breast-cancer-prediction.ipynb

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   "source": [
    "# Using the Wisconsin breast cancer diagnostic data set for predictive analysis\n",
    "Attribute Information:\n",
    "\n",
    " - 1) ID number \n",
    " - 2) Diagnosis (M = malignant, B = benign) \n",
    " \n",
    "-3-32.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) \n",
    " - 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",
    "\n",
    "For this analysis, as a guide to predictive analysis I followed the instructions and discussion on \"A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)\" at Analytics Vidhya."
   ]
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   "source": [
    "#Load Libraries"
   ]
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   "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",
    "import mpld3 as mpl\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import metrics"
   ]
  },
  {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Load the data"
   ]
  },
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
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       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
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       "      <td>1709.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>11.42</td>\n",
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       "      <th>4</th>\n",
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       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
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       "         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",
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       "\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]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv(\"../input/data.csv\",header = 0)\n",
    "df.head()"
   ]
  },
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   },
   "source": [
    "# Clean and prepare data"
   ]
  },
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   ],
   "source": [
    "df.drop('id',axis=1,inplace=True)\n",
    "df.drop('Unnamed: 32',axis=1,inplace=True)\n",
    "len(df)"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "df.diagnosis.unique()"
   ]
  },
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   "outputs": [
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       "      <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  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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert \n",
    "df['diagnosis'] = df['diagnosis'].map({'M':1,'B':0})\n",
    "df.head()\n",
    "\n",
    "# Explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c3c6ba7",
   "metadata": {
    "_cell_guid": "cfd882cd-1719-4093-934a-539faf665353",
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:52.194425Z",
     "iopub.status.busy": "2021-11-25T08:05:52.193779Z",
     "iopub.status.idle": "2021-11-25T08:05:52.279424Z",
     "shell.execute_reply": "2021-11-25T08:05:52.278876Z",
     "shell.execute_reply.started": "2021-11-25T07:49:14.717060Z"
    },
    "papermill": {
     "duration": 0.116726,
     "end_time": "2021-11-25T08:05:52.279608",
     "exception": false,
     "start_time": "2021-11-25T08:05:52.162882",
     "status": "completed"
    },
    "tags": []
   },
   "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>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.372583</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.483918</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        diagnosis  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  569.000000   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     0.372583    14.127292     19.289649       91.969033   654.889104   \n",
       "std      0.483918     3.524049      4.301036       24.298981   351.914129   \n",
       "min      0.000000     6.981000      9.710000       43.790000   143.500000   \n",
       "25%      0.000000    11.700000     16.170000       75.170000   420.300000   \n",
       "50%      0.000000    13.370000     18.840000       86.240000   551.100000   \n",
       "75%      1.000000    15.780000     21.800000      104.100000   782.700000   \n",
       "max      1.000000    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
       "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
       "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
       "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
       "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
       "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
       "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
       "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
       "\n",
       "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f66797d4",
   "metadata": {
    "_cell_guid": "aa80be8a-4022-038b-d7b7-0789df4ef973",
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:52.349767Z",
     "iopub.status.busy": "2021-11-25T08:05:52.346996Z",
     "iopub.status.idle": "2021-11-25T08:05:52.752689Z",
     "shell.execute_reply": "2021-11-25T08:05:52.753574Z",
     "shell.execute_reply.started": "2021-11-25T07:49:14.814531Z"
    },
    "papermill": {
     "duration": 0.445746,
     "end_time": "2021-11-25T08:05:52.753778",
     "exception": false,
     "start_time": "2021-11-25T08:05:52.308032",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\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": "markdown",
   "id": "9e7552c8",
   "metadata": {
