{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Published on April 02, 2023. By Marília Prata, mpwolke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2023-04-02T22:11:04.640536Z",
     "iopub.status.busy": "2023-04-02T22:11:04.640086Z",
     "iopub.status.idle": "2023-04-02T22:11:04.675820Z",
     "shell.execute_reply": "2023-04-02T22:11:04.674545Z",
     "shell.execute_reply.started": "2023-04-02T22:11:04.640485Z"
    }
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-02T22:11:15.868876Z",
     "iopub.status.busy": "2023-04-02T22:11:15.867831Z",
     "iopub.status.idle": "2023-04-02T22:11:32.323625Z",
     "shell.execute_reply": "2023-04-02T22:11:32.322233Z",
     "shell.execute_reply.started": "2023-04-02T22:11:15.868817Z"
    }
   },
   "outputs": [],
   "source": [
    "!pip install -U kaleido"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://pbs.twimg.com/profile_images/1544321423799472133/q52AENtW_400x400.jpg)twitter.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-02T22:11:51.604609Z",
     "iopub.status.busy": "2023-04-02T22:11:51.604203Z",
     "iopub.status.idle": "2023-04-02T22:12:02.956664Z",
     "shell.execute_reply": "2023-04-02T22:12:02.955622Z",
     "shell.execute_reply.started": "2023-04-02T22:11:51.604572Z"
    }
   },
   "outputs": [],
   "source": [
    "!pip install yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-02T22:12:09.726575Z",
     "iopub.status.busy": "2023-04-02T22:12:09.725781Z",
     "iopub.status.idle": "2023-04-02T22:12:21.413987Z",
     "shell.execute_reply": "2023-04-02T22:12:21.412852Z",
     "shell.execute_reply.started": "2023-04-02T22:12:09.726527Z"
    }
   },
   "outputs": [],
   "source": [
    "!pip install Kneed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:13:10.882723Z",
     "iopub.status.busy": "2023-04-02T22:13:10.882326Z",
     "iopub.status.idle": "2023-04-02T22:13:13.930673Z",
     "shell.execute_reply": "2023-04-02T22:13:13.929367Z",
     "shell.execute_reply.started": "2023-04-02T22:13:10.882688Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "# for visuallization\n",
    "import plotly.express as px\n",
    "# import kaleido # comment out for reproducing purposes\n",
    "import scipy\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "# from kneed import KneeLocator\n",
    "\n",
    "# %notebook matplotlib\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:13:49.239975Z",
     "iopub.status.busy": "2023-04-02T22:13:49.238568Z",
     "iopub.status.idle": "2023-04-02T22:13:49.340114Z",
     "shell.execute_reply": "2023-04-02T22:13:49.338955Z",
     "shell.execute_reply.started": "2023-04-02T22:13:49.239900Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matchDate</th>\n",
       "      <th>symbol</th>\n",
       "      <th>matchPri</th>\n",
       "      <th>matchQty</th>\n",
       "      <th>tolMatchQty</th>\n",
       "      <th>matchTime</th>\n",
       "      <th>bidPri1</th>\n",
       "      <th>bidPri2</th>\n",
       "      <th>bidPri3</th>\n",
       "      <th>bidPri4</th>\n",
       "      <th>...</th>\n",
       "      <th>askQty3</th>\n",
       "      <th>askQty4</th>\n",
       "      <th>askQty5</th>\n",
       "      <th>openPri</th>\n",
       "      <th>highPri</th>\n",
       "      <th>lowPri</th>\n",
       "      <th>refPri</th>\n",
       "      <th>upPri</th>\n",
       "      <th>dnPri</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>94.6</td>\n",
       "      <td>216</td>\n",
       "      <td>216</td>\n",
       "      <td>90003845</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>94.3</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>58</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>94.6</td>\n",
       "      <td>1</td>\n",
       "      <td>217</td>\n",
       "      <td>90003921</td>\n",
