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2022-074
Commit 8ceb3408 authored by Anuththara18's avatar Anuththara18
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clustering model files added

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HerokuDeployment/Alternating - KMeans - Age 4 & 5.ipynb 0 → 100644
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HerokuDeployment/Alternating - KMeans - Age 6 & 7.ipynb 0 → 100644
View file @ 8ceb3408
{
"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": {
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
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"\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",
" <th>correct_responses</th>\n",
" <th>commission_errors</th>\n",
" <th>omission_errors</th>\n",
" <th>mean_reaction_time</th>\n",
" <th>total_duration</th>\n",
" <th>diagnosis</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>game</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
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" <td>No</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>Alternating</td>\n",
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" <td>0.000000</td>\n",
" <td>Alternating</td>\n",
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" <td>0.000000</td>\n",
" <td>52.631579</td>\n",
" <td>Alternating</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
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" <td>No</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>42.105263</td>\n",
" <td>Alternating</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
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" <td>57.894737</td>\n",
" <td>Alternating</td>\n",
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" <td>6</td>\n",
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" <td>1</td>\n",
" <td>994</td>\n",
" <td>57000</td>\n",
" <td>No</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>10.526316</td>\n",
" <td>Alternating</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
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" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>15.789474</td>\n",
" <td>Alternating</td>\n",
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" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>36.842105</td>\n",
" <td>Alternating</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
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" <td>2</td>\n",
" <td>6</td>\n",
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" <td>No</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>26.315789</td>\n",
" <td>Alternating</td>\n",
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" <th>31</th>\n",
" <td>29</td>\n",
" <td>1</td>\n",
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" <td>100.000000</td>\n",
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" <td>Alternating</td>\n",
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" <td>5.263158</td>\n",
" <td>47.368421</td>\n",
" <td>Alternating</td>\n",
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" <td>10.526316</td>\n",
" <td>47.368421</td>\n",
" <td>Alternating</td>\n",
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" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>Alternating</td>\n",
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" <td>57000</td>\n",
" <td>No</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>5.263158</td>\n",
" <td>Alternating</td>\n",
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" <td>0.000000</td>\n",
" <td>Alternating</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id child_gender child_age total_correct_responses correct_responses \\\n",
"22 23 2 6 19 19 \n",
"23 24 1 6 19 19 \n",
"24 25 1 6 19 19 \n",
"25 26 1 6 19 19 \n",
"26 27 1 6 19 19 \n",
"27 28 1 6 19 18 \n",
"28 29 2 6 19 18 \n",
"29 29 2 6 19 18 \n",
"30 29 2 6 19 18 \n",
"31 29 1 7 19 19 \n",
"32 30 1 7 19 18 \n",
"33 31 1 7 19 17 \n",
"34 32 1 7 19 19 \n",
"35 33 1 7 19 19 \n",
"36 37 2 7 19 18 \n",
"37 38 2 7 19 17 \n",
"38 39 2 7 19 19 \n",
"\n",
" commission_errors omission_errors mean_reaction_time total_duration \\\n",
"22 0 0 1023 57000 \n",
"23 0 0 1397 57000 \n",
"24 10 0 961 57000 \n",
