Machine Learning Workflow on Diabetes Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Workflow on Diabetes Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 0 -  Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial I will use the “Pima Indians Diabetes Database” provided by the UCI Machine Learning Repository (famous repository for machine learning data sets).\n",
    "\n",
    "URL : [https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 1 -  Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "diabetes = pd.read_csv('datasets/diabetes.csv')\n",
    "print(diabetes.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diabetes data set dimensions : (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Diabetes data set dimensions : {}\".format(diabetes.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.groupby('Outcome').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000001C916D1160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000001C91712588>,\n",
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       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000001C917720B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000001C91797630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000001C917BFBA8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000001C917EE160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000001C91816710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000001C91816748>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes.hist(figsize=(9, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    [[AxesSubplot(0.125,0.670278;0.215278x0.209722...\n",
       "1    [[AxesSubplot(0.125,0.670278;0.215278x0.209722...\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes.groupby('Outcome').hist(figsize=(9, 9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Phase 2 - Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing or Null Data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unexpected Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total :  35\n",
      "Outcome\n",
      "0    19\n",
      "1    16\n",
      "Name: Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total : \", diabetes[diabetes.BloodPressure == 0].shape[0])\n",
    "print(diabetes[diabetes.BloodPressure == 0].groupby('Outcome')['Age'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total :  5\n",
      "Outcome\n",
      "0    3\n",
      "1    2\n",
      "Name: Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total : \", diabetes[diabetes.Glucose == 0].shape[0])\n",
    "print(diabetes[diabetes.Glucose == 0].groupby('Outcome')['Age'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total :  227\n",
      "Outcome\n",
      "0    139\n",
      "1     88\n",
