Lab6-1(NaiveBayes).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b98f1aad-61a5-42c5-b081-299e63658555",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "42cddf95-0864-429a-b6f5-b817b171363e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/baraa/PythonCodes/archive/data.csv\")      # Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44c8a9a9-9755-45dd-a7ab-d43c808b2e40",
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "564  926424         M        21.56         22.39          142.00     1479.0   \n",
       "565  926682         M        20.13         28.25          131.20     1261.0   \n",
       "566  926954         M        16.60         28.08          108.30      858.1   \n",
       "567  927241         M        20.60         29.33          140.10     1265.0   \n",
       "568   92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "564  ...          26.40           166.10      2027.0           0.14100   \n",
       "565  ...          38.25           155.00      1731.0           0.11660   \n",
       "566  ...          34.12           126.70      1124.0           0.11390   \n",
       "567  ...          39.42           184.60      1821.0           0.16500   \n",
       "568  ...          30.37            59.16       268.6           0.08996   \n",
       "\n",
       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "564            0.21130           0.4107                0.2216          0.2060   \n",
       "565            0.19220           0.3215                0.1628          0.2572   \n",
       "566            0.30940           0.3403                0.1418          0.2218   \n",
       "567            0.86810           0.9387                0.2650          0.4087   \n",
       "568            0.06444           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  Unnamed: 32  \n",
       "564                  0.07115          NaN  \n",
       "565                  0.06637          NaN  \n",
       "566                  0.07820          NaN  \n",
       "567                  0.12400          NaN  \n",
       "568                  0.07039          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "938f4d3f-945b-4275-b75e-b7bcbb699dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Unnamed: 32\",\"id\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "118109d8-00bb-4a41-b2fb-df02c3adf8c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "M= data[data.diagnosis ==\"M\"]\n",
    "B= data[data.diagnosis ==\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "000ea76f-53b7-483c-8a0e-1f45d38a0bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(M.radius_mean,M.texture_mean,color=\"red\",label=\"kotu\",alpha=0.3)\n",
    "plt.scatter(B.radius_mean,B.texture_mean,color=\"green\",label=\"iyi\",alpha=0.3)\n",
    "plt.xlabel(\"radius_mean(radius of the tumor)\")\n",
    "plt.ylabel(\"texture_mean(texture of the tumor)\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30ea9c7d-d49f-40e7-87ab-603861816127",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.diagnosis = [1 if each == \"M\" else 0 for each in data.diagnosis]\n",
    "y= data.diagnosis.values\n",
    "x_data= data.drop([\"diagnosis\"],axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e844a71e-5715-48e3-ab90-5690bbb52f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=(x_data - np.min(x_data))/(np.max(x_data)-np.min(x_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b54a3c7c-d8ba-4e6b-9ce3-760e74041bdb",
   "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=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3493a5c2-5e99-4c6e-9e4a-83ca3ff63f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb= GaussianNB()\n",
    "nb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c74daa17-f5dc-4a1a-b589-1140c20db0a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of svm algorithm:  0.9473684210526315\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy of svm algorithm: \",nb.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0a43c9a-c010-44bd-944a-1e34a9426872",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}