"25 8 0 804 57000 \n",
"26 11 0 1180 57000 \n",
"27 2 1 994 57000 \n",
"28 3 1 448 57000 \n",
"29 7 1 733 57000 \n",
"30 5 1 1083 57000 \n",
"31 15 0 771 57000 \n",
"32 9 1 668 57000 \n",
"33 9 2 838 57000 \n",
"34 0 0 1338 57000 \n",
"35 0 0 1106 57000 \n",
"36 1 1 987 57000 \n",
"37 2 2 1181 57000 \n",
"38 0 0 1179 57000 \n",
"\n",
" diagnosis percentage_no_of_correct_responses oer cer \\\n",
"22 No 100.000000 0.000000 0.000000 \n",
"23 No 100.000000 0.000000 0.000000 \n",
"24 No 100.000000 0.000000 52.631579 \n",
"25 No 100.000000 0.000000 42.105263 \n",
"26 No 100.000000 0.000000 57.894737 \n",
"27 No 94.736842 5.263158 10.526316 \n",
"28 No 94.736842 5.263158 15.789474 \n",
"29 No 94.736842 5.263158 36.842105 \n",
"30 No 94.736842 5.263158 26.315789 \n",
"31 No 100.000000 0.000000 78.947368 \n",
"32 No 94.736842 5.263158 47.368421 \n",
"33 No 89.473684 10.526316 47.368421 \n",
"34 No 100.000000 0.000000 0.000000 \n",
"35 No 100.000000 0.000000 0.000000 \n",
"36 No 94.736842 5.263158 5.263158 \n",
"37 No 89.473684 10.526316 10.526316 \n",
"38 No 100.000000 0.000000 0.000000 \n",
"\n",
" game \n",
"22 Alternating \n",
"23 Alternating \n",
"24 Alternating \n",
"25 Alternating \n",
"26 Alternating \n",
"27 Alternating \n",
"28 Alternating \n",
"29 Alternating \n",
"30 Alternating \n",
"31 Alternating \n",
"32 Alternating \n",
"33 Alternating \n",
"34 Alternating \n",
"35 Alternating \n",
"36 Alternating \n",
"37 Alternating \n",
"38 Alternating "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Importing the dataset \n",
"dataset = pd.read_csv('data.csv') \n",
"\n",
"dataset.drop(dataset.index[dataset['game'] == 'Divided'], 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'] == 'Focused'], inplace = True)\n",
"\n",
"dataset.drop(dataset.index[dataset['child_age'] == 4], inplace = True)\n",
"dataset.drop(dataset.index[dataset['child_age'] == 5], inplace = True)\n",
"\n",
"display(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "12841129",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1023. , 100. , 0. , 0. ],\n",
" [1397. , 100. , 0. , 0. ],\n",
" [ 961. , 100. , 0. , 52.63157895],\n",
" [ 804. , 100. , 0. , 42.10526316],\n",
" [1180. , 100. , 0. , 57.89473684],\n",
" [ 994. , 94.73684211, 5.26315789, 10.52631579],\n",
" [ 448. , 94.73684211, 5.26315789, 15.78947368],\n",
" [ 733. , 94.73684211, 5.26315789, 36.84210526],\n",
" [1083. , 94.73684211, 5.26315789, 26.31578947],\n",
" [ 771. , 100. , 0. , 78.94736842],\n",
" [ 668. , 94.73684211, 5.26315789, 47.36842105],\n",
" [ 838. , 89.47368421, 10.52631579, 47.36842105],\n",
" [1338. , 100. , 0. , 0. ],\n",
" [1106. , 100. , 0. , 0. ],\n",
" [ 987. , 94.73684211, 5.26315789, 5.26315789],\n",
" [1181. , 89.47368421, 10.52631579, 10.52631579],\n",
" [1179. , 100. , 0. , 0. ]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# extracting only 11-comission & 12-omission\n",
"x = dataset.iloc[:, [7, 10, 11, 12]].values \n",
"display(x)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d569e05b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.17087754, 0.85125653, -0.85125653, -1.03606733],\n",
" [ 1.72293381, 0.85125653, -0.85125653, -1.03606733],\n",
" [-0.08641521, 0.85125653, -0.85125653, 1.11187713],\n",
" [-0.73794686, 0.85125653, -0.85125653, 0.68228824],\n",
" [ 0.82240918, 0.85125653, -0.85125653, 1.32667158],\n",
" [ 0.05053093, -0.59587957, 0.59587957, -0.60647843],\n",
" [-2.21530524, -0.59587957, 0.59587957, -0.39168399],\n",
" [-1.03258856, -0.59587957, 0.59587957, 0.46749379],\n",
" [ 0.41987052, -0.59587957, 0.59587957, 0.0379049 ],\n",
" [-0.874893 , 0.85125653, -0.85125653, 2.18584936],\n",
" [-1.30233096, -0.59587957, 0.59587957, 0.89708268],\n",
" [-0.59685083, -2.04301567, 2.04301567, 0.89708268],\n",
" [ 1.47809071, 0.85125653, -0.85125653, -1.03606733],\n",
" [ 0.51531784, 0.85125653, -0.85125653, -1.03606733],\n",
" [ 0.02148175, -0.59587957, 0.59587957, -0.82127288],\n",
" [ 0.82655907, -2.04301567, 2.04301567, -0.60647843],\n",
" [ 0.8182593 , 0.85125653, -0.85125653, -1.03606733]])"
]
},
"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([[ 6. , 0.17087754, 0.85125653, -0.85125653, -1.03606733],\n",
" [ 6. , 1.72293381, 0.85125653, -0.85125653, -1.03606733],\n",
