,41,Female,Minimal Change Disease,High,Yes,Low,Elevated,2,Yes
+32,69,Female,Membranous Nephropathy,Very High,No,Low,Elevated,1,Yes
+33,52,Male,Minimal Change Disease,Low,Yes,Low,Normal,0,Yes
+34,45,Male,Focal Segmental GS,Normal,No,Very Low,High,0,Yes
+35,43,Male,Healthy,High,No,Normal,High,1,No
+36,25,Male,Membranous Nephropathy,Moderate,Yes,Very Low,High,1,Yes
+37,60,Female,Healthy,Moderate,Yes,Very Low,Normal,0,No
+38,47,Female,Healthy,Very High,Yes,Very Low,Normal,0,No
+39,54,Female,Focal Segmental GS,High,No,Normal,Normal,3,Yes
+40,40,Male,Minimal Change Disease,Very High,Yes,Very Low,High,2,Yes
+41,57,Female,Minimal Change Disease,Low,No,Very Low,Elevated,0,Yes
+42,57,Male,Minimal Change Disease,Normal,No,Normal,Elevated,1,Yes
+43,37,Male,Membranous Nephropathy,Low,No,Normal,High,0,Yes
+44,80,Female,Focal Segmental GS,Very High,Yes,Low,Elevated,0,Yes
+45,24,Male,Membranous Nephropathy,Normal,No,Very Low,High,2,Yes
+46,42,Male,Minimal Change Disease,High,No,Low,Elevated,0,Yes
+47,54,Female,Membranous Nephropathy,Very High,No,Low,High,2,Yes
+48,47,Female,Minimal Change Disease,Very High,Yes,Low,Normal,0,Yes
+49,56,Male,Healthy,High,Yes,Low,High,1,No
+50,24,Male,Membranous Nephropathy,High,Yes,Very Low,High,2,Yes
+51,31,Male,Healthy,Normal,Yes,Low,Normal,3,No
+52,62,Male,Focal Segmental GS,Low,No,Normal,Normal,0,Yes
+53,77,Female,Focal Segmental GS,High,Yes,Very Low,Normal,2,Yes
+54,49,Female,Healthy,High,Yes,Normal,High,0,No
+55,47,Male,Membranous Nephropathy,Normal,No,Low,Normal,2,Yes
+56,71,Female,Healthy,Low,Yes,Low,Normal,3,No
+57,48,Male,Minimal Change Disease,High,No,Low,Normal,1,Yes
+58,50,Male,Membranous Nephropathy,Moderate,No,Low,Elevated,1,Yes
+59,34,Female,Minimal Change Disease,Normal,Yes,Normal,Normal,1,Yes
+60,80,Female,Membranous Nephropathy,Low,Yes,Low,High,1,Yes
+61,42,Female,Healthy,Moderate,Yes,Low,Elevated,2,No
+62,54,Male,Focal Segmental GS,Moderate,Yes,Low,Normal,0,Yes
+63,75,Female,Focal Segmental GS,Low,No,Normal,Normal,0,Yes
+64,22,Male,Minimal Change Disease,High,Yes,Low,Normal,1,Yes
+65,53,Male,Focal Segmental GS,Low,No,Low,Normal,2,Yes
+66,42,Female,Healthy,Very High,No,Very Low,Elevated,2,No
+67,63,Female,Membranous Nephropathy,High,No,Very Low,Elevated,2,Yes
+68,53,Male,Focal Segmental GS,Normal,No,Very Low,Elevated,2,Yes
+69,34,Male,Focal Segmental GS,Low,No,Normal,Elevated,2,Yes
+70,34,Female,Minimal Change Disease,Moderate,Yes,Normal,Normal,3,Yes
+71,38,Female,Membranous Nephropathy,Moderate,No,Very Low,Elevated,3,Yes
+72,66,Female,Focal Segmental GS,Moderate,Yes,Very Low,Elevated,3,Yes
+73,36,Female,Focal Segmental GS,Normal,Yes,Normal,Elevated,1,Yes
+74,27,Female,Membranous Nephropathy,High,Yes,Very Low,Elevated,0,Yes
+75,23,Female,Healthy,High,Yes,Very Low,Normal,3,No
+76,57,Female,Membranous Nephropathy,Low,No,Normal,Elevated,2,Yes
+77,47,Female,Minimal Change Disease,Normal,Yes,Normal,Normal,0,Yes
