Iris data set try 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'iris.data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"Sepal Length\", \"Sepal Width\", \"Petal Length\", \"Petal Width\", \"Class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(file,names = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Sepal Length  Sepal Width  Petal Length  Petal Width        Class\n",
      "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
      "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
      "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
      "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
      "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
      "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
      "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
      "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
      "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
      "9           4.9          3.1           1.5          0.1  Iris-setosa\n"
     ]
    }
   ],
   "source": [
    "print(dataset.head(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Sepal Length  Sepal Width  Petal Length  Petal Width\n",
      "count    150.000000   150.000000    150.000000   150.000000\n",
      "mean       5.843333     3.054000      3.758667     1.198667\n",
      "std        0.828066     0.433594      1.764420     0.763161\n",
      "min        4.300000     2.000000      1.000000     0.100000\n",
      "25%        5.100000     2.800000      1.600000     0.300000\n",
      "50%        5.800000     3.000000      4.350000     1.300000\n",
      "75%        6.400000     3.300000      5.100000     1.800000\n",
      "max        7.900000     4.400000      6.900000     2.500000\n"
     ]
    }
   ],
   "source": [
    "print(dataset.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001E0575E7C18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E057910550>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001E05793ABE0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E05796E2B0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_array = dataset.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(data_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_learning = data_array[:80][:,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_learning = data_array[:80][:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_learning,Y_learning)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_array[-20:][:,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data_array[-20:][:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = svc.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted results:\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted results:\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor']\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual results:\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual results:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor']\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  1.000000\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:  %f\" % (accuracy_score(Y, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}