Breast Cancer - Answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breast cancer\n",
    "\n",
    "Le but de cet exercice est de construire un modèle de regression logistique pour **prédire la présence d'un cancer du sein**.\n",
    "\n",
    "- Chargez le dataset *Breast Cancer.csv* sous forme d'un Pandas DataFrame.\n",
    "- Débarassez-vous des colonnes inutiles:\n",
    "  - id\n",
    "  - Unnamed: 32\n",
    "- Le champs \"diagnosis\" indique la présence d'un cancer (M) ou son absence (B). Transformez cette valeur en valeur numérique (0 et 1).\n",
    "- Quelle est la répartition entre les échantillons positifs vs. négatifs?\n",
    "- Préparez les données pour l'entrainement/test.\n",
    "- Standardisez les variables explicatives.\n",
    "- Evaluez le modèle et commentez les résultats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\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 accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "</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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "df = pd.read_csv(os.path.join(\"data\", \"Breast Cancer.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         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",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['id', 'Unnamed: 32'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"diagnosis\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>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>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         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",
       "   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  label  \n",
       "0          0.4601                  0.11890      1  \n",
       "1          0.2750                  0.08902      1  \n",
       "2          0.3613                  0.08758      1  \n",
       "3          0.6638                  0.17300      1  \n",
       "4          0.2364                  0.07678      1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def diagnosis(x):\n",
    "    if x == 'M':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "df['label'] = df['diagnosis'].apply(diagnosis)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['diagnosis','label'], axis = 1).to_numpy()\n",
    "y = df['label'].to_numpy()\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardise Data\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#fit scaler \n",
    "scaler.fit(x_train)\n",
    "\n",
    "x_train_scale = scaler.transform(x_train)\n",
    "x_test_scale = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_scale, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Evaluation de model\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_predict_train = model.predict(x_train_scale)\n",
    "cm_train = confusion_matrix(y_train, y_predict_train)\n",
    "cm_train_perc = confusion_matrix(y_train, y_predict_train, normalize = 'true')\n",
    "\n",
    "acc_score_train = accuracy_score(y_train, y_predict_train)\n",
    "\n",
    "name = ['True Negative', 'False Positive', 'False Negative', 'True Positive']\n",
    "counts = [cm_train[0][0], cm_train[0][1], cm_train[1][0], cm_train[1][1]]\n",
    "percentage = [cm_train_perc[0][0]*100, cm_train_perc[0][1]*100, cm_train_perc[1][0]*100, cm_train_perc[1][1]*100]\n",
    "\n",
    "\n",
    "boxlabels = [f\"{n}\\n{c}\\n{p.round(2)}%\" for n,c,p in zip(name, counts, percentage)]\n",
    "boxlabels = np.asarray(boxlabels).reshape(cm_train.shape[0],cm_train.shape[1])\n",
    "\n",
    "plt.figure(figsize = (12,10))\n",
    "plt.title('Confusion Matrix Train, Accuracy = '+ str(acc_score_train.round(2)*100)+ '%')\n",
    "\n",
    "sns.heatmap(cm_train_perc, \n",
    "            cmap = 'Blues',\n",
    "            xticklabels = ['Negative', 'Positive'],\n",
    "            yticklabels = ['Negative', 'Positive'],\n",
    "            fmt = '',\n",
    "            annot = boxlabels)\n",
    "    \n",
    "plt.xlabel('Predict')\n",
    "plt.ylabel('Real')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision =  1.0\n",
      "Training Recall =  0.9647058823529412\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "#precision - how many were actually positive?\n",
    "precision_train = precision_score(y_train, y_predict_train,)\n",
    "\n",
    "#recall - True Positive Rate\n",
    "recall_train = recall_score(y_train, y_predict_train,)\n",
    "\n",
    "print(\"Training Precision = \", precision_train)\n",
    "print(\"Training Recall = \", recall_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict_test = model.predict(x_test_scale)\n",
    "cm_test = confusion_matrix(y_test, y_predict_test)\n",
    "cm_test_perc = confusion_matrix(y_test, y_predict_test, normalize = 'true')\n",
    "acc_score_test = accuracy_score(y_test, y_predict_test)\n",
    "\n",
    "name = ['True Negative', 'False Positive', 'False Negative', 'True Positive']\n",
    "counts = [cm_test[0][0], cm_test[0][1], cm_test[1][0], cm_test[1][1]]\n",
    "percentage = [cm_test_perc[0][0]*100, cm_test_perc[0][1]*100, cm_test_perc[1][0]*100, cm_test_perc[1][1]*100]\n",
    "\n",
    "\n",
    "boxlabels = [f\"{n}\\n{c}\\n{p.round(2)}%\" for n,c,p in zip(name, counts, percentage)]\n",
    "boxlabels = np.asarray(boxlabels).reshape(2,2)\n",
    "\n",
    "plt.figure(figsize = (12,10))\n",
    "plt.title('Confusion Matrix Test, Accuracy = '+ str(acc_score_test.round(2)*100)+ '%')\n",
    "\n",
    "sns.heatmap(cm_test_perc, \n",
    "            cmap = 'Blues',\n",
    "            xticklabels = ['Negative', 'Positive'],\n",
    "            yticklabels = ['Negative', 'Positive'],\n",
    "            fmt = '',\n",
    "            annot = boxlabels)\n",
    "    \n",
    "plt.xlabel('Predict')\n",
    "plt.ylabel('Real')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Precision =  0.975\n",
      "Test Recall =  0.9285714285714286\n"
     ]
    }
   ],
   "source": [
    "#precision - how many were actually positive?\n",
    "precision_test = precision_score(y_test, y_predict_test,)\n",
    "\n",
    "#recall - True Positive Rate\n",
    "recall_test = recall_score(y_test, y_predict_test,)\n",
    "\n",
    "print(\"Test Precision = \", precision_test)\n",
    "print(\"Test Recall = \", recall_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}