KNN_breast_cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_data = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(cancer_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
      "0        17.99         10.38          122.80     1001.0          0.11840   \n",
      "1        20.57         17.77          132.90     1326.0          0.08474   \n",
      "2        19.69         21.25          130.00     1203.0          0.10960   \n",
      "3        11.42         20.38           77.58      386.1          0.14250   \n",
      "4        20.29         14.34          135.10     1297.0          0.10030   \n",
      "\n",
      "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
      "0           0.27760          0.3001              0.14710         0.2419   \n",
      "1           0.07864          0.0869              0.07017         0.1812   \n",
      "2           0.15990          0.1974              0.12790         0.2069   \n",
      "3           0.28390          0.2414              0.10520         0.2597   \n",
      "4           0.13280          0.1980              0.10430         0.1809   \n",
      "\n",
      "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
      "0                 0.07871  ...          17.33           184.60      2019.0   \n",
      "1                 0.05667  ...          23.41           158.80      1956.0   \n",
      "2                 0.05999  ...          25.53           152.50      1709.0   \n",
      "3                 0.09744  ...          26.50            98.87       567.7   \n",
      "4                 0.05883  ...          16.67           152.20      1575.0   \n",
      "\n",
      "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
      "0            0.1622             0.6656           0.7119                0.2654   \n",
      "1            0.1238             0.1866           0.2416                0.1860   \n",
      "2            0.1444             0.4245           0.4504                0.2430   \n",
      "3            0.2098             0.8663           0.6869                0.2575   \n",
      "4            0.1374             0.2050           0.4000                0.1625   \n",
      "\n",
      "   worst symmetry  worst fractal dimension  target  \n",
      "0          0.4601                  0.11890       0  \n",
      "1          0.2750                  0.08902       0  \n",
      "2          0.3613                  0.08758       0  \n",
      "3          0.6638                  0.17300       0  \n",
      "4          0.2364                  0.07678       0  \n",
      "\n",
      "[5 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(cancer_data.data, columns = cancer_data.feature_names)\n",
    "df['target'] = cancer_data.target\n",
    "print(df.head())\n",
    "x_train, x_test, y_train, y_test = train_test_split(cancer_data.data, cancer_data.target,\n",
    "                                                    stratify = cancer_data.target, \n",
    "                                                    shuffle = True,\n",
    "                                                    random_state = 144)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(x_train)\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Prediction: [0 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 0 0 0\n",
      " 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 0 1 1 1 1 0 0\n",
      " 1 1 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 1 1 1 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "print('Test Set Prediction: {}'. format(knn.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.97\n"
     ]
    }
   ],
   "source": [
    "print('Test Set Accuracy: {:.2f}'.format(knn.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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