BreastCancerPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"10\">About the dataset</font>\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1) ID number \n",
    "2) Diagnosis (M = malignant, B = benign) 3-32)\n",
    "\n",
    "Ten real-valued features are computed for each cell nucleus:\n",
    "\n",
    "a) radius (mean of distances from center to points on the perimeter) \n",
    "b) texture (standard deviation of gray-scale values) \n",
    "c) perimeter \n",
    "d) area \n",
    "e) smoothness (local variation in radius lengths) \n",
    "f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) \n",
    "h) concave points (number of concave portions of the contour) \n",
    "i) symmetry \n",
    "j) fractal dimension (\"coastline approximation\" - 1)\n",
    "\n",
    "The mean, standard error and \"worst\" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.\n",
    "\n",
    "All feature values are recoded with four significant digits.\n",
    "\n",
    "Missing attribute values: none\n",
    "\n",
    "Class distribution: 357 benign, 212 malignant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Importing the libraries</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.gridspec as gridspec \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold   \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Loading Data</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Cleaning Data</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('id',axis=1,inplace=True)\n",
    "df.drop('Unnamed: 32',axis=1,inplace=True)\n",
    "\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diagnosis.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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>1</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>1</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>1</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>1</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>1</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          1        17.99         10.38          122.80     1001.0   \n",
       "1          1        20.57         17.77          132.90     1326.0   \n",
       "2          1        19.69         21.25          130.00     1203.0   \n",
       "3          1        11.42         20.38           77.58      386.1   \n",
       "4          1        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  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "\n",
       "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "1           158.80      1956.0            0.1238             0.1866   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "\n",
       "   concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "1           0.2416                0.1860          0.2750   \n",
       "2           0.4504                0.2430          0.3613   \n",
       "3           0.6869                0.2575          0.6638   \n",
