secom_file_hayate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82873ae8",
   "metadata": {},
   "source": [
    "# 2.0 DATA UNDERSTANDING STAGE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc768422",
   "metadata": {},
   "source": [
    "### 2.1 libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc43b761",
   "metadata": {},
   "source": [
    "##### 2.1 -1 Install required libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03b140fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# !pip install numpy\n",
    "# !pip install pandas\n",
    "# !pip install requests\n",
    "# !pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e242f3f",
   "metadata": {},
   "source": [
    "##### 2.1 -2 Import Required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "43a2e69a-926e-426f-b91d-1fed666dc9ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import zipfile\n",
    "from io import BytesIO\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "\n",
    "import plotly.graph_objs as go # bar and pie charts, Plotly\n",
    "import plotly\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.offline as plof\n",
    "\n",
    "\n",
    "# defning colour setting \n",
    "\n",
    "htw_green = \"#76B900\"\n",
    "non_important = \"#000000\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f76181-4b97-4daa-822e-751e0c49f719",
   "metadata": {},
   "source": [
    "## 2.2 Gathering data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf62ae6",
   "metadata": {},
   "source": [
    "#### 2.2 -1 importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40733590-fa15-4443-bd32-643dce8cf3d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['secom.data', 'secom.names', 'secom_labels.data']\n"
     ]
    }
   ],
   "source": [
    "# URL of the zipped folder\n",
    "url = \"https://archive.ics.uci.edu/static/public/179/secom.zip\"\n",
    "\n",
    "# download the zipped folder using request library\n",
    "zip_file = requests.get(url)\n",
    "\n",
    "# opening the zipped folder\n",
    "secom_files =  zipfile.ZipFile(BytesIO(zip_file.content), 'r')\n",
    "\n",
    "# printing the file names inside the zip\n",
    "secom_files_names =  (zipfile.ZipFile(BytesIO(zip_file.content))).namelist()\n",
    "print(secom_files_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea31b4fb-cecd-4f16-8136-ff1090e43133",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the relevant files \n",
    "\n",
    "f_file = secom_files.open('secom.data')\n",
    "l_file = secom_files.open('secom_labels.data')\n",
    "n_file = secom_files.open('secom.names')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "982e0394",
   "metadata": {},
   "source": [
    "#### 2.2 -2 labels data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17410d0e-7217-4e08-b8d3-8d6ab4ecc539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of Labels data:  (1567, 2)\n",
      "   classifications            date_time\n",
      "0               -1  19/07/2008 11:55:00\n",
      "1               -1  19/07/2008 12:32:00\n",
      "2                1  19/07/2008 13:17:00\n",
      "3               -1  19/07/2008 14:43:00\n",
      "4               -1  19/07/2008 15:22:00\n"
     ]
    }
   ],
   "source": [
    "#Create column names and pandas dataframes for labels data\n",
    "\n",
    "l_column_names=['classifications', 'date_time'] #### create column labels\n",
    "\n",
    "labels_df = pd.read_csv(l_file, sep=r'\\s+', header=None, names= l_column_names)\n",
    "\n",
    "print(f\"Dimensions of Labels data: \", labels_df.shape)\n",
    "\n",
    "print(labels_df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f5a69bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classifications    0\n",
       "date_time          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88ee4755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The timestamps column is in ascending (oldest to newest) order.\n"
     ]
    }
   ],
   "source": [
    "# checking the order of data time\n",
    "\n",
    "labels_df['date_time'] = pd.to_datetime(labels_df['date_time'], format='%d/%m/%Y %H:%M:%S')\n",
    "\n",
    "is_ascending = labels_df['date_time'].is_monotonic_increasing\n",
    "\n",
    "if is_ascending:\n",
    "    print(\"The timestamps column is in ascending (oldest to newest) order.\")\n",
    "else:\n",
    "    print(\"The timestamps column is not in ascending (newest to oldest) order.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a4e097",
   "metadata": {},
   "source": [
    "### 2-2 -3 feature.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91e88989",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of Features' data:  (1567, 591)\n",
      "---------------------------------------------------------\n",
      "   feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
      "0    3030.93    2564.00  2187.7333  1411.1265     1.3602      100.0   \n",
      "1    3095.78    2465.14  2230.4222  1463.6606     0.8294      100.0   \n",
      "2    2932.61    2559.94  2186.4111  1698.0172     1.5102      100.0   \n",
      "3    2988.72    2479.90  2199.0333   909.7926     1.3204      100.0   \n",
      "4    3032.24    2502.87  2233.3667  1326.5200     1.5334      100.0   \n",
      "\n",
      "   feature_7  feature_8  feature_9  feature_10  ...  feature_582  feature_583  \\\n",
      "0    97.6133     0.1242     1.5005      0.0162  ...          NaN       0.5005   \n",
      "1   102.3433     0.1247     1.4966     -0.0005  ...     208.2045       0.5019   \n",
      "2    95.4878     0.1241     1.4436      0.0041  ...      82.8602       0.4958   \n",
      "3   104.2367     0.1217     1.4882     -0.0124  ...      73.8432       0.4990   \n",
      "4   100.3967     0.1235     1.5031     -0.0031  ...          NaN       0.4800   \n",
      "\n",
      "   feature_584  feature_585  feature_586  feature_587  feature_588  \\\n",
      "0       0.0118       0.0035       2.3630          NaN          NaN   \n",
      "1       0.0223       0.0055       4.4447       0.0096       0.0201   \n",
      "2       0.0157       0.0039       3.1745       0.0584       0.0484   \n",
      "3       0.0103       0.0025       2.0544       0.0202       0.0149   \n",
      "4       0.4766       0.1045      99.3032       0.0202       0.0149   \n",
      "\n",
      "   feature_589  feature_590  feature_591  \n",
      "0          NaN          NaN          NaN  \n",
      "1       0.0060     208.2045          NaN  \n",
      "2       0.0148      82.8602          NaN  \n",
      "3       0.0044      73.8432          NaN  \n",
      "4       0.0044      73.8432          NaN  \n",
      "\n",
      "[5 rows x 591 columns]\n"
     ]
    }
   ],
   "source": [
    "# adding column name as \"feature-##\"\n",
    "f_column_names = [f\"feature_{i}\" for i in range(1, 592)]\n",
    "\n",
    "features_df = pd.read_csv(f_file, sep=r'\\s+', header=None, names= f_column_names)\n",
    "print(f\"Dimensions of Features' data: \", features_df.shape)\n",
    "print(\"---------------------------------------------------------\")\n",
    "print(features_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd98a791",
   "metadata": {},
   "source": [
    "### 2-2 -4 merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d6b8b27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of merged data:  (1567, 593)\n",
      "   classifications           date_time  feature_1  feature_2  feature_3  \\\n",
      "0               -1 2008-07-19 11:55:00    3030.93    2564.00  2187.7333   \n",
      "1               -1 2008-07-19 12:32:00    3095.78    2465.14  2230.4222   \n",
      "2                1 2008-07-19 13:17:00    2932.61    2559.94  2186.4111   \n",
      "3               -1 2008-07-19 14:43:00    2988.72    2479.90  2199.0333   \n",
      "4               -1 2008-07-19 15:22:00    3032.24    2502.87  2233.3667   \n",
      "\n",
      "   feature_4  feature_5  feature_6  feature_7  feature_8  ...  feature_582  \\\n",
      "0  1411.1265     1.3602      100.0    97.6133     0.1242  ...          NaN   \n",
      "1  1463.6606     0.8294      100.0   102.3433     0.1247  ...     208.2045   \n",
      "2  1698.0172     1.5102      100.0    95.4878     0.1241  ...      82.8602   \n",
      "3   909.7926     1.3204      100.0   104.2367     0.1217  ...      73.8432   \n",
      "4  1326.5200     1.5334      100.0   100.3967     0.1235  ...          NaN   \n",
      "\n",
      "   feature_583  feature_584  feature_585  feature_586  feature_587  \\\n",
      "0       0.5005       0.0118       0.0035       2.3630          NaN   \n",
      "1       0.5019       0.0223       0.0055       4.4447       0.0096   \n",
      "2       0.4958       0.0157       0.0039       3.1745       0.0584   \n",
      "3       0.4990       0.0103       0.0025       2.0544       0.0202   \n",
      "4       0.4800       0.4766       0.1045      99.3032       0.0202   \n",
      "\n",
      "   feature_588  feature_589  feature_590  feature_591  \n",
      "0          NaN          NaN          NaN          NaN  \n",
      "1       0.0201       0.0060     208.2045          NaN  \n",
      "2       0.0484       0.0148      82.8602          NaN  \n",
      "3       0.0149       0.0044      73.8432          NaN  \n",
