Naive_Bayes.ipynb

{
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   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "# Naive Bayes :\n",
    "- N B algorithms is a suprervised learning algorithms , which  is based on Bayes Theorem , and used for solving classification problems .\n",
    "\" it is a probabilistics classifier , which means it prediict on the basis of the probability of a object\n",
    "# Bayes Theorem:\n",
    "which is used to determine the probability  of a hypothesis with prior knowledge.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EstimatedSalary  Purchased\n",
       "0     19            19000          0\n",
       "1     35            20000          0\n",
       "2     26            43000          0\n",
       "3     27            57000          0\n",
       "4     19            76000          0\n",
       "..   ...              ...        ...\n",
       "395   46            41000          1\n",
       "396   51            23000          1\n",
       "397   50            20000          1\n",
       "398   36            33000          0\n",
       "399   49            36000          1\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"Social_Network_Ads.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[: , :-1].values\n",
    "y = dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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=0.2 , random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[30 , 85000]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_pred.reshape(len(y_pred) , 1)\n",
    "y_test = y_pred.reshape(len(y_test) , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate((y_pred , y_test) , axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[59  0]\n",
      " [ 0 21]] 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix  , accuracy_score\n",
    "cm = confusion_matrix(y_pred , y_test)\n",
    "acc = accuracy_score(y_pred , y_test)\n",
    "print(cm , acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sana\\AppData\\Local\\Temp\\ipykernel_11032\\1764185017.py:11: UserWarning: *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 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  plt.scatter(X_set[y_set ==j , 0 ] , X_set[y_set ==j , 1] , c = ListedColormap(('red' , 'green')) (i), label = j)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set , y_set = sc.inverse_transform(X_train) , y_train \n",
    "x1 , x2 = np.meshgrid(np.arange(start = X_set[: , 0].min() - 10, stop = X_set[: , 0].max() + 10 , step = 1), \n",
    "                      np.arange(start = X_set[: , 0].min() - 1000, stop = X_set[: , 0].max() + 1000 , step = 1))\n",
    "\n",
    "plt.contourf(x1 , x2 , classifier.predict(sc.transform(np.array([x1.ravel() , x2.ravel()]).T)).reshape(x1.shape),\n",
    "             alpha = 0.75 , cmap = ListedColormap(('red' , 'green')))\n",
    "plt.xlim(x1.min() , x1.max())\n",
    "plt.ylim(x2.min() , x2.max())\n",
    "for i , j in enumerate(np.unique(y_set)):\n",
    "      plt.scatter(X_set[y_set ==j , 0 ] , X_set[y_set ==j , 1] , c = ListedColormap(('red' , 'green')) (i), label = j) \n",
    "      plt.title('Naive Bayes(Training set)')\n",
    "      plt.xlabel('Age')\n",
    "      plt.ylabel('Estimated Salary')\n",
    "      plt.show()"
   ]
  }
 ],
 "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": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}