notebook.ipynb

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    "# Classify Benign or Malignant\n",
    "\n",
    "Here [Breast Cancer](https://www.kaggle.com/karteek93/breast-cancer) by [karteek menda](https://www.kaggle.com/karteek93) is used to create a `machine learning` model to classify `Breast Cancer`. `EDA` is `Bengin` or `Malignant`.\n",
    "\n",
    "![](https://media.giphy.com/media/fzZzoftMBR8is/giphy.gif)"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, GridSearchCV, learning_curve\n",
    "import statsmodels.api as sm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, classification_report, confusion_matrix\n",
    "import itertools\n",
    "from sklearn.pipeline import Pipeline\n",
    "from joblib import dump"
   ]
  },
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       "<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>Clump</th>\n",
       "      <th>UnifSize</th>\n",
       "      <th>UnifShape</th>\n",
       "      <th>MargAdh</th>\n",
       "      <th>SingEpiSize</th>\n",
       "      <th>BareNuc</th>\n",
       "      <th>BlandChrom</th>\n",
       "      <th>NormNucl</th>\n",
       "      <th>Mit</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>1350319</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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       "      <th>341</th>\n",
       "      <td>810104</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1091262</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>695219</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1170420</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "          ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "668  1350319      5         7          4        1            6       1   \n",
       "341   810104      1         1          1        1            2       1   \n",
       "39   1091262      2         5          3        3            6       7   \n",
       "578   695219      1         1          1        1            2       1   \n",
       "107  1170420      1         6          8       10            8      10   \n",
       "\n",
       "     BlandChrom  NormNucl  Mit  Class  \n",
       "668           7        10    3      4  \n",
       "341           3         1    1      2  \n",
       "39            7         5    1      4  \n",
       "578           2         1    1      2  \n",
       "107           5         7    1      4  "
      ]
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   "source": [
    "def load_dataset():\n",
    "    path = '/kaggle/input/breast-cancer/cell_samples.csv'\n",
    "    return pd.read_csv(path)\n",
    "\n",
    "\n",
    "df = load_dataset()\n",
    "df.sample(5)"
   ]
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   "source": [
    " benign (value = 2) or malignant (value = 4)"
   ]
  },
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    "tags": []
   },
   "source": [
    "## Data Preparation"
   ]
  },
  {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   ID           699 non-null    int64 \n",
      " 1   Clump        699 non-null    int64 \n",
      " 2   UnifSize     699 non-null    int64 \n",
      " 3   UnifShape    699 non-null    int64 \n",
      " 4   MargAdh      699 non-null    int64 \n",
      " 5   SingEpiSize  699 non-null    int64 \n",
      " 6   BareNuc      699 non-null    object\n",
      " 7   BlandChrom   699 non-null    int64 \n",
      " 8   NormNucl     699 non-null    int64 \n",
      " 9   Mit          699 non-null    int64 \n",
      " 10  Class        699 non-null    int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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    "tags": []
   },
   "source": [
    "The above info tells us that the data doesn't has any null values, but missing data can be in other forms too. All the above data are numeric but the column `BareNuc's`dtype is `object`, so checking it unique value"
   ]
  },
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    {
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     "text": [
      "['1', '10', '2', '4', '3', '9', '7', '?', '5', '8', '6']\n",
      "['1', '10', '2', '4', '3', '9', '7', nan, '5', '8', '6']\n"
     ]
    }
   ],
   "source": [
    "print(df.BareNuc.unique().tolist())\n",
    "# `BareNuc` column has missing data\n",
    "\n",
    "# replacing missing values with nan\n",
    "df.BareNuc.replace('?', np.nan, inplace=True)\n",
    "print(df.BareNuc.unique().tolist())\n",
    "\n",
    "# converting dtype\n",
    "df.BareNuc = df.BareNuc.astype(np.float64)"
   ]
  },
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    {
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     "text": [
      "BareNuc column has 16 missing values\n"
     ]
    }
   ],
   "source": [
    "print(f'BareNuc column has {len(df[df.BareNuc.isnull()])} missing values')"
   ]
  },
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   "source": [
    "def plot_histplot(column):\n",
    "    sns.histplot(x=column, color='#adfc03', alpha=.7) \n",
    "    \n",
    "\n",
    "def plot_scatterplot(x_column, y_column):\n",
    "    sns.scatterplot(x=x_column, y=y_column, color='#fc03ad', alpha=.7, s=80)\n",
    "    \n",
    "    \n",
    "def plot_stripplot(x_column, y_column):\n",
    "    sns.stripplot(\n",
    "        x=x_column, y=y_column,\n",
    "        color='#3b3b3b', alpha=.7, jitter=.1\n",
    "    )\n",
    "    \n",
    "    \n",
    "def plot_countplot(column):\n",
    "    sns.countplot(x=column, color='#4aff95', alpha=.7)\n",
    "    \n",
    "    \n",
    "def plot_corr(df):\n",
    "    # the `corr` method uses pearson correaltion\n",
    "    corr = df.corr()\n",
    "    \n",
    "    _, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "    g = sns.heatmap(corr, ax=ax, cmap=sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True))\n",
    "\n",
    "    for _ax in g.get_xticklabels():\n",
    "        _ax.set_rotation(75)\n",
    "        \n",
    "        \n",
    "def plot_boxplot(column):\n",
    "    sns.boxplot(x=df.Class, y=column)"
   ]
  },
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    {
     "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": [
    "plot_histplot(df.BareNuc)\n",
    "\n",
    "# The data points majorly have 1 as their value\n",
    "# so replacing missing values with that\n",
    "df.BareNuc.fillna(1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028582,
     "end_time": "2021-04-02T12:49:46.552418",
     "exception": false,
     "start_time": "2021-04-02T12:49:46.523836",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "`Missing values` problem solved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:46.614557Z",
