3.breast-cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0aad882e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea29bea6",
   "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>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</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",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv(\"breast-cancer.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7fdc5f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave points_mean        0\n",
       "symmetry_mean              0\n",
       "fractal_dimension_mean     0\n",
       "radius_se                  0\n",
       "texture_se                 0\n",
       "perimeter_se               0\n",
       "area_se                    0\n",
       "smoothness_se              0\n",
       "compactness_se             0\n",
       "concavity_se               0\n",
       "concave points_se          0\n",
       "symmetry_se                0\n",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "area_worst                 0\n",
       "smoothness_worst           0\n",
       "compactness_worst          0\n",
       "concavity_worst            0\n",
       "concave points_worst       0\n",
       "symmetry_worst             0\n",
       "fractal_dimension_worst    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= df.iloc[:, 2:-1]\n",
    "x.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7e1f0c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 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, 1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= df.iloc[:, 1]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le= LabelEncoder()\n",
    "y= le.fit_transform(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fa7a3362",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc= StandardScaler()\n",
    "x= sc.fit_transform(x)\n",
    "\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "x_train, x_test, y_train, y_test= tts(x, y, test_size= 0.25, random_state= 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "44054467",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten\n",
    "\n",
    "model= Sequential([\n",
    "    Flatten(input_shape= (30,)),\n",
    "    Dense(20, activation= 'relu'),\n",
    "    Dense(2, activation= 'sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "812a2a2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "10/10 [==============================] - 0s 10ms/step - loss: 0.9082 - accuracy: 0.4463 - val_loss: 0.7451 - val_accuracy: 0.5547\n",
      "Epoch 2/10\n",
      "10/10 [==============================] - 0s 2ms/step - loss: 0.7129 - accuracy: 0.5839 - val_loss: 0.5840 - val_accuracy: 0.6484\n",
      "Epoch 3/10\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 0.5659 - accuracy: 0.6879 - val_loss: 0.4671 - val_accuracy: 0.7422\n",
      "Epoch 4/10\n",
      "10/10 [==============================] - 0s 1ms/step - loss: 0.4571 - accuracy: 0.7651 - val_loss: 0.3834 - val_accuracy: 0.8281\n",
      "Epoch 5/10\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 0.3752 - accuracy: 0.8221 - val_loss: 0.3232 - val_accuracy: 0.8672\n",
      "Epoch 6/10\n",
      "10/10 [==============================] - 0s 3ms/step - loss: 0.3144 - accuracy: 0.9060 - val_loss: 0.2784 - val_accuracy: 0.8984\n",
      "Epoch 7/10\n",
      "10/10 [==============================] - 0s 2ms/step - loss: 0.2666 - accuracy: 0.9362 - val_loss: 0.2441 - val_accuracy: 0.9297\n",
      "Epoch 8/10\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 0.2295 - accuracy: 0.9430 - val_loss: 0.2190 - val_accuracy: 0.9375\n",
      "Epoch 9/10\n",
      "10/10 [==============================] - 0s 2ms/step - loss: 0.2039 - accuracy: 0.9430 - val_loss: 0.1984 - val_accuracy: 0.9453\n",
      "Epoch 10/10\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 0.1808 - accuracy: 0.9530 - val_loss: 0.1830 - val_accuracy: 0.9453\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', metrics=['accuracy'], loss= 'sparse_categorical_crossentropy')\n",
    "history = model.fit(x_train, y_train, validation_split=0.3, epochs=10, shuffle= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "222fd629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x282d26437c0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.legend(['training data', 'validation data'], loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c5720911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x282d37295e0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.legend(['training data', 'validation data'], loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae2b2f80",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}