+78,51,Male,Membranous Nephropathy,Low,No,Normal,Elevated,3,Yes
+79,31,Male,Minimal Change Disease,High,No,Low,Normal,3,Yes
+80,25,Male,Healthy,High,No,Very Low,Normal,2,No
+81,63,Female,Membranous Nephropathy,High,Yes,Normal,Normal,1,Yes
+82,36,Male,Minimal Change Disease,Moderate,Yes,Low,Normal,2,Yes
+83,66,Female,Membranous Nephropathy,High,Yes,Low,High,0,Yes
+84,54,Male,Healthy,High,No,Normal,Elevated,1,No
+85,71,Male,Focal Segmental GS,High,No,Normal,High,3,Yes
+86,70,Male,Healthy,Very High,No,Very Low,Elevated,3,No
+87,67,Female,Minimal Change Disease,High,No,Very Low,High,1,Yes
+88,69,Male,Healthy,Normal,Yes,Low,High,1,No
+89,19,Male,Healthy,Low,No,Very Low,Elevated,2,No
+90,55,Female,Minimal Change Disease,Low,Yes,Normal,Normal,3,Yes
+91,29,Female,Healthy,High,No,Low,High,0,No
+92,61,Female,Membranous Nephropathy,Normal,Yes,Normal,Elevated,1,Yes
+93,52,Male,Healthy,Moderate,No,Normal,High,1,No
+94,60,Male,Membranous Nephropathy,Very High,Yes,Low,Normal,0,Yes
+95,61,Female,Focal Segmental GS,Normal,Yes,Normal,Elevated,0,Yes
+96,62,Female,Minimal Change Disease,Moderate,Yes,Low,Elevated,2,Yes
+97,26,Female,Membranous Nephropathy,Moderate,Yes,Normal,Elevated,3,Yes
+98,79,Female,Minimal Change Disease,Moderate,Yes,Very Low,High,2,Yes
+99,37,Female,Minimal Change Disease,Very High,Yes,Normal,Elevated,1,Yes
+100,53,Female,Healthy,Very High,No,Low,Elevated,0,No

IT20151010 - PP1/patient_data.csv

+PatientID,Age,Gender,AKIDiagnosis,AKISeverity,InitialCreatinine,PeakCreatinine,UrineOutput,Swelling,HasDisease
+1,82,Female,No,Severe,0.5,1.3,881,Yes,No
+2,38,Male,Yes,Moderate,1.7,2.7,685,Yes,Yes
+3,48,Female,No,Mild,0.8,2.1,406,Yes,No
+4,43,Female,No,Mild,1.8,2.3,407,Yes,No
+5,64,Female,No,Moderate,0.7,1.7,672,Yes,No
+6,33,Female,Yes,Mild,1.7,2.5,456,Yes,Yes
+7,46,Male,No,Mild,1.4,1.6,654,Yes,No
+8,63,Male,No,N/A,0.8,0.8,835,Yes,No
+9,74,Female,No,N/A,1.1,1.4,301,No,No
+10,28,Female,No,Mild,0.9,2.6,719,Yes,No
+11,26,Male,No,Moderate,1.3,2.6,996,No,No
+12,43,Male,Yes,Moderate,1.3,1.6,264,Yes,Yes
+13,68,Male,No,Severe,0.9,2.0,228,No,No
+14,90,Male,Yes,Moderate,0.8,1.1,363,No,Yes
+15,44,Male,No,Moderate,1.5,2.7,442,No,No
+16,86,Male,No,Severe,1.8,3.5,780,No,No
+17,58,Female,No,N/A,0.5,2.0,298,No,No
+18,50,Female,Yes,N/A,1.9,3.1,228,No,Yes
+19,74,Male,Yes,Mild,1.7,2.7,420,Yes,Yes
+20,72,Male,Yes,Moderate,1.4,2.7,504,Yes,Yes
+21,28,Female,Yes,Moderate,1.2,1.9,255,No,Yes
+22,66,Male,No,N/A,1.0,2.6,827,No,No
+23,85,Female,No,Moderate,1.8,2.5,318,Yes,No
+24,64,Female,No,Severe,0.6,2.0,315,No,No
+25,24,Female,No,Severe,0.5,1.4,602,No,No
+26,83,Female,Yes,Mild,1.2,1.9,555,Yes,Yes
+27,34,Male,Yes,Moderate,1.1,2.3,248,No,Yes
+28,82,Female,Yes,Mild,1.8,2.4,638,No,Yes
+29,40,Female,Yes,Mild,0.8,1.0,761,No,Yes
+30,77,Female,No,N/A,0.7,2.6,553,No,No
+31,85,Male,Yes,Severe,1.1,2.9,589,Yes,Yes