     "iopub.status.busy": "2021-04-02T12:49:46.613968Z",
     "iopub.status.idle": "2021-04-02T12:49:46.619748Z",
     "shell.execute_reply": "2021-04-02T12:49:46.619144Z"
    },
    "papermill": {
     "duration": 0.038688,
     "end_time": "2021-04-02T12:49:46.619893",
     "exception": false,
     "start_time": "2021-04-02T12:49:46.581205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def drop_column(df, column_name, inplace=True):\n",
    "    return df.drop([column_name], axis='columns', inplace=inplace)\n",
    "\n",
    "\n",
    "drop_column(df, 'ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028887,
     "end_time": "2021-04-02T12:49:46.677784",
     "exception": false,
     "start_time": "2021-04-02T12:49:46.648897",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:46.739158Z",
     "iopub.status.busy": "2021-04-02T12:49:46.738579Z",
     "iopub.status.idle": "2021-04-02T12:49:47.026486Z",
     "shell.execute_reply": "2021-04-02T12:49:47.026945Z"
    },
    "papermill": {
     "duration": 0.32009,
     "end_time": "2021-04-02T12:49:47.027121",
     "exception": false,
     "start_time": "2021-04-02T12:49:46.707031",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:47.096693Z",
     "iopub.status.busy": "2021-04-02T12:49:47.095779Z",
     "iopub.status.idle": "2021-04-02T12:49:47.193701Z",
     "shell.execute_reply": "2021-04-02T12:49:47.193209Z"
    },
    "papermill": {
     "duration": 0.135893,
     "end_time": "2021-04-02T12:49:47.193840",
     "exception": false,
     "start_time": "2021-04-02T12:49:47.057947",
     "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": [
    "plot_stripplot(df.Class, df.Mit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:47.275726Z",
     "iopub.status.busy": "2021-04-02T12:49:47.274338Z",
     "iopub.status.idle": "2021-04-02T12:49:47.437153Z",
     "shell.execute_reply": "2021-04-02T12:49:47.436215Z"
    },
    "papermill": {
     "duration": 0.213051,
     "end_time": "2021-04-02T12:49:47.437288",
     "exception": false,
     "start_time": "2021-04-02T12:49:47.224237",
     "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": [
    "plot_countplot(df.Mit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:47.504806Z",
     "iopub.status.busy": "2021-04-02T12:49:47.504184Z",
     "iopub.status.idle": "2021-04-02T12:49:47.508690Z",
     "shell.execute_reply": "2021-04-02T12:49:47.508217Z"
    },
    "papermill": {
     "duration": 0.040439,
     "end_time": "2021-04-02T12:49:47.508838",
     "exception": false,
     "start_time": "2021-04-02T12:49:47.468399",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc',\n",
       "       'BlandChrom', 'NormNucl', 'Mit', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:47.592140Z",
     "iopub.status.busy": "2021-04-02T12:49:47.578707Z",
     "iopub.status.idle": "2021-04-02T12:49:48.022626Z",
     "shell.execute_reply": "2021-04-02T12:49:48.023124Z"
    },
    "papermill": {
     "duration": 0.482532,
     "end_time": "2021-04-02T12:49:48.023299",
     "exception": false,
     "start_time": "2021-04-02T12:49:47.540767",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab64f5ad50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(10, 4))\n",
    "sns.scatterplot(x='Clump', y='UnifSize', size='UnifShape', data=df[df.Class == 4], color='#141414', s=80, ax=ax)\n",
    "sns.scatterplot(x='Clump', y='UnifSize', size='UnifShape', data=df[df.Class == 2], color='#00bfff', s=80, ax=ax)\n",
    "plt.legend(bbox_to_anchor=(1.01, 1),borderaxespad=0)\n",
    "\n",
    "# black == 4\n",
    "# blue == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:48.093500Z",
     "iopub.status.busy": "2021-04-02T12:49:48.092827Z",
     "iopub.status.idle": "2021-04-02T12:49:48.201285Z",
     "shell.execute_reply": "2021-04-02T12:49:48.201752Z"
    },
    "papermill": {
     "duration": 0.145641,
     "end_time": "2021-04-02T12:49:48.201930",
     "exception": false,
     "start_time": "2021-04-02T12:49:48.056289",
     "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": [
    "plot_boxplot(df.Clump)\n",
    "# malignant has higher clump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:48.272082Z",
     "iopub.status.busy": "2021-04-02T12:49:48.271503Z",
     "iopub.status.idle": "2021-04-02T12:49:48.276531Z",
     "shell.execute_reply": "2021-04-02T12:49:48.277056Z"
    },
    "papermill": {
     "duration": 0.041619,
     "end_time": "2021-04-02T12:49:48.277213",
     "exception": false,
     "start_time": "2021-04-02T12:49:48.235594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc',\n",
       "       'BlandChrom', 'NormNucl', 'Mit', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:48.351287Z",
     "iopub.status.busy": "2021-04-02T12:49:48.350708Z",
     "iopub.status.idle": "2021-04-02T12:49:48.353306Z",
     "shell.execute_reply": "2021-04-02T12:49:48.352866Z"
    },
    "papermill": {
     "duration": 0.042312,
     "end_time": "2021-04-02T12:49:48.353439",
     "exception": false,
     "start_time": "2021-04-02T12:49:48.311127",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_box_and_count(df, column_name):\n",
    "    _, ax = plt.subplots(1, 2, figsize=(20, 4))\n",
    "    sns.boxplot(x=df.Class, y=df[column_name], ax=ax[0])\n",
    "    sns.countplot(x=column_name, data=df[df.Class == 2], color='#7300ff', ax=ax[1])\n",
    "    ax[1].set_xlabel(f'Benign - {column_name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:48.425432Z",
     "iopub.status.busy": "2021-04-02T12:49:48.424790Z",
     "iopub.status.idle": "2021-04-02T12:49:48.680950Z",
     "shell.execute_reply": "2021-04-02T12:49:48.681376Z"
    },
    "papermill": {
     "duration": 0.294192,
     "end_time": "2021-04-02T12:49:48.681558",
     "exception": false,
     "start_time": "2021-04-02T12:49:48.387366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'UnifSize')\n",
    "\n",
    "# benign has most of the UnifSize as 1 which also explains the bloxplot\n",
    "# by looking at both countplot & boxplot we can say that malignant has much higher  \n",
    "# UnifSize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:48.875069Z",
     "iopub.status.busy": "2021-04-02T12:49:48.874415Z",
     "iopub.status.idle": "2021-04-02T12:49:49.114857Z",
     "shell.execute_reply": "2021-04-02T12:49:49.115289Z"
    },
    "papermill": {
     "duration": 0.398442,
     "end_time": "2021-04-02T12:49:49.115452",
     "exception": false,
     "start_time": "2021-04-02T12:49:48.717010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'UnifShape')\n",
