PIMA Indian Diabetes Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIMA Indian Diabetes Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict the onset of Diabetes for the Pima Indians based on the available diagnostic data: https://www.kaggle.com/uciml/pima-indians-diabetes-database\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import and seed various random functions for same result \n",
    "from numpy.random import seed\n",
    "from tensorflow import set_random_seed\n",
    "seed(123)\n",
    "set_random_seed(124)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import Pandas, Sequential and Dense from Keras\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the csv file\n",
    "diabetes = pd.read_csv('diabetes.csv')\n",
    "diabetes.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X and Y variables\n",
    "X = diabetes.iloc[:, 0:-1]\n",
    "Y = diabetes.iloc[:,   -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = \\\n",
    "train_test_split(X, Y, test_size = 0.2, random_state = 1234, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the keras sequential model with 3 hidden layers\n",
    "model = Sequential()\n",
    "model.add(Dense(24, \n",
    "                input_shape=(8,), \n",
    "                activation='relu', \n",
    "                kernel_initializer='RandomNormal'))\n",
    "\n",
    "model.add(Dense(12, \n",
    "                activation='relu',\n",
    "                kernel_initializer='RandomNormal'))\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the keras model for classification accuracy\n",
    "model.compile(loss='binary_crossentropy', \n",
    "              optimizer='adam', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x18b70c47470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model on the training dataset\n",
    "model.fit( X_train, Y_train, epochs=160, batch_size=10,verbose=0, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "154/154 [==============================] - 0s 162us/step\n",
      "\n",
      "Test Accuracy     : 0.7467532436569015\n"
     ]
    }
   ],
   "source": [
    "# Evaluate and print the model accuracy on test dataset\n",
    "accuracy_test = model.evaluate(X_test, Y_test)\n",
    "print(\"\")\n",
    "print(\"Test Accuracy     : \" + str(accuracy_test[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the classes using test data and the compiled model\n",
    "Y_predict = model.predict_classes(X_test)\n",
    "Y_pred_prob = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(Y_test, Y_predict)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}