      "4       0.0149       0.0044      73.8432          NaN  \n",
      "\n",
      "[5 rows x 593 columns]\n"
     ]
    }
   ],
   "source": [
    "# merging \n",
    "\n",
    "merged_df = pd.merge(labels_df, features_df, left_index=True, right_index=True)\n",
    "\n",
    "print(f\"Dimensions of merged data: \", merged_df.shape)\n",
    "\n",
    "print(merged_df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "545397d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>classifications</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
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       "      <th>feature_582</th>\n",
       "      <th>feature_583</th>\n",
       "      <th>feature_584</th>\n",
       "      <th>feature_585</th>\n",
       "      <th>feature_586</th>\n",
       "      <th>feature_587</th>\n",
       "      <th>feature_588</th>\n",
       "      <th>feature_589</th>\n",
       "      <th>feature_590</th>\n",
       "      <th>feature_591</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>3030.93</td>\n",
       "      <td>2564.00</td>\n",
       "      <td>2187.7333</td>\n",
       "      <td>1411.1265</td>\n",
       "      <td>1.3602</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.6133</td>\n",
       "      <td>0.1242</td>\n",
       "      <td>1.5005</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5005</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>2.3630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>3095.78</td>\n",
       "      <td>2465.14</td>\n",
       "      <td>2230.4222</td>\n",
       "      <td>1463.6606</td>\n",
       "      <td>0.8294</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.3433</td>\n",
       "      <td>0.1247</td>\n",
       "      <td>1.4966</td>\n",
       "      <td>...</td>\n",
       "      <td>208.2045</td>\n",
       "      <td>0.5019</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>4.4447</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>208.2045</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2932.61</td>\n",
       "      <td>2559.94</td>\n",
       "      <td>2186.4111</td>\n",
       "      <td>1698.0172</td>\n",
       "      <td>1.5102</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.4878</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>1.4436</td>\n",
       "      <td>...</td>\n",
       "      <td>82.8602</td>\n",
       "      <td>0.4958</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>3.1745</td>\n",
       "      <td>0.0584</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>82.8602</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>2988.72</td>\n",
       "      <td>2479.90</td>\n",
       "      <td>2199.0333</td>\n",
       "      <td>909.7926</td>\n",
       "      <td>1.3204</td>\n",
       "      <td>100.0</td>\n",
       "      <td>104.2367</td>\n",
       "      <td>0.1217</td>\n",
       "      <td>1.4882</td>\n",
       "      <td>...</td>\n",
       "      <td>73.8432</td>\n",
       "      <td>0.4990</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>2.0544</td>\n",
       "      <td>0.0202</td>\n",
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       "      <td>0.0044</td>\n",
       "      <td>73.8432</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>3032.24</td>\n",
       "      <td>2502.87</td>\n",
       "      <td>2233.3667</td>\n",
       "      <td>1326.5200</td>\n",
       "      <td>1.5334</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.3967</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>1.5031</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.4766</td>\n",
       "      <td>0.1045</td>\n",
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       "      <td>0.0202</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>-1</td>\n",
       "      <td>2899.41</td>\n",
       "      <td>2464.36</td>\n",
       "      <td>2179.7333</td>\n",
       "      <td>3085.3781</td>\n",
       "      <td>1.4843</td>\n",
       "      <td>100.0</td>\n",
       "      <td>82.2467</td>\n",
       "      <td>0.1248</td>\n",
       "      <td>1.3424</td>\n",
       "      <td>...</td>\n",
       "      <td>203.1720</td>\n",
       "      <td>0.4988</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>2.8669</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>203.1720</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>-1</td>\n",
       "      <td>3052.31</td>\n",
       "      <td>2522.55</td>\n",
       "      <td>2198.5667</td>\n",
       "      <td>1124.6595</td>\n",
       "      <td>0.8763</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.4689</td>\n",
       "      <td>0.1205</td>\n",
       "      <td>1.4333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4975</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>2.6238</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>203.1720</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>-1</td>\n",
       "      <td>2978.81</td>\n",
       "      <td>2379.78</td>\n",
       "      <td>2206.3000</td>\n",
       "      <td>1110.4967</td>\n",
       "      <td>0.8236</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.4122</td>\n",
       "      <td>0.1208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>43.5231</td>\n",
       "      <td>0.4987</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>3.0590</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>43.5231</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>-1</td>\n",
       "      <td>2894.92</td>\n",
       "      <td>2532.01</td>\n",
       "      <td>2177.0333</td>\n",
       "      <td>1183.7287</td>\n",
       "      <td>1.5726</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.7978</td>\n",
       "      <td>0.1213</td>\n",
       "      <td>1.4622</td>\n",
       "      <td>...</td>\n",
       "      <td>93.4941</td>\n",
       "      <td>0.5004</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>3.5662</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>93.4941</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>-1</td>\n",
       "      <td>2944.92</td>\n",
       "      <td>2450.76</td>\n",
       "      <td>2195.4444</td>\n",
       "      <td>2914.1792</td>\n",
       "      <td>1.5978</td>\n",
       "      <td>100.0</td>\n",
       "      <td>85.1011</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>137.7844</td>\n",
       "      <td>0.4987</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>3.6275</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>137.7844</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1567 rows × 592 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      classifications  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n",
       "0                  -1    3030.93    2564.00  2187.7333  1411.1265     1.3602   \n",
       "1                  -1    3095.78    2465.14  2230.4222  1463.6606     0.8294   \n",
       "2                   1    2932.61    2559.94  2186.4111  1698.0172     1.5102   \n",
       "3                  -1    2988.72    2479.90  2199.0333   909.7926     1.3204   \n",
       "4                  -1    3032.24    2502.87  2233.3667  1326.5200     1.5334   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "1562               -1    2899.41    2464.36  2179.7333  3085.3781     1.4843   \n",
       "1563               -1    3052.31    2522.55  2198.5667  1124.6595     0.8763   \n",
       "1564               -1    2978.81    2379.78  2206.3000  1110.4967     0.8236   \n",
       "1565               -1    2894.92    2532.01  2177.0333  1183.7287     1.5726   \n",
       "1566               -1    2944.92    2450.76  2195.4444  2914.1792     1.5978   \n",
       "\n",
       "      feature_6  feature_7  feature_8  feature_9  ...  feature_582  \\\n",
       "0         100.0    97.6133     0.1242     1.5005  ...          NaN   \n",
       "1         100.0   102.3433     0.1247     1.4966  ...     208.2045   \n",
       "2         100.0    95.4878     0.1241     1.4436  ...      82.8602   \n",
       "3         100.0   104.2367     0.1217     1.4882  ...      73.8432   \n",
       "4         100.0   100.3967     0.1235     1.5031  ...          NaN   \n",
       "...         ...        ...        ...        ...  ...          ...   \n",
       "1562      100.0    82.2467     0.1248     1.3424  ...     203.1720   \n",
       "1563      100.0    98.4689     0.1205     1.4333  ...          NaN   \n",
       "1564      100.0    99.4122     0.1208        NaN  ...      43.5231   \n",
       "1565      100.0    98.7978     0.1213     1.4622  ...      93.4941   \n",
       "1566      100.0    85.1011     0.1235        NaN  ...     137.7844   \n",
       "\n",
       "      feature_583  feature_584  feature_585  feature_586  feature_587  \\\n",
       "0          0.5005       0.0118       0.0035       2.3630          NaN   \n",
       "1          0.5019       0.0223       0.0055       4.4447       0.0096   \n",
       "2          0.4958       0.0157       0.0039       3.1745       0.0584   \n",
       "3          0.4990       0.0103       0.0025       2.0544       0.0202   \n",