    "# UnifShape case is also similar to UnifSize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:49.192889Z",
     "iopub.status.busy": "2021-04-02T12:49:49.192270Z",
     "iopub.status.idle": "2021-04-02T12:49:49.442066Z",
     "shell.execute_reply": "2021-04-02T12:49:49.441462Z"
    },
    "papermill": {
     "duration": 0.29119,
     "end_time": "2021-04-02T12:49:49.442210",
     "exception": false,
     "start_time": "2021-04-02T12:49:49.151020",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SpEkLRklOBd4MvIbOmEV/B5zQvDlNkiRJT998Om+jralukOQwOgWlXwEeB1ZU1UXNINr/AMwDNgH/rqp+0GxzHvA2OkWps6vqK3vzJCRJ0syyu0fSvkLnjtdvVtVbq+o6OhclkiRJ2jvuplP4eSpGgXdU1cuAVwFnJTmazptn11TVUcCaZp5m2WnAy+n0Fv9Ykll7Kb8kSZqBdvdI2ivpXFzckOQ7wBWAFxeSJEl7z4HAPUluBR4ba6yq393VBlW1BdjSTP84yb3AXGAxsKBZbSWwFnhn035FVT0G3J9kI3AC8LW9fTKSJGlmmLRgVFV3AHcA70xyIp3H056R5J+Aq6tqRR8ySpIkzWTvfTobJ5kHHAfcAhzcFJOoqi1JDmpWmwt8fdxmm5u2ifZ3JnAmwOGHH/50okmSpGlsSoNeA1TVfwP+W5KzgVPp9DyyYCRJkvQ0VNU/7+m2SZ5D52Uk51TVj5LsctWJDr2LPCtorvHmz58/5XGVJEnSzDLVt6Qdv1PTNuD/TTJSVaN7P5YkSdJwSPJjnizePAPYF/hJVT13N9vtS6dY9Nmq+kLT/HCSQ5reRYcAW5v2zcBh4zY/FHhob52DJEmaeabaw+hjwPHAnXTuUB3TTM9O8n9W1fU9yidJkjSjVdUvjZ9P8no64wvtUjpdiS4F7q2qC8ctuhZYAry/+b5mXPvnklwIvAA4Crh1b+SXJEkz0+7ekjZmE3BcVc2vqlfSeU7+buAU4K96lE2SJGnoVNV/ARbuZrUTgdOBhUnWN5/X0ikUnZrkPjpDCLy/2ecG4ErgHmAVcFZV7ejRKUiSpBlgqj2MXtpcaABQVfckOa6qvjPJs/KSJEnajSS/N252H2A+uxhfaExV3czE4xIBnLyLbZYDy/ckoyRJGj5TLRj99yQfB65o5t/UtO0H/LwnySRJkobD68ZNj9Lp2b24nSiSJEkdUy0YLQH+BDiHzt2sm4H/SKdYdFJPkkmSJA2BqvrDtjNIkiTtbLcFoySzgOuq6hTgQxOs8sheTyVJkjQkkhwKXEJnXKKic2NuWVVtbjWYJEkaarsd9LoZEPGnSX65D3mkJ9x4440sWLCAm266qe0okiT10qfpvMXsBcBc4LqmTZIkqTVTfUva/wTuSnJpkovHPr0MJl1wwQUALF/u+JySpBltTlV9uqpGm89ngDlth5IkScNtqgWjLwH/CfgqcNu4j9QTN954I6OjowCMjo7ay0iSNJP9a5K3JpnVfN4KbG87lCRJGm5TGvS6qlb2Oog03ljvojHLly/npJMcX117z4MPPshPfjyLC25/bttRpIH23R/P4tkPPth2jJnuPwAfBT5MZwyjfwEcCFuSJLVqSgWjJEcBfwkcDTxzrL2qXtSjXBpyY72LdjUvSdIM8hfAkqr6AUCS5wMfpFNIkiRJasWUCkZ0Bl58D507XyfRueuVXoWSRkZGuopEIyNT/VGVpmbu3Lk8NrqF84//UdtRpIF2we3PZb+5c9uOMdP92lixCKCqvp/kuDYDSZIkTXUMo/2rag2QqvpuVb0XWNi7WBp2559/ftf8u9/97paSSJLUc/skOWBspulh5J0SSZLUqim/JS3JPsB9Sd6e5A3AQT3MpSG3cOHCJ3oVjYyMOH6RJGkm+xDwL0n+Isl/pjOG0V+1nEmSJA25qRaMzgGeBZwNvBI4HVjSo0wS8GQvI3sXSZJmsqq6DPh94GFgG/B7VfV37aaSJEnDbqpvSftGM/kIvrVDfbJw4UIWLvTJR0nSzFdV9wD3tJ1DkiRpzKQFoyTXTra8qn5378aRJEmSJElS23bXw+jXge8BlwO34JvRJEmSJEmSZrzdFYx+BTgVeDPwFuBLwOVVtaHXwSRJkiRJktSOSQe9rqodVbWqqpYArwI2AmuTLO1LOkmSJEmSJPXdbge9TrIf8Nt0ehnNAy4GvtDbWJIkSZIkSWrL7ga9XgkcA/wT8L6qursvqSRJkiRJktSaSR9JA04HXgIsA/4lyY+az4+T/Kj38SRJkjSRJJ9KsjXJ3ePa3pvkwSTrm89rxy07L8nGJN9O8pp2UkuSpOli0h5GVbW7gpIkSZLa8Rngo8BlO7V/uKo+OL4hydHAacDLgRcANyR5SVXt6EdQSZI0/VgQkiRJmoaq6qvA96e4+mLgiqp6rKrup/MikxN6Fk6SJE17FowkSZJmlrcnubN5ZO2Apm0u8L1x62xu2n5BkjOTrEuybtu2bb3OKkmSBpQFI0mSpJnj48CLgWOBLcCHmvZMsG5NtIOqWlFV86tq/pw5c3oSUpIkDT4LRpIkSTNEVT1cVTuq6nHgEzz52Nlm4LBxqx4KPNTvfJIkafqwYKSBdc0117BgwQKuu+66tqNIkjQtJDlk3OwbgLE3qF0LnJZkvyRHAEcBt/Y7nyRJmj5aKxglmZXkjiRfbCuDBttHPvIRAC688MJ2g0iSNICSXA58DfjVJJuTvA34qyR3JbkTOAk4F6CqNgBXAvcAq4CzfEOaJEmazEiLx14G3As8t8UMGlDXXHMNVZ2hFaqK6667jte97nUtp5IkaXBU1ZsnaL50kvWXA8t7l0iSJM0krfQwSnIo8NvAJ9s4vgbfWO+iMfYykiRJkiSpf9p6JO0jwJ8Dj+9qBV/pOtzGehftal6SJEmSJPVO3wtGSX4H2FpVt022nq90HW5JJp2XJEmSJEm900YPoxOB302yCbgCWJjk71vIoQF2zjnndM3/6Z/+aTtBJEmSJEkaQn0vGFXVeVV1aFXNA04Dbqyqt/Y7hwbb4sWLn+hVlMQBryVJkiRJ6qO2xjCSdmusl5G9iyRJkiRJ6q+RNg9eVWuBtW1m0OBavHgxixcvbjuGJEmSJElDxx5GkiRJkiRJ6mLBSJIkSZIkSV0sGEmSJEmSJKmLBSNJkiRJkiR1sWAkSZIkSZKkLhaMJEmSJEmS1MWCkSRJkiRJkrpYMJIkSZIkSVIXC0aSJEmSJEnqYsFIkiRJkiRJXSwYSZIkSZIkqYsFI0mSpGkoyaeSbE1y97i25ydZneS+5vuAccvOS7IxybeTvKad1JIkabqwYKSBdfLJJ7NgwQJOOeWUtqNIkjSIPgMs2qntXcCaqjoKWNPMk+Ro4DTg5c02H0syq39RJUnSdGPBSANrx44dAIyOjracRJKkwVNVXwW+v1PzYmBlM70SeP249iuq6rGquh/YCJzQj5ySJGl6smCkgXTyySd3zdvLSJKkKTm4qrYANN8HNe1zge+NW29z0yZJkjShkbYDSBMZ6100xl5G6oUHHpnFBbc/t+0Yajz80849jIOf9XjLSTTeA4/M4qi2Q2hvyARtNeGKyZnAmQCHH354LzNJkqQBZsFI0lA68sgj246gnfxs40YA9nuh/20GyVH4+zLNPJzkkKrakuQQYGvTvhk4bNx6hwIPTbSDqloBrACYP3/+hEUlSZI081kwkjSUli5d2nYE7WTZsmUAXHTRRS0nkaa1a4ElwPub72vGtX8uyYXAC+jUAm9tJaEkSZoWLBhpIM2aNavrsbSREX9UJUkaL8nlwALgwCSbgffQKRRdmeRtwAPAGwGqakOSK4F7gFHgrKraMeGOJUmSsGCkAbVmzRoWLFjwxPwNN9zQXhhJkgZQVb15F4tOnqixqpYDy3uXSJIkzSS+JU0Da9asWYC9iyRJkiRJ6jf/n7gG1po1a9qOIEmSJEnSULKHkSRJkiRJkrpYMJIkSZIkSVIXC0aSJEmSJEnqYsFIkiRJkiRJXSwYSZIkSZIkqYsFI0mSJEmSJHWxYCRJkiRJkqQuFowkSZIkSZLUxYKRJEmSJEmSulgwkiRJkiRJUhcLRpIkSZIkSepiwUiSJEmSJEldRtoOIO3KggULnpheu3ZtazkkSZIkSRo2fe9hlOSwJDcluTfJhiTL+p1BkiRJkiRJu9bGI2mjwDuq6mXAq4CzkhzdQg4NsPG9iyaalyRJkiRJvdP3glFVbamq25vpHwP3AnP7nUOSJEmSJEkTa3XQ6yTzgOOAWyZYdmaSdUnWbdu2re/ZJEmSJEmShlVrBaMkzwGuAs6pqh/tvLyqVlTV/KqaP2fOnP4HlCRJmqaSbEpyV5L1SdY1bc9PsjrJfc33AW3nlCRJg6uVglGSfekUiz5bVV9oI4MkSdIMd1JVHVtV85v5dwFrquooYE0zL0mSNKE23pIW4FLg3qq6sN/H1/Swdu3aSeclSdJTthhY2UyvBF7fXhRJkjTo2uhhdCJwOrCw6Sa9PslrW8ghSZI0UxVwfZLbkpzZtB1cVVug8xIS4KCJNnQcSUmSBDDS7wNW1c1A+n1cTT/2KpIkaY+dWFUPJTkIWJ3kW1PdsKpWACsA5s+fX70KKEmSBlurb0mTJEnS3ldVDzXfW4GrgROAh5McAtB8b20voSRJGnQWjCRJkmaQJM9O8ktj08BvAXcD1wJLmtWWANe0k1CSJE0HfX8kTZIkST11MHB15z0jjACfq6pVSb4BXJnkbcADwBtbzChJkgacBSNJkqQZpKq+A7xigvbtwMn9TyRJkqYjH0mTJEmSJElSF3sYSZIkSVP0ziM2tR1hr/nA/fPajiBJGmD2MJIkSZIkSVIXC0aSJEmSJEnqYsFIkiRJkiRJXRzDSJIkSdKUOIaTJA0PexhJkiRJkiSpiwUjSZIkSZIkdbFgJEmSJEmSpC4WjCRJkiRJktTFgpEkSZIkSZK6+JY04JJLLmHVqlVtx9grfvrTn1JVbcfQTpLwrGc9q+0Ye8WiRYtYunRp2zE0ziWXXMLGjRvbjvG0jZ3DsmXLWk6ydxx55JH+rkiSJGnasmAkSRoI+++/f9sRJEmSJDUsGAFLly71LrCkact/vyRJkiTtbY5hJEmSJEmSpC4WjCRJkiRJktTFgpEkSZIkSZK6WDCSJEmSJElSFwtGkiRJkiRJ6uJb0iRJkoZEkkXARcAs4JNV9f6WI0nTxjuP2NR2hL3mA/fPazuCpGnAgpEkSdIQSDIL+GvgVGAz8I0k11bVPe0mkzQdzJSC2Z4Uy2bKuYPFQj01FowkSZKGwwnAxqr6DkCSK4DFgAUjSdKEhr1YNuznn6ra+0n2siTbgO+2nUOtOBD417ZDSOobf+eH1wurak7bIWayJH8ALKqq/6OZPx34X6vq7TutdyZwZjP7q8C3+xr0ScP+78Ewn/8wnzsM9/l77sNrmM+/7XPf5TXYtOhh5AXk8Eqyrqrmt51DUn/4Oy/1VCZo+4U7h1W1AljR+ziTG/Z/D4b5/If53GG4z99zH85zh+E+/0E+d9+SJkmSNBw2A4eNmz8UeKilLJIkacBZMJIkSRoO3wCOSnJEkmcApwHXtpxJkiQNqGnxSJqGWutd4iX1lb/zUo9U1WiStwNfAWYBn6qqDS3Hmsyw/3swzOc/zOcOw33+nvvwGubzH9hznxaDXkuSJEmSJKl/fCRNkiRJkiRJXSwYSZIkSZIkqYsFIw2cJIcluSnJvUk2JFnWdiZJ/ZFkVpI7knyx7SyS2pHkU0m2Jrm77Sz9NuzXQEmemeTWJN9szv99bWfqt2H+O5hkU5K7kqxPsq7tPP2U5HlJPp/kW83v/6+3nalfkixLcnfzO39O23l6baK/cUmen2R1kvua7wPazDieBSMNolHgHVX1MuBVwFlJjm45k6T+WAbc23YISa36DLCo7RAtGfZroMeAhVX1CuBYYFGSV7Ubqe+G/e/gSVV1bFXNbztIn10ErKqqlwKvYEh+BpIcA/wRcAKd8/6dJEe1m6rnPsMv/o17F7Cmqo4C1jTzA8GCkQZOVW2pqtub6R/T+QdzbrupJPVakkOB3wY+2XYWSe2pqq8C3287RxuG/RqoOh5pZvdtPkPzhh7/Dg6nJM8FXg1cClBVP6uqH7Yaqn9eBny9qn5aVaPAPwNvaDlTT+3ib9xiYGUzvRJ4fT8zTcaCkQZaknnAccAtLUeR1HsfAf4ceLzlHJLUumG9BmoeyVoPbAVWV9Uwnf9HGO6/gwVcn+S2JGe2HaaPXgRsAz7dPI74ySTPbjtUn9wNvDrJ7CTPAl4LHNZypjYcXFVboHPjADio5TxPsGCkgZXkOcBVwDlV9aO280jqnSS/A2ytqtvaziJJbRvma6Cq2lFVxwKHAic0j6zMeP4dBODEqjoe+Ld0Hsd8dduB+mQEOB74eFUdB/yEAXokqZeq6l7gA8BqYBXwTTqP5mpAWDDSQEqyL50Lpc9W1RfaziOp504EfjfJJuAKYGGSv283kiT1n9dAHc0jOWsZnvGshv7vYFU91HxvBa6mM67NMNgMbB7Xm+7zdApIQ6GqLq2q46vq1XQe1bqv7UwteDjJIQDN99aW8zzBgpEGTpLQeYb33qq6sO08knqvqs6rqkOrah5wGnBjVb215ViS1FfDfg2UZE6S5zXT+wOnAN9qNVSfDPvfwSTPTvJLY9PAb9F5XGnGq6r/AXwvya82TScD97QYqa+SHNR8Hw78HnB5u4lacS2wpJleAlzTYpYuI20HkCZwInA6cFfzDDvA+VX15fYiSZKkfkhyObAAODDJZuA9VXVpu6n6ZtivgQ4BViaZRefG9pVVNXSvlx9SBwNXd2qmjACfq6pV7Ubqq6XAZ5M8A/gO8Ict5+mnq5LMBn4OnFVVP2g7UC9N9DcOeD9wZZK3AQ8Ab2wvYbdUDc2LByRJkiRJkjQFPpImSZIkSZKkLhaMJEmSJEmS1MWCkSRJkiRJkrpYMJIkSZIkSVIXC0aSJEmSJEnqYsFIUl8k+ZUkVyT5/5Lck+TLSV6S5O62s0mSJPVKkh1J1if5ZpLbk/zG09jXf05yyt7MN8Ex5iWpJH8xru3AJD9P8tG9fKw3NMd66STrrE0yv5l+ZG8eX9LkLBhJ6rkkAa4G1lbVi6vqaOB84OB2k0mSJPXco1V1bFW9AjgP+Ms93VFV/T9VdcPei7ZL3wF+Z9z8G4ENT2UHSUamsNqbgZuB057KviX1hwUjSf1wEvDzqvqbsYaqWg98b2y+uZv1X5s7b0/cfUtySJKvNnfm7k7yvyWZleQzzfxdSc7t+xlJkiQ9dc8FfjA2k+TPknwjyZ1J3te0zUtyb5JPJNmQ5Pok+zfLPpPkD5rp1yb5VpKbk1yc5ItN+3uTfKrpmfOdJGfvQc5HgXvHevYAbwKuHJf7dUluSXJHkhuSHDzu2CuSXA9clmROktXNtd3fJvlukgObdZ8DnAi8jXEFoyT7N73S70zyD8D+44MlWd701vr62HEl9YYFI0n9cAxw227W2QqcWlXH07koubhpfwvwlao6FngFsB44FphbVcdU1b8BPt2DzJIkSXvD/s2Nr28BnwT+AiDJbwFHASfQubZ5ZZJXN9scBfx1Vb0c+CHw++N3mOSZwN8C/7aqfhOYs9MxXwq8ptn3e5Lsuwe5rwBOS3IosAN4aNyym4FXVdVxzXp/Pm7ZK4HFVfUW4D3Ajc313dXA4ePWez2wqqr+O/D9JMc37f8X8NOq+jVgebO/Mc8Gvt701voq8Ed7cF6Spmgq3QQlqR/2BT6a5Fg6FyUvadq/AXyqudD5L1W1Psl3gBcluQT4EnB9G4ElSZKm4NHmxhdJfp1Oz5tjgN9qPnc06z2HTqHoAeD+pjc2dG66zdtpny8FvlNV9zfzlwNnjlv+pap6DHgsyVY6wwBsfoq5V9Epbj0M/MNOyw4F/iHJIcAzgPvHLbu2qh5tpn8TeANAVa1K8oNx670Z+EgzfUUzfzvwapobh1V1Z5I7x23zM+CLzfRtwKlP8ZwkPQX2MJLUDxvovjs0kXPpXJC8AphP5+KDqvoqnQuHB4G/S3JGVf2gWW8tcBadu3WSJEkDraq+BhxIp0dQgL9sxjc6tqqOrKpLm1UfG7fZDn7xRn92c6hJt28Gm17ffOYzgar6GZ2izDuAq3ZafAnw0aan9x8Dzxy37Ce7y5lkNrAQ+GSSTcCfAW9qxr0EqF2c18+ramzZRP+7SNqLLBhJ6ocbgf2SPNFtOMn/Arxw3Dq/DGypqseB04FZzXovBLZW1SeAS4Hjm2ff96mqq4D/BByPJEnSgGveBjYL2A58BfgPzVg+JJmb5KAp7upbdHpbz2vm3/RUclTV1eMKVesmWfVDwDuravtO7b9M52YewJJJtr8Z+HfwxCN4BzTtfwBcVlUvrKp5VXUYnV5Kv0nnUbP/vdnmGODXnsKpSdqLrMhK6rmqqiRvAD6S5F3A/wQ2AeeMW+1jwFVJ3gjcxJN3pxYAf5bk58AjwBnAXODTScaK3uf1+hwkSZL20P5J1jfTAZZU1Q7g+iQvA77WdKx5BHgrnZ4zk6qqR5P8CbAqyb8Ct/YieFVtYOK3o70X+MckDwJfB47YxS7eB1ye5E3APwNbgB/Tefzs/TutexWdsSv/lM513p10xq7syblJ2r082aNPkiRJkjQdJHlOVT3SPMb118B9VfXhtnONl2Q/YEdVjTbjN318bDwnSYPPHkaSJEmSNP38UZIldMZ9vIPOW9MGzeHAlU2v8J/hW82kacUeRpIkSZIkSerioNeSJEmSJEnqYsFIkiRJkiRJXSwYSZIkSZIkqYsFI0mSJEmSJHWxYCRJkiRJkqQu/z9cHddACFoWrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'MargAdh')\n",
    "\n",
    "# MargAdh case is also similar to UnifSize\n",
    "# But unlike UnifShape & UnifSize here malignant is not too big\n",
    "# but it is surely bigger than benign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:49.527040Z",
     "iopub.status.busy": "2021-04-02T12:49:49.526410Z",
     "iopub.status.idle": "2021-04-02T12:49:49.776527Z",
     "shell.execute_reply": "2021-04-02T12:49:49.776056Z"
    },
    "papermill": {
     "duration": 0.297255,
     "end_time": "2021-04-02T12:49:49.776685",
     "exception": false,
     "start_time": "2021-04-02T12:49:49.479430",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'SingEpiSize')\n",
    "\n",
    "# SingEpiSize case is also similar to UnifSize\n",
    "# But unlike UnifShape & UnifSize here malignant is not that big\n",
    "# but it is surely bigger than benign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:49.873243Z",
     "iopub.status.busy": "2021-04-02T12:49:49.868115Z",
     "iopub.status.idle": "2021-04-02T12:49:50.116240Z",
     "shell.execute_reply": "2021-04-02T12:49:50.115737Z"
    },
    "papermill": {
     "duration": 0.30258,
     "end_time": "2021-04-02T12:49:50.116386",
     "exception": false,
     "start_time": "2021-04-02T12:49:49.813806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'BareNuc')\n",
    "# UnifShape case is also similar to UnifSize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:50.229359Z",
     "iopub.status.busy": "2021-04-02T12:49:50.213145Z",
     "iopub.status.idle": "2021-04-02T12:49:50.453139Z",
     "shell.execute_reply": "2021-04-02T12:49:50.452651Z"
    },
    "papermill": {
     "duration": 0.297932,
     "end_time": "2021-04-02T12:49:50.453277",
     "exception": false,
     "start_time": "2021-04-02T12:49:50.155345",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'BlandChrom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:50.549583Z",
     "iopub.status.busy": "2021-04-02T12:49:50.535791Z",
     "iopub.status.idle": "2021-04-02T12:49:50.800740Z",
     "shell.execute_reply": "2021-04-02T12:49:50.800220Z"
    },
    "papermill": {
     "duration": 0.309234,
     "end_time": "2021-04-02T12:49:50.800886",
     "exception": false,
     "start_time": "2021-04-02T12:49:50.491652",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'NormNucl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:50.921751Z",
     "iopub.status.busy": "2021-04-02T12:49:50.903718Z",
     "iopub.status.idle": "2021-04-02T12:49:51.115273Z",
     "shell.execute_reply": "2021-04-02T12:49:51.114789Z"
    },
    "papermill": {
     "duration": 0.275259,
     "end_time": "2021-04-02T12:49:51.115415",
     "exception": false,
     "start_time": "2021-04-02T12:49:50.840156",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_and_count(df, 'Mit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041337,
     "end_time": "2021-04-02T12:49:51.197803",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.156466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For `benign` except for `BlandChrom` column, all other columns data is highly concentrated to a specific value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.039528,
     "end_time": "2021-04-02T12:49:51.277787",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.238259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:51.367000Z",
     "iopub.status.busy": "2021-04-02T12:49:51.366065Z",
     "iopub.status.idle": "2021-04-02T12:49:51.369332Z",
     "shell.execute_reply": "2021-04-02T12:49:51.368817Z"
    },
    "papermill": {
     "duration": 0.051529,
     "end_time": "2021-04-02T12:49:51.369469",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.317940",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Shuffling the dataset\n",
    "dataset = df.sample(frac=1, random_state=0)\n",
    "\n",
    "features = dataset.columns.tolist()\n",
    "features.remove('Class')\n",
    "\n",
    "x = dataset[features]\n",
    "y = dataset[['Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:51.453169Z",
     "iopub.status.busy": "2021-04-02T12:49:51.452218Z",
     "iopub.status.idle": "2021-04-02T12:49:51.461124Z",
     "shell.execute_reply": "2021-04-02T12:49:51.460401Z"
    },
    "papermill": {
     "duration": 0.051656,
     "end_time": "2021-04-02T12:49:51.461260",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.409604",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our dataset contains 66.0% benign data and 34.0% malignant data\n"
     ]
    }
   ],
   "source": [
    "benign_pct    = round(len(y[y.Class == 2]) / len(y), 2) * 100 \n",
    "malignant_pct = round(len(y[y.Class == 4]) / len(y), 2) * 100 \n",
    "print(f\"Our dataset contains {benign_pct}% benign data and {malignant_pct}% malignant data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:51.548686Z",
     "iopub.status.busy": "2021-04-02T12:49:51.547712Z",
     "iopub.status.idle": "2021-04-02T12:49:51.551693Z",
     "shell.execute_reply": "2021-04-02T12:49:51.551230Z"
    },
    "papermill": {
     "duration": 0.049953,
     "end_time": "2021-04-02T12:49:51.551829",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.501876",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Splitting the dataset (15% as test dataset)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y.values.ravel(), test_size=0.15, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:51.636264Z",
     "iopub.status.busy": "2021-04-02T12:49:51.635727Z",
     "iopub.status.idle": "2021-04-02T12:49:51.638970Z",
     "shell.execute_reply": "2021-04-02T12:49:51.639425Z"
    },
    "papermill": {
     "duration": 0.047346,
     "end_time": "2021-04-02T12:49:51.639582",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.592236",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:51.725114Z",
     "iopub.status.busy": "2021-04-02T12:49:51.724210Z",
     "iopub.status.idle": "2021-04-02T12:49:51.915573Z",
     "shell.execute_reply": "2021-04-02T12:49:51.915077Z"
    },
    "papermill": {
     "duration": 0.234593,
     "end_time": "2021-04-02T12:49:51.915740",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.681147",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LassoCV: 0.8281590397440844\n",
      "SVM(SVC): 0.9646464646464646\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(LassoCV(), x_train, y_train, cv=skf)\n",
    "print(f'LassoCV: {score.mean()}')\n",
    "\n",
    "score = cross_val_score(SVC(), x_train, y_train, cv=skf)\n",
    "print(f'SVM(SVC): {score.mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:52.004959Z",
     "iopub.status.busy": "2021-04-02T12:49:52.004358Z",
     "iopub.status.idle": "2021-04-02T12:49:53.281362Z",
     "shell.execute_reply": "2021-04-02T12:49:53.280747Z"
    },
    "papermill": {
     "duration": 1.324796,
     "end_time": "2021-04-02T12:49:53.281500",
     "exception": false,
     "start_time": "2021-04-02T12:49:51.956704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parameter tuning\n",
    "\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    Cs = [0.001, 0.01, 0.1, 1, 10]\n",
    "    gammas = [0.001, 0.01, 0.1, 1]\n",
    "    kernels = ['linear', 'rbf']\n",
    "    param_grid = {\n",
    "        'C': Cs, \n",
    "        'gamma' : gammas, \n",
    "        'kernel': kernels\n",
    "    }\n",
    "    \n",
    "    grid_search = GridSearchCV(SVC(), param_grid, cv=nfolds)\n",
    "    grid_search.fit(X, y)\n",
    "    grid_search.best_params_\n",
    "    return grid_search.best_params_\n",
    "\n",
    "\n",
    "best_params_ = svc_param_selection(x_train, y_train, skf)\n",
    "best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:53.370785Z",
     "iopub.status.busy": "2021-04-02T12:49:53.369878Z",
     "iopub.status.idle": "2021-04-02T12:49:53.396928Z",
     "shell.execute_reply": "2021-04-02T12:49:53.397365Z"
    },
    "papermill": {
     "duration": 0.074094,
     "end_time": "2021-04-02T12:49:53.397537",
     "exception": false,
     "start_time": "2021-04-02T12:49:53.323443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM(SVC): 0.9646464646464646\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation\n",
    "score = cross_val_score(SVC(C=1, gamma=0.01, kernel='rbf'), x_train, y_train, cv=skf)\n",
    "print(f'SVM(SVC): {score.mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.04094,
     "end_time": "2021-04-02T12:49:53.479895",
     "exception": false,
     "start_time": "2021-04-02T12:49:53.438955",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "NOTE: Recursive Feature Selection cannot not be done on `rbf` kernel of SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041147,
     "end_time": "2021-04-02T12:49:53.562504",
     "exception": false,
     "start_time": "2021-04-02T12:49:53.521357",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If number of features are comparable or even greater than the number of training examples(observations) then the dataset is said to be `high dimensional` or `wide`. When training with high dimensional data we get complex models that will fit the data & in extreme case you can create a model that will exactly predict the training data but at a cost of poorly generalizing. This is not good.\n",
    "\n",
    "To deal with high dimensional data we can use either `Feature Selection` or `Dimensionality Reduction`.\n",
    "\n",
    "There are 3 types of feature selection methods\n",
    "- `Filter`: It is applied before training a model, where you use statistical properties of the features to remove those data with low predictive power and then use this data to train model\n",
    "- `Wrapper`: It trains multiple model with different combinations of training data and models and gives the best training data with best model\n",
    "- `Embedded`: It does feature selection while training the model, the result is model that emphasis on most useful featrues.\n",
    "\n",
    "[Source](https://www.coursera.org/lecture/predictive-modeling-machine-learning/embedded-methods-for-feature-selection-Q9LnS) to know more about `embedded feature selection`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041017,
     "end_time": "2021-04-02T12:49:53.644813",
     "exception": false,
     "start_time": "2021-04-02T12:49:53.603796",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Using embedded method for feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:53.735245Z",
     "iopub.status.busy": "2021-04-02T12:49:53.734518Z",
     "iopub.status.idle": "2021-04-02T12:49:53.794526Z",
     "shell.execute_reply": "2021-04-02T12:49:53.793966Z"
    },
    "papermill": {
     "duration": 0.108573,
     "end_time": "2021-04-02T12:49:53.794675",
     "exception": false,
     "start_time": "2021-04-02T12:49:53.686102",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best regularization parameter using built-in LassoCV: 0.031983445283669094\n",