       "4           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'] = df['diagnosis'].map({'M':1,'B':0})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFqhJREFUeJzt3XuwZWV95vHvIyDGgDbYB9M2re2lnREdbZkWsTQJEccgRhtrNNOUSmtR0+qgk0yoTFCToBOpwjiCscZbWxgajQrxEjsGkxDEQsYBbAVabsaWa9sd+ijQQqFE4Dd/7PeQ7eFc9rnscziL76dq117rXe9a+/eePv3std+99j6pKiRJ3fWoxS5AkjRcBr0kdZxBL0kdZ9BLUscZ9JLUcQa9JHWcQa9ZSfLxJH+y2HVMJsk1SY6axX77J7k2ya8NoawlKcl/T3L6Yteh2TPo9RBJbkrysyR3JbkzybeSvDXJg78vVfXWqvqzxaxzKlX17Kr6xix23QRcXFX/ApDk7CSV5NX9nZJ8qLW/aSYHT7IiydYku9r+q2dR49ixjkryQJK72+1HSd47y2OtTnJRknuSXJ/kZX2bNwNvSHLIbGvV4jLoNZlXVdWBwFOA04E/As5a3JIWxFuAT49r+2dg49hKkn2B1wE/nMXxHwD+HvjPsy1wnF1VdUBVHQC8BDgxyXGzOM7ngCuAJwDvBr6QZASgqn4OfA04YZ5q1gIz6DWlqtpbVVuB/wJsTPIcePBM931t+aAkX00ymuSOtnzo2DGSPDXJxe0Vwj8l+UiSz7Rtq9uZ7cYktyT5cZJ39+27fzt73tVuH0qyf9u2vD3WnUluT/LNsVcd7VXJy9ryEUm2JflpktuSnDHRWJM8GXg6cNm4TX8LvDjJQW39GGA78C+z+HneVlUfBb49030HOPaNwLeAw2ayX5JnAocDp1bVz6rqi8D3+OUno28Ar5ynUrXADHoNpKouB3YCvz7B5kcBf0nv7P/JwM+A/9O3/bPA5fTOFt8DvHGCY7wE+HfA0cCfJnlWa383cCSwFngecATwx23bya2mEeCJwLuAib7T4y+Av6iqx9EL8vMmGeZ/AG6oqvvGtf8c2ApsaOsnAOf0d0jykvaEM9ntJZM85rxJsgZ4MXBpX9v2KWr6aOv2bHrjvqvvcFe19jHX0fv5awnad7EL0JKyCzh4fGNV/QT44th6ktOAi9ryk4EXAEdX1b8ClyTZOsGx31tVPwOuSnIVvVC5Dng98I6q2tOO917gE8CfAL8AVgBPqaodwDcnqfsXwDOSLK+qH9MXhOMsA+6aZNs5wAeSfBb4TXpTOSf1/QwuafsvtCcluZPek+2BwJeBS/rqeu4AxzgA2DuubS+wsm/9LuDxcytVi8Uzes3ESuD28Y1JHpvkE0luTvJT4GJgWZJ9gCcBt1fVPX273DrBsfunQe6hFz60/W/u23ZzawP4ALAD+MckNyQ5ZZK6TwSeCVyf5NtJfmeSfnfQC8uHaEE+Qu/VxFfbk9LDwa6qWtZerSyj92pqywyPcTfwuHFtj+OXn/QO5KFPBloiDHoNJMkL6AX9JRNsPpnetMsLW+D8xthuwG7g4CSP7eu/agYPvYvelNCYJ7c2ququqjq5qp4GvAr4gyRHjz9AVf2gqo4HDgHeT++Nxl+d4LG2A09rb7ZO5DP0xnrO+A1Jfr3v6peJbhNNec2rqtpLb5rsVX11XTNFTR9v3a6hN+7+J7nntfYxz6I3naMlyKDXlJI8rp0Bfx74TFV9b4JuB9I7k7wzycHAqWMbqupmYBvwniSPTvIi+oJoAJ8D/jjJSJLlwJ/SC1yS/E6SZyQJ8FPg/nYbP4Y3JBmpqgeAO1vzQ/pV1U7gB/TeB5jIh4H/RO8Vy/h9vzl29csktwenlZI8Bti/re7f1ifU3vQ+e7Lt4/oeQO99hAcDul1mOllNb219/hm4Ejg1yWOSvAZ4Ln3TcfSmq742SB16+HGOXpP52yT30bsc8FrgDODjk/T9EL0zyR/TO9v+INB/id/rgbOBn9B7U/ZcYJ8B63gfvWmE7W39r1sbwBp6b/qO0Jt2+egk184fA5zRXlXcDGxolwxO5BP03iz+1vgNVXU7cOGAdU+lf9rn+nafSfquovckO5knJbm7Ld9L7/2H18+ipg30/o3uAG4BXltVo/DgE9OxwH+cxXH1MBD/8IgWWpJzgeur6tRpOy+wdunmFfTePN69yLU8mt50yXOr6heLWMc7gFVV9T8XqwbNjUGvoWvz+7cDNwIvB/4GeFFVXbGohUmPEE7daCH8GvAletfR7wTeZshLC8czeknqOK+6kaSOe1hM3SxfvrxWr1692GVI0pLyne9858dVNTJdv4dF0K9evZpt27YtdhmStKQkuXn6Xk7dSFLnGfSS1HEGvSR1nEEvSR1n0EtSxxn0ktRxBr0kdZxBL0kdZ9BLUsc9LD4ZOxerT/m7RXvsm05/5aI9tiQNatoz+vanxS5PclX7+5Pvbe1nJ7kxyZXttra1J8mHk+xIsj3J4cMehCRpcoOc0d8LvLSq7k6yH3BJkrG/HfmHVfWFcf1fQe9PvK0BXgh8rN1LkhbBtGf01TP2Nyn3a7epvsR+PXBO2+9SYFmSFXMvVZI0GwO9GZtknyRXAnuAC6rqsrbptDY9c2b7W5sAK4Fb+3bf2drGH3NTkm1Jto2Ojs5hCJKkqQwU9FV1f1WtBQ4FjkjyHOCdwL8HXgAcDPxR6z7RX7N/yCuAqtpcVeuqat3IyLRfpyxJmqUZXV5ZVXcC3wCOqardbXrmXuAvgSNat53Aqr7dDgV2zUOtkqRZGOSqm5Eky9ryrwAvA64fm3dPEuA44Oq2y1bghHb1zZHA3qraPZTqJUnTGuSqmxXAliT70HtiOK+qvprk60lG6E3VXAm8tfU/HzgW2AHcA7x5/suWJA1q2qCvqu3A8ydof+kk/Qs4ae6lSZLmg1+BIEkdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HEGvSR1nEEvSR1n0EtSxxn0ktRxBr0kdZxBL0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HEGvSR13LRBn+QxSS5PclWSa5K8t7U/NcllSX6Q5Nwkj27t+7f1HW376uEOQZI0lUHO6O8FXlpVzwPWAsckORJ4P3BmVa0B7gBObP1PBO6oqmcAZ7Z+kqRFMm3QV8/dbXW/divgpcAXWvsW4Li2vL6t07YfnSTzVrEkaUYGmqNPsk+SK4E9wAXAD4E7q+q+