{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "bcdc5589", "metadata": {}, "outputs": [], "source": [ "# importing libraries \n", "import numpy as nm \n", "import matplotlib.pyplot as mtp \n", "import pandas as pd \n", "from sklearn.cluster import DBSCAN\n", "from numpy import unique\n", "from numpy import where\n", "from matplotlib import pyplot" ] }, { "cell_type": "code", "execution_count": 2, "id": "f448f999", "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>id</th>\n", " <th>child_gender</th>\n", " <th>child_age</th>\n", " <th>total_correct_responses</th>\n", " 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<td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>116</th>\n", " <td>117</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1785</td>\n", " <td>86000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>117</th>\n", " <td>118</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1628</td>\n", " <td>92000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td>119</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1758</td>\n", " <td>86500</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>119</th>\n", " <td>120</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1215</td>\n", " <td>92000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>121</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1134</td>\n", " <td>89000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>121</th>\n", " <td>122</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1364</td>\n", " <td>89000</td>\n", " <td>No</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>122</th>\n", " <td>123</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1499</td>\n", " <td>89000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>123</th>\n", " <td>124</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1998</td>\n", " <td>88000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>124</th>\n", " <td>125</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1916</td>\n", " <td>85500</td>\n", " <td>No</td>\n", " <td>83.333333</td>\n", " <td>16.666667</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td>126</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1152</td>\n", " <td>89500</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>126</th>\n", " <td>127</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1086</td>\n", " <td>92500</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>127</th>\n", " <td>128</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1207</td>\n", " <td>86500</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td>129</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1047</td>\n", " <td>92000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>129</th>\n", " <td>130</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1162</td>\n", " <td>88500</td>\n", " <td>No</td>\n", " <td>83.333333</td>\n", " <td>16.666667</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " <tr>\n", " <th>130</th>\n", " <td>131</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1278</td>\n", " <td>89000</td>\n", " <td>No</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>Focused</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id child_gender child_age total_correct_responses correct_responses \\\n", "79 80 1 4 10 10 \n", "80 81 1 4 10 10 \n", "81 82 1 4 10 10 \n", "82 83 2 4 10 10 \n", "83 84 2 4 10 9 \n", "84 85 2 4 10 10 \n", "85 86 2 4 10 8 \n", "86 87 2 4 10 10 \n", "87 88 2 4 10 8 \n", "88 89 2 4 10 9 \n", "89 90 2 4 10 10 \n", "90 91 1 4 10 9 \n", "91 92 1 4 10 10 \n", "92 93 1 4 10 10 \n", "93 94 1 4 10 10 \n", "94 95 1 4 10 10 \n", "95 96 1 4 10 10 \n", "96 97 1 4 10 9 \n", "97 98 1 4 10 10 \n", "98 99 1 4 10 9 \n", "99 100 1 4 10 7 \n", "100 101 1 4 10 10 \n", "101 102 1 4 10 10 \n", "102 103 1 4 10 9 \n", "103 104 1 4 10 8 \n", "104 105 1 4 10 7 \n", "105 106 2 5 12 11 \n", "106 107 2 5 12 11 \n", "107 108 2 5 12 12 \n", "108 109 2 5 12 12 \n", "109 110 2 5 12 12 \n", "110 111 2 5 12 12 \n", "111 112 1 5 12 12 \n", "112 113 1 5 12 12 \n", "113 114 1 5 12 10 \n", "114 115 2 5 12 10 \n", "115 116 2 5 12 11 \n", "116 117 2 5 12 12 \n", "117 118 2 5 12 12 \n", "118 119 2 5 12 12 \n", "119 120 2 5 12 12 \n", "120 121 2 5 12 12 \n", "121 122 2 5 12 11 \n", "122 123 2 5 12 12 \n", "123 124 2 5 12 12 \n", "124 125 2 5 12 10 \n", "125 126 2 5 12 12 \n", "126 127 2 5 12 12 \n", "127 128 2 5 12 