      "Best score using built-in LassoCV: 0.8364856912033988\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Clump          0.065597\n",
       "UnifSize       0.044891\n",
       "UnifShape      0.033253\n",
       "MargAdh        0.014070\n",
       "SingEpiSize    0.012444\n",
       "BareNuc        0.086892\n",
       "BlandChrom     0.040159\n",
       "NormNucl       0.035733\n",
       "Mit            0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LassoCV()\n",
    "\n",
    "reg.fit(x_train, y_train)\n",
    "print(f'Best regularization parameter using built-in LassoCV: {reg.alpha_}')\n",
    "print(f'Best score using built-in LassoCV: {reg.score(x_train, y_train)}')\n",
    "\n",
    "coef = pd.Series(reg.coef_, index = x_train.columns)\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:53.884011Z",
     "iopub.status.busy": "2021-04-02T12:49:53.883290Z",
     "iopub.status.idle": "2021-04-02T12:49:53.886861Z",
     "shell.execute_reply": "2021-04-02T12:49:53.887288Z"
    },
    "papermill": {
     "duration": 0.050931,
     "end_time": "2021-04-02T12:49:53.887439",
     "exception": false,
     "start_time": "2021-04-02T12:49:53.836508",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 8 variables and eliminated the other 1 variables\n"
     ]
    }
   ],
   "source": [
    "print(f'Lasso picked {sum(coef != 0)} variables and eliminated the other {sum(coef == 0)} variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:54.003336Z",
     "iopub.status.busy": "2021-04-02T12:49:53.988970Z",
     "iopub.status.idle": "2021-04-02T12:49:54.128924Z",
     "shell.execute_reply": "2021-04-02T12:49:54.128304Z"
    },
    "papermill": {
     "duration": 0.199183,
     "end_time": "2021-04-02T12:49:54.129061",
     "exception": false,
     "start_time": "2021-04-02T12:49:53.929878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(x=coef, y=coef.index, orient='h', color='#bbff5c')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.044127,
     "end_time": "2021-04-02T12:49:54.216705",
     "exception": false,
     "start_time": "2021-04-02T12:49:54.172578",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here Lasso model has taken all the features, except Mit so we will remove that feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.043547,
     "end_time": "2021-04-02T12:49:54.303470",
     "exception": false,
     "start_time": "2021-04-02T12:49:54.259923",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Using Backward Elimination for feature selection\n",
    "\n",
    "[Source_1](https://towardsdatascience.com/backward-elimination-for-feature-selection-in-machine-learning-c6a3a8f8cef4) and [Source_2](https://medium.com/@mayankshah1607/machine-learning-feature-selection-with-backward-elimination-955894654026) to know more about `backward elimination` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:54.395759Z",
     "iopub.status.busy": "2021-04-02T12:49:54.394747Z",
     "iopub.status.idle": "2021-04-02T12:49:54.411039Z",
     "shell.execute_reply": "2021-04-02T12:49:54.410557Z"
    },
    "papermill": {
     "duration": 0.064353,
     "end_time": "2021-04-02T12:49:54.411180",
     "exception": false,
     "start_time": "2021-04-02T12:49:54.346827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const          2.381329e-176\n",
       "Clump           2.861026e-17\n",
       "UnifSize        3.574851e-03\n",
       "UnifShape       1.848212e-02\n",
       "MargAdh         7.304446e-02\n",
       "SingEpiSize     9.902671e-02\n",
       "BareNuc         3.671993e-31\n",
       "BlandChrom      6.902807e-05\n",
       "NormNucl        7.822861e-06\n",
       "Mit             9.614491e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_1 = sm.add_constant(x_train)\n",
    "\n",
    "# Fitting sm.OLS model\n",
    "sm_model = sm.OLS(y_train, x_1).fit()\n",
    "sm_model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:54.510782Z",
     "iopub.status.busy": "2021-04-02T12:49:54.509824Z",
     "iopub.status.idle": "2021-04-02T12:49:54.531650Z",
     "shell.execute_reply": "2021-04-02T12:49:54.532142Z"
    },
    "papermill": {
     "duration": 0.077609,
     "end_time": "2021-04-02T12:49:54.532301",
     "exception": false,
     "start_time": "2021-04-02T12:49:54.454692",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Clump', 'UnifSize', 'UnifShape', 'BareNuc', 'BlandChrom', 'NormNucl']\n"
     ]
    }
   ],
   "source": [
    "# Backward Elimination\n",
    "\n",
    "def backward_elminiation(x_train, y_train):\n",
    "    columns = list(x_train.columns)\n",
    "    p_max = 1\n",
    "    \n",
    "    while len(columns) > 0:\n",
    "        p = []\n",
    "\n",
    "        x_1 = x_train[columns]\n",
    "        x_1 = sm.add_constant(x_1)  # `add_constant`: Add a column of ones to an array\n",
    "        \n",
    "        # Fitting sm.OLS model\n",
    "        model = sm.OLS(y_train, x_1).fit()\n",
    "        \n",
    "        # p-values\n",
    "        p = pd.Series(model.pvalues.values[1:], index=columns)\n",
    "        pmax = max(p)\n",
    "        feature_with_p_max = p.idxmax()\n",
    "        \n",
    "        # if p-value (significance) 0.05 (5%) then remove the column\n",
    "        if pmax > 0.05:\n",
    "            columns.remove(feature_with_p_max)\n",
    "        else:\n",
    "            break\n",
    "            \n",
    "    selected_features_BE = columns\n",
    "    print(selected_features_BE)\n",
    "        \n",
    "\n",
    "backward_elminiation(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.043588,
     "end_time": "2021-04-02T12:49:54.619867",
     "exception": false,
     "start_time": "2021-04-02T12:49:54.576279",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Selecting the best features from embedded method and backward elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:54.712485Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# From embedded method\n",
    "emb_features = ['Clump', 'UnifSize', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'SingEpiSize', 'MargAdh', 'UnifShape']\n",
    "\n",
    "# From backward elimination \n",
    "bem_features = ['Clump', 'UnifSize', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:54.806164Z",
     "iopub.status.busy": "2021-04-02T12:49:54.805565Z",
     "iopub.status.idle": "2021-04-02T12:49:57.428900Z",
     "shell.execute_reply": "2021-04-02T12:49:57.428208Z"
    },
    "papermill": {
     "duration": 2.669772,
     "end_time": "2021-04-02T12:49:57.429097",
     "exception": false,
     "start_time": "2021-04-02T12:49:54.759325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "Embeeded Method Features mean score: 0.9596\n",