       "      <td>94.7</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>94.7</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>90005353</td>\n",
       "      <td>94.7</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.7</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>94.7</td>\n",
       "      <td>1</td>\n",
       "      <td>219</td>\n",
       "      <td>90005400</td>\n",
       "      <td>94.7</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.7</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>94.7</td>\n",
       "      <td>15</td>\n",
       "      <td>234</td>\n",
       "      <td>90006605</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>94.3</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>94.6</td>\n",
       "      <td>94.7</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5596</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>93.3</td>\n",
       "      <td>1</td>\n",
       "      <td>15240</td>\n",
       "      <td>132456151</td>\n",
       "      <td>93.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>93.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.2</td>\n",
       "      <td>93.3</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5597</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>93.5</td>\n",
       "      <td>1</td>\n",
       "      <td>15242</td>\n",
       "      <td>132456435</td>\n",
       "      <td>93.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>93.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.2</td>\n",
       "      <td>93.3</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5598</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>93.3</td>\n",
       "      <td>1</td>\n",
       "      <td>15243</td>\n",
       "      <td>132457241</td>\n",
       "      <td>93.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>93.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.2</td>\n",
       "      <td>93.3</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5599</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2</td>\n",
       "      <td>15245</td>\n",
       "      <td>132458268</td>\n",
       "      <td>93.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>93.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.2</td>\n",
       "      <td>93.3</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5600</th>\n",
       "      <td>20221220</td>\n",
       "      <td>3711</td>\n",
       "      <td>93.3</td>\n",
       "      <td>1</td>\n",
       "      <td>15246</td>\n",
       "      <td>132458352</td>\n",
       "      <td>93.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>93.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>94.6</td>\n",
       "      <td>95.2</td>\n",
       "      <td>93.3</td>\n",
       "      <td>95.8</td>\n",
       "      <td>105</td>\n",
       "      <td>86.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5601 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      matchDate  symbol  matchPri  matchQty  tolMatchQty  matchTime  bidPri1  \\\n",
       "0      20221220    3711      94.6       216          216   90003845     94.6   \n",
       "1      20221220    3711      94.6         1          217   90003921     94.7   \n",
       "2      20221220    3711      94.7         1          218   90005353     94.7   \n",
       "3      20221220    3711      94.7         1          219   90005400     94.7   \n",
       "4      20221220    3711      94.7        15          234   90006605     94.6   \n",
       "...         ...     ...       ...       ...          ...        ...      ...   \n",
       "5596   20221220    3711      93.3         1        15240  132456151     93.3   \n",
       "5597   20221220    3711      93.5         1        15242  132456435     93.3   \n",