" [ 6. , -0.08641521, 0.85125653, -0.85125653, 1.11187713],\n",
" [ 6. , -0.73794686, 0.85125653, -0.85125653, 0.68228824],\n",
" [ 6. , 0.82240918, 0.85125653, -0.85125653, 1.32667158],\n",
" [ 6. , 0.05053093, -0.59587957, 0.59587957, -0.60647843],\n",
" [ 6. , -2.21530524, -0.59587957, 0.59587957, -0.39168399],\n",
" [ 6. , -1.03258856, -0.59587957, 0.59587957, 0.46749379],\n",
" [ 6. , 0.41987052, -0.59587957, 0.59587957, 0.0379049 ],\n",
" [ 7. , -0.874893 , 0.85125653, -0.85125653, 2.18584936],\n",
" [ 7. , -1.30233096, -0.59587957, 0.59587957, 0.89708268],\n",
" [ 7. , -0.59685083, -2.04301567, 2.04301567, 0.89708268],\n",
" [ 7. , 1.47809071, 0.85125653, -0.85125653, -1.03606733],\n",
" [ 7. , 0.51531784, 0.85125653, -0.85125653, -1.03606733],\n",
" [ 7. , 0.02148175, -0.59587957, 0.59587957, -0.82127288],\n",
" [ 7. , 0.82655907, -2.04301567, 2.04301567, -0.60647843],\n",
" [ 7. , 0.8182593 , 0.85125653, -0.85125653, -1.03606733]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = dataset.iloc[:, [2, 7, 10, 11, 12]].copy()\n",
"x[['mean_reaction_time', 'percentage_no_of_correct_responses', 'oer', 'cer']] = new_df\n",
"x.head()\n",
"x = x.to_numpy()\n",
"display(x)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "58284e31",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Finding the optimal number of clusters using the elbow method\n",
"from sklearn.cluster import KMeans \n",
"wcss_list= [] #Initializing the list for the values of WCSS \n",
" \n",
"#Using for loop for iterations from 1 to 10. \n",
"for i in range(1, 11): \n",
" kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42) \n",
" kmeans.fit(x) \n",
" wcss_list.append(kmeans.inertia_) \n",
"mtp.plot(range(1, 11), wcss_list) \n",
"mtp.title('The Elobw Method Graph') \n",
"mtp.xlabel('Number of clusters(k)') \n",
"mtp.ylabel('wcss_list') \n",
"mtp.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5d1c61bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1 2 2 2 0 0 0 0 2 0 0 1 1 0 0 1]\n"
]
}
],
"source": [
"#training the K-means model on a dataset \n",
"kmeans = KMeans(n_clusters = 3, init='k-means++', random_state= 42) \n",
"y_predict= kmeans.fit_predict(x) \n",
"print(y_predict)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "984e35be",
"metadata": {},
"outputs": [],
"source": [
"# save the model to disk\n",
"import pickle\n",
"filename = 'modelaa2.sav'\n",
"pickle.dump(kmeans, open(filename, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2e691585",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</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>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
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" <th>23</th>\n",
" <td>6</td>\n",
" <td>1397</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>6</td>\n",
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" <td>1180</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>2</td>\n",
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" <tr>\n",
" <th>27</th>\n",
" <td>6</td>\n",
" <td>994</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>10.526316</td>\n",
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" <tr>\n",
" <th>28</th>\n",
" <td>6</td>\n",
" <td>448</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>15.789474</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>6</td>\n",
" <td>733</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>36.842105</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>30</th>\n",
" <td>6</td>\n",
" <td>1083</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>26.315789</td>\n",
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" <td>7</td>\n",
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" <td>2</td>\n",
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" <th>32</th>\n",
" <td>7</td>\n",
" <td>668</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>47.368421</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>7</td>\n",
" <td>838</td>\n",
" <td>89.473684</td>\n",
" <td>10.526316</td>\n",
" <td>47.368421</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>7</td>\n",
" <td>1338</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>35</th>\n",
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" <tr>\n",
