03. Prepare Data from Machine Learning .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from numpy import set_printoptions\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] \n",
    "data = pd.read_csv('datasets/pima-indians-diabetes.csv', names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = array[:,0:8]\n",
    "Y = array[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rescaling\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "rescaledX = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_printoptions(precision=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.353 0.744 0.59  0.354 0.    0.501 0.234 0.483]\n",
      " [0.059 0.427 0.541 0.293 0.    0.396 0.117 0.167]\n",
      " [0.471 0.92  0.525 0.    0.    0.347 0.254 0.183]\n",
      " [0.059 0.447 0.541 0.232 0.111 0.419 0.038 0.   ]\n",
      " [0.    0.688 0.328 0.354 0.199 0.642 0.944 0.2  ]]\n"
     ]
    }
   ],
   "source": [
    "print(rescaledX[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardization\n",
    "scaler = StandardScaler().fit(X)\n",
    "rescaledX = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.64   0.848  0.15   0.907 -0.693  0.204  0.468  1.426]\n",
      " [-0.845 -1.123 -0.161  0.531 -0.693 -0.684 -0.365 -0.191]\n",
      " [ 1.234  1.944 -0.264 -1.288 -0.693 -1.103  0.604 -0.106]\n",
      " [-0.845 -0.998 -0.161  0.155  0.123 -0.494 -0.921 -1.042]\n",
      " [-1.142  0.504 -1.505  0.907  0.766  1.41   5.485 -0.02 ]]\n"
     ]
    }
   ],
   "source": [
    "set_printoptions(precision=3)\n",
    "print(rescaledX[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.034 0.828 0.403 0.196 0.    0.188 0.004 0.28 ]\n",
      " [0.008 0.716 0.556 0.244 0.    0.224 0.003 0.261]\n",
      " [0.04  0.924 0.323 0.    0.    0.118 0.003 0.162]\n",
      " [0.007 0.588 0.436 0.152 0.622 0.186 0.001 0.139]\n",
      " [0.    0.596 0.174 0.152 0.731 0.188 0.01  0.144]]\n"
     ]
    }
   ],
   "source": [
    "#Normalizing Data\n",
    "scaler = Normalizer().fit(X)\n",
    "normalizedX = scaler.transform(X)\n",
    "set_printoptions(precision=3)\n",
    "print(normalizedX[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Binarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1. 0. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 0. 1. 1. 1.]\n",
      " [1. 1. 1. 0. 0. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 1. 1. 1.]\n",
      " [0. 1. 1. 1. 1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "#Binarize Data\n",
    "binarizer = Binarizer(threshold=0.0).fit(X)\n",
    "binaryX = binarizer.transform(X)\n",
    "set_printoptions(precision=3)\n",
    "print(binaryX[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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