Level3-Machine-Learning-Python/code/2020 Feb 12 Random Forest Ensemble Classifier on Diabetes Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Practise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drug Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>25.355</td>\n",
       "      <td>drugY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>13.093</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>10.114</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>7.798</td>\n",
       "      <td>drugX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>18.043</td>\n",
       "      <td>drugY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
       "0   23   F    HIGH        HIGH   25.355  drugY\n",
       "1   47   M     LOW        HIGH   13.093  drugC\n",
       "2   47   M     LOW        HIGH   10.114  drugC\n",
       "3   28   F  NORMAL        HIGH    7.798  drugX\n",
       "4   61   F     LOW        HIGH   18.043  drugY"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('datasets/drug.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 6 columns):\n",
      "Age            200 non-null int64\n",
      "Sex            200 non-null object\n",
      "BP             200 non-null object\n",
      "Cholesterol    200 non-null object\n",
      "Na_to_K        200 non-null float64\n",
      "Drug           200 non-null object\n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HIGH      77\n",
       "LOW       64\n",
       "NORMAL    59\n",
       "Name: BP, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HIGH      103\n",
       "NORMAL     97\n",
       "Name: Cholesterol, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cholesterol'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['drugY', 'drugX', 'drugA', 'drugB', 'drugC'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_names = df['Drug'].value_counts().index\n",
    "cls_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K', 'Drug'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "def lb():\n",
    "    lbc = LabelEncoder()\n",
    "    for col in df.columns:\n",
    "        df[col] = lbc.fit_transform(df[col])\n",
    "    return\n",
    "lb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  BP  Cholesterol  Na_to_K  Drug\n",
       "0    8    0   0            0      167     4\n",
       "1   30    1   1            0       89     2\n",
       "2   30    1   1            0       43     2\n",
       "3   12    0   2            0       10     3\n",
       "4   44    0   1            0      133     4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Drug',axis = 1)\n",
    "Y = df['Drug']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.3,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree_clf = DecisionTreeClassifier(max_depth = 3)\n",
    "tree_clf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7,  0,  0,  0,  0],\n",
       "       [ 0,  3,  0,  0,  0],\n",
       "       [ 0,  0,  0,  6,  0],\n",
       "       [ 0,  0,  0, 18,  0],\n",
       "       [ 0,  0,  0,  0, 26]], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = tree_clf.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "\n",
    "confusion_matrix(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(tree_clf,out_file='diabetes.dot',class_names=cls_names,\n",
    "                rounded=True,filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image\n",
    "import pydotplus\n",
    "import os\n",
    "os.environ[\"PATH\"]+=os.environ['PATH']+';'+r'C:\\Users\\Jesus\\Anaconda3\\Lib\\site-packages\\graphviz-2.38\\release\\bin'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "export_graphviz(tree_clf,out_file=dot_data,filled=True, rounded=True,feature_names=X_train.columns,\n",
    "                class_names = cls_names)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Pima Indians Diabetes Database](https://www.kaggle.com/uciml/pima-indians-diabetes-database)\n",
    "## Predict the onset of diabetes based on diagnostic measures\n",
    "\n",
    "## Context\n",
    "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.\n",
    "\n",
    "## Content\n",
    "The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.\n",
    "\n",
    "## About this dataset\n",
    "The datasets consist of several medical predictor (independent) variables and one target (dependent) variable, Outcome. Independent variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.\n",
    "\n",
    "|Features|Description|\n",
    "|--|--|\n",
    "|Pregnancies|Number of times pregnant|\n",
    "|Glucose|Plasma glucose concentration a<br> 2 hours in an oral glucose tolerance test|\n",
    "|BloodPressure|Diastolic blood pressure (mm Hg)|\n",
    "|SkinThickness|Triceps skin fold thickness (mm)|\n",
    "|Insulin|2-Hour serum insulin (mu U/ml)|\n",
    "|BMI|Body mass index **(weight in kg/(height in m)^2)**|\n",
    "|Diabetes|PedigreeFunctionDiabetes pedigree function|\n",
    "|Age|Age (years)|\n",
    "|Outcome|Class variable (0 or 1)<br> 268 of 768 are 1, the others are 0|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv('datasets/diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\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>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b11c367320>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "sns.pointplot(x='Age',y='Glucose',data=df,color='lime',alpha=0.8)\n",
    "sns.pointplot(x='Age',y='BloodPressure',data=df,color='blue',alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Outcome',axis = 1)\n",
    "Y = df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jesus\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7402597402597403"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(Y_test,Y_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}