       "4          0.4800       0.4766       0.1045      99.3032       0.0202   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "1562       0.4988       0.0143       0.0039       2.8669       0.0068   \n",
       "1563       0.4975       0.0131       0.0036       2.6238       0.0068   \n",
       "1564       0.4987       0.0153       0.0041       3.0590       0.0197   \n",
       "1565       0.5004       0.0178       0.0038       3.5662       0.0262   \n",
       "1566       0.4987       0.0181       0.0040       3.6275       0.0117   \n",
       "\n",
       "      feature_588  feature_589  feature_590  feature_591  \n",
       "0             NaN          NaN          NaN          NaN  \n",
       "1          0.0201       0.0060     208.2045          NaN  \n",
       "2          0.0484       0.0148      82.8602          NaN  \n",
       "3          0.0149       0.0044      73.8432          NaN  \n",
       "4          0.0149       0.0044      73.8432          NaN  \n",
       "...           ...          ...          ...          ...  \n",
       "1562       0.0138       0.0047     203.1720          NaN  \n",
       "1563       0.0138       0.0047     203.1720          NaN  \n",
       "1564       0.0086       0.0025      43.5231          NaN  \n",
       "1565       0.0245       0.0075      93.4941          NaN  \n",
       "1566       0.0162       0.0045     137.7844          NaN  \n",
       "\n",
       "[1567 rows x 592 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data = merged_df.drop(columns = \"date_time\", axis=1)\n",
    "merged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3e8a29bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    591.000000\n",
      "mean       5.299320\n",
      "std        9.559892\n",
      "min      -22.169909\n",
      "25%        0.000000\n",
      "50%        0.977121\n",
      "75%        6.468798\n",
      "max       39.562779\n",
      "dtype: float64\n",
      "AxesSubplot(0.125,0.125;0.775x0.755)\n",
      "classifications    3.487359\n",
      "feature_1          0.513657\n",
      "feature_2         -0.043066\n",
      "feature_3         -0.305764\n",
      "feature_4          1.711627\n",
      "                     ...   \n",
      "feature_587        1.438025\n",
      "feature_588        1.947408\n",
      "feature_589        2.029372\n",
      "feature_590        2.714475\n",
      "feature_591             NaN\n",
      "Length: 592, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme()\n",
    "print(merged_data.skew().describe())\n",
    "print(merged_data.skew().hist())\n",
    "\n",
    "print(merged_data.skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f3912095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1216: RuntimeWarning:\n",
      "\n",
      "Mean of empty slice.\n",
      "\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:181: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in true_divide\n",
      "\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3440: RuntimeWarning:\n",
      "\n",
      "Mean of empty slice.\n",
      "\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in double_scalars\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew, kurtosis\n",
    "\n",
    "# Calculate skewness for each column\n",
    "skewness_threshold = 20\n",
    "skewness_levels = {}\n",
    "\n",
    "for column in merged_data.columns:\n",
    "    skewness = kurtosis(merged_data[column].dropna())\n",
    "    skewness_levels[column] = skewness\n",
    "\n",
    "# Filter columns where skewness is higher than the threshold\n",
    "high_skew_columns = [column for column, skewness in skewness_levels.items() if skewness > skewness_threshold]\n",
    "\n",
    "# Select columns that meet the condition\n",
    "selected_columns = merged_data[high_skew_columns]\n",
    "\n",
    "print(len(selected_columns.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "547c1384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data.kurtosis().hist(bins=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0a80c978",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [80]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m kurtosis_levels \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m column \u001b[38;5;129;01min\u001b[39;00m merged_data\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m----> 7\u001b[0m     kurtosis \u001b[38;5;241m=\u001b[39m \u001b[43mkurtosis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmerged_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      8\u001b[0m     kurtosis_levels[column] \u001b[38;5;241m=\u001b[39m kurtosis\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Filter columns kurtosis is higher than the threshold\u001b[39;00m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object is not callable"
     ]
    }
   ],
   "source": [
    "from scipy.stats import kurtosis\n",
    "\n",
    "kurtosis_threshold = 20\n",
    "kurtosis_levels = {}\n",
    "\n",
    "for column in merged_data.columns:\n",
    "    kurtosis = kurtosis(merged_data[column])\n",
    "    kurtosis_levels[column] = kurtosis\n",
    "    \n",
    "# Filter columns kurtosis is higher than the threshold\n",
    "high_kurtosis_columns = [column for column, kurtosis in kurtosis_levels.items() if kurtosis > kurtosis_threshold]\n",
    "\n",
    "# Select columns that meet the condition\n",
    "selected_columns = merged_data[high_kurtosis_columns]\n",
    "\n",
    "print(len(selected_columns.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b4b9a401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Calculate volatilities (standard deviations) for each column\n",
    "volatilities = features_df.std()\n",
    "\n",
    "sns.set_theme()\n",
    "# Plot histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(volatilities, bins=10, color='#76B900', edgecolor='black')\n",
    "plt.xlabel('Standard Deviation of Features')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Volatilities')\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "49025276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591\n",
      "579\n",
      "\n",
      "std_dev : counts\n",
      "   500 : 579\n",
      "          97.97 %\n",
      "\n",
      "   1000 : 584\n",
      "          98.82 %\n",
      "\n",
      "   1500 : 585\n",
      "          98.98 %\n",
      "\n",
      "   2000 : 586\n",
      "          99.15 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(len(volatilities))\n",
    "print((volatilities < 500 ).sum())\n",
    "\n",
    "x = range(1, 5)\n",
    "print()\n",
    "print(\"std_dev : counts\")\n",
    "for n in x:\n",
    " xaxis = n * 500\n",
    " print(f\"   {xaxis} : {(volatilities < xaxis ).sum()}\")\n",
    " print( f\"          {round( ( (volatilities < xaxis ).sum() / len(volatilities) * 100 ) , 2 )} %\")\n",
    " print()\n",
    " \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e1a382d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1005d2c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "252ad2fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "30d5768e-25eb-48ad-8cf9-3fa57ce0aafe",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2-3 Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f153d3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classifications       0\n",
      "feature_1             6\n",
      "feature_2             7\n",
      "feature_3            14\n",
      "feature_4            14\n",
      "                   ... \n",
      "feature_587           1\n",
      "feature_588           1\n",
      "feature_589           1\n",
      "feature_590           1\n",
      "feature_591        1567\n",
      "Length: 592, dtype: int64\n",
      "\n",
      "===============================================================\n",
      "\n",
      "       classifications    feature_1    feature_2    feature_3    feature_4  \\\n",
      "count         8.000000     8.000000     8.000000     8.000000     8.000000   \n",
      "mean        195.453844  2472.883085  2078.990367  1844.894221  1383.052958   \n",
      "std         554.188900  1109.001613   891.252031   770.362628  1096.086719   \n",
      "min          -1.000000    73.621787    80.407705    29.513152     0.000000   \n",
      "25%          -1.000000  2447.680000  2009.062500  1933.745000   921.829760   \n",
      "50%          -0.933631  2988.875000  2474.048865  2190.795859  1340.795514   \n",
      "75%           0.623508  3025.002172  2509.259375  2205.313900  1562.555875   \n",
      "max        1567.000000  3356.350000  2846.440000  2315.266700  3715.041700   \n",
      "\n",
      "         feature_5    feature_6    feature_7    feature_8    feature_9  ...  \\\n",
      "count     8.000000     8.000000     8.000000     8.000000     8.000000  ...   \n",
      "mean    341.578857   269.125000   271.969040   194.828335   196.721732  ...   \n",
      "std     623.789231   519.942699   518.874817   550.804533   552.868134  ...   \n",
      "min       0.681500     0.000000     6.237214     0.000000     0.073897  ...   \n",
      "25%       1.242025   100.000000    93.972775     0.093065     1.356150  ...   \n",
      "50%       2.861357   100.000000   101.312554     0.122111     1.462231  ...   \n",
      "75%     320.900805   100.000000   110.753075     0.125000     1.551775  ...   \n",