1mUnsLItrwRuBWjb9wJPmOCYm5JsS7JtdHR0bqOQJE1qoKCvqvurai1wKHAE8KyJurX7ic7e6yENVZural1VrRsZGRm0XknSDM3oqpuquhP4BnAksCzJvm3TocCutrwTWAXQtj8euH0+ipUkzdwgV92MJFnWln8FeBlwHXAR8NrWbSPwlba8ta3Ttn+9qh5yRi9JWhj7Tt+FFcCWJPvQe2I4r6q+muRa4PNJ3gdcAZzV+p8FfDrJDnpn8huGULckaUDTBn1VbQeeP0H7DfTm68e3/xx43bxUJ0maMz8ZK0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HEGvSR1nEEvSR1n0EtSxxn0ktRxBr0kdZxBL0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HEGvSR13LRBn2RVkouSXJfkmiS/19rfk+RHSa5st2P79nlnkh1Jvp/kt4c5AEnS1PYdoM99wMlV9d0kBwLfSXJB23ZmVf3v/s5JDgM2AM8GngT8U5JnVtX981m4JGkw057RV9XuqvpuW74LuA5YOcUu64HPV9W9VXUjsAM4Yj6KlSTN3Izm6JOsBp4PXNaa3p5ke5JPJTmota0Ebu3bbScTPDEk2ZRkW5Jto6OjMy5ckjSYgYM+yQHAF4Hfr6qfAh8Dng6sBXYDHxzrOsHu9ZCGqs1Vta6q1o2MjMy4cEnSYAYK+iT70Qv5v6qqLwFU1W1VdX9VPQB8kn+bntkJrOrb/VBg1/yVLEmaiUGuuglwFnBdVZ3R176ir9trgKvb8lZgQ5L9kzwVWANcPn8lS5JmYpCrbl4MvBH4XpIrW9u7gOOTrKU3LXMT8BaAqromyXnAtfSu2DnJK24kafFMG/RVdQkTz7ufP8U+pwGnzaEuSdI88ZOxktRxBr0kdZxBL0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HEGvSR1nEEvSR1n0EtSxxn0ktRxBr0kdZxBL0kdZ9BLUscN8hemJKnTVp/yd4v22Ded/sqhP4Zn9JLUcQa9JHWcQS9JHTdt0CdZleSiJNcluSbJ77X2g5NckOQH7f6g1p4kH06yI8n2JIcPexCSpMkNckZ/H3ByVT0LOBI4KclhwCnAhVW1BriwrQO8AljTbpuAj8171ZKkgU0b9FW1u6q+25bvAq4DVgLrgS2t2xbguLa8Hjinei4FliVZMe+VS5IGMqM5+iSrgecDlwFPrKrd0HsyAA5p3VYCt/bttrO1jT/WpiTbkmwbHR2deeWSpIEMHPRJDgC+CPx+Vf10qq4TtNVDGqo2V9W6qlo3MjIyaBmSpBkaKOiT7Ecv5P+qqr7Umm8bm5Jp93ta+05gVd/uhwK75qdcSdJMDXLVTYCzgOuq6oy+TVuBjW15I/CVvvYT2tU3RwJ7x6Z4JEkLb5CvQHgx8Ebge0mubG3vAk4HzktyInAL8Lq27XzgWGAHcA/w5nmtWJI0I9MGfVVdwsTz7gBHT9C/gJPmWJckaZ74yVhJ6jiDXpI6zqCXpI4z6CWp4wx6Seo4g16SOs6gl6SOM+glqeMMeknqOINekjrOoJekjjPoJanjDHpJ6jiDXpI6zqCXpI4z6CWp4wx6Seo4g16SOs6gl6SOM+glqeOmDfokn0qyJ8nVfW3vSfKjJFe227F9296ZZEeS7yf57WEVLkkazCBn9GcDx0zQfmZVrW238wGSHAZsAJ7d9vlokn3mq1hJ0sxNG/RVdTFw+4DHWw98vqruraobgR3AEXOoT5I0R3OZo397ku1taueg1rYSuLWvz87W9hBJNiXZlmTb6OjoHMqQJE1ltkH/MeDpwFpgN/DB1p4J+tZEB6iqzVW1rqrWjYyMzLIMSdJ0ZhX0VXVbVd1fVQ8An+Tfpmd2Aqv6uh4K7JpbiZKkuZhV0CdZ0bf6GmDsipytwIYk+yd5KrAGuHxuJUqS5mLf6Tok+RxwFLA8yU7gVOCoJGvpTcvcBLwFoKquSXIecC1wH3BSVd0/nNIlSYOYNuir6vgJms+aov9pwGlzKUqSNH/8ZKwkdZxBL0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HEGvSR1nEEvSR1n0EtSxxn0ktRxBr0kdZxBL0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HHTBn2STyXZk+TqvraDk1yQ5Aft/qDWniQfTrIjyfYkhw+zeEnS9AY5oz8bOGZc2ynAhVW1BriwrQO8AljTbpuAj81PmZKk2Zo26KvqYuD2cc3rgS1teQtwXF/7OdVzKbAsyYr5KlaSNHOznaN/YlXtBmj3h7T2lcCtff12traHSLIpybYk20ZHR2dZhiRpOvP9ZmwmaKuJOlbV5qpaV1XrRkZG5rkMSdKY2Qb9bWNTMu1+T2vfCazq63cosGv25UmS5mq2Qb8V2NiWNwJf6Ws/oV19cySwd2yKR5K0OPadrkOSzwFHAcuT7AROBU4HzktyInAL8LrW/XzgWGAHcA/w5iHULEmagWmDvqqOn2TT0RP0LeCkuRYlSZo/fjJWkjrOoJekjjPoJanjDHpJ6jiDXpI6zqCXpI4z6CWp4wx6Seo4g16SOs6gl6SOM+glqeMMeknqOINekjrOoJekjjPoJanjDHpJ6jiDXpI6zqCXpI4z6CWp4wx6Seq4af84+FSS3ATcBdwP3FdV65IcDJwLrAZuAn63qu6YW5mSpNmajzP636qqtVW1rq2fAlxYVWuAC9u6JGmRDGPqZj2wpS1vAY4bwmNIkgY016Av4B+TfCfJptb2xKraDdDuD5loxySbkmxLsm10dHSOZUiSJjOnOXrgxVW1K8khwAVJrh90x6raDGwGWLduXc2xDknSJOZ0Rl9Vu9r9HuDLwBHAbUlWALT7PXMtUpI0e7MO+iS/muTAsWXg5cDVwFZgY+u2EfjKXIuUJM3eXKZungh8OcnYcT5bVX+f5NvAeUlOBG4BXjf3MiVJszXroK+qG4DnTdD+E+DouRQlSZo/fjJWkjrOoJekjjPoJanjDHpJ6jiDXpI6zqCXpI4z6CWp4wx6Seo4g16SOs6gl6SOM+glqeMMeknqOINekjrOoJekjjPoJanjDHpJ6jiDXpI6zqCXpI4z6CWp4wx6Seq4oQV9kmOSfD/JjiSnDOtxJElTG0rQJ9kH+AjwCuAw4Pgkhw3jsSRJUxvWGf0RwI6quqGq/hX4PLB+SI8lSZrCvkM67krg1r71ncAL+zsk2QRsaqt3J/n+LB9rOfDjWe47J3n/YjwqsIhjXkSO+ZHhETfmvH9OY37KIJ2GFfSZoK1+aaVqM7B5zg+UbKuqdXM9zlLimB8ZHPMjw0KMeVhTNzuBVX3rhwK7hvRYkqQpDCvovw2sSfLUJI8GNgBbh/RYkqQpDGXqpqruS/J24B+AfYBPVdU1w3gs5mH6ZwlyzI8MjvmRYehjTlVN30uStGT5yVhJ6jiDXpI6bskE/XRfqZBk/yTntu2XJVm98FXOrwHG/AdJrk2yPcmFSQa6pvbhbNCvzkjy2iSVZMlfijfImJP8bvu3vibJZxe6xvk2wO/2k5NclOSK9vt97GLUOV+SfCrJniRXT7I9ST7cfh7bkxw+rwVU1cP+Ru8N3R8CTwMeDVwFHDauz38DPt6WNwDnLnbdCzDm3wIe25bf9kgYc+t3IHAxcCmwbrHrXoB/5zXAFcBBbf2Qxa57Aca8GXhbWz4MuGmx657jmH8DOBy4epLtxwJfo/cZpCOBy+bz8ZfKGf0gX6mwHtjSlr8AHJ1kog9uLRXTjrmqLqqqe9rqpfQ+r7CUDfrVGX8G/Dnw84UsbkgGGfN/BT5SVXcAVNWeBa5xvg0y5gIe15YfzxL/HE5VXQzcPkWX9cA51XMpsCzJivl6/KUS9BN9pcLKyfpU1X3AXuAJC1LdcAwy5n4n0jsjWMqmHXOS5wOrquqrC1nYEA3y7/xM4JlJ/m+SS5Mcs2DVDccgY34P8IYkO4HzgXcsTGmLZqb/32dkWF+BMN+m/UqFAfssJQOPJ8kbgHXAbw61ouGbcsxJHgWcCbxpoQpaAIP8O+9Lb/rmKHqv2r6Z5DlVdeeQaxuWQcZ8PHB2VX0wyYuAT7cxPzD88hbFUPNrqZzRD/KVCg/2SbIvvZd7U71Uergb6GskkrwMeDfw6qq6d4FqG5bpxnwg8BzgG0luojeXuXWJvyE76O/2V6rqF1V1I/B9esG/VA0y5hOB8wCq6v8Bj6H3hWddNdSvjVkqQT/IVypsBTa25dcCX6/2LscSNe2Y2zTGJ+iF/FKft4VpxlxVe6tqeVWtrqrV9N6XeHVVbVuccufFIL/bf0PvjXeSLKc3lXPDglY5vwYZ8y3A0QBJnkUv6EcXtMqFtRU4oV19cySwt6p2z9fBl8TUTU3ylQpJ/hewraq2AmfRe3m3g96Z/IbFq3juBhzzB4ADgL9u7zvfUlWvXrSi52jAMXfKgGP+B+DlSa4F7gf+sKp+snhVz82AYz4Z+GSS/0FvCuNNS/nELcnn6E29LW/vO5wK7AdQVR+n9z7EscAO4B7gzfP6+Ev4ZydJGsBSmbqRJM2SQS9JHWfQS1LHGfSS1HEGvSR1nEEvSR1n0EtSx/1/2SiZRd9xKYYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe()\n",
    "plt.hist(df['diagnosis'])\n",
    "plt.title('Diagnosis (M=1 , B=0)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_mean=list(df.columns[1:11])\n",
    "dfM=df[df['diagnosis'] ==1]\n",
    "dfB=df[df['diagnosis'] ==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 8})\n",
    "fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(8,10))\n",
    "axes = axes.ravel()\n",
    "for idx,ax in enumerate(axes):\n",
    "    ax.figure\n",
    "    binwidth= (max(df[features_mean[idx]]) - min(df[features_mean[idx]]))/50\n",
    "    ax.hist([dfM[features_mean[idx]],dfB[features_mean[idx]]], bins=np.arange(min(df[features_mean[idx]]), max(df[features_mean[idx]]) + binwidth, binwidth) , alpha=0.5,stacked=True, density = True, label=['M','B'],color=['r','g'])\n",
    "    ax.legend(loc='upper right')\n",
    "    ax.set_title(features_mean[idx])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Observations</font>\n",
    "\n",
    "\n",
    "mean values of cell radius, perimeter, area, compactness, concavity and concave points can be used in classification of the cancer. Larger values of these parameters tends to show a correlation with malignant tumors.\n",
    "mean values of texture, smoothness, symmetry or fractual dimension does not show a particular preference of one diagnosis over the other. In any of the histograms there are no noticeable large outliers that warrants further cleanup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindf, testdf = train_test_split(df, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generic function for making a classification model and accessing the performance. \n",