" <th>36</th>\n",
" <td>7</td>\n",
" <td>987</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>5.263158</td>\n",
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" <td>7</td>\n",
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" <td>10.526316</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>38</th>\n",
" <td>7</td>\n",
" <td>1179</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</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",
"22 6 1023 100.000000 \n",
"23 6 1397 100.000000 \n",
"24 6 961 100.000000 \n",
"25 6 804 100.000000 \n",
"26 6 1180 100.000000 \n",
"27 6 994 94.736842 \n",
"28 6 448 94.736842 \n",
"29 6 733 94.736842 \n",
"30 6 1083 94.736842 \n",
"31 7 771 100.000000 \n",
"32 7 668 94.736842 \n",
"33 7 838 89.473684 \n",
"34 7 1338 100.000000 \n",
"35 7 1106 100.000000 \n",
"36 7 987 94.736842 \n",
"37 7 1181 89.473684 \n",
"38 7 1179 100.000000 \n",
"\n",
" oer cer clusters \n",
"22 0.000000 0.000000 1 \n",
"23 0.000000 0.000000 1 \n",
"24 0.000000 52.631579 2 \n",
"25 0.000000 42.105263 2 \n",
"26 0.000000 57.894737 2 \n",
"27 5.263158 10.526316 0 \n",
"28 5.263158 15.789474 0 \n",
"29 5.263158 36.842105 0 \n",
"30 5.263158 26.315789 0 \n",
"31 0.000000 78.947368 2 \n",
"32 5.263158 47.368421 0 \n",
"33 10.526316 47.368421 0 \n",
"34 0.000000 0.000000 1 \n",
"35 0.000000 0.000000 1 \n",
"36 5.263158 5.263158 0 \n",
"37 10.526316 10.526316 0 \n",
"38 0.000000 0.000000 1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"new_df = dataset.iloc[:, [2, 7, 10, 11, 12]].copy()\n",
"new_df['clusters'] = y_predict\n",
"new_df.head()\n",
"display(new_df)"
]
},
{
"cell_type": "markdown",
"id": "900a0d3f",
"metadata": {},
"source": [
"# Cluster Analysis"
]
},
{
"cell_type": "markdown",
"id": "262e8a4f",
"metadata": {},
"source": [
"## Cluster 1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ba8fef3b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6c5b7397",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" 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>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>6</td>\n",
" <td>994</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>10.526316</td>\n",
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" <tr>\n",
" <th>28</th>\n",
" <td>6</td>\n",
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" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>15.789474</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>6</td>\n",
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" <td>36.842105</td>\n",
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" <th>30</th>\n",
" <td>6</td>\n",
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" <td>5.263158</td>\n",
" <td>26.315789</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>32</th>\n",
" <td>7</td>\n",
" <td>668</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>47.368421</td>\n",
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" <tr>\n",
" <th>33</th>\n",
" <td>7</td>\n",
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" <td>47.368421</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>7</td>\n",
" <td>987</td>\n",
" <td>94.736842</td>\n",
" <td>5.263158</td>\n",
" <td>5.263158</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>7</td>\n",
" <td>1181</td>\n",
" <td>89.473684</td>\n",
" <td>10.526316</td>\n",
" <td>10.526316</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",
"27 6 994 94.736842 \n",
"28 6 448 94.736842 \n",
"29 6 733 94.736842 \n",
"30 6 1083 94.736842 \n",
"32 7 668 94.736842 \n",
"33 7 838 89.473684 \n",
"36 7 987 94.736842 \n",
"37 7 1181 89.473684 \n",
"\n",
" oer cer clusters \n",
"27 5.263158 10.526316 0 \n",
"28 5.263158 15.789474 0 \n",
"29 5.263158 36.842105 0 \n",
"30 5.263158 26.315789 0 \n",
"32 5.263158 47.368421 0 \n",
"33 10.526316 47.368421 0 \n",
"36 5.263158 5.263158 0 \n",
"37 10.526316 10.526316 0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n",
"display(cluster_0)\n",
"# cluster_0.boxplot(column =['CER'], grid = False)\n",
"# cluster_0.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "849d9447",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean_reaction_time min - 448\n",
"mean_reaction_time max - 1181\n",
"\n",
"percentage_no_of_correct_responses min - 89.47368421\n",