       "5598   20221220    3711      93.3         1        15243  132457241     93.3   \n",
       "5599   20221220    3711      93.5         2        15245  132458268     93.3   \n",
       "5600   20221220    3711      93.3         1        15246  132458352     93.3   \n",
       "\n",
       "      bidPri2  bidPri3  bidPri4  ...  askQty3  askQty4  askQty5  openPri  \\\n",
       "0        94.5     94.4     94.3  ...        8       13       58     94.6   \n",
       "1        94.6     94.5     94.4  ...        1       18        1     94.6   \n",
       "2        94.6     94.5     94.4  ...        1       18        1     94.6   \n",
       "3        94.6     94.5     94.4  ...       19       33       18     94.6   \n",
       "4        94.5     94.4     94.3  ...       19       33       24     94.6   \n",
       "...       ...      ...      ...  ...      ...      ...      ...      ...   \n",
       "5596     93.2     93.1     93.0  ...       28       16       32     94.6   \n",
       "5597     93.2     93.1     93.0  ...       16       32       36     94.6   \n",
       "5598     93.2     93.1     93.0  ...       16       32       36     94.6   \n",
       "5599     93.2     93.1     93.0  ...       16       32       36     94.6   \n",
       "5600     93.2     93.1     93.0  ...       15       32       36     94.6   \n",
       "\n",
       "      highPri  lowPri  refPri  upPri  dnPri  label  \n",
       "0        94.6    94.6    95.8    105   86.3    2.0  \n",
       "1        94.6    94.6    95.8    105   86.3    1.0  \n",
       "2        94.7    94.6    95.8    105   86.3    2.0  \n",
       "3        94.7    94.6    95.8    105   86.3    2.0  \n",
       "4        94.7    94.6    95.8    105   86.3    0.0  \n",
       "...       ...     ...     ...    ...    ...    ...  \n",
       "5596     95.2    93.3    95.8    105   86.3    1.0  \n",
       "5597     95.2    93.3    95.8    105   86.3    0.0  \n",
       "5598     95.2    93.3    95.8    105   86.3    1.0  \n",
       "5599     95.2    93.3    95.8    105   86.3    0.0  \n",
       "5600     95.2    93.3    95.8    105   86.3    1.0  \n",
       "\n",
       "[5601 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('data/3711.csv');df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.drop('Unnamed: 0',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#No Missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-02T22:14:48.068401Z",
     "iopub.status.busy": "2023-04-02T22:14:48.067971Z",
     "iopub.status.idle": "2023-04-02T22:14:48.079476Z",
     "shell.execute_reply": "2023-04-02T22:14:48.078371Z",
     "shell.execute_reply.started": "2023-04-02T22:14:48.068365Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:15:20.772919Z",
     "iopub.status.busy": "2023-04-02T22:15:20.772521Z",
     "iopub.status.idle": "2023-04-02T22:15:21.422798Z",
     "shell.execute_reply": "2023-04-02T22:15:21.421441Z",
     "shell.execute_reply.started": "2023-04-02T22:15:20.772885Z"
    }
   },
   "outputs": [],
   "source": [
    "list_of_number_of_features=[]\n",
    "for col in df.columns:\n",
    "    new_nunique=df[col].nunique()\n",
    "    list_of_number_of_features.append([col,new_nunique])\n",
    "print(pd.DataFrame(list_of_number_of_features,columns=['column','nunique']))\n",
    "\n",
    "\n",
    "df_value_counts = df.nunique()\n",
    "ax = df_value_counts.sort_values().plot(kind='bar',figsize = (10,5), color = ['#a5c687','#5678f1'],edgecolor='#ff1195')\n",
    "plt.title(\"Number of Feature Values\", size = 15)\n",
    "plt.ylabel(\"Frequency\", size = 20)\n",
    "plt.xlabel(\"Features\", size = 20)\n",
    "for i in ax.containers :\n",
    "    ax.bar_label(i, padding = 5, size = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Duplicated Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:15:52.232686Z",
