exploration and Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1    Male  80.0             0              1          Yes        Private   \n",
       "2  Female  49.0             0              0          Yes        Private   \n",
       "3  Female  79.0             1              0          Yes  Self-employed   \n",
       "4    Male  81.0             0              0          Yes        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             105.92  32.5     never smoked       1  \n",
       "2          Urban             171.23  34.4           smokes       1  \n",
       "3          Rural             174.12  24.0     never smoked       1  \n",
       "4          Urban             186.21  29.0  formerly smoked       1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data  = pd.read_csv(\"/home/adarsh/Downloads/Machine learning/Project/input/brain_stroke.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4981 entries, 0 to 4980\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             4981 non-null   object \n",
      " 1   age                4981 non-null   float64\n",
      " 2   hypertension       4981 non-null   int64  \n",
      " 3   heart_disease      4981 non-null   int64  \n",
      " 4   ever_married       4981 non-null   object \n",
      " 5   work_type          4981 non-null   object \n",
      " 6   Residence_type     4981 non-null   object \n",
      " 7   avg_glucose_level  4981 non-null   float64\n",
      " 8   bmi                4981 non-null   float64\n",
      " 9   smoking_status     4981 non-null   object \n",
      " 10  stroke             4981 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 428.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To find the number of null values.\n",
    "#Luckily we dont have any null values in the dataset.\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nMachine learning models do not tend to perform well on data that contains text as parameters. So it is better that we convert it into\\nnumbers using encoding.\\n\\nThere are various methods to achieve this.\\nLabel encoding\\nOne Hot encoding. etc.,\\n\\nWe will use label encoder here to encode the textual data to numbers.\\n'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Preprocessing\n",
    "\"\"\"\n",
    "Machine learning models do not tend to perform well on data that contains text as parameters. So it is better that we convert it into\n",
    "numbers using encoding.\n",
    "\n",
    "There are various methods to achieve this.\n",
    "Label encoding\n",
    "One Hot encoding. etc.,\n",
    "\n",
    "We will use label encoder here to encode the textual data to numbers.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing label encoder from sklearn.preprocessing  to modify the data as required.\n",
    "#As per the data visualisation above we could see that parameters \"Gender\",\"ever_married\", \"work_type\", \"residence type\", \"smoking status\" have textual data.\n",
    "#do not use transform directly. use fit_transform\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "data[\"gender\"] = labelencoder.fit_transform(data[\"gender\"])\n",
    "data[\"ever_married\"] = labelencoder.fit_transform(data[\"ever_married\"])\n",
    "data[\"work_type\"] = labelencoder.fit_transform(data[\"work_type\"])\n",
    "data[\"Residence_type\"] = labelencoder.fit_transform(data[\"Residence_type\"])\n",
    "data[\"smoking_status\"] = labelencoder.fit_transform(data[\"smoking_status\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4981 entries, 0 to 4980\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             4981 non-null   int64  \n",
      " 1   age                4981 non-null   float64\n",
      " 2   hypertension       4981 non-null   int64  \n",
      " 3   heart_disease      4981 non-null   int64  \n",
      " 4   ever_married       4981 non-null   int64  \n",
      " 5   work_type          4981 non-null   int64  \n",
      " 6   Residence_type     4981 non-null   int64  \n",
      " 7   avg_glucose_level  4981 non-null   float64\n",
      " 8   bmi                4981 non-null   float64\n",
      " 9   smoking_status     4981 non-null   int64  \n",
      " 10  stroke             4981 non-null   int64  \n",
      "dtypes: float64(3), int64(8)\n",
      "memory usage: 428.2 KB\n"
     ]
    }
   ],
   "source": [
    "#Now we are able to see that the data is converted to numbers.\n",
    "data.head()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we are splitting the data into train and test.\n",
    "#we have a feature in scikit to to this. \n",
    "\n",
    "X = data.drop(\"stroke\",axis=1)\n",
    "Y = data[\"stroke\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4414</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>106.47</td>\n",
       "      <td>27.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2751</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>232.12</td>\n",
       "      <td>28.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3236</th>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110.28</td>\n",
       "      <td>22.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2591</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>108.65</td>\n",
