ML, Diabetes Prediction using sklearn classifications .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import export_text\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "moyVGhqxLQ60",
    "outputId": "b8b64028-27be-42d7-a428-12f1f63b0223"
   },
   "outputs": [
    {
     "data": {
      "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>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": [
    "db=pd.read_csv('diabetes.csv')\n",
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "KVPDfe04LQ61",
    "outputId": "89543be2-d136-4b29-c612-41bb65f32552"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "gCmyaDUjLQ61",
    "outputId": "31ce76ae-695f-4cbd-c0b5-575a31833a0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   int64\n",
       "Glucose                       int64\n",
       "BloodPressure                 int64\n",
       "SkinThickness                 int64\n",
       "Insulin                       int64\n",
       "BMI                         float64\n",
       "DiabetesPedigreeFunction    float64\n",
       "Age                           int64\n",
       "Outcome                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Z0e4vlL9LQ61",
    "outputId": "84c873d4-bfa5-4c84-eb8f-53d5b55668a2"
   },
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "kca8JKFzLQ61",
    "outputId": "3172c6c5-bb59-498a-db4f-2f849f958f9a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db['Outcome'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "UgFtUUf6LQ61"
   },
   "outputs": [],
   "source": [
    "X=db.drop('Outcome',axis=1)\n",
    "y=db['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "r7QNkXK8LQ61"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "MJ-ryKZHLQ62",
    "outputId": "e4684ea5-811f-4b79-f085-987002e60f36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(537, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LmLE4DGiLQ62"
   },
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "fRHXBsovLQ62"
   },
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(random_state=0)\n",
    "path = clf.cost_complexity_pruning_path(X_train, y_train)\n",
    "ccp_alphas, impurities = path.ccp_alphas, path.impurities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "QaqytDgVLQ62",
    "outputId": "73a214b3-e750-4b0e-ffc5-ab117d33bceb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.00117939, 0.00159617, 0.00165529, 0.00173805,\n",
       "       0.00173805, 0.00173805, 0.00175476, 0.00181987, 0.00184495,\n",
       "       0.00223464, 0.00223464, 0.00223464, 0.00239425, 0.00248293,\n",
       "       0.00248293, 0.00248293, 0.00269354, 0.0027933 , 0.00283763,\n",
       "       0.00297952, 0.00297952, 0.00331057, 0.00341403, 0.0034379 ,\n",
       "       0.0034952 , 0.00349791, 0.00350031, 0.00352837, 0.00353725,\n",
       "       0.00369471, 0.00395052, 0.00399042, 0.00419615, 0.00446927,\n",
       "       0.00446927, 0.00448857, 0.00472939, 0.00496586, 0.00496586,\n",
       "       0.004966  , 0.00526607, 0.00534722, 0.00536756, 0.00558407,\n",
       "       0.0057851 , 0.0057935 , 0.00624843, 0.01038992, 0.01148145,\n",
       "       0.01413395, 0.01754131, 0.03499904, 0.07989694])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccp_alphas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "oDdM-7iXLQ62",
    "outputId": "06340c36-9593-43af-b78d-7f7d9e4c3e2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes in the last tree is: 1 with ccp_alpha: 0.07989694415988807\n"
     ]
    }
   ],
   "source": [
    "clfs = []\n",
    "for ccp_alpha in ccp_alphas:\n",
    "    clf = DecisionTreeClassifier(random_state=0, ccp_alpha=ccp_alpha)\n",
    "    clf.fit(X_train, y_train)\n",
    "    clfs.append(clf)\n",
    "print(\"Number of nodes in the last tree is: {} with ccp_alpha: {}\".format(\n",
    "      clfs[-1].tree_.node_count, ccp_alphas[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "_ots5M-RLQ62"
   },
   "outputs": [],
   "source": [
    "cvres=[]\n",
    "for a in ccp_alphas:\n",
    "    clf = DecisionTreeClassifier(random_state=0, ccp_alpha= a)\n",
    "    cvclf = cross_validate(clf, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    cvres.append(cvclf['test_score'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "hYmoE1VoLQ62",
    "outputId": "9ee44774-d3de-4dce-a6a1-9604e345d3f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cvres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "fAFXxJerLQ63",
    "outputId": "e22b91e2-0bb7-4da5-c600-30ea7177e7d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Acc')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(list(range(1,55)),cvres)\n",
    "plt.xlabel('indx')\n",
    "plt.ylabel('Acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "B87jh7AcLQ63",