"percentage_no_of_correct_responses max - 94.73684211\n",
"\n",
"oer min - 5.263157895\n",
"oer max - 10.52631579\n",
"\n",
"cer min - 5.263157895\n",
"cer max - 47.36842105\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)\n",
"print()\n",
"\n",
"maxVal = cluster_0['cer'].max()\n",
"minVal = cluster_0['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "markdown",
"id": "dd8d7e4f",
"metadata": {},
"source": [
"## Cluster 2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f9ed816e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 1])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e3eeb500",
"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>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>6</td>\n",
" <td>1023</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>6</td>\n",
" <td>1397</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>7</td>\n",
" <td>1338</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>7</td>\n",
" <td>1106</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>7</td>\n",
" <td>1179</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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 oer \\\n",
"22 6 1023 100.0 0.0 \n",
"23 6 1397 100.0 0.0 \n",
"34 7 1338 100.0 0.0 \n",
"35 7 1106 100.0 0.0 \n",
"38 7 1179 100.0 0.0 \n",
"\n",
" cer clusters \n",
"22 0.0 1 \n",
"23 0.0 1 \n",
"34 0.0 1 \n",
"35 0.0 1 \n",
"38 0.0 1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n",
"display(cluster_1)\n",
"#cluster_1.boxplot(column =['CER'], grid = False)\n",
"#cluster_1.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "2ab1bc45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean_reaction_time min - 1023\n",
"mean_reaction_time max - 1397\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",
"\n",
"cer min - 0.0\n",
"cer max - 0.0\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)\n",
"\n",
"print()\n",
"\n",
"maxVal = cluster_1['cer'].max()\n",
"minVal = cluster_1['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "markdown",
"id": "e62b9a30",
"metadata": {},
"source": [
"## Cluster 3"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "105ff3ad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 2])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "9c9ac4a6",
"metadata": {},
"outputs": [
{
"data": {
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" <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>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
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" <tr>\n",
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" <th>31</th>\n",
" <td>7</td>\n",
" <td>771</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>78.947368</td>\n",
" <td>2</td>\n",
" </tr>\n",
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"</table>\n",
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],
"text/plain": [
" child_age mean_reaction_time percentage_no_of_correct_responses oer \\\n",
"24 6 961 100.0 0.0 \n",
"25 6 804 100.0 0.0 \n",
"26 6 1180 100.0 0.0 \n",
"31 7 771 100.0 0.0 \n",
"\n",
" cer clusters \n",
"24 52.631579 2 \n",
"25 42.105263 2 \n",
"26 57.894737 2 \n",
"31 78.947368 2 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_2 = new_df[new_df[\"clusters\"] == 2 ]\n",
"display(cluster_2)\n",
"#cluster_2.boxplot(column =['CER'], grid = False)\n",
"#cluster_2.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "09b1596d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean_reaction_time min - 771\n",
"mean_reaction_time max - 1180\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",
"\n",
"cer min - 42.10526316\n",
"cer max - 78.94736842\n"
]
}
],
"source": [
"cluster_2 = new_df[new_df[\"clusters\"] == 2 ]\n",
"\n",
"maxVal = cluster_2['mean_reaction_time'].max()\n",
"minVal = cluster_2['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_2['percentage_no_of_correct_responses'].max()\n",
"minVal = cluster_2['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_2['oer'].max()\n",
"minVal = cluster_2['oer'].min()\n",
"\n",
"print(\"oer min - \", minVal)\n",
"print(\"oer max - \", maxVal)\n",
"\n",
"print()\n",
"\n",
"maxVal = cluster_2['cer'].max()\n",