       "      <td>23.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>108.43</td>\n",
       "      <td>32.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "4414       0  46.0             0              0             1          1   \n",
       "2751       1  80.0             1              0             1          2   \n",
       "3236       0  37.0             0              0             1          1   \n",
       "2591       1  14.0             0              0             0          1   \n",
       "1404       1  71.0             0              0             1          1   \n",
       "\n",
       "      Residence_type  avg_glucose_level   bmi  smoking_status  \n",
       "4414               0             106.47  27.2               0  \n",
       "2751               0             232.12  28.8               2  \n",
       "3236               0             110.28  22.3               2  \n",
       "2591               0             108.65  23.1               2  \n",
       "1404               1             108.43  32.8               3  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We would be able to clearlt see that the textual data is now modified\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature scaling\n",
    "# We need to scale our data to a uniform range\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the decision tree algorithm for the data is: 0.9107321965897693\n"
     ]
    }
   ],
   "source": [
    "#trying the pre processed data in various algorithms\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "DecisionTree = DecisionTreeClassifier()\n",
    "DecisionTree.fit(X_train_scaled,Y_train) #fitting the preprocessed data with the decision tree algorithm\n",
    "ycap = DecisionTree.predict(X_test_scaled) \n",
    "accuracy = accuracy_score(Y_test, ycap)\n",
    "print(\"The accuracy of the decision tree algorithm for the data is: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.946285140562249\n"
     ]
    }
   ],
   "source": [
    "#Using machine learning ensembling method to improve the performance of the prediction.\n",
    "\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "bag_model = BaggingClassifier(\n",
    "    base_estimator=DecisionTreeClassifier(), \n",
    "    n_estimators=100, \n",
    "    max_samples=0.8, \n",
    "    oob_score=True,\n",
    "    random_state=0\n",
    ")\n",
    "bag_model.fit(X_train_scaled, Y_train)\n",
    "print(bag_model.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN algorithm for the data is: 0.9518555667001003\n"
     ]
    }
   ],
   "source": [
    "#training the model with KNN algorithm and getting the accuracy score of the prediction\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN=KNeighborsClassifier()\n",
    "\n",
    "KNN.fit(X_train_scaled, Y_train)\n",
    "ycap = KNN.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(Y_test, ycap)\n",
    "print(\"The accuracy of the KNN algorithm for the data is: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Random forest algorithm for the data is: 0.9478435305917753\n"
     ]
    }
   ],
   "source": [
    "#training the model with Random forest and getting the accuracy score of the prediction\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RF = RandomForestClassifier()\n",
    "\n",
    "RF.fit(X_train_scaled, Y_train)\n",
    "ycap = RF.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(Y_test, ycap)\n",
    "print(\"The accuracy of the Random forest algorithm for the data is: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM algorithm for the data is: 0.950852557673019\n"
     ]
    }
   ],
   "source": [
    "#training the model with SVM algorithm and getting the accuracy score of the prediction\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "SVM = SVC()\n",
    "\n",
    "SVM.fit(X_train_scaled, Y_train)\n",
    "ycap = SVM.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(Y_test, ycap)\n",
    "print(\"The accuracy of the SVM algorithm for the data is: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the stacked model for the data is: 0.9498495486459378\n"
     ]
    }
   ],
   "source": [
    "#We stack the models trained to reduce the variance of the model, and this gives a whooping .94> accuracy.\n",
    "# we will save this model and use it to build an user interface\n",
    "\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "models = [\n",
    "    ('DecisionTree',DecisionTree),\n",
    "    ('KNN',KNN),\n",
    "    ('RF',RF),\n",
    "    ('SVM',SVM)]\n",
    "\n",
    "stackmodel = StackingClassifier(estimators = models, final_estimator = LogisticRegression())\n",
    "stackmodel.fit(X_train_scaled, Y_train)\n",
    "ypred = stackmodel.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(Y_test, ypred)\n",
    "print(\"The accuracy of the stacked model for the data is: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/adarsh/Downloads/Machine learning/Project/models/model.bin']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#saving the model\n",
    "\n",
    "import joblib\n",
    "import os\n",
    "\n",
    "joblib.dump(stackmodel, os.path.join('/home/adarsh/Downloads/Machine learning/Project/models','model.bin'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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