12 \n", "128 129 2 5 12 12 \n", "129 130 2 5 12 10 \n", "130 131 2 5 12 12 \n", "\n", " commission_errors omission_errors mean_reaction_time total_duration \\\n", "79 0 0 1448 74000 \n", "80 0 0 1331 78000 \n", "81 0 0 1426 74500 \n", "82 0 0 1632 76000 \n", "83 0 1 1340 72000 \n", "84 0 0 1564 76000 \n", "85 0 2 1366 76000 \n", "86 0 0 1291 74500 \n", "87 0 2 2032 71500 \n", "88 0 1 1789 74000 \n", "89 0 0 1680 73500 \n", "90 0 1 1317 67500 \n", "91 0 0 1040 70500 \n", "92 0 0 1142 75500 \n", "93 0 0 1168 75000 \n", "94 0 0 1150 77000 \n", "95 0 0 1270 76000 \n", "96 0 1 1457 73000 \n", "97 0 0 1180 72500 \n", "98 0 1 1261 73500 \n", "99 0 3 1234 71500 \n", "100 0 0 1165 73000 \n", "101 0 0 1238 71000 \n", "102 0 1 1830 71000 \n", "103 0 2 1657 78000 \n", "104 0 3 1817 74000 \n", "105 0 1 1600 84500 \n", "106 0 1 1396 86500 \n", "107 0 0 1380 89000 \n", "108 0 0 1350 90000 \n", "109 0 0 1310 87000 \n", "110 0 0 1462 94000 \n", "111 0 0 1069 89000 \n", "112 0 0 1221 92000 \n", "113 0 2 1775 90000 \n", "114 0 2 1852 89500 \n", "115 0 1 1598 92000 \n", "116 0 0 1785 86000 \n", "117 0 0 1628 92000 \n", "118 0 0 1758 86500 \n", "119 0 0 1215 92000 \n", "120 0 0 1134 89000 \n", "121 0 1 1364 89000 \n", "122 0 0 1499 89000 \n", "123 0 0 1998 88000 \n", "124 0 2 1916 85500 \n", "125 0 0 1152 89500 \n", "126 0 0 1086 92500 \n", "127 0 0 1207 86500 \n", "128 0 0 1047 92000 \n", "129 0 2 1162 88500 \n", "130 0 0 1278 89000 \n", "\n", " diagnosis percentage_no_of_correct_responses oer cer game \n", "79 No 100.000000 0.000000 0.0 Focused \n", "80 No 100.000000 0.000000 0.0 Focused \n", "81 No 100.000000 0.000000 0.0 Focused \n", "82 No 100.000000 0.000000 0.0 Focused \n", "83 No 90.000000 10.000000 0.0 Focused \n", "84 No 100.000000 0.000000 0.0 Focused \n", "85 No 80.000000 20.000000 0.0 Focused \n", "86 No 100.000000 0.000000 0.0 Focused \n", "87 No 80.000000 20.000000 0.0 Focused \n", "88 No 90.000000 10.000000 0.0 Focused \n", "89 No 100.000000 0.000000 0.0 Focused \n", "90 No 90.000000 10.000000 0.0 Focused \n", "91 No 100.000000 0.000000 0.0 Focused \n", "92 No 100.000000 0.000000 0.0 Focused \n", "93 No 100.000000 0.000000 0.0 Focused \n", "94 No 100.000000 0.000000 0.0 Focused \n", "95 No 100.000000 0.000000 0.0 Focused \n", "96 No 90.000000 10.000000 0.0 Focused \n", "97 No 100.000000 0.000000 0.0 Focused \n", "98 No 90.000000 10.000000 0.0 Focused \n", "99 No 70.000000 30.000000 0.0 Focused \n", "100 No 100.000000 0.000000 0.0 Focused \n", "101 No 100.000000 0.000000 0.0 Focused \n", "102 No 90.000000 10.000000 0.0 Focused \n", "103 No 80.000000 20.000000 0.0 Focused \n", "104 No 70.000000 30.000000 0.0 Focused \n", "105 No 91.666667 8.333333 0.0 Focused \n", "106 No 91.666667 8.333333 0.0 Focused \n", "107 No 100.000000 0.000000 0.0 Focused \n", "108 No 100.000000 0.000000 0.0 Focused \n", "109 No 100.000000 0.000000 0.0 Focused \n", "110 No 100.000000 0.000000 0.0 Focused \n", "111 No 100.000000 0.000000 0.0 Focused \n", "112 No 100.000000 0.000000 0.0 Focused \n", "113 No 83.333333 16.666667 0.0 Focused \n", "114 No 83.333333 16.666667 0.0 Focused \n", "115 No 91.666667 8.333333 0.0 Focused \n", "116 No 100.000000 0.000000 0.0 Focused \n", "117 No 100.000000 0.000000 0.0 Focused \n", "118 No 100.000000 0.000000 0.0 Focused \n", "119 No 100.000000 0.000000 0.0 Focused \n", "120 No 100.000000 0.000000 0.0 Focused \n", "121 No 91.666667 8.333333 0.0 Focused \n", "122 No 100.000000 0.000000 0.0 Focused \n", "123 No 100.000000 0.000000 0.0 Focused \n", "124 No 83.333333 16.666667 0.0 Focused \n", "125 No 100.000000 0.000000 0.0 Focused \n", "126 No 100.000000 0.000000 0.0 Focused \n", "127 No 100.000000 0.000000 0.0 Focused \n", "128 No 100.000000 0.000000 0.0 Focused \n", "129 No 83.333333 16.666667 0.0 Focused \n", "130 No 100.000000 0.000000 0.0 Focused " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Importing the dataset \n", "dataset = pd.read_csv('data.csv') \n", "dataset.drop(dataset.index[dataset['game'] == 'Alternating'], inplace = True)\n", "dataset.drop(dataset.index[dataset['game'] == 'Sustained'], inplace = True)\n", "dataset.drop(dataset.index[dataset['game'] == 'Selective'], inplace = True)\n", "dataset.drop(dataset.index[dataset['game'] == 'Divided'], inplace = True)\n", "\n", "dataset.drop(dataset.index[dataset['child_age'] == 6], inplace = True)\n", "dataset.drop(dataset.index[dataset['child_age'] == 7], inplace = True)\n", "\n", "display(dataset)" ] }, { "cell_type": "code", "execution_count": 3, "id": "12841129", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[1448. , 100. , 0. ],\n", " [1331. , 100. , 0. ],\n", " [1426. , 100. , 0. ],\n", " [1632. , 100. , 0. ],\n", " [1340. , 90. , 10. ],\n", " [1564. , 100. , 0. ],\n", " [1366. , 80. , 20. ],\n", " [1291. , 100. , 0. ],\n", " [2032. , 80. , 20. ],\n", " [1789. , 90. , 10. ],\n", " [1680. , 100. , 0. ],\n", " [1317. , 90. , 10. ],\n", " [1040. , 100. , 0. ],\n", " [1142. , 100. , 0. ],\n", " [1168. , 100. , 0. ],\n", " [1150. , 100. , 0. ],\n", " [1270. , 100. , 0. ],\n", " [1457. , 90. , 10. ],\n", " [1180. , 100. , 0. ],\n", " [1261. , 90. , 10. ],\n", " [1234. , 70. , 30. ],\n", " [1165. , 100. , 0. ],\n", " [1238. , 100. , 0. ],\n", " [1830. , 90. , 10. ],\n", " [1657. , 80. , 20. ],\n", " [1817. , 70. , 30. ],\n", " [1600. , 91.66666667, 8.33333333],\n", " [1396. , 91.66666667, 8.33333333],\n", " [1380. , 100. , 0. ],\n", " [1350. , 100. , 0. ],\n", " [1310. , 100. , 0. ],\n", " [1462. , 100. , 0. ],\n", " [1069. , 100. , 0. ],\n", " [1221. , 100. , 0. ],\n", " [1775. , 83.33333333, 16.66666667],\n", " [1852. , 83.33333333, 16.66666667],\n", " [1598. , 91.66666667, 8.33333333],\n", " [1785. , 100. , 0. ],\n", " [1628. , 100. , 0. ],\n", " [1758. , 100. , 0. ],\n", " [1215. , 100. , 0. ],\n", " [1134. , 100. , 0. ],\n", " [1364. , 91.66666667, 8.33333333],\n", " [1499. , 100. , 0. ],\n", " [1998. , 100. , 0. ],\n", " [1916. , 83.33333333, 16.66666667],\n", " [1152. , 100. , 0. ],\n", " [1086. , 100. , 0. ],\n", " [1207. , 100. , 0. ],\n", " [1047. , 100. , 0. ],\n", " [1162. , 83.33333333, 16.66666667],\n", " [1278. , 100. , 0. ]])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# extracting only 11-comission & 12-omission\n", "x = dataset.iloc[:, [7, 10, 11]].values \n", "display(x)" ] }, { "cell_type": "code", "execution_count": 4, "id": "d569e05b", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.08876077, 0.65810029, -0.65810029],\n", " [-0.35063754, 0.65810029, -0.65810029],\n", " [ 0.00613887, 0.65810029, -0.65810029],\n", " [ 0.77978033, 0.65810029, -0.65810029],\n", " [-0.31683767, -0.56408597, 0.56408597],\n", " [ 0.52440354, 0.65810029, -0.65810029],\n", " [-0.2191936 , -1.78627223, 1.78627223],\n", " [-0.50085918, 0.65810029, -0.65810029],\n", " [ 2.28199678, -1.78627223, 1.78627223],\n", " [ 1.36940029, -0.56408597, 0.56408597],\n", " [ 0.96004631, 0.65810029, -0.65810029],\n", " [-0.40321512, -0.56408597, 0.56408597],\n", " [-1.4435 , 0.65810029, -0.65810029],\n", " [-1.06043481, 0.65810029, -0.65810029],\n", " [-0.96279074, 0.65810029, -0.65810029],\n", " [-1.03039048, 0.65810029, -0.65810029],\n", " [-0.57972555, 0.65810029, -0.65810029],\n", " [ 0.12256064, -0.56408597, 0.56408597],\n", " [-0.91772425, 0.65810029, -0.65810029],\n", " [-0.61352542, -0.56408597, 0.56408597],\n", " [-0.71492503, -3.00845849, 3.00845849],\n", " [-0.97405736, 0.65810029, -0.65810029],\n", " [-0.69990286, 0.65810029, -0.65810029],\n", " [ 1.52337747, -0.56408597, 0.56408597],\n", " [ 0.87366886, -1.78627223, 1.78627223],\n", " [ 1.47455544, -3.00845849, 3.00845849],\n", " [ 0.65960302, -0.36038826, 0.36038826],\n", " [-0.10652737, -0.36038826, 0.36038826],\n", " [-0.16661603, 0.65810029, -0.65810029],\n", " [-0.27928226, 0.65810029, -0.65810029],\n", " [-0.4295039 , 0.65810029, -0.65810029],\n", " [ 0.14133835, 0.65810029, -0.65810029],\n", " [-1.33458931, 0.65810029, -0.65810029],\n", " [-0.76374706, 0.65810029, -0.65810029],\n", " [ 1.31682271, -1.37887681, 1.37887681],\n", " [ 1.60599938, -1.37887681, 1.37887681],\n", " [ 0.65209194, -0.36038826, 0.36038826],\n", " [ 1.35437812, 0.65810029, -0.65810029],\n", " [ 0.76475817, 0.65810029, -0.65810029],\n", " [ 1.25297851, 0.65810029, -0.65810029],\n", " [-0.78628031, 0.65810029, -0.65810029],\n", " [-1.09047914, 0.65810029, -0.65810029],\n", " [-0.22670468, -0.36038826, 0.36038826],\n", " [ 0.28029337, 0.65810029, -0.65810029],\n", " [ 2.15430838, 0.65810029, -0.65810029],\n", " [ 1.84635401, -1.37887681, 1.37887681],\n", " [-1.0228794 , 0.65810029, -0.65810029],\n", " [-1.27074511, 0.65810029, -0.65810029],\n", " [-0.81632464, 0.65810029, -0.65810029],\n", " [-1.41721122, 0.65810029, -0.65810029],\n", " [-0.98532399, -1.37887681, 1.37887681],\n", " [-0.54968122, 0.65810029, -0.65810029]])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# standardizing