Irish-flower.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris=load_iris()\n",
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0]\n"
     ]
    }
   ],
   "source": [
    "targets= (iris.target==0).astype(np.int8)\n",
    "print(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "datasets = train_test_split(iris.data,targets,test_size=0.2)\n",
    "train_data, teat_data, train_labels, test_labels = datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "Perceptron(max_iter=10, random_state=42)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "p = Perceptron(random_state=42,\n",
    "             max_iter=10,\n",
    "             tol=0.001)\n",
    "p.fit(train_data,train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58 [1]\n",
      "117 [1]\n",
      "62 [0]\n",
      "98 [0]\n",
      "45 [1]\n",
      "114 [0]\n",
      "67 [0]\n",
      "26 [0]\n",
      "74 [1]\n",
      "94 [0]\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "sample = random.sample(range(len(train_data)),10)\n",
    "for i in sample:\n",
    "    print(i,p.predict([train_data[i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        80\n",
      "           1       1.00      1.00      1.00        40\n",
      "\n",
      "    accuracy                           1.00       120\n",
      "   macro avg       1.00      1.00      1.00       120\n",
      "weighted avg       1.00      1.00      1.00       120\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(p.predict(train_data),train_labels))"
   ]
  }
 ],
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