     "iopub.status.busy": "2023-04-02T22:15:52.231754Z",
     "iopub.status.idle": "2023-04-02T22:15:52.280021Z",
     "shell.execute_reply": "2023-04-02T22:15:52.278841Z",
     "shell.execute_reply.started": "2023-04-02T22:15:52.232644Z"
    }
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates()  # We don't have any duplicated datapoint in out dataset???"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:17:29.400711Z",
     "iopub.status.busy": "2023-04-02T22:17:29.400280Z",
     "iopub.status.idle": "2023-04-02T22:17:29.411929Z",
     "shell.execute_reply": "2023-04-02T22:17:29.410522Z",
     "shell.execute_reply.started": "2023-04-02T22:17:29.400677Z"
    }
   },
   "outputs": [],
   "source": [
    "class ZScore:\n",
    "    def __init__(self,threshold,**kwargs):\n",
    "        self._threshold = threshold # we can change it later\n",
    "        self._outlier={} # by this definition , we can access it out of this area\n",
    "        for k,v in kwargs.items():\n",
    "            setattr(self,k,v)\n",
    "\n",
    "    def z_score_calculation(self,dataframe):\n",
    "        for col in dataframe.columns.values:\n",
    "            Mean = np.mean(dataframe[col])\n",
    "            Std =  np.std(dataframe[col])\n",
    "            z = pd.Series(dataframe[col]-Mean) / Std\n",
    "            \n",
    "            list_of_index=[]\n",
    "    \n",
    "            for index,value in enumerate(list(z)):\n",
    "                if  abs(value) > self._threshold: \n",
    "                    list_of_index.append(index)\n",
    "                    self._outlier.update({df[col].name:list_of_index})\n",
    "\n",
    "\n",
    "outliers=ZScore(3)\n",
    "#outliers.z_score_calculation(df.drop(['Country'],axis=1))\n",
    "removing_indexes=outliers._outlier\n",
    "# removing_indexes\n",
    "\n",
    "Sum=0\n",
    "for index,value in enumerate(removing_indexes.values()):\n",
    "    item=df.columns.values[index]\n",
    "    Sum +=len(value)\n",
    "    print(item,':', len(value))\n",
    "print('*******************************')\n",
    "print('the total number of outliers:',Sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:22:32.178725Z",
     "iopub.status.busy": "2023-04-02T22:22:32.178110Z",
     "iopub.status.idle": "2023-04-02T22:22:33.385223Z",
     "shell.execute_reply": "2023-04-02T22:22:33.383752Z",
     "shell.execute_reply.started": "2023-04-02T22:22:32.178676Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 500x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "fig, ax = plt.subplots(6, 1, figsize=(5, 20))#Original was 9,1\n",
    "\n",
    "for i, col in enumerate(list(df.columns.values)):\n",
    "    # fix -----------df.columns length is greater than ax\n",
    "    if i >= len(ax):\n",
    "        break\n",
    "        \n",
    "    axes_box  = ax[i]  \n",
    "    sns.boxplot(data=df, x=col, ax=axes_box,color='#a5c687')\n",
    "    ax[i].set_title(col,fontsize=15,color='magenta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print (\"percent of Outliers:  %\", round((100 * (Sum)/df.shape[0]),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:24:59.821780Z",
     "iopub.status.busy": "2023-04-02T22:24:59.821025Z",
     "iopub.status.idle": "2023-04-02T22:25:05.825572Z",
     "shell.execute_reply": "2023-04-02T22:25:05.824304Z",
     "shell.execute_reply.started": "2023-04-02T22:24:59.821743Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x4500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "fig, ax = plt.subplots(6,1, figsize=(20, 45))#Original was 9,1\n",
    "for i, col in enumerate(list(df.columns)):\n",
    "    \n",
    "    # fix -----------df.columns length is greater than ax\n",
    "    if i >= len(ax):\n",
