breast_cancer_practice.ipynb

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   "source": [
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c611924",
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      "     Sample code number  Clump Thickness  Uniformity of Cell Size  \\\n",
      "0               1000025                5                        1   \n",
      "1               1002945                5                        4   \n",
      "2               1015425                3                        1   \n",
      "3               1016277                6                        8   \n",
      "4               1017023                4                        1   \n",
      "..                  ...              ...                      ...   \n",
      "678              776715                3                        1   \n",
      "679              841769                2                        1   \n",
      "680              888820                5                       10   \n",
      "681              897471                4                        8   \n",
      "682              897471                4                        8   \n",
      "\n",
      "     Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
      "0                           1                  1                            2   \n",
      "1                           4                  5                            7   \n",
      "2                           1                  1                            2   \n",
      "3                           8                  1                            3   \n",
      "4                           1                  3                            2   \n",
      "..                        ...                ...                          ...   \n",
      "678                         1                  1                            3   \n",
      "679                         1                  1                            2   \n",
      "680                        10                  3                            7   \n",
      "681                         6                  4                            3   \n",
      "682                         8                  5                            4   \n",
      "\n",
      "     Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
      "0              1                3                1        1      2  \n",
      "1             10                3                2        1      2  \n",
      "2              2                3                1        1      2  \n",
      "3              4                3                7        1      2  \n",
      "4              1                3                1        1      2  \n",
      "..           ...              ...              ...      ...    ...  \n",
      "678            2                1                1        1      2  \n",
      "679            1                1                1        1      2  \n",
      "680            3                8               10        2      4  \n",
      "681            4               10                6        1      4  \n",
      "682            5               10                4        1      4  \n",
      "\n",
      "[683 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('breast_cancer.csv')\n",
    "print(dataset)\n",
    "#importing the dataset of breast cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3a8166c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:, 1:-1].values\n",
    "y = dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "602011fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we train our model\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a547cd42",
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   "outputs": [
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      ],
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       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ea98c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a5335d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf97d310",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "[[84  3]\n",
      " [ 3 47]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f6fb748",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we have 84 predictions that are correct that the tumor is Benigno\n",
    "# 47 predictions that the tumor is malignant\n",
    "# and 3 incorrect predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bd6ed36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9562043795620438"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here we calculate the number accuracies \n",
    "(84+47)/(84+47+3+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a09b1c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator=classifier, X= x_train, y= y_train, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da99dc98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.70 %\n",
      "Standard Deviation: 1.97 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "449efd08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we have 96.7% of correct predictions\n",
    "# those predictions can variate between 94.73 and 98.73"
   ]
  }
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