breast_cancer_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breast Cancer Analysis with Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import math\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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>symmetry_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>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</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>0.2419</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</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>0.1812</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</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>0.2069</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</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>0.2597</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</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>0.1809</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>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        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",
       "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "0         0.2419  ...          17.33           184.60      2019.0   \n",
       "1         0.1812  ...          23.41           158.80      1956.0   \n",
       "2         0.2069  ...          25.53           152.50      1709.0   \n",
       "3         0.2597  ...          26.50            98.87       567.7   \n",
       "4         0.1809  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\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",
       "   symmetry_worst  fractal_dimension_worst  diagnosis  \n",
       "0          0.4601                  0.11890          M  \n",
       "1          0.2750                  0.08902          M  \n",
       "2          0.3613                  0.08758          M  \n",
       "3          0.6638                  0.17300          M  \n",
       "4          0.2364                  0.07678          M  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset = pd.read_csv('breast_cancer_data.csv')\n",
    "cancer_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions : (569, 32)\n"
     ]
    }
   ],
   "source": [
    "X_axis = cancer_dataset.iloc[:, 1:31].values\n",
    "Y_axis = cancer_dataset.iloc[:, 31].values\n",
    "print(\"Dimensions : {}\".format(cancer_dataset.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis\n",
       "B    357\n",
       "M    212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset.groupby('diagnosis').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis\n",
       "B    [[AxesSubplot(0.125,0.779333;0.103333x0.100667...\n",
       "M    [[AxesSubplot(0.125,0.779333;0.103333x0.100667...\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cancer_dataset.groupby('diagnosis').hist(figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "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",
       "diagnosis                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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>symmetry_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>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</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>0.2419</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</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>0.1812</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</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>0.2069</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</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>0.2597</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>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</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>0.1809</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>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        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",
       "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "0         0.2419  ...          17.33           184.60      2019.0   \n",
       "1         0.1812  ...          23.41           158.80      1956.0   \n",
       "2         0.2069  ...          25.53           152.50      1709.0   \n",
       "3         0.2597  ...          26.50            98.87       567.7   \n",
       "4         0.1809  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\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",
       "   symmetry_worst  fractal_dimension_worst  diagnosis  \n",
       "0          0.4601                  0.11890          M  \n",
       "1          0.2750                  0.08902          M  \n",
       "2          0.3613                  0.08758          M  \n",
       "3          0.6638                  0.17300          M  \n",
       "4          0.2364                  0.07678          M  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pd.DataFrame(Y_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_Y_axis = LabelEncoder()\n",
    "Y_axis = labelencoder_Y_axis.fit_transform(Y_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.005e+01, 1.753e+01, 6.441e+01, ..., 6.499e-02, 2.894e-01,\n",
       "        7.664e-02],\n",
       "       [1.080e+01, 2.198e+01, 6.879e+01, ..., 7.485e-02, 2.965e-01,\n",
       "        7.662e-02],\n",
       "       [1.614e+01, 1.486e+01, 1.043e+02, ..., 1.129e-01, 2.778e-01,\n",
       "        7.012e-02],\n",
       "       ...,\n",
       "       [9.436e+00, 1.832e+01, 5.982e+01, ..., 5.052e-02, 2.454e-01,\n",
       "        8.136e-02],\n",
       "       [9.720e+00, 1.822e+01, 6.073e+01, ..., 0.000e+00, 1.909e-01,\n",
       "        6.559e-02],\n",
       "       [1.151e+01, 2.393e+01, 7.452e+01, ..., 9.653e-02, 2.112e-01,\n",
       "        8.732e-02]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spliting the dataset into the Training set and the Test set\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_axis, Y_axis, test_size = 0.20, random_state = 0)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling\n",
    "sc = StandardScaler()\n",
    "#Split it so you can train and test from diffrent sets\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the logistic Regression Algorithm to the Training set\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicte the test set results\n",
    "Y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression: 0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "#Create the confusion matrix\n",
    "cm = confusion_matrix(Y_test, Y_pred)\n",
    "print(\"Accuracy of Logistic Regression:\", accuracy_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision of Logistic Regression: 0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision of Logistic Regression:\", precision_score(Y_test, Y_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear', random_state=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the Support Vector Machines Algorithm to the Training set\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the test result\n",
    "Y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVC: 0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "#Create a confusion matrix\n",
    "cm = confusion_matrix(Y_test, Y_pred)\n",
    "print(\"Accuracy of SVC:\", accuracy_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision of SVC: 0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision of SVC:\", precision_score(Y_test, Y_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets use the training data as the test set and the test set as the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2, X_test2, Y_train2, Y_test2 = train_test_split(X_axis, Y_axis, test_size = 0.20, random_state = 0)\n",
    "\n",
    "#Feature Scaling\n",
    "sc = StandardScaler()\n",
    "#Split it so you can train and test from diffrent sets, this is where the switch happeneds\n",
    "X_train2 = sc.fit_transform(X_test2)\n",
    "X_test2 = sc.transform(X_train2)\n",
    "\n",
    "classifier2 = LogisticRegression(random_state = 0)\n",
    "classifier2.fit(X_train2, Y_test2)\n",
    "#predicting the Test set results\n",
    "Y_pred2 = classifier2.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of reverse Logistic Regression: 0.5789473684210527\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(Y_test2, Y_pred2)\n",
    "print(\"Accuracy of reverse Logistic Regression:\", accuracy_score(Y_test2, Y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision of reverse Logistic Regression: 0.5845823709600799\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision of reverse Logistic Regression:\", precision_score(Y_test2, Y_pred2, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fitting the Support Vector Machines Algorithm to the Training set\n",
    "classifier2 = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier2.fit(X_train2, Y_test2)\n",
    "#predict the test ser results\n",
    "Y_pred2 = classifier2.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of reverse SVC: 0.5087719298245614\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(Y_test2, Y_pred2)\n",
    "print(\"Accuracy of reverse SVC:\",accuracy_score(Y_test2,Y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision of reverse SVC: 0.5241612803939674\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision of reverse SVC:\", precision_score(Y_test2, Y_pred2, average='weighted'))"
   ]
  }
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