Machine Learning with Scikit-Learn - The Cancer Dataset - 6 - KNN 2.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
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    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mglearn\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Knowledge Gathering \n",
    "\n",
    "#print(cancer.DESCR)\n",
    "#cancer.data\n",
    "#cancer.data.shape\n",
    "#print(cancer.feature_names)\n",
    "#print(cancer.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "<center> \n",
    "# Process Outline (for many ML projects)\n",
    "#### 1. Get the data (pre-process it)\n",
    "#### 2. Pick an algorithm (classifier)\n",
    "#### 3. Train the algorithm. Verify accuracy. Optimize.\n",
    "#### 4. Predict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Looking into the raw dataset (not pre-processed like the one that comes with scikit-learn)\n",
    "\n",
    "import pandas as pd\n",
    "raw_data=pd.read_csv('breast-cancer-wisconsin-data.csv', delimiter=',')\n",
    "#raw_data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
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    "deletable": true,
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   "outputs": [
    {
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YZY8eXcNtFkNNioAView3UNdrE6Hv7n+PxecvXQSspqoqkFWV3Nzcare98847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\nyy+/5MEHHzQ0FkcgiV0IUS3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x240173b1b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN Classifier Overview\n",
    "\n",
    "mglearn.plots.plot_knn_classification(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target, random_state=42)\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN n-5, on the training set: 0.946\n",
      "Accuracy of KNN n-5, on the test set: 0.930\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of KNN n-5, on the training set: {:.3f}'.format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of KNN n-5, on the test set: {:.3f}'.format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}