"minVal = cluster_2['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37d3d977",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "796872b0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"version": 3
},
"file_extension": ".py",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/Divided Attention - BIRCH.ipynb 0 → 100644
View file @ 8ceb3408
{
"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": {
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" <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",
" <th>correct_responses</th>\n",
" <th>commission_errors</th>\n",
" <th>omission_errors</th>\n",
" <th>mean_reaction_time</th>\n",
" <th>total_duration</th>\n",
" <th>diagnosis</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
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" <th>cer</th>\n",
" <th>game</th>\n",
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" <tr>\n",
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" <td>Divided</td>\n",
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" <td>70000</td>\n",
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" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>Divided</td>\n",
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" <td>Divided</td>\n",
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" <th>47</th>\n",
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" <td>No</td>\n",
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" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>Divided</td>\n",
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" <td>25.0</td>\n",
" <td>37.5</td>\n",
" <td>Divided</td>\n",
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" <th>52</th>\n",
" <td>53</td>\n",
" <td>2</td>\n",
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" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1160</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>54</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
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" <td>1053</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
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" <th>54</th>\n",
" <td>55</td>\n",
" <td>2</td>\n",
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" <td>87.5</td>\n",
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" <td>0.0</td>\n",
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" <td>25.0</td>\n",
" <td>37.5</td>\n",
" <td>Divided</td>\n",
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" <td>6</td>\n",
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" <td>60000</td>\n",
" <td>No</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Divided</td>\n",
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" <tr>\n",
" <th>58</th>\n",
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" <td>5</td>\n",
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" <td>7</td>\n",
" <td>7</td>\n",
" <td>1188</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>12.5</td>\n",
" <td>87.5</td>\n",
" <td>87.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>60</td>\n",
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" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>61</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>614</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>50.0</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
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" <td>2</td>\n",
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" <td>8</td>\n",
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" <td>60000</td>\n",
" <td>No</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
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" <td>No</td>\n",
" <td>62.5</td>\n",
" <td>37.5</td>\n",
" <td>50.0</td>\n",
" <td>Divided</td>\n",