    "outputId": "88bfc4af-a3fa-4d35-cc7e-9df28758781f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(cvres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "5PAxyrptLQ63"
   },
   "outputs": [],
   "source": [
    "al=ccp_alphas[48]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "a9nX5BlYLQ63"
   },
   "outputs": [],
   "source": [
    "our_clf = DecisionTreeClassifier(random_state=0, ccp_alpha=al).fit(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "34vRXyPyLQ63",
    "outputId": "4d25d2a2-f2c6-4073-f32b-c2cf155695e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8917748917748918"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, our_clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "FbNK5PwlLQ63",
    "outputId": "956287e3-5102-46c4-86fc-7f27e7c0996a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- Glucose <= 139.50\n",
      "|   |--- BMI <= 43.15\n",
      "|   |   |--- BMI <= 27.35\n",
      "|   |   |   |--- class: 0\n",
      "|   |   |--- BMI >  27.35\n",
      "|   |   |   |--- Glucose <= 101.00\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- Glucose >  101.00\n",
      "|   |   |   |   |--- DiabetesPedigreeFunction <= 0.92\n",
      "|   |   |   |   |   |--- DiabetesPedigreeFunction <= 0.24\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- DiabetesPedigreeFunction >  0.24\n",
      "|   |   |   |   |   |   |--- BMI <= 33.90\n",
      "|   |   |   |   |   |   |   |--- Insulin <= 22.00\n",
      "|   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- Insulin >  22.00\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- BMI >  33.90\n",
      "|   |   |   |   |   |   |   |--- Age <= 41.50\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- Age >  41.50\n",
      "|   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- DiabetesPedigreeFunction >  0.92\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |--- BMI >  43.15\n",
      "|   |   |--- class: 1\n",
      "|--- Glucose >  139.50\n",
      "|   |--- BMI <= 27.85\n",
      "|   |   |--- class: 0\n",
      "|   |--- BMI >  27.85\n",
      "|   |   |--- Insulin <= 482.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- Insulin >  482.50\n",
      "|   |   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out=export_text(our_clf,feature_names=list(X.columns))\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Y35tzZHtLQ63",
    "outputId": "27e38151-4950-4837-fe74-23898532b4c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1, 8)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "nfeat=X.shape[1]\n",
    "plt.barh(np.arange(nfeat),our_clf.feature_importances_,align='center')\n",
    "plt.yticks(np.arange(nfeat),list(X.columns))\n",
    "plt.ylim(-1,nfeat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = BaggingClassifier(base_estimator=DecisionTreeClassifier(),\n",
    "                        n_estimators=400,random_state=0).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7792207792207793"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(clf.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfclf = RandomForestClassifier(n_estimators=400, \n",
    "                               max_features='sqrt',random_state=0).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can do slightly better with max_depth=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7835497835497836"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(rfclf.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "bst_clf = GradientBoostingClassifier(n_estimators=600, \n",
    "                                     learning_rate=0.01,\n",
    "                                     max_depth=1, \n",
    "                                     random_state=0).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8008658008658008"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(bst_clf.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_tree_clf = DecisionTreeClassifier(random_state=0)\n",
    "path = single_tree_clf.cost_complexity_pruning_path(X_train, y_train)\n",
    "ccp_alphas, impurities = path.ccp_alphas, path.impurities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvres=[]\n",
    "for a in ccp_alphas:\n",
    "    st_clf = DecisionTreeClassifier(random_state=0, ccp_alpha= a)\n",
    "    cvclf = cross_validate(st_clf, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    cvres.append(cvclf['test_score'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(cvres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "al=ccp_alphas[np.argmax(cvres)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_clf = DecisionTreeClassifier(random_state=0, ccp_alpha=al).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7359307359307359"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(our_clf.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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