python/model_building.ipynb

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 "cells": [
  {
   "source": [
    "# Diabetes Data Diving\n",
    "\n",
    "Model Building"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Importing Libraries"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General data analysis libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Preprocessing (Label Encoder)\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "# Basic Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Ensemble Models\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Validation\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n",
    "\n",
    "# Model Parameter Tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# ML Scoring Metrics\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "random.seed(0)\n",
    "np.random.seed(0)"
   ]
  },
  {
   "source": [
    "## Read in Data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Age Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n",
       "0   40   Male       No        Yes                 No      Yes         No   \n",
       "1   58   Male       No         No                 No      Yes         No   \n",
       "2   41   Male      Yes         No                 No      Yes        Yes   \n",
       "3   45   Male       No         No                Yes      Yes        Yes   \n",
       "4   60   Male      Yes        Yes                Yes      Yes        Yes   \n",
       "\n",
       "  Genital thrush visual blurring Itching Irritability delayed healing  \\\n",
       "0             No              No     Yes           No             Yes   \n",
       "1             No             Yes      No           No              No   \n",
       "2             No              No     Yes           No             Yes   \n",
       "3            Yes              No     Yes           No             Yes   \n",
       "4             No             Yes     Yes          Yes             Yes   \n",
       "\n",
       "  partial paresis muscle stiffness Alopecia Obesity     class  \n",
       "0              No              Yes      Yes     Yes  Positive  \n",
       "1             Yes               No      Yes      No  Positive  \n",
       "2              No              Yes      Yes      No  Positive  \n",
       "3              No               No       No      No  Positive  \n",
       "4             Yes              Yes      Yes     Yes  Positive  "
      ],
      "text/html": "<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>Age</th>\n      <th>Gender</th>\n      <th>Polyuria</th>\n      <th>Polydipsia</th>\n      <th>sudden weight loss</th>\n      <th>weakness</th>\n      <th>Polyphagia</th>\n      <th>Genital thrush</th>\n      <th>visual blurring</th>\n      <th>Itching</th>\n      <th>Irritability</th>\n      <th>delayed healing</th>\n      <th>partial paresis</th>\n      <th>muscle stiffness</th>\n      <th>Alopecia</th>\n      <th>Obesity</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>40</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>58</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>41</td>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>45</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>60</td>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Positive</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df = pd.read_csv('../data/diabetes_data_upload.csv')\n",
    "df.head()"
   ]
  },
  {
   "source": [
    "## Clean up labels "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace(\" \", \"_\")"
   ]
  },
  {
   "source": [
    "## Label Encoding\n",
    "\n",
    "Encode `Yes` or `No` as numeric values"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                    count       mean        std   min   25%   50%   75%   max\n",
       "Age                 520.0  48.028846  12.151466  16.0  39.0  47.5  57.0  90.0\n",
       "Gender              520.0   0.630769   0.483061   0.0   0.0   1.0   1.0   1.0\n",
       "Polyuria            520.0   0.496154   0.500467   0.0   0.0   0.0   1.0   1.0\n",
       "Polydipsia          520.0   0.448077   0.497776   0.0   0.0   0.0   1.0   1.0\n",
       "sudden_weight_loss  520.0   0.417308   0.493589   0.0   0.0   0.0   1.0   1.0\n",
       "weakness            520.0   0.586538   0.492928   0.0   0.0   1.0   1.0   1.0\n",
       "Polyphagia          520.0   0.455769   0.498519   0.0   0.0   0.0   1.0   1.0\n",
       "Genital_thrush      520.0   0.223077   0.416710   0.0   0.0   0.0   0.0   1.0\n",
       "visual_blurring     520.0   0.448077   0.497776   0.0   0.0   0.0   1.0   1.0\n",
       "Itching             520.0   0.486538   0.500300   0.0   0.0   0.0   1.0   1.0\n",
       "Irritability        520.0   0.242308   0.428892   0.0   0.0   0.0   0.0   1.0\n",
       "delayed_healing     520.0   0.459615   0.498846   0.0   0.0   0.0   1.0   1.0\n",
       "partial_paresis     520.0   0.430769   0.495661   0.0   0.0   0.0   1.0   1.0\n",
       "muscle_stiffness    520.0   0.375000   0.484589   0.0   0.0   0.0   1.0   1.0\n",
       "Alopecia            520.0   0.344231   0.475574   0.0   0.0   0.0   1.0   1.0\n",
       "Obesity             520.0   0.169231   0.375317   0.0   0.0   0.0   0.0   1.0\n",
       "class               520.0   0.615385   0.486973   0.0   0.0   1.0   1.0   1.0"
      ],
      "text/html": "<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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>520.0</td>\n      <td>48.028846</td>\n      <td>12.151466</td>\n      <td>16.0</td>\n      <td>39.0</td>\n      <td>47.5</td>\n      <td>57.0</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th>Gender</th>\n      <td>520.0</td>\n      <td>0.630769</td>\n      <td>0.483061</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Polyuria</th>\n      <td>520.0</td>\n      <td>0.496154</td>\n      <td>0.500467</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Polydipsia</th>\n      <td>520.0</td>\n      <td>0.448077</td>\n      <td>0.497776</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sudden_weight_loss</th>\n      <td>520.0</td>\n      <td>0.417308</td>\n      <td>0.493589</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>weakness</th>\n      <td>520.0</td>\n      <td>0.586538</td>\n      <td>0.492928</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Polyphagia</th>\n      <td>520.0</td>\n      <td>0.455769</td>\n      <td>0.498519</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Genital_thrush</th>\n      <td>520.0</td>\n      <td>0.223077</td>\n      <td>0.416710</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>visual_blurring</th>\n      <td>520.0</td>\n      <td>0.448077</td>\n      <td>0.497776</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Itching</th>\n      <td>520.0</td>\n      <td>0.486538</td>\n      <td>0.500300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Irritability</th>\n      <td>520.0</td>\n      <td>0.242308</td>\n      <td>0.428892</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>delayed_healing</th>\n      <td>520.0</td>\n      <td>0.459615</td>\n      <td>0.498846</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>partial_paresis</th>\n      <td>520.0</td>\n      <td>0.430769</td>\n      <td>0.495661</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>muscle_stiffness</th>\n      <td>520.0</td>\n      <td>0.375000</td>\n      <td>0.484589</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Alopecia</th>\n      <td>520.0</td>\n      <td>0.344231</td>\n      <td>0.475574</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Obesity</th>\n      <td>520.0</td>\n      <td>0.169231</td>\n      <td>0.375317</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>class</th>\n      <td>520.0</td>\n      <td>0.615385</td>\n      <td>0.486973</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "for c in df.columns:\n",
    "    if df[c].dtype == 'object':\n",
    "        lbl = LabelEncoder()\n",
    "        lbl.fit(list(df[c].values)) \n",
    "        df[c] = lbl.transform(list(df[c].values))\n",
    "        \n",
    "df.describe().T"
   ]
  },
  {
   "source": [
    "## Split data into test/train"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train:  (416, 16)\nX_test:  (104, 16)\ny_train:  (416,)\ny_test:  (104,)\n"
     ]
    }
   ],
   "source": [
    "X = df.drop('class', axis=1)\n",
    "y = df['class']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "print('X_train: ',X_train.shape)\n",
    "print('X_test: ',X_test.shape)\n",
    "print('y_train: ',y_train.shape)\n",
    "print('y_test: ',y_test.shape)"
   ]
  },
  {
   "source": [
    "## Train Models\n",
    "\n",
    "Train basic classification models and evaluate using 10-fold cross validation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Logistic Regression: \t 0.9184088269454124 +/- 0.04144262875410617\n",
      "K-Nearest Neighbors: \t 0.858362369337979 +/- 0.05795193742377788\n",
      "Naive Bayes: \t 0.8752613240418119 +/- 0.053223748943650846\n",
      "Support Vector Machine: \t 0.6154471544715446 +/- 0.061106435617389765\n"
     ]
    }
   ],
   "source": [
    "models = [\n",
    "    ( 'Logistic Regression' , LogisticRegression()),\n",
    "    ( 'K-Nearest Neighbors' , KNeighborsClassifier()),\n",
    "    ( 'Naive Bayes' , GaussianNB()),\n",
    "    ( 'Support Vector Machine' , SVC())\n",
    "]\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    # training with 10-fold cross validation\n",
    "    Kfold = KFold(n_splits=10, random_state=3)\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=Kfold, scoring= 'accuracy')\n",
    "    \n",
    "    # save results\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "\n",
    "    # print out results\n",
    "    print(f'{name}: \\t {cv_results.mean()} +/- {cv_results.std()}')"
   ]
  },
  {
   "source": [
    "## Training Ensemble Models"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "AdaBoost: \t 0.9256097560975609 +/- 0.04571373833510797\n",
      "Bagging: \t 0.9618466898954704 +/- 0.030464345822262418\n",
      "Gradient Boosted: \t 0.976074332171893 +/- 0.026190154281572776\n",
      "Random Forest: \t 0.9761324041811846 +/- 0.028172347166595148\n"
     ]
    }
   ],
   "source": [
    "models = [\n",
    "    ( 'AdaBoost' , AdaBoostClassifier()),\n",
    "    ( 'Bagging' , BaggingClassifier()),\n",
    "    ( 'Gradient Boosted' ,  GradientBoostingClassifier()),\n",
    "    ( 'Random Forest' , RandomForestClassifier())\n",
    "]\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    # training with 10-fold cross validation\n",
    "    Kfold = KFold(n_splits=10, random_state=3)\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=Kfold, scoring= 'accuracy')\n",
    "    \n",
    "    # save results\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "\n",
    "    # print out results\n",
    "    print(f'{name}: \\t {cv_results.mean()} +/- {cv_results.std()}')"
   ]
  },
  {
   "source": [
    "The best model is the random forest!"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Parameter Tuning"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Best Score: 0.9784552845528456 using these parameters: {'max_features': 'auto', 'n_estimators': 20}\n"
     ]
    }
   ],
   "source": [
    "# preprocess with standard scaler transformation\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "\n",
    "# find best parameters\n",
    "model = RandomForestClassifier()\n",
    "param_grid = { \n",
    "    'n_estimators': [10,20,50,100],\n",
    "    'max_features': ['auto', 'sqrt', 'log2']\n",
    "}\n",
    "\n",
    "kfold = KFold(n_splits=10, random_state=0)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy', cv=kfold)\n",
    "grid_result = grid.fit(X_train, y_train)\n",
    "\n",
    "print(f'Best Score: {grid_result.best_score_} using these parameters: {grid_result.best_params_}')"
   ]
  },
  {
   "source": [
    "## Testing the best model on the test set"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.9807692307692307\n"
     ]
    }
   ],
   "source": [
    "RF = RandomForestClassifier(max_features=grid_result.best_params_['max_features'], n_estimators=grid_result.best_params_['n_estimators']).fit(X_train,y_train)\n",
    "y_pred = RF.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "source": [
    "## Evaluating Error"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True)\n",
    "plt.title(\"Confusion Matrix\",fontweight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.95      1.00      0.98        40\n           1       1.00      0.97      0.98        64\n\n    accuracy                           0.98       104\n   macro avg       0.98      0.98      0.98       104\nweighted avg       0.98      0.98      0.98       104\n\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}