    "        break\n",
    "        \n",
    "    axes_hist = ax[i]\n",
    "    sns.histplot(data=df, x=col, ax=axes_hist,color = '#a5c687', stat = \"density\",edgecolor='#ff1195')\n",
    "    sns.kdeplot(x = col, data = df,ax=axes_hist, color = '#5678f1', linewidth = 5)\n",
    "    ax[i].set_title(col,fontsize=35,color='#561195')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:26:52.480072Z",
     "iopub.status.busy": "2023-04-02T22:26:52.479695Z",
     "iopub.status.idle": "2023-04-02T22:26:56.013270Z",
     "shell.execute_reply": "2023-04-02T22:26:56.011767Z",
     "shell.execute_reply.started": "2023-04-02T22:26:52.480040Z"
    }
   },
   "outputs": [],
   "source": [
    "fig,ax =plt.subplots(9,2,figsize=(20,50))\n",
    "for i ,c in enumerate(list(df.columns)):\n",
    "    axes_left_bar = ax[i][0]\n",
    "    axes_right_bar  = ax[i][1]\n",
    "    df.sort_values(by=c,ascending=False).iloc[:3].plot(\n",
    "        kind='bar',title='High ' +c ,y=c,x='tolMatchQty',fontsize=16,color = ['#5678f1','#a5c687'],ax=axes_left_bar,edgecolor='#ff1195')\n",
    "    ax[i][0].set_title('High: '+ c,fontsize=25,color='magenta')\n",
    "    df.sort_values(by=c,ascending=False).iloc[-3:].plot(\n",
    "            kind='bar',title='Low '+c,y=c,x='tolMatchQty',fontsize=16,color = ['#5678f1','#a5c687'],edgecolor='#ff1195',ax=axes_right_bar)\n",
    "    ax[i][1].set_title('Low: '+c,fontsize=25,color='magenta')\n",
    "plt.tight_layout()    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:27:42.590012Z",
     "iopub.status.busy": "2023-04-02T22:27:42.589009Z",
     "iopub.status.idle": "2023-04-02T22:27:44.068077Z",
     "shell.execute_reply": "2023-04-02T22:27:44.066964Z",
     "shell.execute_reply.started": "2023-04-02T22:27:42.589971Z"
    }
   },
   "outputs": [],
   "source": [
    "fig = px.imshow(df.corr(), title='Heatmap of numerical values of the data')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:28:29.887928Z",
     "iopub.status.busy": "2023-04-02T22:28:29.887070Z",
     "iopub.status.idle": "2023-04-02T22:28:29.926174Z",
     "shell.execute_reply": "2023-04-02T22:28:29.924985Z",
     "shell.execute_reply.started": "2023-04-02T22:28:29.887881Z"
    }
   },
   "outputs": [],
   "source": [
    "df1=df.copy();df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:29:26.142052Z",
     "iopub.status.busy": "2023-04-02T22:29:26.141678Z",
     "iopub.status.idle": "2023-04-02T22:29:26.242406Z",
     "shell.execute_reply": "2023-04-02T22:29:26.241230Z",
     "shell.execute_reply.started": "2023-04-02T22:29:26.142021Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler ,MinMaxScaler\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.cluster import KMeans, DBSCAN\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n",
    "import scipy.cluster.hierarchy as shc\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:29:31.304461Z",
     "iopub.status.busy": "2023-04-02T22:29:31.303514Z",
     "iopub.status.idle": "2023-04-02T22:29:31.346796Z",
     "shell.execute_reply": "2023-04-02T22:29:31.345545Z",
     "shell.execute_reply.started": "2023-04-02T22:29:31.304419Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "df2=pd.DataFrame(scaler.fit_transform(df1), columns = scaler.feature_names_in_);df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:30:15.253673Z",
     "iopub.status.busy": "2023-04-02T22:30:15.252507Z",
     "iopub.status.idle": "2023-04-02T22:30:15.310472Z",
     "shell.execute_reply": "2023-04-02T22:30:15.308666Z",
     "shell.execute_reply.started": "2023-04-02T22:30:15.253613Z"
    }
   },
   "outputs": [],
   "source": [
    "pca = PCA()\n",
    "pca_df2 = pd.DataFrame(pca.fit_transform(df2))\n",
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:30:30.024044Z",