      "Name: Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total : \", diabetes[diabetes.SkinThickness == 0].shape[0])\n",
    "print(diabetes[diabetes.SkinThickness == 0].groupby('Outcome')['Age'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total :  11\n",
      "Outcome\n",
      "0    9\n",
      "1    2\n",
      "Name: Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total : \", diabetes[diabetes.BMI == 0].shape[0])\n",
    "print(diabetes[diabetes.BMI == 0].groupby('Outcome')['Age'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total :  374\n",
      "Outcome\n",
      "0    236\n",
      "1    138\n",
      "Name: Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total : \", diabetes[diabetes.Insulin == 0].shape[0])\n",
    "print(diabetes[diabetes.Insulin == 0].groupby('Outcome')['Age'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(724, 9)\n"
     ]
    }
   ],
   "source": [
    "diabetes_mod = diabetes[(diabetes.BloodPressure != 0) & (diabetes.BMI != 0) & (diabetes.Glucose != 0)]\n",
    "print(diabetes_mod.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 3 - Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features/Response\n",
    "feature_names = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
    "X = diabetes_mod[feature_names]\n",
    "y = diabetes_mod.Outcome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 4 - Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial model selection process\n",
    "models = []\n",
    "\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('SVC', SVC(gamma='scale')))\n",
    "models.append(('LR', LogisticRegression(solver='lbfgs', max_iter=4000)))\n",
    "models.append(('DT', DecisionTreeClassifier()))\n",
    "models.append(('GNB', GaussianNB()))\n",
    "models.append(('RF', RandomForestClassifier(n_estimators=100)))\n",
    "models.append(('GB', GradientBoostingClassifier()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Train/Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train/Test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = diabetes_mod.Outcome, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Name     Score\n",
      "0  KNN  0.729282\n",
      "1  SVC  0.740331\n",
      "2   LR  0.779006\n",
      "3   DT  0.729282\n",
      "4  GNB  0.734807\n",
      "5   RF  0.751381\n",
      "6   GB  0.773481\n"
     ]
    }
   ],
   "source": [
    "names = []\n",
    "scores = []\n",
    "\n",
    "for name, model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    scores.append(accuracy_score(y_test, y_pred))\n",
    "    names.append(name)\n",
    "\n",
    "tr_split = pd.DataFrame({'Name': names, 'Score': scores})\n",
    "print(tr_split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using K-Fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Name     Score\n",
