Tutorial2.ipynb

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    "%matplotlib inline\n",
    "# imports\n",
    "# you need the first sentence to enable plot in notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# for this import you need to import datasets level\n",
    "# import sklearn does not work\n",
    "import sklearn.datasets"
   ]
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    "# Import a sample dataset from sklearn\n",
    "data, label=sklearn.datasets.load_breast_cancer(return_X_y=True)\n",
    "# split your data into training and testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(data, label)"
   ]
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      "569\n",
      "426\n"
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   "source": [
    "print (len(data))\n",
    "print (len(x_train))"
   ]
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      "text/plain": [
       "'\\nIn this demo we will be using SVM to classify the breast canser dataset.\\nYou can also use other methods\\nSee Sklearn documentation for details\\n'"
      ]
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    "\"\"\"\n",
    "In this demo we will be using SVM to classify the breast canser dataset.\n",
    "You can also use other methods\n",
    "See Sklearn documentation for details\n",
    "\"\"\""
   ]
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    "# Basic procedure of using a classifier\n",
    "# Train the model using training set, \n",
    "# see how it performs using test set\n",
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel=\"linear\")\n",
    "clf.fit(x_train,y_train)\n",
    "clf.score(x_test, y_test)"
   ]
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