      "\n",
      "{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "Backward Elimination Features mean score: 0.9613\n"
     ]
    }
   ],
   "source": [
    "# Doing Cross Validation on emb_features\n",
    "\n",
    "\n",
    "emb_x_train = x_train[emb_features]\n",
    "emb_y_train = y_train.copy()\n",
    "\n",
    "emb_cv_clf = SVC(C=1, kernel='rbf', gamma=0.001)\n",
    "scores = cross_val_score(emb_cv_clf, emb_x_train, emb_y_train, cv=skf)\n",
    "emb_best_params_ = svc_param_selection(x_train, y_train, skf)\n",
    "print(emb_best_params_)\n",
    "print(f'Embeeded Method Features mean score: {round(scores.mean(), 4)}')\n",
    "\n",
    "\n",
    "print()\n",
    "\n",
    "\n",
    "bem_x_train = x_train[bem_features]\n",
    "bem_y_train = y_train.copy()\n",
    "\n",
    "bem_cv_clf = SVC(C=1, kernel='rbf', gamma=0.001)\n",
    "scores = cross_val_score(bem_cv_clf, bem_x_train, bem_y_train, cv=skf)\n",
    "bem_best_params_ = svc_param_selection(x_train, y_train, skf)\n",
    "print(bem_best_params_)\n",
    "print(f'Backward Elimination Features mean score: {round(scores.mean(), 4)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:57.527501Z",
     "iopub.status.busy": "2021-04-02T12:49:57.526707Z",
     "iopub.status.idle": "2021-04-02T12:49:57.528660Z",
     "shell.execute_reply": "2021-04-02T12:49:57.529248Z"
    },
    "papermill": {
     "duration": 0.054745,
     "end_time": "2021-04-02T12:49:57.529407",
     "exception": false,
     "start_time": "2021-04-02T12:49:57.474662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Selecting features which gave the highest cross-validation mean score\n",
    "\n",
    "final_features = ['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']\n",
    "target = ['Class']\n",
    "\n",
    "x_train = x_train[final_features]\n",
    "x_test = x_test[final_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:57.622031Z",
     "iopub.status.busy": "2021-04-02T12:49:57.621468Z",
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     "shell.execute_reply": "2021-04-02T12:49:58.938564Z"
    },
    "papermill": {
     "duration": 1.365041,
     "end_time": "2021-04-02T12:49:58.939218",
     "exception": false,
     "start_time": "2021-04-02T12:49:57.574177",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_param_selection(x_train, y_train, skf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:59.043135Z",
     "iopub.status.busy": "2021-04-02T12:49:59.039090Z",
     "iopub.status.idle": "2021-04-02T12:49:59.068172Z",
     "shell.execute_reply": "2021-04-02T12:49:59.067350Z"
    },
    "papermill": {
     "duration": 0.083418,
     "end_time": "2021-04-02T12:49:59.068413",
     "exception": false,
     "start_time": "2021-04-02T12:49:58.984995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM(SVC): 0.9663299663299663\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation\n",
    "score = cross_val_score(SVC(C=1, gamma=0.001, kernel='rbf'), x_train, y_train, cv=skf)\n",
    "print(f'SVM(SVC): {score.mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:59.169662Z",
     "iopub.status.busy": "2021-04-02T12:49:59.169025Z",
     "iopub.status.idle": "2021-04-02T12:49:59.585443Z",
     "shell.execute_reply": "2021-04-02T12:49:59.585963Z"
    },
    "papermill": {
     "duration": 0.471094,
     "end_time": "2021-04-02T12:49:59.586139",
     "exception": false,
     "start_time": "2021-04-02T12:49:59.115045",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py\", line 173, in fit\n",
      "    y = self._validate_targets(y)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py\", line 560, in _validate_targets\n",
      "    \" class\" % len(cls))\n",
      "ValueError: The number of classes has to be greater than one; got 1 class\n",
      "\n",
      "  FitFailedWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py\", line 173, in fit\n",
      "    y = self._validate_targets(y)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py\", line 560, in _validate_targets\n",
      "    \" class\" % len(cls))\n",
      "ValueError: The number of classes has to be greater than one; got 1 class\n",
      "\n",
      "  FitFailedWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py\", line 173, in fit\n",
      "    y = self._validate_targets(y)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py\", line 560, in _validate_targets\n",
      "    \" class\" % len(cls))\n",
      "ValueError: The number of classes has to be greater than one; got 1 class\n",
      "\n",
      "  FitFailedWarning)\n"
     ]
    }
   ],
   "source": [
    "# Plotting learning curve\n",
    "\n",
    "_size = np.arange(0.01, 1.01, 0.06)\n",
    "train_sizes = np.array(_size)\n",
    "scoring = 'neg_mean_squared_error'\n",
    "\n",
    "train_sizes_abs, train_scores, cv_scores = learning_curve(\n",
    "    SVC(C=1, gamma=0.001, kernel='rbf'), \n",
    "    x_train, y_train, \n",
    "    train_sizes=train_sizes, cv=skf, scoring=scoring\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:59.683757Z",
     "iopub.status.busy": "2021-04-02T12:49:59.683177Z",
     "iopub.status.idle": "2021-04-02T12:49:59.685822Z",
     "shell.execute_reply": "2021-04-02T12:49:59.685280Z"
    },
    "papermill": {
     "duration": 0.053941,
     "end_time": "2021-04-02T12:49:59.685958",
     "exception": false,
     "start_time": "2021-04-02T12:49:59.632017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_scores_mean = []\n",
    "for row in train_scores:\n",
    "    _mean = row.mean()\n",
    "    train_scores_mean.append(_mean)\n",
    "    \n",
    "cv_scores_mean = []\n",
    "for row in cv_scores:\n",
    "    _mean = row.mean()\n",
    "    cv_scores_mean.append(_mean)    \n",
    "    \n",
    "train_scores_mean = -np.array(train_scores_mean)\n",
    "cv_scores_mean = -np.array(cv_scores_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:49:59.780902Z",
     "iopub.status.busy": "2021-04-02T12:49:59.780320Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab649c85d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "ax.plot(train_sizes_abs, train_scores_mean, label='Train')\n",
    "ax.plot(train_sizes_abs, cv_scores_mean, label='Cross Validation')\n",
    "\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:50:00.046383Z",
     "iopub.status.busy": "2021-04-02T12:50:00.045707Z",
     "iopub.status.idle": "2021-04-02T12:50:00.059470Z",
     "shell.execute_reply": "2021-04-02T12:50:00.059962Z"
    },
    "papermill": {
     "duration": 0.065856,
     "end_time": "2021-04-02T12:50:00.060128",
     "exception": false,
