data_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Göğüs Kanseri Veri Setini Anlamak - Daha fazla bilgi için tıklayın.](https://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+original)\n",
    "### Veri Seti Genel Bilgileri:\n",
    "- Örnek Sayısı: 699\n",
    "- Özellik Sayısı: 9\n",
    "- Eksik Veri Durumu: Var\n",
    "### Ek Değişken Bilgileri:\n",
    "Sıra_No [Kısaltması] Öznitelik_adı--->Değer_aralığı\n",
    "1. **Sample code number (Numara):** --->kimlik_numarası\n",
    "2. **Clump Thickness (Yığın Kalınlığı):** --->[1 - 10]\n",
    "3. **Uniformity of Cell Size (Hücre Boyutunun Eşbiçimliliği):** --->[1 - 10]\n",
    "4. **Uniformity of Cell Shape (Hücre Şekil Eşbiçimliliği):** --->[1 - 10]\n",
    "5. **Marginal Adhesion (Marjinal Yapışma):** --->[1 - 10]\n",
    "6. **Single Epithelial Cell Size (Tek Epitel Hücre Boyutu):** --->[1 - 10]\n",
    "7. **Bare Nuclei (Çıplak Çekirdekler):** --->[1 - 10]\n",
    "8. **Bland Chromatin (Yumuşak Kromatin):** --->[1 - 10]\n",
    "9. **Normal Nucleoli(Normal Nükleoli):** --->[1 - 10]\n",
    "10. **Mitoses (Mitoz):** --->[1 - 10]\n",
    "11. **Class (Sınıf):** --->(2=İyi Huylu, 4=Kötü Huylu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daha Çok Bilgi/Makale:\n",
    "* [Meme Kanseri Tanısı İçin Özniteliklerin Öneminin Değerlendirilmesi Üzerine Bir\n",
    "Çalışma](https://dergipark.org.tr/en/download/article-file/519015)\n",
    "* [webmd.com - Breast Cancer: Symptoms, Causes, Treatment](https://www.webmd.com/breast-cancer/understanding-breast-cancer-basics)\n",
    "* [On the Bias of Precision Estimation Under Separate Sampling](https://journals.sagepub.com/doi/pdf/10.1177/1176935119860822)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "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>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  clump_thickness  size_uniformity  shape_uniformity  \\\n",
       "0  1000025                5                1                 1   \n",
       "1  1002945                5                4                 4   \n",
       "2  1015425                3                1                 1   \n",
       "3  1016277                6                8                 8   \n",
       "4  1017023                4                1                 1   \n",
       "\n",
       "   marginal_adhesion  epithelial_size bare_nucleoli  bland_chromatin  \\\n",
       "0                  1                2             1                3   \n",
       "1                  5                7            10                3   \n",
       "2                  1                2             2                3   \n",
       "3                  1                3             4                3   \n",
       "4                  3                2             1                3   \n",
       "\n",
       "   normal_nucleoli  mitoses  class  \n",
       "0                1        1      2  \n",
       "1                2        1      2  \n",
       "2                1        1      2  \n",
       "3                7        1      2  \n",
       "4                1        1      2  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset= pd.read_csv(\"breastCancer.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "#sonraki kullanımlarda ve okunmalarda kolaylık sağlanması açısından özelliklerin isimleri yukarda belirtilmiş şekilde kısa türkçe karşılıkları ile değiştirildi\n",
    "dataset.columns= [\"numara\", \"yigin_kalinligi\", \"hucre_boyutu_esbicim\", \"hucre_sekil_esbicim\", \"marjinal_yapisma\", \"tek_epitel_hucre_boyut\", \"ciplak_cekirdekler\", \"yumusak_kromatin\", \"normal_nukleoli\", \"mitoz\", \"sinif\" ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   numara                  699 non-null    int64 \n",
      " 1   yigin_kalinligi         699 non-null    int64 \n",
      " 2   hucre_boyutu_esbicim    699 non-null    int64 \n",
      " 3   hucre_sekil_esbicim     699 non-null    int64 \n",