      "0  KNN  0.711521\n",
      "1  SVC  0.728074\n",
      "2   LR  0.773662\n",
      "3   DT  0.684162\n",
      "4  GNB  0.755681\n",
      "5   RF  0.769551\n",
      "6   GB  0.765442\n"
     ]
    }
   ],
   "source": [
    "strat_k_fold = StratifiedKFold(n_splits=10, random_state=10)\n",
    "\n",
    "names = []\n",
    "scores = []\n",
    "\n",
    "for name, model in models:\n",
    "    \n",
    "    score = cross_val_score(model, X, y, cv=strat_k_fold, scoring='accuracy').mean()\n",
    "    names.append(name)\n",
    "    scores.append(score)\n",
    "\n",
    "kf_cross_val = pd.DataFrame({'Name': names, 'Score': scores})\n",
    "print(kf_cross_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jj9G4cWPatWvHvffeu8G8GbP6kGpSkNQf+CVQBNwdETdW2D4G6Jst7gQ0j4hN/qvoMaJ2N91Oy6ujz6jvEGwTFDLPZcGCBYwaNYrp06fTtGnT5M5eL774ItOnT0+WP/7GN77B1KlTk7ktf/7zn9lll102eL5+/foxatQoGjVqxGWXXcaoUaO46aab6ubFmm1EasNHkoqAscAAoBNwqqQNFvGJiOER0T0iugO3AX9OKx6z6hQyz+Wuu+7i/PPPp2nTpgA0b94cyFzl9uWXX7JmzRpWr17N2rVr+drXvgbAZ599xq233rrBCqMARx11VHIj+V69evlKMGsQ0jyncDCwMCIWRcQaYDxwXBXtTwXGpRiPWZUKmecyf/585s+fT+/evenVqxdPPvkkAIceeih9+/alRYsWtGjRIjmvBXDVVVdx8cUXs9NOO230ue+55x4GDBiQwqsy2zRpDh/tAyzNKZcCh+RrKGlfoA2Qdx1aSUOBoQCtWrXavFGaZRUyz6WsrIwFCxbw/PPPU1paymGHHcZbb73FypUrmTt3bvJtv1+/fkybNo3ddtuNhQsXMmbMGBYvXpz3ea+//noaNWrEaaedttlfk9mmSjMp5LsMZ2M3bxgMTIyIdfk2RsSdwJ2QuZ/C5gnPbEOFzHMpLi6mV69ebL/99rRp04b9998/SRK9evVKzhsMGDCAl19+mV133ZVXX32V1q1bU1ZWxvLly+nTpw/PP/88APfffz+PP/44zzzzjK9cswYhzeGjUqBlTrkYWLaRtoPx0JHVs0LmuRx//PE899xzAKxcuZL58+fTtm1bWrVqxdSpUykrK2Pt2rVMnTqVjh078uMf/5hly5axePFi/vrXv9KhQ4ckITz55JPcdNNNTJo0qcqhJbO6lGZSmAm0l9RGUmMyH/yTKjaStD/QFHgpxVjMqlXIPJejjz6aPfbYg06dOtG3b19Gjx7NHnvswaBBg2jXrh1du3alpKSEkpISvvOdqm80M2zYMD799FP69etH9+7dOffcc+viZZpVKbXho4gokzQMmELmktR7ImKOpGuBWRFRniBOBcbHlnZfUNsqVTfPRRK33nort9566wZtioqKKi25XlHr1q2TOQpAcq9ss4Yk1XkKETEZmFyhbmSF8jVpxmBWlanfPLy+Q9iow6dNre8QbBu0VS1zYWZmteOkYGZmCScFMzNLOCmYmVnCScHMzBJOCmZmlnBSMDOzhJOCmZklnBTMzCzhpGBmZgknBTMzSzgpmJlZwknBzMwSTgpmZpZwUjAzs4STgpmZJZwUzMws4aRgZmaJVJOCpP6S5klaKOnyjbT5rqS3Jc2R9GCa8ZiZWdVSu0ezpCJgLNAPKAVmSpoUEW/ntGkP/AzoHRGrJDVPKx4zM6temkcKBwMLI2JRRKwBxgPHVWjzQ2BsRKwCiIjlKcZjZmbVSDMp7AMszSmXZutydQA6SJou6WVJ/fN1JGmopFmSZq1YsSKlcM3MLM2koDx1UaHcCGgP9AFOBe6W1KTSThF3RkTPiOjZrFmzzR6omZllpJkUSoGWOeViYFmeNo9GxNqIeBeYRyZJmJlZPUgzKcwE2ktqI6kxMBiYVKHNI0BfAEl7khlOWpRiTGZmVoXUkkJElAHDgCnAXGBCRMyRdK2kgdlmU4APJb0NPAeMiIgP04rJzMyqltolqQARMRmYXKFuZM7jAC7K/piZWT3zjGYzM0s4KZiZWcJJwczMEk4KZmaWcFIwM7OEk4KZmSWcFMzMLOGkYGZmCScFMzNLOCmYmVnCScHMzBJOCmZmlnBSMDOzhJOCmZklnBTMzCzhpGBmZgknBTMzS6SaFCT1lzRP0kJJl+fZfqakFZJmZ3/OSTMeMzOrWmq345RUBIwF+gGlwExJkyLi7QpN/xgRw9KKw8zMCpfmkcLBwMKIWBQRa4DxwHEpPp+ZmdVSmklhH2BpTrk0W1fRSZLekDRRUssU4zEzs2qkmRSUpy4qlB8DWkdEN+D/gPvzdiQNlTRL0qwVK1Zs5jDNzKxcmkmhFMj95l8MLMttEBEfRsTqbPEuoEe+jiLizojoGRE9mzVrlkqwZmaWblKYCbSX1EZSY2AwMCm3gaQWOcWBwNwU4zEzs2qkdvVRRJRJGgZMAYqAeyJijqRrgVkRMQm4QNJAoAz4CDgzrXjMzKx61SaF7Af7HyJi1aZ2HhGTgckV6kbmPP4Z8LNN7dfMzNJRyPDRXmTmGEzITkbLdwLZzMy2AtUmhYi4EmgP/C+Z4Z0Fkm6Q1C7l2MzMrI4VdKI5IgL4R/anDGgKTJR0c4qxmZlZHSvknMIFwBBgJXA3MCIi1kraDlgAXJpuiGZmVlcKufpoT+DEiFiSWxkR6yUdm05YZmZWHwoZPppM5nJRACTtKukQgIjwvAIzs61IIUnhN8BnOeXPs3VmZraVKSQpKHuiGcgMG5HipDczM6s/hSSFRZIukLR99udCYFHagZmZWd0rJCmcC/wX8D6ZRe4OAYamGZSZmdWPaoeBImI5mcXszMxsK1fIPIUdgbOBzsCO5fUR8YMU4zIzs3pQyPDR78isf3Q0MJXMfRE+TTMoMzOrH4Ukhf0i4irg84i4H/g20DXdsMzMrD4UkhTWZv/9l6QuwO5A69QiMjOzelPIfIM7JTUFriRz57RdgKtSjcrMzOpFlUkhu+jdJ9kb7EwD2tZJVGZmVi+qHD7Kzl4eVkexmJlZPSvknMLTki6R1FLSV8t/Cuk8e6e2eZIWSrq8inaDJIWkngVHbmZmm10h5xTK5yOcn1MXVDOUJKkIGAv0IzMTeqakSRHxdoV2uwIXAK8UGrSZmaWjkBnNbWrY98HAwohYBCBpPHAc8HaFdr8AbgYuqeHzmJnZZlLIjOYz8tVHxAPV7LoPsDSnXL5uUm7fBwAtI+JxSU4KZmb1rJDho4NyHu8IHAm8BlSXFJSnLlmCO3tl0xjgzOoCkDSU7CJ8rVq1qq65mZnVUCHDRz/JLUvanczSF9UpBVrmlIuBZTnlXYEuwPOSILOUxiRJAyNiVoUY7gTuBOjZs2dgZmapKOTqo4q+ANoX0G4m0F5SG0mNyay0Oql8Y0R8HBF7RkTriGgNvAxUSghmZlZ3Cjmn8Bj/GfbZDugETKhuv4gokzQMmAIUAfdExBxJ1wKzImJS1T2YmVldK+Scwi05j8uAJRFRWkjnETEZmFyhbuRG2vYppE8zM0tPIUnhPeCDiPgSQNJXJLWOiMWpRmZmZnWukHMKDwHrc8rrsnVmZraVKSQpNIqINeWF7OPG6YVkZmb1pZCksELSwPKCpOOAlemFZGZm9aWQcwrnAn+QdHu2XArkneVsZmZbtkImr70D9JK0C6CI8P2Zzcy2UtUOH0m6QVKTiPgsIj6V1FTSdXURnJmZ1a1CzikMiIh/lReyd2E7Jr2QzMysvhSSFIok7VBekPQVYIcq2puZ2RaqkBPNvweekXRvtnwWcH96IZmZWX0p5ETzzZLeAL5FZjnsJ4F90w7MzMzqXqGrpP6DzKzmk8jcT2FuahGZmVm92eiRgqQOZJa7PhX4EPgjmUtS+9ZRbGZmVseqGj76O/AC8J2IWAggaXidRGVmZvWiquGjk8gMGz0n6S5JR5L/FptmZraV2GhSiIiHI+IU4OvA88Bw4GuSfiPpqDqKz8zM6lC1J5oj4vOI+ENEHEvmPsuzgctTj8zMzOrcJt2jOSI+iog7IuKItAIyM7P6s0lJYVNJ6i9pnqSFkiodXUg6V9KbkmZL+qukTmnGY2ZmVUstKUgqAsYCA4BOwKl5PvQfjIiuEdEduBm4Na14zMysemkeKRwMLIyIRdm7tY0HjsttEBGf5BR3BiLFeMzMrBqFrH1UU/sAS3PKpcAhFRtJOh+4iMwtPvOeq5A0FBgK0KpVq80eqJmZZaR5pJBvTkOlI4GIGBsR7YDLgCvzdRQRd0ZEz4jo2axZs80cppmZlUszKZQCLXPKxcCyKtqPB45PMR4zM6tGmklhJtBeUhtJjcmsozQpt4Gk9jnFbwMLUozHzMyqkdo5hYgokzQMmAIUAfdExBxJ1wKzImISMEzSt4C1wCpgSFrxmJlZ9dI80UxETAYmV6gbmfP4wjSf38zMNk2qk9fMzGzL4qRgZmYJJwUzM0s4KZiZWcJJwczMEk4KZmaWcFIwM7OEk4KZmSWcFMzMLOGkYGZmCScFMzNLOCmYmVnCScHMzBJOCmZmlnBSMDOzhJOCmZklnBTMzCzhpGBmZolUk4Kk/pLmSVoo6fI82y+S9LakNyQ9I2nfNOMxM7OqpZYUJBUBY4EBQCfgVEmdKjR7HegZEd2AicDNacVjZmbVS/NI4WBgYUQsiog1wHjguNwGEfFcRHyRLb4MFKcYj5mZVSPNpLAPsDSnXJqt25izgb/k2yBpqKRZkmatWLFiM4ZoZma50kwKylMXeRtKpwM9gdH5tkfEnRHRMyJ6NmvWbDOGaGZmuRql2Hcp0DKnXAwsq9hI0reAK4DDI2J1ivGYmVk10jxSmAm0l9RGUmNgMDApt4GkA4A7gIERsTzFWMzMrACpJYWIKAOGAVOAucCEiJgj6VpJA7PNRgO7AA9Jmi1p0ka6MzOzOpDm8BERMRmYXKFuZM7jb6X5/GZmtmk8o9nMzBJOCmZmlnBSMDOzhJOCmZklnBTMzCzhpGBmZgknBTMzSzgpmJlZwknBzMwSTgpmZpZwUjAzs4STgpmZJZwUzMws4aRgZmYJJwUzM0s4KZiZWcJJwczMEqkmBUn9Jc2TtFDS5Xm2f1PSa5LKJA1KMxYzM6teaklBUhEwFhgAdAJOldSpQrP3gDOBB9OKw8zMCpfmPZoPBhZGxCIASeOB44C3yxtExOLstvUpxmFmZgVKc/hoH2BpTrk0W2dmZg1UmklBeeqiRh1JQyXNkjRrxYoVtQzLzMw2Js2kUAq0zCkXA8tq0lFE3BkRPSOiZ7NmzTZLcGZmVlmaSWEm0F5SG0mNgcHApBSfz8zMaim1pBARZcAwYAowF5gQEXMkXStpIICkgySVAicDd0iak1Y8ZmZWvTSvPiIiJgOTK9SNzHk8k8ywkpmZNQCe0WxmZgknBTMzSzgpmJlZwknBzMwSTgpmZpZwUjAzs4STgpmZJZwUzMws4aRgZmYJJwUzM0s4KZiZWcJJwczMEk4KZmaWcFIwM7OEk4KZmSWcFMzMLOGkYGZmCScFMzNLpJoUJPWXNE/SQkmX59m+g6Q/Zre/Iql1mvGYmVnVUksKkoqAscAAoBNwqqROFZqdDayKiP2AMcBNacVjZmbVS/NI4WBgYUQsiog1wHjguAptjgPuzz6eCBwpSSnGZGZmVUgzKewDLM0pl2br8raJiDLgY2CPFGMyM7MqKCLS6Vg6GTg6Is7Jlr8PHBwRP8lpMyfbpjRbfifb5sMKfQ0FhmaL+wPzUgk6Y09gZYr9p83x158tOXZw/PUt7fj3jYhm1TVqlGIApUDLnHIxsGwjbUolNQJ2Bz6q2FFE3AncmVKcG5A0KyJ61sVzpcHx158tOXZw/PWtocSf5vDRTKC9pDaSGgODgUkV2kwChmQfDwKejbQOXczMrFqpHSlERJmkYcAUoAi4JyLmSLoWmBURk4D/BX4naSGZI4TBacVjZmbVS3P4iIiYDEyuUDcy5/GXwMlpxlADdTJMlSLHX3+25NjB8de3BhF/aieazcxsy+NlLszMLLHNJAVJn+U8PkbSAkmtJF0j6QtJzTfSNiT9d075EknX1FngOSRdIWmOpDckzZb0F0mjKrTpLmlu9vEuku6Q9E52v2mSDqmP2CvK/R3n1F0j6f3sa3tb0qn1EVshJK3LxjlH0t8kXSRpO0lHZ+tnS/osu8zLbEkP1HO8X5P0oKRFkl6V9JKkEyT1yf4f/05O28cl9ck+fj7nNczNXh5e73J+/29JekxSk2x9a0n/znkPZmcvdGkwqnkvPs7G/Iak/8v9XKor20xSKCfpSOA2oH9EvJetXglcvJFdVgMnStqzLuLbGEmHAscCB0ZEN+BbwI3AKRWaDgYezD6+m8wJ/PYR0Rk4k8y10A3ZmIjoTma2+x2Stq/vgDbi3xHRPft77QccA1wdEVOy9d2BWcBp2fIZ9RVodpWAR4DB3Ti2AAAGHUlEQVRpEdE2InqQ+X9SnG1SClxRRRenZV9Pb+CmBvIhW/7770Lm//j5OdveKX8Psj9r6inGSgp4L17IxtyNzBWc52+kq9RsU0lB0mHAXcC3I+KdnE33AKdI+mqe3crInAAaXgchVqUFsDIiVgNExMqImAr8q8K3/+8C4yW1Aw4BroyI9dl9FkXEE3UdeE1ExALgC6BpfcdSnYhYTmZy5bAGukzLEcCaiPhteUVELImI27LFvwEfS+pXTT+7AJ8D69IJs8ZeovJqCQ1Vde8FkCSPXYFVdRzfNpUUdgAeBY6PiL9X2PYZmcRw4Ub2HQucJmn3FOOrzlNAS0nzJf1a0uHZ+nFkL+WV1Av4MPuB2hmYHREN7Q+4IJIOBBZkP3AbvIhYRObvqc4P9wvQGXitmjbXAVduZNsfJL1BZiWBXzSk/1PKLLx5JBvOgWqXM3Q0tp5C25jq3ovDJM0G3iMzGnBPnUSVY1tKCmuBF8mszJrPr4AhknaruCEiPgEeAC5IL7yqRcRnQA8y30hXAH+UdCaZhQYHSdqOTHIYV18xbibDJc0DXgGuqedYNlVDPEqoRNLY7HmQmeV1EfFCdttheXY5LTuc0Qq4RNK+dRRqVb6S/fD8EPgq8HTOttzhozofftkUed6L8uGjlsC9wM11HdO2lBTWkxlaOUjS/6u4MSL+RWYs/ryN7P8/ZBLKzqlFWI2IWBcRz0fE1cAw4KSIWAosBg4HTgImZJvPAUqyyWJLMiYi9idzruQBSTvWd0CFkNSWzLBKQzyymQMcWF7IflAeCVRcB+d6qji3EBEryHzLbQgXK/w7e55jX6Ax9TD2XkOFvheQOfr5Zh3FldjSPjBqJSK+IHOy9jRJ+Y4YbgV+RJ5JfRHxEZkP3I0daaRK0v6S2udUdQeWZB+PI3M/infKFxfMnjOZBfy8fJxbUntJFZcvb5Ai4s9k4h9SXdv6JqkZ8Fvg9ga6TMuzwI6SfpxTt1PFRhHxFJlzOCX5OpG0E3AA8E6+7fUhIj4mcwR/SQO+KCFXQe9F1jeoh991qjOaG6KI+EhSf2CapJUVtq2U9DAbP6n832S+odeHXYDbspfelQEL+c/KsQ8BvwR+UmGfc8jEvFDSF2QOtUfUTbjV2klSaU751jxtrgUelHRX+cnyBqR8+GJ7Mu/H78j/GupdRISk44Exki4lM/z4OXBZnubXkzn3lusPkv5N5rzcfRHxaqoBb6KIeF3S38gMn75Q3/FUpYD3ovycgsjcSuCcuo7RM5rNzCyxTQ0fmZlZ1ZwUzMws4aRgZmYJJwUzM0s4KZiZWcJJwbZJkvaSNF6ZFWTfljRZUgdJb23G57hW0reyjw9TZkXV2ZL2kTRxcz2P2ebkS1Jtm5OdzPcicH/5wmSSupNZgOw32ZU3N/dz/hZ4JSLurcG+RQ1pvSHbuvlIwbZFfYG1FVaqnA0sLS9n1+V/QdJr2Z//yta3UOa+FOVr+R8mqUjSfdnym5KGZ9veJ2mQpHPILLEyUtIfsn2/lW1TJGm0pJnZNfR/lK3vI+k5SQ8Cb9bZb8a2edvcjGYzoAtQ3azc5UC/iPgyu7zIOKAn8D1gSkRcn12hcycyS47sU36EkZ11noiIuyV9A3g8IiZKap2z+Wzg44g4SNIOwHRJT2W3HQx0iYh3a/NizTaFk4JZftsDt2eHldYBHbL1M4F7suvsPBIRsyUtAtpKug14gswy54U6CugmaVC2vDvQHlgDzHBCsLrm4SPbFs0hswx5VYYD/ySzOFxPMitxEhHTyKxc+T7wO0lnRMSqbLvnyazWefcmxCLgJzlLPbfJLkwHmTVxzOqUk4Jti54FdpD0w/IKSQeRWYa53O7AB9mF+L4PFGXb7Qssj4i7gP8FDlTmVq3bRcSfgKvIWRq5AFOAH5ev8Jm9Aqrelmc38/CRbXOyK1WeAPyPpMuBL8nck+KnOc1+DfxJ0snAc/znW3sfYISktWTu2HcGmVtB3ptz74qfbUI4dwOtgdeyV0WtAI6vwcsy2yx8SaqZmSU8fGRmZgknBTMzSzgpmJlZwknBzMwSTgpmZpZwUjAzs4STgpmZJZwUzMws8f8BDTQNmQ+fG4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis = sns.barplot(x = 'Name', y = 'Score', data = kf_cross_val)\n",
    "axis.set(xlabel='Classifier', ylabel='Accuracy')\n",
    "\n",
    "for p in axis.patches:\n",
    "    height = p.get_height()\n",
    "    axis.text(p.get_x() + p.get_width()/2, height + 0.005, '{:1.4f}'.format(height), ha=\"center\") \n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 5 - Feature Selection (Revisited)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFECV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg_model = LogisticRegression(solver='lbfgs', max_iter=4000)\n",
    "\n",
    "rfecv = RFECV(estimator=logreg_model, step=1, cv=strat_k_fold, scoring='accuracy')\n",
    "rfecv.fit(X, y)\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Logistic Regression CV score vs No of Features')\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pregnancies', 'Glucose', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n"
     ]
    }
   ],
   "source": [
    "feature_importance = list(zip(feature_names, rfecv.support_))\n",
    "\n",
    "new_features = []\n",
    "\n",
    "for key,value in enumerate(feature_importance):\n",
    "    if(value[1]) == True:\n",
    "        new_features.append(value[0])\n",
    "        \n",
    "print(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial accuracy : 0.7736617574549275 \n",
      "Accuracy after Feature Selection : 0.7750125945934357 \n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy scores \n",
    "X_new = diabetes_mod[new_features]\n",
    "\n",
    "initial_score = cross_val_score(logreg_model, X, y, cv=strat_k_fold, scoring='accuracy').mean()\n",
    "print(\"Initial accuracy : {} \".format(initial_score))\n",
    "\n",
    "fe_score = cross_val_score(logreg_model, X_new, y, cv=strat_k_fold, scoring='accuracy').mean()\n",
    "print(\"Accuracy after Feature Selection : {} \".format(fe_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb_model = GradientBoostingClassifier()\n",
    "\n",
    "gb_rfecv = RFECV(estimator=gb_model, step=1, cv=strat_k_fold, scoring='accuracy')\n",