      "max    1553.000000  1553.000000  1553.000000  1558.000000  1565.000000  ...   \n",
      "\n",
      "       feature_582  feature_583  feature_584  feature_585  feature_586  \\\n",
      "count     8.000000     8.000000     8.000000     8.000000     8.000000   \n",
      "mean    221.971636   196.123947   195.819625   195.765571   210.188236   \n",
      "std     285.247330   553.513539   553.636501   553.658319   548.873105   \n",
      "min       0.000000     0.003404     0.006000     0.001700     1.197500   \n",
      "25%      65.762900     0.492875     0.013250     0.003475     2.644862   \n",
      "50%      92.727670     0.500148     0.015909     0.003783     3.181501   \n",
      "75%     241.904362     0.504231     0.132035     0.029200    27.509325   \n",
      "max     737.304800  1566.000000  1566.000000  1566.000000  1566.000000   \n",
      "\n",
      "       feature_587  feature_588  feature_589  feature_590  feature_591  \n",
      "count     8.000000     8.000000     8.000000     8.000000          1.0  \n",
      "mean    195.772655   195.769260   195.756506   340.985698          0.0  \n",
      "std     553.655457   553.656828   553.661981   548.355979          NaN  \n",
      "min      -0.016900     0.003200     0.001000     0.000000          0.0  \n",
      "25%       0.013158     0.010152     0.003192    65.017525          0.0  \n",
      "50%       0.020979     0.015637     0.004942    96.780993          0.0  \n",
      "75%       0.046400     0.035200     0.011950   270.388475          0.0  \n",
      "max    1566.000000  1566.000000  1566.000000  1566.000000          0.0  \n",
      "\n",
      "[8 rows x 592 columns]\n"
     ]
    }
   ],
   "source": [
    "print(merged_data.isna().sum())\n",
    "print(\"\\n===============================================================\\n\")\n",
    "print(merged_data.describe().describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a19baa55",
   "metadata": {},
   "source": [
    "### 2-3 -1 Target value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "54021934-1491-4811-bbe5-c233991c484a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of failure: 104 \n",
      "The number of pass:    1463\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": [
           "#000000",
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          "line": {
           "color": "#000000",
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          }
         },
         "opacity": 0.8,
         "orientation": "h",
         "type": "bar",
         "x": [
          104,
          1463
         ],
         "y": [
          "Fail ",
          "Pass "
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
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            "error_y": {
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             "line": {
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           "bgcolor": "#E5ECF6",
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          "scene": {
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          "shapedefaults": {
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          },
          "ternary": {
           "aaxis": {
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           "baxis": {
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
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           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Count of target variable for faile and pass data"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import plotly.offline as plof\n",
    "# import plotly.graph_objs as go\n",
    "\n",
    "result_fail = merged_data[merged_data['classifications'] != -1]\n",
    "result_pass = merged_data[merged_data['classifications'] == -1]\n",
    "\n",
    "print(f\"The number of failure: {len(result_fail)} \\nThe number of pass:    {len(result_pass)}\")\n",
    "\n",
    "trace = go.Bar(x = (len(result_fail), len(result_pass)), \n",
    "               y = ['Fail ', 'Pass '], \n",
    "               orientation = 'h', \n",
    "               opacity = 0.8, \n",
    "               marker=dict(\n",
    "                       color=[ '#000000', '#76B900'],\n",
    "                       line=dict(color='#000000',width=1.5)\n",
    "                       )\n",
    "               )\n",
    "\n",
    "layout = dict(title =  'Count of target variable for faile and pass data')\n",
    "                    \n",
    "fig = dict(data = [trace], layout=layout)\n",
    "plotly.offline.iplot(fig)\n",
    "\n",
    "\n",
    "#Then, we will plot the percentage\n",
    "trace = go.Pie(labels = ['Pass ', 'Fail '], \n",
    "               values = labels_df['classifications'].value_counts(), \n",
    "               textfont=dict(size=30), opacity = 0.8,\n",
    "               marker=dict(colors=['#76B900', '#000000'], \n",
    "                           line=dict(color='#000000', \n",
    "                                     width=1.5)\n",
    "                           )\n",
    "               )\n",
    "\n",
    "\n",
    "layout = dict(title =  'Count of target variable for faile and pass data')\n",
    "           \n",
    "fig = dict(data = [trace], layout=layout)\n",
    "plof.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd2c51b",
   "metadata": {},
   "source": [
    "### 2-3 -2 Other Variables / Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14a60507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1567, 592)\n",
      "(1567, 53)\n"
     ]
    }
   ],
   "source": [
    "# missing values\n",
    "\n",
    "merged_noNull = merged_data.dropna(axis=1)\n",
    "\n",
    "print(merged_data.shape)\n",
    "print(merged_noNull.shape) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c66554e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>classifications</th>\n",
       "      <th>feature_21</th>\n",
       "      <th>feature_87</th>\n",
       "      <th>feature_88</th>\n",
       "      <th>feature_89</th>\n",
       "      <th>feature_114</th>\n",
       "      <th>feature_115</th>\n",
       "      <th>feature_116</th>\n",
       "      <th>feature_117</th>\n",
       "      <th>feature_118</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_527</th>\n",
       "      <th>feature_528</th>\n",
       "      <th>feature_571</th>\n",
       "      <th>feature_572</th>\n",
       "      <th>feature_573</th>\n",
       "      <th>feature_574</th>\n",
       "      <th>feature_575</th>\n",
       "      <th>feature_576</th>\n",
       "      <th>feature_577</th>\n",
       "      <th>feature_578</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>1.4026</td>\n",
       "      <td>2.3895</td>\n",
       "      <td>0.9690</td>\n",
       "      <td>1747.6049</td>\n",
       "      <td>0.9460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>748.6115</td>\n",
       "      <td>0.9908</td>\n",
       "      <td>58.4306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5064</td>\n",
       "      <td>6.6926</td>\n",
       "      <td>533.8500</td>\n",
       "      <td>2.1113</td>\n",
       "      <td>8.95</td>\n",
       "      <td>0.3157</td>\n",
       "      <td>3.0624</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>1.6765</td>\n",
       "      <td>14.9509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>1.3825</td>\n",
       "      <td>2.3754</td>\n",
       "      <td>0.9894</td>\n",
       "      <td>1931.6464</td>\n",
       "      <td>0.9425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>731.2517</td>\n",
       "      <td>0.9902</td>\n",
       "      <td>58.6680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8832</td>\n",
       "      <td>8.8370</td>\n",
       "      <td>535.0164</td>\n",
       "      <td>2.4335</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.2653</td>\n",
       "      <td>2.0111</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>1.1065</td>\n",
       "      <td>10.9003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   classifications  feature_21  feature_87  feature_88  feature_89  \\\n",
       "0               -1      1.4026      2.3895      0.9690   1747.6049   \n",
       "1               -1      1.3825      2.3754      0.9894   1931.6464   \n",
       "\n",
       "   feature_114  feature_115  feature_116  feature_117  feature_118  ...  \\\n",
       "0       0.9460          0.0     748.6115       0.9908      58.4306  ...   \n",
       "1       0.9425          0.0     731.2517       0.9902      58.6680  ...   \n",
       "\n",
       "   feature_527  feature_528  feature_571  feature_572  feature_573  \\\n",
       "0       0.5064       6.6926     533.8500       2.1113         8.95   \n",
       "1       0.8832       8.8370     535.0164       2.4335         5.92   \n",
       "\n",
       "   feature_574  feature_575  feature_576  feature_577  feature_578  \n",
       "0       0.3157       3.0624       0.1026       1.6765      14.9509  \n",
       "1       0.2653       2.0111       0.0772       1.1065      10.9003  \n",
       "\n",
       "[2 rows x 53 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_noNull.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c6b22ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['feature_21',\n",