     "start_time": "2021-04-02T12:49:59.994272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, gamma=0.001)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting model\n",
    "\n",
    "model = SVC(C=1, gamma=0.001, kernel='rbf')\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.049216,
     "end_time": "2021-04-02T12:50:00.159865",
     "exception": false,
     "start_time": "2021-04-02T12:50:00.110649",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:50:00.261145Z",
     "iopub.status.busy": "2021-04-02T12:50:00.260569Z",
     "iopub.status.idle": "2021-04-02T12:50:00.266796Z",
     "shell.execute_reply": "2021-04-02T12:50:00.267249Z"
    },
    "papermill": {
     "duration": 0.057885,
     "end_time": "2021-04-02T12:50:00.267420",
     "exception": false,
     "start_time": "2021-04-02T12:50:00.209535",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:50:00.368799Z",
     "iopub.status.busy": "2021-04-02T12:50:00.368228Z",
     "iopub.status.idle": "2021-04-02T12:50:00.381580Z",
     "shell.execute_reply": "2021-04-02T12:50:00.380927Z"
    },
    "papermill": {
     "duration": 0.064902,
     "end_time": "2021-04-02T12:50:00.381780",
     "exception": false,
     "start_time": "2021-04-02T12:50:00.316878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9809523809523809\n",
      "0.9809523809523809\n",
      "0.9809523809523809\n",
      "0.9809523809523809\n"
     ]
    }
   ],
   "source": [
    "# Metrics\n",
    "\n",
    "print(model.score(x_test, y_test))\n",
    "print(f1_score(y_test, y_test_pred, average=\"weighted\"))\n",
    "print(precision_score(y_test, y_test_pred, average=\"weighted\"))\n",
    "print(recall_score(y_test, y_test_pred, average=\"weighted\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:50:00.491023Z",
     "iopub.status.busy": "2021-04-02T12:50:00.490234Z",
     "iopub.status.idle": "2021-04-02T12:50:00.493055Z",
     "shell.execute_reply": "2021-04-02T12:50:00.492568Z"
    },
    "papermill": {
     "duration": 0.061071,
     "end_time": "2021-04-02T12:50:00.493187",
     "exception": false,
     "start_time": "2021-04-02T12:50:00.432116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix'):\n",
    "    '''\n",
    "        This function prints and plots the confusion matrix.\n",
    "        Normalization can be applied by setting `normalize=True`.\n",
    "    '''\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print('Normalized confusion matrix')\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True))\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=10)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(\n",
    "            j, i, format(cm[i, j], fmt),\n",
    "            horizontalalignment='center',\n",
    "            color='white' if cm[i, j] > thresh else 'black'\n",
    "        )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:50:00.598913Z",
     "iopub.status.busy": "2021-04-02T12:50:00.598272Z",
     "iopub.status.idle": "2021-04-02T12:50:00.768490Z",
     "shell.execute_reply": "2021-04-02T12:50:00.769065Z"
    },
    "papermill": {
     "duration": 0.226735,
     "end_time": "2021-04-02T12:50:00.769239",
     "exception": false,
     "start_time": "2021-04-02T12:50:00.542504",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       0.99      0.99      0.99        68\n",
      "           4       0.97      0.97      0.97        37\n",
      "\n",
      "    accuracy                           0.98       105\n",
      "   macro avg       0.98      0.98      0.98       105\n",
      "weighted avg       0.98      0.98      0.98       105\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[67  1]\n",
      " [ 1 36]]\n"
     ]
    },
    {
     "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": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, y_test_pred, labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print(classification_report(y_test, y_test_pred))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:50:00.873581Z",
     "iopub.status.busy": "2021-04-02T12:50:00.872962Z",
     "iopub.status.idle": "2021-04-02T12:50:00.901786Z",
     "shell.execute_reply": "2021-04-02T12:50:00.900972Z"
    },
    "papermill": {
     "duration": 0.082218,
     "end_time": "2021-04-02T12:50:00.901977",
     "exception": false,
     "start_time": "2021-04-02T12:50:00.819759",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score: 0.9619047619047619\n",
      "f1-score: 0.961651713825627\n",
      "precision score: 0.9621768707482994\n",
      "recall score: 0.9619047619047619\n"
     ]
    }
   ],
   "source": [
    "# Pipeline\n",
    "\n",
    "scaling = ('scale', StandardScaler())\n",
    "model = ('model', SVC(C=1, gamma=0.001, kernel='rbf'))\n",
    "\n",
    "# Steps in the pipeline\n",
    "steps = [scaling, model]\n",
    "\n",
    "# Instantiating pipeline\n",
    "pipe = Pipeline(steps=steps)\n",
    "\n",
    "# Fiitting the model\n",
    "model = pipe.fit(x_train, y_train)\n",
    "\n",
    "# Out-Of-Sample Forecast\n",
    "y_test_pred = model.predict(x_test)\n",
    "\n",
    "# Evaluation\n",
    "print(f'Model Score: {model.score(x_test, y_test)}')\n",
    "print(f'f1-score: {f1_score(y_test, y_test_pred, average=\"weighted\")}')\n",
    "print(f'precision score: {precision_score(y_test, y_test_pred, average=\"weighted\")}')\n",
    "print(f'recall score: {recall_score(y_test, y_test_pred, average=\"weighted\")}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T12:50:01.011128Z",
     "iopub.status.busy": "2021-04-02T12:50:01.008406Z",
     "iopub.status.idle": "2021-04-02T12:50:01.015781Z",
     "shell.execute_reply": "2021-04-02T12:50:01.015172Z"
    },
    "papermill": {
     "duration": 0.062022,
     "end_time": "2021-04-02T12:50:01.015918",
     "exception": false,
     "start_time": "2021-04-02T12:50:00.953896",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.joblib']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the model\n",
    "dump(model, 'model.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.051267,
     "end_time": "2021-04-02T12:50:01.118136",
     "exception": false,
     "start_time": "2021-04-02T12:50:01.066869",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "I'll wrap things up there. If you want to find some other answers then go ahead `edit` this kernel. If you have any `questions` then do let me know.\n",
    "\n",
    "If this kernel helped you then don't forget to 🔼 `upvote` and share your 🎙 `feedback` on improvements of the kernel.\n",
    "\n",
    "![](https://media.giphy.com/media/qTD9EXZRgI1y0/giphy.gif)\n",
    "\n",
    "---"
   ]
  }
 ],
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