     "iopub.status.busy": "2023-04-02T22:30:30.023228Z",
     "iopub.status.idle": "2023-04-02T22:30:30.030689Z",
     "shell.execute_reply": "2023-04-02T22:30:30.029231Z",
     "shell.execute_reply.started": "2023-04-02T22:30:30.024001Z"
    }
   },
   "outputs": [],
   "source": [
    "print(pca.components_)\n",
    "print('Number of Components:', pca.n_components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-02T22:32:36.572914Z",
     "iopub.status.busy": "2023-04-02T22:32:36.572525Z",
     "iopub.status.idle": "2023-04-02T22:32:36.861891Z",
     "shell.execute_reply": "2023-04-02T22:32:36.860307Z",
     "shell.execute_reply.started": "2023-04-02T22:32:36.572881Z"
    },
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "highest_ratio=np.cumsum(pca.explained_variance_ratio_)\n",
    "plt.step(list(range(1,8)),highest_ratio)\n",
    "plt.plot(highest_ratio)\n",
    "\n",
    "plt.xlabel('Number of Components', fontsize = 20)\n",
    "plt.ylabel('Variance (%)', fontsize = 20) \n",
    "plt.title('Explained Variance', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Above: ValueError: x and y must have same first dimension, but have shapes (7,) and (33,)\n",
    "\n",
    "I've No clue how to fix it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:33:26.039522Z",
     "iopub.status.busy": "2023-04-02T22:33:26.038280Z",
     "iopub.status.idle": "2023-04-02T22:33:26.094219Z",
     "shell.execute_reply": "2023-04-02T22:33:26.092547Z",
     "shell.execute_reply.started": "2023-04-02T22:33:26.039463Z"
    }
   },
   "outputs": [],
   "source": [
    "main_pca = PCA(4)\n",
    "main_pca_df2 = pd.DataFrame(main_pca.fit_transform(df2),columns=['PC1','PC2','PC3','PC4'])\n",
    "dff=pd.concat([df['tolMatchQty'],main_pca_df2],axis=1);dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:34:40.274328Z",
     "iopub.status.busy": "2023-04-02T22:34:40.273381Z",
     "iopub.status.idle": "2023-04-02T22:34:40.279675Z",
     "shell.execute_reply": "2023-04-02T22:34:40.278359Z",
     "shell.execute_reply.started": "2023-04-02T22:34:40.274284Z"
    }
   },
   "outputs": [],
   "source": [
    "df_kmeans=main_pca_df2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:34:56.667980Z",
     "iopub.status.busy": "2023-04-02T22:34:56.667146Z",
     "iopub.status.idle": "2023-04-02T22:34:56.747490Z",
     "shell.execute_reply": "2023-04-02T22:34:56.746066Z",
     "shell.execute_reply.started": "2023-04-02T22:34:56.667924Z"
    }
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(init=\"random\",n_clusters=3, n_init=10,max_iter=300, random_state=42)\n",
    "kmeans.fit_predict(df_kmeans)\n",
    "print(kmeans.cluster_centers_)\n",
    "print(kmeans.n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:35:12.123076Z",
     "iopub.status.busy": "2023-04-02T22:35:12.122654Z",
     "iopub.status.idle": "2023-04-02T22:35:48.026995Z",
     "shell.execute_reply": "2023-04-02T22:35:48.025884Z",
     "shell.execute_reply.started": "2023-04-02T22:35:12.123039Z"
    }
   },
   "outputs": [],
   "source": [
    "model_kmeans_sill = KMeans(random_state=42)\n",
    "visualizer_kmeans_sill = KElbowVisualizer(model_kmeans_sill, k=(2,20),metric='silhouette', timings= True)\n",
    "visualizer_kmeans_sill.fit(df_kmeans)\n",
    "visualizer_kmeans_sill.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:35:56.145360Z",
     "iopub.status.busy": "2023-04-02T22:35:56.144918Z",
     "iopub.status.idle": "2023-04-02T22:36:36.669283Z",
     "shell.execute_reply": "2023-04-02T22:36:36.668210Z",
     "shell.execute_reply.started": "2023-04-02T22:35:56.145315Z"
    }
   },
   "outputs": [],
   "source": [
    "model_kmeans_ch = KMeans(random_state=42)\n",
    "visualizer_kmeans_ch = KElbowVisualizer(model_kmeans_ch, k=(2,30),metric='calinski_harabasz', timings= True)\n",