the data\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "new_df = scaler.fit_transform(x)\n", "\n", "# statistics of scaled data\n", "pd.DataFrame(new_df).describe()\n", "\n", "display(new_df)\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "b5fc4f60", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 4. , 0.08876077, 0.65810029, -0.65810029],\n", " [ 4. , -0.35063754, 0.65810029, -0.65810029],\n", " [ 4. , 0.00613887, 0.65810029, -0.65810029],\n", " [ 4. , 0.77978033, 0.65810029, -0.65810029],\n", " [ 4. , -0.31683767, -0.56408597, 0.56408597],\n", " [ 4. , 0.52440354, 0.65810029, -0.65810029],\n", " [ 4. , -0.2191936 , -1.78627223, 1.78627223],\n", " [ 4. , -0.50085918, 0.65810029, -0.65810029],\n", " [ 4. , 2.28199678, -1.78627223, 1.78627223],\n", " [ 4. , 1.36940029, -0.56408597, 0.56408597],\n", " [ 4. , 0.96004631, 0.65810029, -0.65810029],\n", " [ 4. , -0.40321512, -0.56408597, 0.56408597],\n", " [ 4. , -1.4435 , 0.65810029, -0.65810029],\n", " [ 4. , -1.06043481, 0.65810029, -0.65810029],\n", " [ 4. , -0.96279074, 0.65810029, -0.65810029],\n", " [ 4. , -1.03039048, 0.65810029, -0.65810029],\n", " [ 4. , -0.57972555, 0.65810029, -0.65810029],\n", " [ 4. , 0.12256064, -0.56408597, 0.56408597],\n", " [ 4. , -0.91772425, 0.65810029, -0.65810029],\n", " [ 4. , -0.61352542, -0.56408597, 0.56408597],\n", " [ 4. , -0.71492503, -3.00845849, 3.00845849],\n", " [ 4. , -0.97405736, 0.65810029, -0.65810029],\n", " [ 4. , -0.69990286, 0.65810029, -0.65810029],\n", " [ 4. , 1.52337747, -0.56408597, 0.56408597],\n", " [ 4. , 0.87366886, -1.78627223, 1.78627223],\n", " [ 4. , 1.47455544, -3.00845849, 3.00845849],\n", " [ 5. , 0.65960302, -0.36038826, 0.36038826],\n", " [ 5. , -0.10652737, -0.36038826, 0.36038826],\n", " [ 5. , -0.16661603, 0.65810029, -0.65810029],\n", " [ 5. , -0.27928226, 0.65810029, -0.65810029],\n", " [ 5. , -0.4295039 , 0.65810029, -0.65810029],\n", " [ 5. , 0.14133835, 0.65810029, -0.65810029],\n", " [ 5. , -1.33458931, 0.65810029, -0.65810029],\n", " [ 5. , -0.76374706, 0.65810029, -0.65810029],\n", " [ 5. , 1.31682271, -1.37887681, 1.37887681],\n", " [ 5. , 1.60599938, -1.37887681, 1.37887681],\n", " [ 5. , 0.65209194, -0.36038826, 0.36038826],\n", " [ 5. , 1.35437812, 0.65810029, -0.65810029],\n", " [ 5. , 0.76475817, 0.65810029, -0.65810029],\n", " [ 5. , 1.25297851, 0.65810029, -0.65810029],\n", " [ 5. , -0.78628031, 0.65810029, -0.65810029],\n", " [ 5. , -1.09047914, 0.65810029, -0.65810029],\n", " [ 5. , -0.22670468, -0.36038826, 0.36038826],\n", " [ 5. , 0.28029337, 0.65810029, -0.65810029],\n", " [ 5. , 2.15430838, 0.65810029, -0.65810029],\n", " [ 5. , 1.84635401, -1.37887681, 1.37887681],\n", " [ 5. , -1.0228794 , 0.65810029, -0.65810029],\n", " [ 5. , -1.27074511, 0.65810029, -0.65810029],\n", " [ 5. , -0.81632464, 0.65810029, -0.65810029],\n", " [ 5. , -1.41721122, 0.65810029, -0.65810029],\n", " [ 5. , -0.98532399, -1.37887681, 1.37887681],\n", " [ 5. , -0.54968122, 0.65810029, -0.65810029]])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = dataset.iloc[:, [2, 7, 10, 11]].copy()\n", "x[['mean_reaction_time', 'percentage_no_of_correct_responses', 'oer']] = new_df\n", "x.head()\n", "x = x.to_numpy()\n", "display(x)\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "5d1c61bf", "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import make_classification\n", "from sklearn.cluster import MeanShift\n", "\n", "# define the model\n", "model = MeanShift()\n", "# fit model and predict clusters\n", "yhat = model.fit_predict(x)\n", "# retrieve unique clusters\n", "clusters = unique(yhat)" ] }, { "cell_type": "code", "execution_count": 7, "id": "3343196f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 4.53125 -0.47609609 0.59444476 -0.59444476]\n", " [ 4.4 0.26602231 -0.48260688 0.48260688]\n", " [ 4.42857143 1.54537422 -1.26247812 1.26247812]\n", " [ 4.33333333 -0.11028291 -1.65047376 1.65047376]\n", " [ 4. 