    "_cell_guid": "56b72979-5155-2a99-1b6e-a55cbf72d2a3",
    "papermill": {
     "duration": 0.028712,
     "end_time": "2021-11-25T08:05:52.812326",
     "exception": false,
     "start_time": "2021-11-25T08:05:52.783614",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### nucleus features vs diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a3a8ed9",
   "metadata": {
    "_cell_guid": "bc36c937-c5d8-8635-480b-777a94571310",
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:52.879050Z",
     "iopub.status.busy": "2021-11-25T08:05:52.877892Z",
     "iopub.status.idle": "2021-11-25T08:05:52.883678Z",
     "shell.execute_reply": "2021-11-25T08:05:52.883106Z",
     "shell.execute_reply.started": "2021-11-25T07:49:15.275643Z"
    },
    "papermill": {
     "duration": 0.040922,
     "end_time": "2021-11-25T08:05:52.883830",
     "exception": false,
     "start_time": "2021-11-25T08:05:52.842908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features_mean=list(df.columns[1:11])\n",
    "# split dataframe into two based on diagnosis\n",
    "dfM=df[df['diagnosis'] ==1]\n",
    "dfB=df[df['diagnosis'] ==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07052e03",
   "metadata": {
    "_cell_guid": "3f3b5e1b-605d-51b4-28c7-c551b5d13a48",
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:52.946942Z",
     "iopub.status.busy": "2021-11-25T08:05:52.946173Z",
     "iopub.status.idle": "2021-11-25T08:05:56.833813Z",
     "shell.execute_reply": "2021-11-25T08:05:56.833155Z",
     "shell.execute_reply.started": "2021-11-25T07:49:15.283932Z"
    },
    "papermill": {
     "duration": 3.920665,
     "end_time": "2021-11-25T08:05:56.833955",
     "exception": false,
     "start_time": "2021-11-25T08:05:52.913290",
     "status": "completed"
    },
    "tags": []
   },
   "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": [
    "\n",
    "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]]], alpha=0.5,stacked=True, label=['M','B'],color=['r','g'],bins=np.arange(min(df[features_mean[idx]]), max(df[features_mean[idx]]) + binwidth, binwidth) , density = True,)\n",
    "    ax.legend(loc='upper right')\n",
    "    ax.set_title(features_mean[idx])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be91c4b",
   "metadata": {
    "_cell_guid": "4b8d6133-427b-1ecf-0e24-9ec2afea0a0e",
    "papermill": {
     "duration": 0.030816,
     "end_time": "2021-11-25T08:05:56.895903",
     "exception": false,
     "start_time": "2021-11-25T08:05:56.865087",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Observations\n",
    "\n",
    "1. 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",
    "2. 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": "markdown",
   "id": "8a2af98c",
   "metadata": {
    "_cell_guid": "ac11039f-0418-3553-9412-ae3d50bef4e4",
    "papermill": {
     "duration": 0.030468,
     "end_time": "2021-11-25T08:05:56.957557",
     "exception": false,
     "start_time": "2021-11-25T08:05:56.927089",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creating a test set and a training set\n",
    "Since this data set is not ordered, I am going to do a simple 70:30 split to create a training data set and a test data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be273552",
   "metadata": {
    "_cell_guid": "1390898b-a338-7395-635f-6e1c216861e6",
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:57.026221Z",
     "iopub.status.busy": "2021-11-25T08:05:57.025468Z",
     "iopub.status.idle": "2021-11-25T08:05:57.028513Z",
     "shell.execute_reply": "2021-11-25T08:05:57.027962Z",
     "shell.execute_reply.started": "2021-11-25T07:49:19.304589Z"
    },
    "papermill": {
     "duration": 0.040684,
     "end_time": "2021-11-25T08:05:57.028656",
     "exception": false,
     "start_time": "2021-11-25T08:05:56.987972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "traindf, testdf = train_test_split(df, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87c7dc7",
   "metadata": {
    "_cell_guid": "45dac047-52fb-b847-a521-fa6883ebd5f6",
    "papermill": {
     "duration": 0.029978,
     "end_time": "2021-11-25T08:05:57.089625",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.059647",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Building\n",
    "\n",
    "Here we are going to build a classification model and evaluate its performance using the training set.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d02c25",
   "metadata": {
    "papermill": {
     "duration": 0.029945,
     "end_time": "2021-11-25T08:05:57.150680",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.120735",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Naive Bayes model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ec725dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:57.214759Z",
     "iopub.status.busy": "2021-11-25T08:05:57.214071Z",
     "iopub.status.idle": "2021-11-25T08:05:57.220925Z",
     "shell.execute_reply": "2021-11-25T08:05:57.221457Z",
     "shell.execute_reply.started": "2021-11-25T07:49:19.314453Z"
    },
    "papermill": {
     "duration": 0.04056,
     "end_time": "2021-11-25T08:05:57.221667",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.181107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model=GaussianNB()\n",
    "predictor_var = ['radius_mean','perimeter_mean','area_mean','compactness_mean','concave points_mean']\n",
    "outcome_var='diagnosis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f004cd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:57.288868Z",
     "iopub.status.busy": "2021-11-25T08:05:57.288229Z",