      " 4   marjinal_yapisma        699 non-null    int64 \n",
      " 5   tek_epitel_hucre_boyut  699 non-null    int64 \n",
      " 6   ciplak_cekirdekler      699 non-null    object\n",
      " 7   yumusak_kromatin        699 non-null    int64 \n",
      " 8   normal_nukleoli         699 non-null    int64 \n",
      " 9   mitoz                   699 non-null    int64 \n",
      " 10  sinif                   699 non-null    int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Veriseti üzerinde eksik değer, veri tipi gibi genel bilgileri öğrenmek için info() özelliği kullanılır\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "verisetinde eksik değerler sadece \"ciplak_cekirdekler\" satırında bulunmakta ve \"?\" işareti ile belirtilmiştir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "#\"ciplak_cekirdekler\" satırında bulunan \n",
    "dataset['ciplak_cekirdekler'] = dataset['ciplak_cekirdekler'].replace('?', np.nan)\n",
    "dataset.dropna(inplace=True)\n",
    "dataset['ciplak_cekirdekler'] = pd.to_numeric(dataset['ciplak_cekirdekler'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 683 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      " #   Column                  Non-Null Count  Dtype\n",
      "---  ------                  --------------  -----\n",
      " 0   numara                  683 non-null    int64\n",
      " 1   yigin_kalinligi         683 non-null    int64\n",
      " 2   hucre_boyutu_esbicim    683 non-null    int64\n",
      " 3   hucre_sekil_esbicim     683 non-null    int64\n",
      " 4   marjinal_yapisma        683 non-null    int64\n",
      " 5   tek_epitel_hucre_boyut  683 non-null    int64\n",
      " 6   ciplak_cekirdekler      683 non-null    int64\n",
      " 7   yumusak_kromatin        683 non-null    int64\n",
      " 8   normal_nukleoli         683 non-null    int64\n",
      " 9   mitoz                   683 non-null    int64\n",
      " 10  sinif                   683 non-null    int64\n",
      "dtypes: int64(11)\n",
      "memory usage: 64.0 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "metadata": {}
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    444\n",
       "4    239\n",
       "Name: sinif, dtype: int64"
      ]
     },
     "execution_count": 73,
     "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": [
    "dataset['sinif'].plot(kind='hist',color='green')\n",
    "dataset['sinif'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "# sinif sutununda bulunan 2 ve 4 değerleri 2(iyi huylu)-->0 ve 4(kötü huylu)-->1 olucak şekilde değiştirildi.\n",
    "dataset['sinif'] = dataset['sinif'].replace(2, 0)\n",
    "dataset['sinif'] = dataset['sinif'].replace(4, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    444\n",
       "1    239\n",
       "Name: sinif, dtype: int64"
      ]
     },
     "execution_count": 75,
     "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": [
    "dataset['sinif'].plot(kind='hist',color='green')\n",
    "dataset['sinif'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numara                   -0.084701\n",
       "mitoz                     0.423448\n",
       "tek_epitel_hucre_boyut    0.690958\n",
       "marjinal_yapisma          0.706294\n",
       "yigin_kalinligi           0.714790\n",
       "normal_nukleoli           0.718677\n",
       "yumusak_kromatin          0.758228\n",
       "hucre_boyutu_esbicim      0.820801\n",
       "hucre_sekil_esbicim       0.821891\n",
       "ciplak_cekirdekler        0.822696\n",
       "sinif                     1.000000\n",
       "Name: sinif, dtype: float64"
      ]
     },
     "execution_count": 76,
     "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": [
    "da\n",
    "dataset.corr()[\"sinif\"].sort_values().plot(kind=\"bar\")\n",
    "dataset.corr()[\"sinif\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "dataset.to_csv(\"breastCancer_processed.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}