    "\n",
    "def classification_model(model, data, predictors, outcome):\n",
    "  #Fit the model:\n",
    "  model.fit(data[predictors],data[outcome])\n",
    "  \n",
    "  #Make predictions on training set:\n",
    "  predictions = model.predict(data[predictors])\n",
    "  \n",
    "  #Print accuracy\n",
    "  accuracy = metrics.accuracy_score(predictions,data[outcome])\n",
    "  print(\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))\n",
    "\n",
    "  #Perform k-fold cross-validation with 5 folds\n",
    "  kf = KFold(data.shape[0], n_folds=5)\n",
    "  error = []\n",
    "  for train, test in kf:\n",
    "    # Filter training data\n",
    "    train_predictors = (data[predictors].iloc[train,:])\n",
    "    \n",
    "    # The target we're using to train the algorithm.\n",
    "    train_target = data[outcome].iloc[train]\n",
    "    \n",
    "    # Training the algorithm using the predictors and target.\n",
    "    model.fit(train_predictors, train_target)\n",
    "    \n",
    "    #Record error from each cross-validation run\n",
    "    error.append(model.score(data[predictors].iloc[test,:], data[outcome].iloc[test]))\n",
    "    \n",
    "    print(\"Cross-Validation Score : %s\" % \"{0:.3%}\".format(np.mean(error)))\n",
    "    \n",
    "  #Fit the model again so that it can be refered outside the function:\n",
    "  model.fit(data[predictors],data[outcome])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Logistic Regression Model</font>\n",
    "\n",
    "Logistic regression is widely used for classification of discrete data. In this case we will use it for binary (1,0) classification.\n",
    "\n",
    "Based on the observations in the histogram plots, we can reasonably hypothesize that the cancer diagnosis depends on the mean cell radius, mean perimeter, mean area, mean compactness, mean concavity and mean concave points. We can then perform a logistic regression analysis using those features as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 89.447%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-c4de59fa0c78>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0moutcome_var\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'diagnosis'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-23-8f223f3409d2>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m   \u001b[1;31m#Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "predictor_var = ['radius_mean','perimeter_mean','area_mean','compactness_mean','concave points_mean']\n",
    "outcome_var='diagnosis'\n",
    "model=LogisticRegression()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction accuracy is reasonable. What happens if we use just one predictor? Use the mean_radius:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 89.196%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-d1a039349335>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'radius_mean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-23-8f223f3409d2>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m   \u001b[1;31m#Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "predictor_var = ['radius_mean']\n",