       " 'feature_87',\n",
       " 'feature_88',\n",
       " 'feature_89',\n",
       " 'feature_114',\n",
       " 'feature_115',\n",
       " 'feature_116',\n",
       " 'feature_117',\n",
       " 'feature_118',\n",
       " 'feature_120',\n",
       " 'feature_121',\n",
       " 'feature_157',\n",
       " 'feature_222',\n",
       " 'feature_223',\n",
       " 'feature_224',\n",
       " 'feature_249',\n",
       " 'feature_250',\n",
       " 'feature_251',\n",
       " 'feature_252',\n",
       " 'feature_253',\n",
       " 'feature_255',\n",
       " 'feature_256',\n",
       " 'feature_292',\n",
       " 'feature_360',\n",
       " 'feature_361',\n",
       " 'feature_362',\n",
       " 'feature_387',\n",
       " 'feature_388',\n",
       " 'feature_389',\n",
       " 'feature_390',\n",
       " 'feature_391',\n",
       " 'feature_393',\n",
       " 'feature_394',\n",
       " 'feature_430',\n",
       " 'feature_494',\n",
       " 'feature_495',\n",
       " 'feature_496',\n",
       " 'feature_521',\n",
       " 'feature_522',\n",
       " 'feature_523',\n",
       " 'feature_524',\n",
       " 'feature_525',\n",
       " 'feature_527',\n",
       " 'feature_528',\n",
       " 'feature_571',\n",
       " 'feature_572',\n",
       " 'feature_573',\n",
       " 'feature_574',\n",
       " 'feature_575',\n",
       " 'feature_576',\n",
       " 'feature_577',\n",
       " 'feature_578']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_name = merged_noNull.columns.tolist()\n",
    "calssifications = col_name.pop(0)\n",
    "col_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7cc08c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import plotly.figure_factory as ff\n",
    "# import plotly.offline as plof\n",
    "\n",
    "def plot_distribution(data_select, size_bin) :  \n",
    "    tmp1 = result_fail[data_select]\n",
    "    tmp2 = result_pass[data_select]\n",
    "    hist_data = [tmp1, tmp2]\n",
    "    \n",
    "    group_labels = ['fail', 'pass']\n",
    "    colors = ['#228C22', '#0000FF']\n",
    "\n",
    "    fig = ff.create_distplot(hist_data, group_labels, colors = colors, show_hist = True, bin_size = size_bin, curve_type='kde')\n",
    "    \n",
    "    fig['layout'].update(title = data_select, xaxis_title='Observed Value', yaxis_title=\"frequency\",)\n",
    "\n",
    "    plof.iplot(fig, filename = 'Density plot for faile and pass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9bf34000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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         "autobinx": false,
         "histnorm": "probability density",
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         "opacity": 0.7,
         "type": "histogram",
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        "yaxis": {
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   "source": [
    "plot_distribution('feature_21', 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "484dc9d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a90a000",
   "metadata": {},
   "source": [
    "### 2-3 -3 Correlation matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939da2ac",
   "metadata": {},
   "source": [
    "#### 2-3 -3-1 correlation between features without any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7e1f4b9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>feature_21</th>\n",
       "      <th>feature_87</th>\n",
       "      <th>feature_88</th>\n",
       "      <th>feature_89</th>\n",
       "      <th>feature_114</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.4026</td>\n",
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       "      <td>0.9460</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>58.4306</td>\n",
       "      <td>0.9804</td>\n",
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       "      <td>4.8560</td>\n",
       "      <td>0.5064</td>\n",
       "      <td>6.6926</td>\n",
       "      <td>533.8500</td>\n",
       "      <td>2.1113</td>\n",
       "      <td>8.95</td>\n",
       "      <td>0.3157</td>\n",
       "      <td>3.0624</td>\n",
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       "      <td>2.3754</td>\n",
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       "      <td>0.9425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>731.2517</td>\n",
       "      <td>0.9902</td>\n",
       "      <td>58.6680</td>\n",
       "      <td>0.9731</td>\n",
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       "      <td>6.6475</td>\n",
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       "      <td>5.92</td>\n",
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       "      <td>1685.8514</td>\n",
       "      <td>0.9231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>718.5777</td>\n",
       "      <td>0.9899</td>\n",
       "      <td>58.4808</td>\n",
       "      <td>0.9772</td>\n",
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       "      <td>7.0870</td>\n",
       "      <td>0.6451</td>\n",
       "      <td>6.4568</td>\n",
       "      <td>535.0245</td>\n",
       "      <td>2.0293</td>\n",
       "      <td>11.21</td>\n",
       "      <td>0.1882</td>\n",
       "      <td>4.0923</td>\n",
       "      <td>0.0640</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.4011</td>\n",
       "      <td>2.4004</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>5.4751</td>\n",
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       "      <td>6.4865</td>\n",
       "      <td>530.5682</td>\n",
       "      <td>2.0253</td>\n",
       "      <td>9.33</td>\n",
       "      <td>0.1738</td>\n",
       "      <td>2.8971</td>\n",
       "      <td>0.0525</td>\n",
       "      <td>1.7585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.3888</td>\n",
       "      <td>2.4530</td>\n",
       "      <td>0.9902</td>\n",
       "      <td>1828.3846</td>\n",
       "      <td>0.9424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>796.5950</td>\n",
       "      <td>0.9908</td>\n",
       "      <td>58.3858</td>\n",
       "      <td>0.9628</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4146</td>\n",
       "      <td>2.2181</td>\n",
       "      <td>6.3745</td>\n",
       "      <td>532.0155</td>\n",
       "      <td>2.0275</td>\n",
       "      <td>8.83</td>\n",
       "      <td>0.2224</td>\n",
       "      <td>3.1776</td>\n",
       "      <td>0.0706</td>\n",
       "      <td>1.6597</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>1.4072</td>\n",
       "      <td>2.3762</td>\n",
       "      <td>0.9861</td>\n",
       "      <td>1869.4215</td>\n",
       "      <td>0.9520</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>58.3514</td>\n",
       "      <td>0.9636</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0178</td>\n",
       "      <td>1.2587</td>\n",
       "      <td>2.6649</td>\n",
       "      <td>536.3418</td>\n",
       "      <td>2.0153</td>\n",
       "      <td>7.98</td>\n",
       "      <td>0.2363</td>\n",
       "      <td>2.6401</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>1.4879</td>\n",
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       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>1.3949</td>\n",
       "      <td>2.4880</td>\n",
       "      <td>0.9911</td>\n",
       "      <td>1872.5133</td>\n",
       "      <td>0.9561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>755.7527</td>\n",
       "      <td>0.9899</td>\n",
       "      <td>57.1020</td>\n",
       "      <td>0.9806</td>\n",
       "      <td>...</td>\n",
       "      <td>4.6482</td>\n",
       "      <td>0.4512</td>\n",
       "      <td>6.0192</td>\n",
       "      <td>537.9264</td>\n",
       "      <td>2.1814</td>\n",
       "      <td>5.48</td>\n",
       "      <td>0.3891</td>\n",
       "      <td>1.9077</td>\n",
       "      <td>0.1213</td>\n",
       "      <td>1.0187</td>\n",
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       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>1.4256</td>\n",
       "      <td>2.4590</td>\n",
       "      <td>0.9869</td>\n",
       "      <td>1820.3629</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>704.2686</td>\n",
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       "      <td>59.2046</td>\n",
       "      <td>0.9767</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2114</td>\n",
       "      <td>0.4867</td>\n",
       "      <td>5.4641</td>\n",
       "      <td>530.3709</td>\n",
       "      <td>2.3435</td>\n",
       "      <td>6.49</td>\n",
       "      <td>0.4154</td>\n",
       "      <td>2.1760</td>\n",
       "      <td>0.1352</td>\n",
       "      <td>1.2237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>1.3868</td>\n",
       "      <td>2.3600</td>\n",
       "      <td>0.9796</td>\n",
       "      <td>1627.4714</td>\n",
       "      <td>0.9485</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>58.2686</td>\n",
       "      <td>0.9805</td>\n",
       "      <td>...</td>\n",
       "      <td>6.6428</td>\n",
       "      <td>0.3866</td>\n",
       "      <td>6.5512</td>\n",
       "      <td>534.3936</td>\n",