     "iopub.status.idle": "2021-11-25T08:05:57.300609Z",
     "shell.execute_reply": "2021-11-25T08:05:57.301588Z",
     "shell.execute_reply.started": "2021-11-25T07:49:19.330033Z"
    },
    "papermill": {
     "duration": 0.048417,
     "end_time": "2021-11-25T08:05:57.301871",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.253454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 90.704%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model.fit(traindf[predictor_var],traindf[outcome_var])\n",
    "predictions = model.predict(traindf[predictor_var])\n",
    "accuracy = metrics.accuracy_score(predictions,traindf[outcome_var])\n",
    "print(\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19639376",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:57.371093Z",
     "iopub.status.busy": "2021-11-25T08:05:57.370229Z",
     "iopub.status.idle": "2021-11-25T08:05:57.757474Z",
     "shell.execute_reply": "2021-11-25T08:05:57.756170Z",
     "shell.execute_reply.started": "2021-11-25T07:49:19.347310Z"
    },
    "papermill": {
     "duration": 0.422861,
     "end_time": "2021-11-25T08:05:57.757672",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.334811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(metrics.confusion_matrix(predictions,traindf[outcome_var]),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a0f844b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:57.832559Z",
     "iopub.status.busy": "2021-11-25T08:05:57.831725Z",
     "iopub.status.idle": "2021-11-25T08:05:57.863876Z",
     "shell.execute_reply": "2021-11-25T08:05:57.863247Z",
     "shell.execute_reply.started": "2021-11-25T07:49:28.221603Z"
    },
    "papermill": {
     "duration": 0.073701,
     "end_time": "2021-11-25T08:05:57.864024",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.790323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90.94936708860759\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from statistics import mean\n",
    "print(mean(cross_val_score(model, traindf[predictor_var],traindf[outcome_var], cv=5))*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5e1536",
   "metadata": {
    "papermill": {
     "duration": 0.032521,
     "end_time": "2021-11-25T08:05:57.928943",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.896422",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0e0e75c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:57.999628Z",
     "iopub.status.busy": "2021-11-25T08:05:57.998927Z",
     "iopub.status.idle": "2021-11-25T08:05:58.147872Z",
     "shell.execute_reply": "2021-11-25T08:05:58.147217Z",
     "shell.execute_reply.started": "2021-11-25T07:52:30.319382Z"
    },
    "papermill": {
     "duration": 0.186638,
     "end_time": "2021-11-25T08:05:58.148025",
     "exception": false,
     "start_time": "2021-11-25T08:05:57.961387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model=KNeighborsClassifier(n_neighbors=4)\n",
    "predictor_var = ['radius_mean','perimeter_mean','area_mean','compactness_mean','concave points_mean']\n",
    "outcome_var='diagnosis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5b17dcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:58.227322Z",
     "iopub.status.busy": "2021-11-25T08:05:58.226641Z",
     "iopub.status.idle": "2021-11-25T08:05:58.247742Z",
     "shell.execute_reply": "2021-11-25T08:05:58.247095Z",
     "shell.execute_reply.started": "2021-11-25T07:52:33.583800Z"
    },
    "papermill": {
     "duration": 0.067455,
     "end_time": "2021-11-25T08:05:58.247898",
     "exception": false,
     "start_time": "2021-11-25T08:05:58.180443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 89.698%\n"
     ]
    }
   ],
   "source": [
    "model.fit(traindf[predictor_var],traindf[outcome_var])\n",
    "predictions = model.predict(traindf[predictor_var])\n",
    "accuracy = metrics.accuracy_score(predictions,traindf[outcome_var])\n",
    "print(\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf7243a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:58.326477Z",
     "iopub.status.busy": "2021-11-25T08:05:58.323454Z",
     "iopub.status.idle": "2021-11-25T08:05:58.368930Z",
     "shell.execute_reply": "2021-11-25T08:05:58.368336Z",
     "shell.execute_reply.started": "2021-11-25T07:53:12.275997Z"
    },
    "papermill": {
     "duration": 0.086077,
     "end_time": "2021-11-25T08:05:58.369076",
     "exception": false,
     "start_time": "2021-11-25T08:05:58.282999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85.92405063291139\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from statistics import mean\n",
    "print(mean(cross_val_score(model, traindf[predictor_var],traindf[outcome_var], cv=5))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "48e44ec6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:58.449546Z",
     "iopub.status.busy": "2021-11-25T08:05:58.448823Z",
     "iopub.status.idle": "2021-11-25T08:05:59.900865Z",
     "shell.execute_reply": "2021-11-25T08:05:59.901356Z",
     "shell.execute_reply.started": "2021-11-25T07:55:22.855345Z"
    },
    "papermill": {
     "duration": 1.498948,
     "end_time": "2021-11-25T08:05:59.901562",
     "exception": false,
     "start_time": "2021-11-25T08:05:58.402614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x_train=traindf[predictor_var]\n",
    "y_train=traindf[outcome_var]\n",
    "x_test=testdf[predictor_var]\n",
    "y_test=testdf[outcome_var]\n",
    "trainAccuracy=[]\n",
    "testAccuracy=[]\n",
    "errorRate=[]\n",
    "for k in range(1,40):\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "    model.fit(x_train,y_train)\n",