    "model=LogisticRegression()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of the predictions are good but not great. Can we do better with another model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Decision Tree Model</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 100.000%\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-de62ec510706>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'radius_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'perimeter_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'area_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'compactness_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'concave points_mean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-23-8f223f3409d2>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m   \u001b[1;31m#Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "predictor_var = ['radius_mean','perimeter_mean','area_mean','compactness_mean','concave points_mean']\n",
    "model = DecisionTreeClassifier()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are over-fitting the model probably due to the large number of predictors. Let use a single predictor, the obvious one is the radius of the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 97.236%\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-91f97f55fce9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'radius_mean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-23-8f223f3409d2>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m   \u001b[1;31m#Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "predictor_var = ['radius_mean']\n",
    "model = DecisionTreeClassifier()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of the prediction is much much better here. But does it depend on the predictor?\n",
    "\n",
    "Using a single predictor gives a 97% prediction accuracy for this model. Let us try Random forest algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Random Forest</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 95.729%\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-632baa1df055>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeatures_mean\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmin_samples_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-23-8f223f3409d2>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m   \u001b[1;31m#Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "# Use all the features of the nucleus\n",
    "predictor_var = features_mean\n",
    "model = RandomForestClassifier(n_estimators=100,min_samples_split=25, max_depth=7, max_features=2)\n",
    "classification_model(model, traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using all the features improves the prediction accuracy\n",
    "An advantage with Random Forest is that it returns a feature importance matrix which can be used to select features. So lets select the top 5 features and use them as predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "concave points_mean       0.244548\n",