       "      <td>1.9098</td>\n",
       "      <td>9.13</td>\n",
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       "      <td>3.2524</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>1.7085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>1.4048</td>\n",
       "      <td>2.3701</td>\n",
       "      <td>0.9758</td>\n",
       "      <td>1759.9908</td>\n",
       "      <td>0.9432</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.1495</td>\n",
       "      <td>1.2878</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1567 rows × 51 columns</p>\n",
       "</div>"
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       "      feature_21  feature_87  feature_88  feature_89  feature_114  \\\n",
       "0         1.4026      2.3895      0.9690   1747.6049       0.9460   \n",
       "1         1.3825      2.3754      0.9894   1931.6464       0.9425   \n",
       "2         1.4123      2.4532      0.9880   1685.8514       0.9231   \n",
       "3         1.4011      2.4004      0.9904   1752.0968       0.9564   \n",
       "4         1.3888      2.4530      0.9902   1828.3846       0.9424   \n",
       "...          ...         ...         ...         ...          ...   \n",
       "1562      1.4072      2.3762      0.9861   1869.4215       0.9520   \n",
       "1563      1.3949      2.4880      0.9911   1872.5133       0.9561   \n",
       "1564      1.4256      2.4590      0.9869   1820.3629       0.9488   \n",
       "1565      1.3868      2.3600      0.9796   1627.4714       0.9485   \n",
       "1566      1.4048      2.3701      0.9758   1759.9908       0.9432   \n",
       "\n",
       "      feature_115  feature_116  feature_117  feature_118  feature_120  ...  \\\n",
       "0             0.0     748.6115       0.9908      58.4306       0.9804  ...   \n",
       "1             0.0     731.2517       0.9902      58.6680       0.9731  ...   \n",
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       "...           ...          ...          ...          ...          ...  ...   \n",
       "1562          0.0     727.6761       0.9894      58.3514       0.9636  ...   \n",
       "1563          0.0     755.7527       0.9899      57.1020       0.9806  ...   \n",
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       "1566          0.0     683.5622       0.9893      59.8578       0.9636  ...   \n",
       "\n",
       "      feature_525  feature_527  feature_528  feature_571  feature_572  \\\n",
       "0          4.8560       0.5064       6.6926     533.8500       2.1113   \n",
       "1          6.6475       0.8832       8.8370     535.0164       2.4335   \n",
       "2          7.0870       0.6451       6.4568     535.0245       2.0293   \n",
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       "...           ...          ...          ...          ...          ...   \n",
       "1562       6.0178       1.2587       2.6649     536.3418       2.0153   \n",
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       "1566       3.8350       1.5629       4.1651     528.7918       2.0831   \n",
       "\n",
       "      feature_573  feature_574  feature_575  feature_576  feature_577  \n",
       "0            8.95       0.3157       3.0624       0.1026       1.6765  \n",
       "1            5.92       0.2653       2.0111       0.0772       1.1065  \n",
       "2           11.21       0.1882       4.0923       0.0640       2.0952  \n",
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       "4            8.83       0.2224       3.1776       0.0706       1.6597  \n",
       "...           ...          ...          ...          ...          ...  \n",
       "1562         7.98       0.2363       2.6401       0.0785       1.4879  \n",
       "1563         5.48       0.3891       1.9077       0.1213       1.0187  \n",
       "1564         6.49       0.4154       2.1760       0.1352       1.2237  \n",
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       "1566         6.81       0.4774       2.2727       0.1495       1.2878  \n",
       "\n",
       "[1567 rows x 51 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "merged_noNull.iloc[:,1:-1]"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c3c9aca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correlation\n",
    "correlation = merged_noNull.iloc[:,1:-1].corr()\n",
    "\n",
    "#tick labels\n",
    "matrix_cols = correlation.columns.tolist()\n",
    "\n",
    "#convert to array\n",
    "corr_array  = np.array(correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f0ad2742",
   "metadata": {},
   "outputs": [
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   "source": [
    "#Plotting\n",
    "trace = go.Heatmap(z = corr_array,\n",
    "                   x = matrix_cols,\n",
    "                   y = matrix_cols,\n",
    "                   xgap = 2,\n",
    "                   ygap = 2,\n",
    "                   colorscale='Rainbow',\n",
    "                   colorbar   = dict() ,\n",
    "                  )\n",
    "layout = go.Layout(dict(title = 'Correlation Matrix',\n",
    "                        autosize = False,\n",
    "                        height  = 720,\n",
    "                        width   = 800,\n",
    "                        margin  = dict(r = 0 ,l = 210,\n",
    "                                       t = 25,b = 210,\n",
    "                                     ),\n",
    "                        yaxis   = dict(tickfont = dict(size = 9)),\n",
    "                        xaxis   = dict(tickfont = dict(size = 9)),\n",
    "                       )\n",
    "                  )\n",
    "fig = go.Figure(data = [trace],layout = layout)\n",
    "plof.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb80286b",
   "metadata": {},
   "source": [
    "#### 2-3 -3-2 correlation between top 30 correalted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1a8ddc2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to determine highly correlated pair of features above threshold of 0.95\n",
    "# Determine which features to drop according to Munirathinam & Ramadoss (2016)\n",
    "# Among each pair, Drop the feature with the highest average correlation with all features\n",
    "def find_highly_correlated_pairs(df, correlation_threshold=0.95):\n",
    "    # Step 1: Calculate the correlation matrix\n",
    "    correlation_matrix = df.corr()\n",
    "    \n",
    "    # Step 2: Find pairs of predictors with correlation >= threshold\n",
    "    high_correlation_pairs = []\n",
    "    columns = correlation_matrix.columns\n",
    "    \n",
    "    # Loop over the upper triangle of the correlation matrix (excluding diagonal)\n",
    "    for i in range(len(columns)):\n",
    "        for j in range(i + 1, len(columns)):\n",
    "            if abs(correlation_matrix.iloc[i, j]) >= correlation_threshold:\n",
    "                high_correlation_pairs.append((columns[i], columns[j]))\n",
    "\n",
    "    # Step 3: Create a DataFrame to store the pairs and their correlations\n",
    "    high_corr_df = pd.DataFrame(high_correlation_pairs, columns=['Predictor_A', 'Predictor_B'])\n",
    "    high_corr_df['Correlation'] = [\n",
    "        correlation_matrix.loc[pair[0], pair[1]] for pair in high_correlation_pairs\n",
    "    ]\n",
    "    \n",
    "    # Step 4: Calculate average correlation with other predictors for each element of each pair\n",
    "    avg_correlation = {}\n",
    "    \n",
    "    for col in columns:\n",
    "        # Calculate the average correlation of a predictor with all others excluding itself\n",
    "        avg_correlation[col] = correlation_matrix[col].drop(col).abs().mean()\n",
    "    \n",
    "    # Add the average correlation to the DataFrame\n",
    "    high_corr_df['Avg_Corr_A'] = high_corr_df['Predictor_A'].apply(lambda x: avg_correlation[x])\n",
    "    high_corr_df['Avg_Corr_B'] = high_corr_df['Predictor_B'].apply(lambda x: avg_correlation[x])\n",
    "\n",
    "    # Step 5: Determine which element has the higher average correlation with other predictors\n",
    "    # You need to compare the values for each row and assign the predictor with the higher correlation\n",
    "    high_corr_df['highest_to_delete'] = high_corr_df.apply(\n",
    "        lambda row: row['Predictor_A'] if row['Avg_Corr_A'] > row['Avg_Corr_B'] else row['Predictor_B'], axis=1\n",
    "    )\n",
    "\n",
    "    return high_corr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8c35411d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Predictor_A  Predictor_B  Correlation  Avg_Corr_A  Avg_Corr_B  \\\n",
      "0  feature_115  feature_250     0.999583    0.195849    0.195900   \n",
      "1  feature_115  feature_388     0.999615    0.195849    0.195896   \n",
      "2  feature_118  feature_253     0.998528    0.165673    0.168508   \n",
      "3  feature_118  feature_391     0.998545    0.165673    0.168476   \n",
      "4  feature_118  feature_525     0.995203    0.165673    0.173319   \n",
      "\n",
      "  highest_to_delete  \n",
      "0       feature_250  \n",
      "1       feature_388  \n",
      "2       feature_253  \n",
      "3       feature_391  \n",
      "4       feature_525  \n",
      "    Predictor_A  Predictor_B  Correlation  Avg_Corr_A  Avg_Corr_B  \\\n",