    "    pred_i = model.predict(x_test)\n",
    "    errorRate.append(np.mean(pred_i != y_test))\n",
    "\n",
    "    trainAccuracy.append(model.score(x_train,y_train))\n",
    "    testAccuracy.append(model.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9fa623cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:05:59.976321Z",
     "iopub.status.busy": "2021-11-25T08:05:59.975264Z",
     "iopub.status.idle": "2021-11-25T08:06:00.226090Z",
     "shell.execute_reply": "2021-11-25T08:06:00.226636Z",
     "shell.execute_reply.started": "2021-11-25T07:55:53.943003Z"
    },
    "papermill": {
     "duration": 0.292232,
     "end_time": "2021-11-25T08:06:00.226818",
     "exception": false,
     "start_time": "2021-11-25T08:05:59.934586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum error:- 0.0935672514619883 at K = 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),errorRate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')\n",
    "print(\"Minimum error:-\",min(errorRate),\"at K =\",errorRate.index(min(errorRate))+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0afecae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:06:00.298770Z",
     "iopub.status.busy": "2021-11-25T08:06:00.297990Z",
     "iopub.status.idle": "2021-11-25T08:06:00.562210Z",
     "shell.execute_reply": "2021-11-25T08:06:00.562718Z",
     "shell.execute_reply.started": "2021-11-25T07:56:27.599985Z"
    },
    "papermill": {
     "duration": 0.302149,
     "end_time": "2021-11-25T08:06:00.562904",
     "exception": false,
     "start_time": "2021-11-25T08:06:00.260755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt,style\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1,40),trainAccuracy,label=\"Train Score\",marker=\"o\",markerfacecolor=\"teal\",color=\"blue\",linestyle=\"dashed\")\n",
    "plt.plot(range(1,40),testAccuracy,label=\"Test Score\",marker=\"o\",markerfacecolor=\"red\",color=\"black\",linestyle=\"dashed\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.title(\"Nbd Vs Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa5a290",
   "metadata": {
    "papermill": {
     "duration": 0.035819,
     "end_time": "2021-11-25T08:06:00.634531",
     "exception": false,
     "start_time": "2021-11-25T08:06:00.598712",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Testing with new K Value= 30\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63d05c25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:06:00.708828Z",
     "iopub.status.busy": "2021-11-25T08:06:00.708131Z",
     "iopub.status.idle": "2021-11-25T08:06:00.712305Z",
     "shell.execute_reply": "2021-11-25T08:06:00.712791Z",
     "shell.execute_reply.started": "2021-11-25T08:05:09.688194Z"
    },
    "papermill": {
     "duration": 0.043365,
     "end_time": "2021-11-25T08:06:00.712988",
     "exception": false,
     "start_time": "2021-11-25T08:06:00.669623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model=KNeighborsClassifier(n_neighbors=31)\n",
    "predictor_var = ['radius_mean','perimeter_mean','area_mean','compactness_mean','concave points_mean']\n",
    "outcome_var='diagnosis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4530a3fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:06:00.787766Z",
     "iopub.status.busy": "2021-11-25T08:06:00.787090Z",
     "iopub.status.idle": "2021-11-25T08:06:00.814549Z",
     "shell.execute_reply": "2021-11-25T08:06:00.815262Z",
     "shell.execute_reply.started": "2021-11-25T08:05:10.195831Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2021-11-25T08:06:00.748854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 88.945%\n"
     ]
    }
   ],
   "source": [
    "model.fit(traindf[predictor_var],traindf[outcome_var])\n",
    "predictions = model.predict(traindf[predictor_var])\n",
    "accuracy = metrics.accuracy_score(predictions,traindf[outcome_var])\n",
    "print(\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e837f5a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-25T08:06:00.915543Z",
     "iopub.status.busy": "2021-11-25T08:06:00.914428Z",
     "iopub.status.idle": "2021-11-25T08:06:00.971113Z",
     "shell.execute_reply": "2021-11-25T08:06:00.972049Z",
     "shell.execute_reply.started": "2021-11-25T08:05:10.708095Z"
    },
    "papermill": {
     "duration": 0.110472,
     "end_time": "2021-11-25T08:06:00.972381",
     "exception": false,
     "start_time": "2021-11-25T08:06:00.861909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.43987341772151\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from statistics import mean\n",
    "print(mean(cross_val_score(model, traindf[predictor_var],traindf[outcome_var], cv=5))*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9744c2b",
   "metadata": {
    "papermill": {
     "duration": 0.036383,
     "end_time": "2021-11-25T08:06:01.046268",
     "exception": false,
     "start_time": "2021-11-25T08:06:01.009885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can see after using K values of 31 our cross-validation and accuracy scores are very close to each other compared to our initial assumption of 4 where cross validation scores were lower and accuracy scores were lot higher on train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae02d731",
   "metadata": {
    "papermill": {
     "duration": 0.036314,
     "end_time": "2021-11-25T08:06:01.119614",
     "exception": false,
     "start_time": "2021-11-25T08:06:01.083300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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