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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "## Pima Indians - diabetes prediction\n",
  8. "## Neural Network for binary classification"
  9. ]
  10. },
  11. {
  12. "cell_type": "code",
  13. "execution_count": 1,
  14. "metadata": {},
  15. "outputs": [
  16. {
  17. "name": "stderr",
  18. "output_type": "stream",
  19. "text": [
  20. "Using TensorFlow backend.\n"
  21. ]
  22. },
  23. {
  24. "name": "stdout",
  25. "output_type": "stream",
  26. "text": [
  27. "768/768 [==============================] - 0s 75us/step\n",
  28. "\n",
  29. "acc: 80.60%\n",
  30. "Confusion Matrix\n",
  31. "================\n",
  32. "True negatives: 443\n",
  33. "False negatives: 92\n",
  34. "False positives: 57\n",
  35. "True positives: 176\n"
  36. ]
  37. }
  38. ],
  39. "source": [
  40. "# Import necessary libraries\n",
  41. "from keras.models import Sequential\n",
  42. "from keras.layers import Dense\n",
  43. "from keras import optimizers\n",
  44. "import numpy as np\n",
  45. "from sklearn.metrics import classification_report, confusion_matrix\n",
  46. "\n",
  47. "# set random seed for reproducibility\n",
  48. "np.random.seed(7)\n",
  49. "\n",
  50. "# load pima indians dataset\n",
  51. "dataset = np.loadtxt(\"pima-indians-diabetes.csv\", delimiter=\",\")\n",
  52. "\n",
  53. "# split into input (X) and output (Y) variables\n",
  54. "X = dataset[:,0:8]\n",
  55. "Y = dataset[:,8] \n",
  56. "\n",
  57. "# create model (requires completion)\n",
  58. "model = \n",
  59. "model.add()\n",
  60. "model.add()\n",
  61. "model.add()\n",
  62. "\n",
  63. "# Compile model (requires completion)\n",
  64. "adam = optimizers.Adam()\n",
  65. "\n",
  66. "model.compile()\n",
  67. "\n",
  68. "# Fit the model (requires completion)\n",
  69. "history = model.fit()\n",
  70. "\n",
  71. "# Evaluate the model\n",
  72. "scores = model.evaluate(X, Y)\n",
  73. "Y_predict = model.predict(X)\n",
  74. "\n",
  75. "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
  76. "\n",
  77. "# I have included this code for you which will \n",
  78. "# create confusion matrix details\n",
  79. "rounded = [round(i[0]) for i in Y_predict]\n",
  80. "y_pred = np.array(rounded,dtype='int64')\n",
  81. "print('Confusion Matrix')\n",
  82. "print('================')\n",
  83. "CM = confusion_matrix(Y, y_pred)\n",
  84. "print('True negatives: ',CM[0,0])\n",
  85. "print('False negatives: ',CM[1,0])\n",
  86. "print('False positives: ',CM[0,1])\n",
  87. "print('True positives: ',CM[1,1])"
  88. ]
  89. },
  90. {
  91. "cell_type": "code",
  92. "execution_count": 19,
  93. "metadata": {},
  94. "outputs": [
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