{
 "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"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 6 is out of bounds for axis 0 with size 6",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-7b9c24c4c900>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0maxes_box\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes_box\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#a5c687'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'magenta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 6 is out of bounds for axis 0 with size 6"
     ]
    },
    {
     "data": {
      "image/png": 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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",
    "    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": null,
   "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"
    }
   },
   "outputs": [],
   "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",
    "    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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