      "44  feature_574  feature_576     0.999225    0.160131    0.158600   \n",
      "45  feature_574  feature_577     0.963690    0.160131    0.160158   \n",
      "46  feature_575  feature_576     0.958001    0.160632    0.158600   \n",
      "47  feature_575  feature_577     0.999648    0.160632    0.160158   \n",
      "48  feature_576  feature_577     0.959632    0.158600    0.160158   \n",
      "\n",
      "   highest_to_delete  \n",
      "44       feature_574  \n",
      "45       feature_577  \n",
      "46       feature_575  \n",
      "47       feature_575  \n",
      "48       feature_577  \n",
      "['feature_388' 'feature_253' 'feature_391' 'feature_525' 'feature_255']\n",
      "Number of highly correlated Features to drop: 49\n",
      "Unique number of highly correlated Features to drop: 28\n"
     ]
    }
   ],
   "source": [
    "# Create a dataframe to determine the highly correlated pair of features\n",
    "correlation_result = find_highly_correlated_pairs(correlation, correlation_threshold=0.95)\n",
    "print(correlation_result.head())\n",
    "print(correlation_result.tail())\n",
    "\n",
    "# Determine the List of unique Features to Drop\n",
    "features_to_drop =correlation_result['highest_to_delete']\n",
    "features_to_drop_unq =correlation_result['highest_to_delete'].unique()\n",
    "# Print the first 5 features and the number of features to drop\n",
    "print(features_to_drop_unq[1:6])\n",
    "\n",
    "print(\"Number of highly correlated Features to drop:\", len(features_to_drop))\n",
    "\n",
    "print(\"Unique number of highly correlated Features to drop:\", len(features_to_drop_unq))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d060a57b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns with correlations below -0.95 or above 0.95 :\n",
      "329\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.95\n",
    "threshold_plus = threshold\n",
    "threshold_minus = threshold * -1\n",
    "\n",
    "# Compute the correlation matrix\n",
    "correlation_matrix1 = features_df.corr()\n",
    "\n",
    "# Create a mask to remove the lower triangle and diagonal\n",
    "mask = np.triu(np.ones_like(correlation_matrix1, dtype=bool))\n",
    "correlation_matrix1[~mask] = np.nan\n",
    "# List of pairs of columns with correlations above 0.95\n",
    "high_correlation_pairs1 = []\n",
    "\n",
    "# Iterate over the upper triangle of the correlation matrix\n",
    "for i in range(len(correlation_matrix1.columns)):\n",
    "    for j in range(i + 1, len(correlation_matrix1.columns)):\n",
    "        if correlation_matrix1.iloc[i, j] > threshold_plus or correlation_matrix1.iloc[i, j] < threshold_minus:\n",
    "            column1 = correlation_matrix1.columns[i]\n",
    "            column2 = correlation_matrix1.columns[j]\n",
    "            high_correlation_pairs1.append((column1, column2))\n",
    "\n",
    "# Output the high-correlation pairs\n",
    "print(f\"Number of Columns with correlations below {threshold_minus} or above {threshold_plus} :\")\n",
    "print(len(high_correlation_pairs1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b611b955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    }
   ],
   "source": [
    "# Select first 30 sets of highly correlated features\n",
    "# Create a list of unique feature which are highly correlated\n",
    "first_30 = high_correlation_pairs1[:30]\n",
    "unique_30_set = set()\n",
    "\n",
    "# Iterate through each pair and add the elements and create a unique list\n",
    "for pair in first_30:\n",
    "    unique_30_set.update(pair)\n",
    "\n",
    "# Convert the set to a sorted list\n",
    "unique_30_list = sorted(list(unique_30_set))\n",
    "print(len(unique_30_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc40cb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dataframe of first 30 highly correlated features\n",
    "unique_30_df = pd.DataFrame(features_df, columns=unique_30_list)\n",
    "\n",
    "unique_30_matrix= unique_30_df.corr()\n",
    "\n",
    "# Visualise the heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(unique_30_matrix, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Heatmap of first 30 pairs of highly correlated Features')\n",
    "plt.xticks(rotation=90)  # Rotate x-axis labels for better readability\n",
    "plt.yticks(rotation=0)   # Rotate y-axis labels for better readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aaf2ce4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2dea6995",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(unique_30_df)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9bc9025c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    'A': [1, 2, 3, 4, 5],\n",
    "    'B': [4, 5, 6, 7, 8],\n",
    "    'C': [7, 8, 9, 10, 11]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Create a pairplot\n",
    "sns.pairplot(df)\n",
    "\n",
    "# Display the pairplot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd280fe3-f7f1-445a-863f-502ec2cbb515",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1ff2efe5",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e44f84e",
   "metadata": {},
   "source": [
    "#### 3.2 splitting training and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "a99afb12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the module\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = merged_df.drop([\"classifications\", \"date_time\"], axis=1).values\n",
    "y = merged_df[\"classifications\"].values\n",
    "\n",
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f171354c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of features training data: \n",
      "(1175, 591)\n",
      "Shape of target training data: \n",
      "(1175,)\n",
      "Shape of features test data: \n",
      "(392, 591)\n",
      "Shape of taget test data: \n",
      "(392,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of features training data: \")\n",
    "print(np.shape(X_train))\n",
    "print(\"Shape of target training data: \")\n",
    "print(np.shape(y_train))\n",
    "print(\"Shape of features test data: \")\n",
    "print(np.shape(X_test))\n",
    "print(\"Shape of taget test data: \")\n",
    "print(np.shape(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "6a636b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of target values in train data:\n",
      "   Class  Frequency  Percentage\n",
      "0   -1.0     1097.0   93.361702\n",
      "1    1.0       78.0    6.638298\n",
      "Distribution of target values in test data:\n",
      "   Class  Frequency  Percentage\n",
      "0   -1.0      366.0   93.367347\n",
      "1    1.0       26.0    6.632653\n"
     ]
    }
   ],
   "source": [
    "#Create a function to create frequency table\n",
    "\n",
    "def draw_frequency(data):\n",
    "    # Calculate frequency distribution\n",
    "    unique_values, counts = np.unique(data, return_counts=True)\n",
    "\n",
    "    # Calculate total count\n",
    "    total_count = len(data)\n",
    "\n",
    "    # Calculate percentage distribution\n",
    "    percentage_counts = counts / total_count * 100\n",
    "\n",
    "    # Merge frequency and percentage distributions into a single NumPy array\n",
    "    distribution = np.stack((unique_values, counts, percentage_counts), axis=-1)\n",
    "    \n",
    "    # Convert them to pandas DataFrame\n",
    "    distribution_df = pd.DataFrame(distribution, columns=['Class', 'Frequency', 'Percentage'])\n",
    "\n",
    "    return distribution_df\n",
    "\n",
    "# Draw the distribution of target variable in training and test data set\n",
    "freuency_y_trn = draw_frequency(y_train)\n",
    "frequency_y_tst= draw_frequency(y_test)\n",
    "# Print the result\n",
    "print(\"Distribution of target values in train data:\")\n",
    "print(freuency_y_trn)\n",
    "\n",
    "print(\"Distribution of target values in test data:\")\n",
    "print(frequency_y_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd3236b",
   "metadata": {},
   "source": [
    "## Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "6fe61a07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns with zero variance: 116\n",
      "Proportion of Columns with zero variance:\n",
      "19.627749576988155 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert X_train data to Pandas df\n",
    "X_train_df = pd.DataFrame(X_train, columns=f_column_names)\n",
    "# Calculate the variance of each column\n",
    "column_variances = X_train_df.var()\n",
    "\n",
    "# Subset the columns with zero variance\n",
    "zero_variance_columns = column_variances[column_variances == 0].index.tolist()\n",
    "\n",
    "# Print the columns with zero variance\n",
    "print(\"Number of columns with zero variance:\", len(zero_variance_columns))\n",
    "\n",
    "print(\"Proportion of Columns with zero variance:\")\n",
    "\n",
    "print(len(zero_variance_columns)/len(X_train_df.columns)*100, \"%\")\n",
    "\n",
    "#plot histogam of variance\n",
    "\n",
    "plt.hist(column_variances, bins=10, color=htw_green, edgecolor='black')\n",
    "plt.title('Histogram of Column Variances')\n",
    "plt.xlabel('Variance')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3fc20c",
