notebooks/classifier_for_iris_data_set.ipynb

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   "source": [
    "%matplotlib inline"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Classifier For Iris Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets, neighbors, linear_model, svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas as pd"
   ]
  },
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   "cell_type": "code",
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   },
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     "text": [
      "KNN score: 1.000000\n",
      "LogisticRegression score: 1.000000\n",
      "Linear SVM Classifier: 1.0\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "X_iris = iris.data\n",
    "y_iris = iris.target\n",
    "\n",
    "n_samples = len(X_iris)\n",
    "\n",
    "X_train = X_iris[:int(.9 * n_samples)]\n",
    "y_train = y_iris[:int(.9 * n_samples)]\n",
    "X_test = X_iris[int(.9 * n_samples):]\n",
    "y_test = y_iris[int(.9 * n_samples):]\n",
    "\n",
    "knn_iris = neighbors.KNeighborsClassifier()\n",
    "logistic_iris = linear_model.LogisticRegression()\n",
    "svc_iris = svm.SVC(kernel='poly')\n",
    "\n",
    "print('KNN score: %f' % knn_iris.fit(X_train, y_train).score(X_test, y_test))\n",
    "print('LogisticRegression score: %f'\n",
    "      % logistic_iris.fit(X_train, y_train).score(X_test, y_test))\n",
    "print('Linear SVM Classifier: {}'.format(svc_iris.fit(X_train, y_train).score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numpy.savetxt(\"iris_y.csv\", y_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numpy.savetxt(\"iris_x.csv\", X_iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something to investigate later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "svc_iris.predict([[1, 2, 3, 4]])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "2"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "logistic_iris.predict([[1, 2, 3, 4]])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "1"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "knn_iris.predict([[1, 2, 3, 4]])[0]"
   ]
  }
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