1.47455544 -3.00845849 3.00845849]\n", " [ 4. -0.71492503 -3.00845849 3.00845849]]\n", "Estimated clusters: 6\n" ] } ], "source": [ "ms = MeanShift()\n", "ms.fit(x)\n", "labels = ms.labels_\n", "cluster_centers = ms.cluster_centers_\n", "print(cluster_centers)\n", "n_clusters_ = len(nm.unique(labels))\n", "print(\"Estimated clusters:\", n_clusters_)" ] }, { "cell_type": "code", "execution_count": 9, "id": "2e691585", "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>child_age</th>\n", " <th>mean_reaction_time</th>\n", " <th>percentage_no_of_correct_responses</th>\n", " <th>oer</th>\n", " <th>clusters</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>79</th>\n", " <td>4</td>\n", " <td>1448</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>4</td>\n", " <td>1331</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>4</td>\n", " <td>1426</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>4</td>\n", " <td>1632</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td>4</td>\n", " <td>1340</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td>4</td>\n", " <td>1564</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td>4</td>\n", " <td>1366</td>\n", " <td>80.000000</td>\n", " <td>20.000000</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td>4</td>\n", " <td>1291</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td>4</td>\n", " <td>2032</td>\n", " <td>80.000000</td>\n", " <td>20.000000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>4</td>\n", " <td>1789</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td>4</td>\n", " <td>1680</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>4</td>\n", " <td>1317</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>4</td>\n", " <td>1040</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td>4</td>\n", " <td>1142</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>4</td>\n", " <td>1168</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td>4</td>\n", " <td>1150</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td>4</td>\n", " <td>1270</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>4</td>\n", " <td>1457</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>4</td>\n", " <td>1180</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>4</td>\n", " <td>1261</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td>4</td>\n", " <td>1234</td>\n", " <td>70.000000</td>\n", " <td>30.000000</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>4</td>\n", " <td>1165</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>101</th>\n", " <td>4</td>\n", " <td>1238</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>102</th>\n", " <td>4</td>\n", " <td>1830</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>103</th>\n", " <td>4</td>\n", " <td>1657</td>\n", " <td>80.000000</td>\n", " <td>20.000000</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>104</th>\n", " <td>4</td>\n", " <td>1817</td>\n", " <td>70.000000</td>\n", " <td>30.000000</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>5</td>\n", " <td>1600</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>5</td>\n", " <td>1396</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>107</th>\n", " <td>5</td>\n", " <td>1380</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>108</th>\n", " <td>5</td>\n", " <td>1350</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>109</th>\n", " <td>5</td>\n", " <td>1310</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>110</th>\n", " <td>5</td>\n", " <td>1462</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>111</th>\n", " <td>5</td>\n", " <td>1069</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>112</th>\n", " <td>5</td>\n", " <td>1221</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>113</th>\n", " <td>5</td>\n", " <td>1775</td>\n", " <td>83.333333</td>\n", " <td>16.666667</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>114</th>\n", " <td>5</td>\n", " <td>1852</td>\n", " <td>83.333333</td>\n", " <td>16.666667</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>115</th>\n", " <td>5</td>\n", " <td>1598</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>116</th>\n", " <td>5</td>\n", " <td>1785</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>117</th>\n", " <td>5</td>\n", " <td>1628</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td>5</td>\n", " <td>1758</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>119</th>\n", " <td>5</td>\n", " <td>1215</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>5</td>\n", " <td>1134</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>121</th>\n", " <td>5</td>\n", " <td>1364</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>122</th>\n", " <td>5</td>\n", " <td>1499</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>123</th>\n", " <td>5</td>\n", " <td>1998</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>124</th>\n", " <td>5</td>\n", " <td>1916</td>\n", " <td>83.333333</td>\n", " <td>16.666667</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td>5</td>\n", " <td>1152</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>126</th>\n", " <td>5</td>\n", " <td>1086</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>127</th>\n", " <td>5</td>\n", " <td>1207</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td>5</td>\n", " <td>1047</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>129</th>\n", " <td>5</td>\n", " <td>1162</td>\n", " <td>83.333333</td>\n", " <td>16.666667</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>130</th>\n", " <td>5</td>\n", " <td>1278</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " child_age mean_reaction_time percentage_no_of_correct_responses \\\n", "79 4 1448 100.000000 \n", "80 4 1331 100.000000 \n", "81 4 1426 100.000000 \n", "82 4 1632 100.000000 \n", "83 4 1340 90.000000 \n", "84 4 1564 100.000000 \n", "85 4 1366 80.000000 \n", "86 4 1291 100.000000 \n", "87 4 2032 80.000000 \n", "88 4 1789 90.000000 \n", "89 4 1680 100.000000 \n", "90 4 1317 90.000000 \n", "91 4 1040 100.000000 \n", "92 4 1142 100.000000 \n", "93 4 1168 100.000000 \n", "94 4 1150 100.000000 \n", "95 4 1270 100.000000 \n", "96 4 1457 90.000000 \n", "97 4 1180 100.000000 \n", "98 4 1261 90.000000 \n", "99 4 1234 70.000000 \n", "100 4 1165 100.000000 \n", "101 4 1238 100.000000 \n", "102 4 1830 90.000000 \n", "103 4 1657 80.000000 \n", "104 4 1817 70.000000 \n", "105 5 1600 91.666667 \n", "106 5 1396 91.666667 \n", "107 5 1380 100.000000 \n", "108 5 1350 100.000000 \n", "109 5 1310 100.000000 \n", "110 5 1462 100.000000 \n", "111 5 1069 100.000000 \n", "112 5 1221 100.000000 \n", "113 5 1775 83.333333 \n", "114 5 1852 83.333333 \n", "115 5 1598 91.666667 \n", "116 5 1785 100.000000 \n", "117 5 1628 100.000000 \n", "118 5 1758 100.000000 \n", "119 5 1215 100.000000 \n", "120 5 1134 100.000000 \n", "121 5 1364 91.666667 \n", "122 5 1499 100.000000 \n", "123 5 1998 100.000000 \n", "124 5 1916 83.333333 \n", "125 5 1152 100.000000 \n", "126 5 1086 100.000000 \n", "127 5 1207 100.000000 \n", "128 5 1047 100.000000 \n", "129 5 1162 83.333333 \n", "130 5 1278 100.000000 \n", "\n", " oer clusters \n", "79 0.000000 0 \n", "80 0.000000 0 \n", "81 0.000000 0 \n", "82 0.000000 0 \n", "83 10.000000 1 \n", "84 0.000000 0 \n", "85 20.000000 3 \n", "86 0.000000 0 \n", "87 20.000000 2 \n", "88 10.000000 2 \n", "89 0.000000 0 \n", "90 10.000000 1 \n", "91 0.000000 0 \n", "92 0.000000 0 \n", "93 0.000000 0 \n", "94 0.000000 0 \n", "95 0.000000 0 \n", "96 10.000000 1 \n", "97 0.000000 0 \n", "98 10.000000 1 \n", "99 30.000000 5 \n", "100 0.000000 0 \n", "101 0.000000 0 \n", "102 10.000000 2 \n", "103 20.000000 3 \n", "104 30.000000 4 \n", "105 8.333333 1 \n", "106 8.333333 1 \n", "107 0.000000 0 \n", "108 0.000000 0 \n", "109 0.000000 0 \n", "110 0.000000 0 \n", "111 0.000000 0 \n", "112 0.000000 0 \n", "113 16.666667 2 \n", "114 16.666667 2 \n", "115 8.333333 1 \n", "116 0.000000 0 \n", "117 0.000000 0 \n", "118 0.000000 0 \n", "119 0.000000 0 \n", "120 0.000000 0 \n", "121 8.333333 1 \n", "122 0.000000 0 \n", "123 0.000000 1 \n", "124 16.666667 2 \n", "125 0.000000 0 \n", "126 0.000000 0 \n", "127 0.000000 0 \n", "128 0.000000 0 \n", "129 16.666667 3 \n", "130 0.000000 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_df = dataset.iloc[:, [2, 7, 10, 11]].copy()\n", "new_df['clusters'] = yhat\n", "new_df.head()\n", "display(new_df)" ] }, { "cell_type": "code", "execution_count": 10, "id": "50a9adbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Silhouette Coefficient: 0.361\n", "Calinski-Harabasz Index: 25.252\n", "Davies-Bouldin Index: 0.833\n" ] } ], "source": [ "from sklearn.metrics import silhouette_score,calinski_harabasz_score,davies_bouldin_score\n", "\n", "print(\"Silhouette Coefficient: %0.3f\" % silhouette_score(x, yhat))\n", "print(\"Calinski-Harabasz Index: %0.3f\" % calinski_harabasz_score(x, yhat))\n", "print(\"Davies-Bouldin Index: %0.3f\" % davies_bouldin_score(x, yhat))" ] }, { "cell_type": "markdown", "id": "900a0d3f", "metadata": {}, "source": [ "# Cluster Analysis" ] }, { "cell_type": "markdown", "id": "262e8a4f", "metadata": {}, "source": [ "## Cluster 1" ] }, { "cell_type": "code", "execution_count": 11, "id": "ba8fef3b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(new_df[new_df[\"clusters\"] == 0])" ] }, { "cell_type": "code", "execution_count": 12, "id": "6c5b7397", "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>child_age</th>\n", " <th>mean_reaction_time</th>\n", " <th>percentage_no_of_correct_responses</th>\n", " <th>oer</th>\n", " <th>clusters</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>79</th>\n", " <td>4</td>\n", " <td>1448</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>4</td>\n", " <td>1331</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>4</td>\n", " <td>1426</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>4</td>\n", " <td>1632</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td>4</td>\n", " <td>1564</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td>4</td>\n", " <td>1291</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td>4</td>\n", " <td>1680</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>4</td>\n", " <td>1040</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td>4</td>\n", " <td>1142</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>4</td>\n", " <td>1168</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td>4</td>\n", " <td>1150</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td>4</td>\n", " <td>1270</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>4</td>\n", " <td>1180</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>4</td>\n", " <td>1165</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>101</th>\n", " <td>4</td>\n", " <td>1238</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>107</th>\n", " <td>5</td>\n", " <td>1380</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>108</th>\n", " <td>5</td>\n", " <td>1350</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>109</th>\n", " <td>5</td>\n", " <td>1310</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>110</th>\n", " <td>5</td>\n", " <td>1462</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>111</th>\n", " <td>5</td>\n", " <td>1069</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>112</th>\n", " <td>5</td>\n", " <td>1221</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>116</th>\n", " <td>5</td>\n", " <td>1785</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>117</th>\n", " <td>5</td>\n", " <td>1628</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td>5</td>\n", " <td>1758</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>119</th>\n", " <td>5</td>\n", " <td>1215</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>5</td>\n", " <td>1134</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>122</th>\n", " <td>5</td>\n", " <td>1499</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td>5</td>\n", " <td>1152</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>126</th>\n", " <td>5</td>\n", " <td>1086</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>127</th>\n", " <td>5</td>\n", " <td>1207</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td>5</td>\n", " <td>1047</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>130</th>\n", " <td>5</td>\n", " <td>1278</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " child_age mean_reaction_time percentage_no_of_correct_responses oer \\\n", "79 4 1448 100.0 0.0 \n", "80 4 1331 100.0 0.0 \n", "81 4 1426 100.0 0.0 \n", "82 4 1632 100.0 0.0 \n", "84 4 1564 100.0 0.0 \n", "86 4 1291 100.0 0.0 \n", "89 4 1680 100.0 0.0 \n", "91 4 1040 100.0 0.0 \n", "92 4 1142 100.0 0.0 \n", "93 4 1168 100.0 0.0 \n", "94 4 1150 100.0 0.0 \n", "95 4 1270 100.0 0.0 \n", "97 4 1180 100.0 0.0 \n", "100 4 1165 100.0 0.0 \n", "101 4 1238 100.0 0.0 \n", "107 5 1380 100.0 0.0 \n", "108 5 1350 100.0 0.0 \n", "109 5 1310 100.0 0.0 \n", "110 5 1462 100.0 0.0 \n", "111 5 1069 100.0 0.0 \n", "112 5 1221 100.0 0.0 \n", "116 5 1785 100.0 0.0 \n", "117 5 1628 100.0 0.0 \n", "118 5 1758 100.0 0.0 \n", "119 5 1215 100.0 0.0 \n", "120 5 1134 100.0 0.0 \n", "122 5 1499 100.0 0.0 \n", "125 5 1152 100.0 0.0 \n", "126 5 1086 100.0 0.0 \n", "127 5 1207 100.0 0.0 \n", "128 5 1047 100.0 0.0 \n", "130 5 1278 100.0 0.0 \n", "\n", " clusters \n", "79 0 \n", "80 0 \n", "81 0 \n", "82 0 \n", "84 0 \n", "86 0 \n", "89 0 \n", "91 0 \n", "92 0 \n", "93 0 \n", "94 0 \n", "95 0 \n", "97 0 \n", "100 0 \n", "101 0 \n", "107 0 \n", "108 0 \n", "109 0 \n", "110 0 \n", "111 0 \n", "112 0 \n", "116 0 \n", "117 0 \n", "118 0 \n", "119 0 \n", "120 0 \n", "122 0 \n", "125 0 \n", "126 0 \n", "127 0 \n", "128 0 \n", "130 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n", "display(cluster_0)" ] }, { "cell_type": "code", "execution_count": 