    "gb_rfecv.fit(X, y)\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Gradient Boost CV score vs No of Features')\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(gb_rfecv.grid_scores_) + 1), gb_rfecv.grid_scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pregnancies', 'Glucose', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n"
     ]
    }
   ],
   "source": [
    "feature_importance = list(zip(feature_names, gb_rfecv.support_))\n",
    "\n",
    "new_features = []\n",
    "\n",
    "for key,value in enumerate(feature_importance):\n",
    "    if(value[1]) == True:\n",
    "        new_features.append(value[0])\n",
    "        \n",
    "print(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial accuracy : 0.7627023174051921 \n",
      "Accuracy after Feature Selection : 0.7847157909404678 \n"
     ]
    }
   ],
   "source": [
    "X_new_gb = diabetes_mod[new_features]\n",
    "\n",
    "initial_score = cross_val_score(gb_model, X, y, cv=strat_k_fold, scoring='accuracy').mean()\n",
    "print(\"Initial accuracy : {} \".format(initial_score))\n",
    "\n",
    "fe_score = cross_val_score(gb_model, X_new_gb, y, cv=strat_k_fold, scoring='accuracy').mean()\n",
    "print(\"Accuracy after Feature Selection : {} \".format(fe_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 6 - Model Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify parameters\n",
    "c_values = list(np.arange(1, 10))\n",
    "\n",
    "param_grid = [\n",
    "    {'C': c_values, 'penalty': ['l1'], 'solver' : ['liblinear'], 'multi_class' : ['ovr']},\n",
    "    {'C': c_values, 'penalty': ['l2'], 'solver' : ['liblinear', 'newton-cg', 'lbfgs'], 'multi_class' : ['ovr']}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=10, shuffle=False),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=False, n_jobs=None,\n",
       "       param_grid=[{'C': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'penalty': ['l1'], 'solver': ['liblinear'], 'multi_class': ['ovr']}, {'C': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'penalty': ['l2'], 'solver': ['liblinear', 'newton-cg', 'lbfgs'], 'multi_class': ['ovr']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(LogisticRegression(), param_grid, cv=strat_k_fold, scoring='accuracy', iid=False)\n",
    "grid.fit(X_new, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1, 'multi_class': 'ovr', 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_new = LogisticRegression(C=1, multi_class='ovr', penalty='l2', solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final accuracy : 0.7778093982464039 \n"
     ]
    }
   ],
   "source": [
    "initial_score = cross_val_score(logreg_new, X_new, y, cv=strat_k_fold, scoring='accuracy').mean()\n",
    "print(\"Final accuracy : {} \".format(initial_score))"
   ]
  }
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