      "perimeter_mean            0.173601\n",
      "area_mean                 0.164839\n",
      "concavity_mean            0.129610\n",
      "radius_mean               0.120212\n",
      "texture_mean              0.055786\n",
      "compactness_mean          0.052936\n",
      "smoothness_mean           0.024780\n",
      "symmetry_mean             0.018850\n",
      "fractal_dimension_mean    0.014837\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Create a series with feature importances:\n",
    "featimp = pd.Series(model.feature_importances_, index=predictor_var).sort_values(ascending=False)\n",
    "print(featimp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 95.477%\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-d4ef47d62c89>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'concave points_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'area_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'radius_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'perimeter_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'concavity_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_samples_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-23-8f223f3409d2>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m   \u001b[1;31m#Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "# Using top 5 features\n",
    "predictor_var = ['concave points_mean','area_mean','radius_mean','perimeter_mean','concavity_mean',]\n",
    "model = RandomForestClassifier(n_estimators=100, min_samples_split=25, max_depth=7, max_features=2)\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Using Test Set</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 95.322%\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-4847b20b85e2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeatures_mean\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmin_samples_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtestdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-23-8f223f3409d2>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m   \u001b[1;31m#Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m   \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m   \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "# Use all the features of the nucleus\n",
    "predictor_var = features_mean\n",
    "model = RandomForestClassifier(n_estimators=100,min_samples_split=25, max_depth=7, max_features=2)\n",
    "classification_model(model, testdf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction accuracy for the test data set using the above Random Forest model is 95%!\n",
    "\n",
    "<font size=\"5\">Conclusion</font>\n",
    "The best model to be used for diagnosing breast cancer as found in this analysis is the Random Forest model with the top 5 predictors, 'concave points_mean','area_mean','radius_mean','perimeter_mean','concavity_mean'. It gives a prediction accuracy of ~95% "
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}