MLPractical18(Binarizer).ipynb

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   "source": [
    "#Binarization\n",
    "from sklearn.preprocessing import Binarizer\n",
    "from pandas import read_csv\n",
    "from numpy import set_printoptions\n",
    "filename='/home/diwakar/Downloads/IITK ML Data/indians-diabetes.data.csv'\n",
    "hnames=['preg','plas','pres','skin','test','mass','pedi','age','class']\n",
    "dataframe=read_csv(filename,names=hnames)\n",
    "array = dataframe.values\n",
    "print(array)\n",
    "#seprate array into input and output components\n",
    "X= array[: ,0:8] #[row ,cols]\n",
    "Y= array[: ,8]\n",
    "binarizer=Binarizer(threshold=5)\n",
    "binaryX=binarizer.fit_transform(X)\n",
    "#summarize transformed data\n",
    "print(binaryX[0:30, :])"
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