    "visualizer_kmeans_ch.fit(df_kmeans)      \n",
    "visualizer_kmeans_ch.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:36:53.003339Z",
     "iopub.status.busy": "2023-04-02T22:36:53.002943Z",
     "iopub.status.idle": "2023-04-02T22:37:18.617684Z",
     "shell.execute_reply": "2023-04-02T22:37:18.616834Z",
     "shell.execute_reply.started": "2023-04-02T22:36:53.003305Z"
    }
   },
   "outputs": [],
   "source": [
    "def DB_score(data, center):\n",
    "    \n",
    "    kmeans = KMeans(n_clusters=center,random_state=42)\n",
    "    model = kmeans.fit_predict(data)\n",
    "\n",
    "    score = davies_bouldin_score(data, model)\n",
    "    \n",
    "    return score\n",
    "scores = []\n",
    "centers = list(range(2,20))\n",
    "for center in centers:\n",
    "    scores.append(DB_score(df_kmeans, center))\n",
    "    \n",
    "plt.plot(centers, scores, linestyle='--', marker='o', color='b');\n",
    "plt.xlabel('K');\n",
    "plt.ylabel('Davies Bouldin score');\n",
    "plt.title('Davies Bouldin score vs. K');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:37:25.649039Z",
     "iopub.status.busy": "2023-04-02T22:37:25.648595Z",
     "iopub.status.idle": "2023-04-02T22:37:26.966482Z",
     "shell.execute_reply": "2023-04-02T22:37:26.965392Z",
     "shell.execute_reply.started": "2023-04-02T22:37:25.648999Z"
    }
   },
   "outputs": [],
   "source": [
    "main_kmeans = KMeans(n_clusters=4, random_state=42)\n",
    "y_kmeans = main_kmeans.fit_predict(df_kmeans)\n",
    "main_cluster_kmeans = pd.DataFrame(data = main_kmeans.cluster_centers_, columns= [main_pca_df2.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:37:32.916630Z",
     "iopub.status.busy": "2023-04-02T22:37:32.916227Z",
     "iopub.status.idle": "2023-04-02T22:37:32.946062Z",
     "shell.execute_reply": "2023-04-02T22:37:32.944448Z",
     "shell.execute_reply.started": "2023-04-02T22:37:32.916596Z"
    }
   },
   "outputs": [],
   "source": [
    "indexes=pd.Index(['Cluster_0','Cluster_1','Cluster_2','Cluster_3'])\n",
    "centers = main_pca.inverse_transform(main_kmeans.cluster_centers_)\n",
    "new_centers_kmeans = np.exp(centers)\n",
    "new_centers_kmeans = pd.DataFrame(new_centers_kmeans,\n",
    "                                  columns=df2.columns,index=indexes)\n",
    "new_centers_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-02T22:37:37.604132Z",
     "iopub.status.busy": "2023-04-02T22:37:37.603734Z",
     "iopub.status.idle": "2023-04-02T22:37:37.635326Z",
     "shell.execute_reply": "2023-04-02T22:37:37.634199Z",
     "shell.execute_reply.started": "2023-04-02T22:37:37.604096Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler2= MinMaxScaler()\n",
    "df_scaled=pd.DataFrame(scaler2.fit_transform(new_centers_kmeans), \n",
    "                       columns = scaler2.feature_names_in_,index=indexes);df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-04-02T22:37:43.045377Z",
     "iopub.status.busy": "2023-04-02T22:37:43.044959Z",
     "iopub.status.idle": "2023-04-02T22:37:43.923805Z",
     "shell.execute_reply": "2023-04-02T22:37:43.922623Z",
     "shell.execute_reply.started": "2023-04-02T22:37:43.045338Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_scaled.plot(kind='bar',figsize = (15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Conclusion\n",
    "\n",
    "I've just wanted to check how Kaleido, Kneed and Yellowbrick libraries. However, the original code removed Country, then worked with df_without_country. Besides, I had some issues that I wasn't able to fix too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Acknowledgements:\n",
    "\n",
    "Reza Semyari https://www.kaggle.com/code/rezasemyari/country-clustering-kmeans-by-using-pca/notebook"
   ]
  }
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