13, "id": "849d9447", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean_reaction_time min - 1040\n", "mean_reaction_time max - 1785\n", "\n", "percentage_no_of_correct_responses min - 100.0\n", "percentage_no_of_correct_responses max - 100.0\n", "\n", "oer min - 0.0\n", "oer max - 0.0\n" ] } ], "source": [ "cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n", "\n", "maxVal = cluster_0['mean_reaction_time'].max()\n", "minVal = cluster_0['mean_reaction_time'].min()\n", "\n", "print(\"mean_reaction_time min - \", minVal)\n", "print(\"mean_reaction_time max - \", maxVal)\n", "print()\n", "\n", "maxVal = cluster_0['percentage_no_of_correct_responses'].max()\n", "minVal = cluster_0['percentage_no_of_correct_responses'].min()\n", "\n", "print(\"percentage_no_of_correct_responses min - \", minVal)\n", "print(\"percentage_no_of_correct_responses max - \", maxVal)\n", "print()\n", "\n", "maxVal = cluster_0['oer'].max()\n", "minVal = cluster_0['oer'].min()\n", "\n", "print(\"oer min - \", minVal)\n", "print(\"oer max - \", maxVal)" ] }, { "cell_type": "markdown", "id": "dd8d7e4f", "metadata": {}, "source": [ "## Cluster 2" ] }, { "cell_type": "code", "execution_count": 14, "id": "f9ed816e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(new_df[new_df[\"clusters\"] == 1])" ] }, { "cell_type": "code", "execution_count": 15, "id": "e3eeb500", "metadata": { "scrolled": true }, "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>child_age</th>\n", " <th>mean_reaction_time</th>\n", " <th>percentage_no_of_correct_responses</th>\n", " <th>oer</th>\n", " <th>clusters</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>83</th>\n", " <td>4</td>\n", " <td>1340</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>4</td>\n", " <td>1317</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>4</td>\n", " <td>1457</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>4</td>\n", " <td>1261</td>\n", " <td>90.000000</td>\n", " <td>10.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>5</td>\n", " <td>1600</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>5</td>\n", " <td>1396</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>115</th>\n", " <td>5</td>\n", " <td>1598</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>121</th>\n", " <td>5</td>\n", " <td>1364</td>\n", " <td>91.666667</td>\n", " <td>8.333333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>123</th>\n", " <td>5</td>\n", " <td>1998</td>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " child_age mean_reaction_time percentage_no_of_correct_responses \\\n", "83 4 1340 90.000000 \n", "90 4 1317 90.000000 \n", "96 4 1457 90.000000 \n", "98 4 1261 90.000000 \n", "105 5 1600 91.666667 \n", "106 5 1396 91.666667 \n", "115 5 1598 91.666667 \n", "121 5 1364 91.666667 \n", "123 5 1998 100.000000 \n", "\n", " oer clusters \n", "83 10.000000 1 \n", "90 10.000000 1 \n", "96 10.000000 1 \n", "98 10.000000 1 \n", "105 8.333333 1 \n", "106 8.333333 1 \n", "115 8.333333 1 \n", "121 8.333333 1 \n", "123 0.000000 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n", "display(cluster_1)" ] }, { "cell_type": "code", "execution_count": 16, "id": "bb910e6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean_reaction_time min - 1261\n", "mean_reaction_time max - 1998\n", "\n", "percentage_no_of_correct_responses min - 90.0\n", "percentage_no_of_correct_responses max - 100.0\n", "\n", "oer min - 0.0\n", "oer max - 10.0\n" ] } ], "source": [ "cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n", "\n", "maxVal = cluster_1['mean_reaction_time'].max()\n", "minVal = cluster_1['mean_reaction_time'].min()\n", "\n", "print(\"mean_reaction_time min - \", minVal)\n", "print(\"mean_reaction_time max - \", maxVal)\n", "print()\n", "\n", "maxVal = cluster_1['percentage_no_of_correct_responses'].max()\n", "minVal = cluster_1['percentage_no_of_correct_responses'].min()\n", "\n", "print(\"percentage_no_of_correct_responses min - \", minVal)\n", "print(\"percentage_no_of_correct_responses max - \", maxVal)\n", "print()\n", "\n", "maxVal = cluster_1['oer'].max()\n", "minVal = cluster_1['oer'].min()\n", "\n", "print(\"oer min - \", minVal)\n", "print(\"oer max - \", maxVal)" ] }, { "cell_type": "code", "execution_count": null, "id": "e7f55f44", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.7" } }, "nbformat": 4, "nbformat_minor": 5 }