+32,56,Male,No,N/A,0.6,0.7,328,No,No
+33,54,Male,No,Severe,1.1,1.6,654,Yes,No
+34,26,Male,No,N/A,1.3,3.2,506,No,No
+35,77,Male,Yes,Mild,1.7,2.3,793,Yes,Yes
+36,56,Female,Yes,N/A,1.1,3.0,847,No,Yes
+37,64,Female,Yes,Mild,1.7,2.9,655,Yes,Yes
+38,62,Male,Yes,Severe,1.8,2.4,272,Yes,Yes
+39,85,Male,Yes,N/A,0.8,1.1,394,No,Yes
+40,57,Female,No,Mild,1.5,1.8,535,No,No
+41,33,Female,Yes,N/A,1.3,3.1,460,No,Yes
+42,59,Female,No,Severe,1.9,3.7,312,Yes,No
+43,20,Male,No,Moderate,1.1,2.5,680,No,No
+44,42,Female,No,Severe,1.0,2.5,523,No,No
+45,52,Female,No,Mild,0.7,1.9,211,No,No
+46,18,Male,Yes,N/A,1.1,2.3,319,No,Yes
+47,57,Female,Yes,Severe,0.6,2.0,883,Yes,Yes
+48,55,Female,No,N/A,1.7,2.5,217,Yes,No
+49,90,Female,No,Moderate,1.4,2.3,660,No,No
+50,81,Female,Yes,Severe,0.5,2.5,240,Yes,Yes
+51,40,Female,Yes,Mild,1.3,1.3,337,No,Yes
+52,67,Male,Yes,Mild,0.8,1.0,363,No,Yes
+53,68,Female,Yes,Moderate,1.4,3.1,889,Yes,Yes
+54,51,Male,No,N/A,1.2,1.6,890,No,No
+55,39,Female,Yes,Moderate,1.7,2.7,500,No,Yes
+56,37,Female,No,Mild,1.6,3.3,901,No,No
+57,28,Female,Yes,Mild,1.5,2.1,573,Yes,Yes
+58,29,Male,Yes,Severe,1.1,2.3,810,Yes,Yes
+59,83,Male,Yes,N/A,2.0,3.0,742,Yes,Yes
+60,44,Female,Yes,N/A,0.7,1.7,692,Yes,Yes
+61,23,Female,Yes,Moderate,1.8,2.4,700,No,Yes
+62,22,Male,No,Moderate,1.4,3.3,385,No,No
+63,24,Female,Yes,Moderate,0.8,2.3,795,Yes,Yes
+64,67,Male,No,N/A,1.4,2.3,637,Yes,No
+65,81,Male,No,Mild,1.1,2.8,635,No,No
+66,32,Female,No,Moderate,1.9,3.4,647,Yes,No
+67,36,Male,No,Mild,1.6,2.2,285,Yes,No
+68,59,Male,Yes,N/A,2.0,3.3,808,Yes,Yes
+69,19,Female,Yes,Mild,1.8,2.8,857,No,Yes
+70,89,Female,No,Severe,1.4,1.9,218,No,No
+71,57,Male,No,Severe,1.5,2.6,519,No,No
+72,24,Male,Yes,Mild,1.1,1.3,862,No,Yes
+73,18,Female,Yes,N/A,2.0,3.1,532,Yes,Yes
+74,29,Female,No,Mild,1.0,2.9,973,Yes,No
+75,46,Female,No,Moderate,1.4,3.2,407,No,No
+76,44,Male,No,Moderate,0.9,2.5,543,No,No
+77,35,Female,Yes,Mild,1.4,1.7,207,Yes,Yes
+78,23,Female,No,Moderate,1.9,3.6,728,No,No
+79,83,Female,No,N/A,0.9,1.0,470,No,No
+80,64,Male,Yes,Severe,1.9,2.3,940,No,Yes
+81,83,Male,No,Moderate,1.9,3.3,336,No,No
+82,81,Female,No,Moderate,0.7,1.6,743,Yes,No
+83,44,Male,No,Severe,0.7,2.1,436,No,No
+84,73,Female,No,Moderate,1.4,3.4,243,No,No
+85,59,Male,No,Moderate,1.2,2.6,506,Yes,No
+86,74,Male,No,Moderate,1.3,2.6,655,No,No
+87,31,Female,No,Severe,0.8,1.8,567,No,No
+88,28,Female,Yes,N/A,2.0,2.6,966,No,Yes
+89,47,Female,Yes,Mild,1.5,2.4,232,No,Yes
+90,20,Male,Yes,Moderate,0.9,1.2,973,Yes,Yes
+91,25,Female,Yes,Mild,1.8,2.1,752,Yes,Yes
+92,42,Female,No,Severe,1.8,3.0,291,Yes,No
+93,45,Male,Yes,Mild,0.9,1.7,497,No,Yes
+94,37,Female,Yes,Severe,1.2,2.2,927,No,Yes
+95,45,Female,No,Mild,0.8,1.5,560,Yes,No
+96,38,Male,No,Moderate,1.3,2.7,710,No,No
+97,73,Female,No,Mild,0.6,2.1,747,No,No
+98,33,Male,No,Moderate,1.0,2.0,578,No,No
+99,31,Male,No,Severe,1.5,2.3,513,No,No
+100,18,Female,No,Moderate,1.5,2.5,836,No,No