Lab10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fcb4f48f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3            4\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"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as nm  \n",
    "import matplotlib.pyplot as mtp  \n",
    "import pandas as pd  \n",
    "\n",
    "path = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "\n",
    "dataset = pd.read_csv(path,header=None)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f45ecb13",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:, [2, 3]].values  \n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d6b8a3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f01eb0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler  \n",
    "sc = StandardScaler()  \n",
    "x_train = sc.fit_transform(x_train)  \n",
    "x_test = sc.transform(x_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5fe555bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB  \n",
    "classifier = GaussianNB()  \n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f0ab4d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(x_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b46bffe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13,  0,  0],\n",
       "       [ 0, 15,  1],\n",
       "       [ 0,  0,  9]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix  \n",
    "cm = confusion_matrix(y_test, y_pred)  \n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "61c6fac8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap  \n",
    "x_set, y_set = x_train, y_train  \n",
    "X1, X2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step = 0.01),  \n",
    "                     nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))  \n",
    "#mtp.contourf(X1, X2, classifier.predict(nm.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),  \n",
    "             #alpha = 0.75, cmap = ListedColormap(('purple', 'green')))  \n",
    "mtp.xlim(X1.min(), X1.max())  \n",
    "mtp.ylim(X2.min(), X2.max())  \n",
    "for i, j in enumerate(nm.unique(y_set)):  \n",
    "    mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],  \n",
    "                c = ListedColormap(('purple', 'green','red'))(i), label = j)  \n",
    "mtp.title('Naive Bayes (Training set)')  \n",
    "mtp.xlabel('Age')  \n",
    "mtp.ylabel('Estimated Salary')  \n",
    "mtp.legend()  \n",
    "mtp.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a7d390a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7555eb06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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