   "metadata": {},
   "source": [
    "### Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "dac6072c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to determine the List  and Number OF Columns with NAs below a given treshhold\n",
    "\n",
    "def identify_na_columns(df, threshold): # threshold in absolute percentage e.g. 90\n",
    "\n",
    "    # Create a DF of percentage of missing values per column \n",
    "    # na_df = df.isnull().sum().any()\n",
    "    na_df = round(df.isnull().mean() * 100)\n",
    "\n",
    "    # create a list of columns with NAs above threshold\n",
    "    nas_above_threshold = list(na_df[na_df > threshold].index)\n",
    "    nas_above_threshold_df = na_df[na_df > threshold]\n",
    "    sns.set_theme()\n",
    "    nas_above_threshold_df.hist(bins=20, color=htw_green, edgecolor='black')\n",
    "    plt.title('Percentage of missing value in a column')\n",
    "    plt.xlabel('Percentage')\n",
    "    plt.ylabel('Count')\n",
    "    \n",
    "    # plt.annotate(\"{}\".format(height),\n",
    "    #        xy = (x, height),             # top left corner of the histogram bar\n",
    "    #        xytext = (0,0.2),             # offsetting label position above its bar\n",
    "    #        textcoords = \"offset points\", # Offset (in points) from the *xy* value\n",
    "    #        ha = 'center', va = 'bottom'\n",
    "    #        )\n",
    "    \n",
    "    # plt.show()\n",
    "\n",
    "\n",
    "    # print the Number and list of columns with NAs\n",
    "    print(f\"Total Number of columns with more than {threshold}% of NAs: \", \n",
    "          len(nas_above_threshold))\n",
    "    # print(f\"List of columns with more than {threshold}% of NAs: \")     \n",
    "    # print(nas_above_threshold)\n",
    "\n",
    "\n",
    "    # Return a new dataframe with column-wise NAs  within threshold\n",
    "    # return(nas_above_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "2bc64aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of columns with more than 5% of NAs:  53\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# identify the List  and Number OF Features above 55% of NAs Threshold\n",
    "identify_na_columns(X_train_df, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "f82ae2fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1175\n",
      "591\n",
      "Total Number of columns with more than 20% of NAs:  33\n",
      "Total Number of columns with more than 30% of NAs:  33\n",
      "Total Number of columns with more than 40% of NAs:  33\n",
      "Total Number of columns with more than 50% of NAs:  25\n",
      "Total Number of columns with more than 60% of NAs:  25\n",
      "Total Number of columns with more than 70% of NAs:  9\n",
      "Total Number of columns with more than 80% of NAs:  9\n",
      "Total Number of columns with more than 90% of NAs:  1\n",
      "Total Number of columns with more than 100% of NAs:  0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(20, 101, 10)\n",
    "print(len(X_train_df))\n",
    "print(X_train_df.isnull().sum().count())\n",
    "\n",
    "for n in x:\n",
    "  # print(n)\n",
    "  identify_na_columns(X_train_df, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b668fe5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "96020c39",
   "metadata": {},
   "source": [
    "### Outliers after Splitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "5e6418f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define function\n",
    "def identify_outliers(df):\n",
    "   \n",
    "    # Calculate mean and standard deviation\n",
    "    mean = df.mean()\n",
    "    std_dev = df.std()\n",
    "    \n",
    "    # Define boundaries for outliers\n",
    "    upper_bound = mean + 3 * std_dev\n",
    "    lower_bound = mean - 3 * std_dev\n",
    "    \n",
    "    # Create a boolean mask for outliers\n",
    "    outliers_mask = (df > upper_bound) | (df < lower_bound)\n",
    "\n",
    "    # Print number and percentage of outliers in the datafame\n",
    "    print(\"The total number of records in the entire DF: \", df.sum().sum())\n",
    "    print(\"The total number of outliers in the entire DF: \", outliers_mask.sum().sum())\n",
    "    print(f\"The percentage of outliers in the entire DF: { round(outliers_mask.sum().sum()/df.sum().sum() * 100, 5)} %\")\n",
    "    \n",
    "    outliers_mask.sum().hist(bins=10, color=htw_green, edgecolor='black')\n",
    "    plt.title('Histogram of Outlies')\n",
    "    plt.xlabel('???')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n",
    "    \n",
    "    return outliers_mask.sum().sum(), "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "d0f9e847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# FOR THE WHOLE DATASET\n",
      "The total number of records in the entire DF:  76723183.5839\n",
      "The total number of outliers in the entire DF:  6116\n",
      "The percentage of outliers in the entire DF: 0.00797 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6116,)\n",
      "\n",
      "======================================================\n",
      "\n",
      "# FOR THE TRAINING DATASET\n",
      "The total number of records in the entire DF:  57416581.8036\n",
      "The total number of outliers in the entire DF:  4610\n",
      "The percentage of outliers in the entire DF: 0.00803 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4610,)\n"
     ]
    }
   ],
   "source": [
    "print(\"# FOR THE WHOLE DATASET\")\n",
    "print(identify_outliers(features_df))\n",
    "print()\n",
    "print(\"======================================================\")\n",
    "print()\n",
    "print(\"# FOR THE TRAINING DATASET\")\n",
    "print(identify_outliers(X_train_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f27ff08a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "972c35e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0ee32d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e0e6c96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a151ca19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # splitting the failed into 25% and 75% \n",
    "\n",
    "# train_data_failed, test_data_failed = train_test_split(failed_chip, test_size=0.2, random_state=42)\n",
    "\n",
    "# print(f\"failed chips for training are: {len(train_data_failed)} \\nfailed chips for testing are:  {len(test_data_failed)}\")\n",
    "# print(f\"the percentage of the testing is {round( len(test_data_failed) / len(train_data_failed ), 4) * 100} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3883b6e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'passed_chip' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [31]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# splitting the passed into 25% and 75% \u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m train_data_passed, test_data_passed \u001b[38;5;241m=\u001b[39m train_test_split(\u001b[43mpassed_chip\u001b[49m, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassed chips for training are: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_data_passed)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mpassed chips for testing are:  \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(test_data_passed)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe percentage of the testing is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mround\u001b[39m( \u001b[38;5;28mlen\u001b[39m(test_data_passed) \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(train_data_passed ), \u001b[38;5;241m3\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m %\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'passed_chip' is not defined"
     ]
    }
   ],
   "source": [
    "# splitting the passed into 25% and 75% \n",
    "\n",
    "train_data_passed, test_data_passed = train_test_split(passed_chip, test_size=0.2, random_state=42)\n",
    "\n",
    "print(f\"passed chips for training are: {len(train_data_passed)} \\npassed chips for testing are:  {len(test_data_passed)}\")\n",
    "print(f\"the percentage of the testing is {round( len(test_data_passed) / len(train_data_passed ), 3) * 100} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9face367",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining the train_failed and train_passed\n",
    "training_df = pd.concat([train_data_failed, train_data_passed])\n",
    "### counting the num of rows to check if it's properly combined \n",
    "print(len(training_df))\n",
    "print(f\"check: train_failed {len(train_data_failed)} + train_passed {len(train_data_passed)} = {len(train_data_failed) + len(train_data_passed)}\")\n",
    "\n",
    "print(\"---------------------------------------------------------------\")\n",
    "\n",
    "# combining the testing_failed and testing_passed  \n",
    "testing_df = pd.concat([test_data_failed, test_data_passed])\n",
    "### counting the num of rows to check if it's properly combined \n",
    "print(len(testing_df))\n",
    "print(f\"check: train_failed {len(test_data_failed)} + train_passed {len(test_data_passed)} = {len(test_data_failed) + len(test_data_passed)}\")\n",
    "\n"
   ]
  }
 ],
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