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" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>67</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1025</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>68</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1012</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>37.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>69</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>845</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>62.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>850</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>37.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>71</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>586</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>75.0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>72</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>845</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>25.0</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>73</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1033</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>74</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>867</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>75</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>901</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>75.0</td>\n",
" <td>25.0</td>\n",
" <td>37.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>76</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>955</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>77</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>780</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>78</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>694</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>12.5</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>79</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>719</td>\n",
" <td>60000</td>\n",
" <td>No</td>\n",
" <td>62.5</td>\n",
" <td>37.5</td>\n",
" <td>0.0</td>\n",
" <td>Divided</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id child_gender child_age total_correct_responses correct_responses \\\n",
"39 40 2 4 8 5 \n",
"40 41 1 4 8 5 \n",
"41 42 2 4 8 7 \n",
"42 43 2 4 8 4 \n",
"43 44 1 4 8 6 \n",
"44 45 1 4 8 8 \n",
"45 46 1 4 8 7 \n",
"46 47 1 4 8 8 \n",
"47 48 1 4 8 8 \n",
"48 49 1 4 8 7 \n",
"49 50 1 4 8 5 \n",
"50 51 2 5 8 8 \n",
"51 52 2 5 8 6 \n",
"52 53 2 5 8 8 \n",
"53 54 2 5 8 7 \n",
"54 55 2 5 8 7 \n",
"55 56 2 5 8 6 \n",
"56 57 2 5 8 3 \n",
"57 58 1 5 8 8 \n",
"58 59 1 5 8 1 \n",
"59 60 2 6 8 7 \n",
"60 61 2 6 8 7 \n",
"61 62 2 6 8 8 \n",
"62 63 2 6 8 8 \n",
"63 64 2 6 8 5 \n",
"64 65 1 6 8 7 \n",
"65 66 1 6 8 7 \n",
"66 67 1 6 8 7 \n",
"67 68 1 6 8 7 \n",
"68 69 1 6 8 7 \n",
"69 70 1 7 8 8 \n",
"70 71 1 7 8 6 \n",
"71 72 1 7 8 7 \n",
"72 73 1 7 8 8 \n",
"73 74 1 7 8 7 \n",
"74 75 1 7 8 6 \n",
"75 76 2 7 8 7 \n",
"76 77 2 7 8 8 \n",
"77 78 2 7 8 8 \n",
"78 79 2 7 8 5 \n",
"\n",
" commission_errors omission_errors mean_reaction_time total_duration \\\n",
"39 3 3 1303 70000 \n",
"40 2 3 1384 70000 \n",
"41 4 1 1191 70000 \n",
"42 2 4 1335 70000 \n",
"43 2 2 1253 70000 \n",
"44 2 0 1239 70000 \n",
"45 4 1 1109 70000 \n",
"46 2 0 952 70000 \n",
"47 0 0 928 70000 \n",
"48 3 1 1428 70000 \n",
"49 6 3 1115 70000 \n",
"50 2 0 1157 60000 \n",
"51 3 2 1097 60000 \n",
"52 0 0 1160 60000 \n",
"53 1 1 1053 60000 \n",
"54 0 1 953 60000 \n",
"55 3 2 1303 60000 \n",
"56 6 5 1257 60000 \n",
"57 0 0 1008 60000 \n",
"58 7 7 1188 60000 \n",
"59 1 1 670 60000 \n",
"60 4 1 614 60000 \n",
"61 2 0 778 60000 \n",
"62 1 0 778 60000 \n",
"63 4 3 832 60000 \n",
"64 1 1 1173 60000 \n",
"65 1 1 1007 60000 \n",
"66 1 1 1025 60000 \n",
"67 3 1 1012 60000 \n",
"68 5 1 845 60000 \n",
"69 3 0 850 60000 \n",
"70 0 2 586 60000 \n",
"71 2 1 845 60000 \n",
"72 0 0 1033 60000 \n",
"73 1 1 867 60000 \n",
"74 3 2 901 60000 \n",
"75 1 1 955 60000 \n",
"76 1 0 780 60000 \n",
"77 1 0 694 60000 \n",
"78 0 3 719 60000 \n",
"\n",
" diagnosis percentage_no_of_correct_responses oer cer game \n",
"39 Yes 62.5 37.5 37.5 Divided \n",
"40 No 62.5 37.5 25.0 Divided \n",
"41 No 87.5 12.5 50.0 Divided \n",
"42 No 50.0 50.0 25.0 Divided \n",
"43 No 75.0 25.0 25.0 Divided \n",
"44 No 100.0 0.0 25.0 Divided \n",
"45 No 87.5 12.5 50.0 Divided \n",
"46 No 100.0 0.0 25.0 Divided \n",
"47 No 100.0 0.0 0.0 Divided \n",
"48 No 87.5 12.5 37.5 Divided \n",
"49 No 62.5 37.5 75.0 Divided \n",
"50 No 100.0 0.0 25.0 Divided \n",
"51 No 75.0 25.0 37.5 Divided \n",
"52 No 100.0 0.0 0.0 Divided \n",
"53 No 87.5 12.5 12.5 Divided \n",
"54 No 87.5 12.5 0.0 Divided \n",
"55 No 75.0 25.0 37.5 Divided \n",
"56 No 37.5 62.5 75.0 Divided \n",
"57 No 100.0 0.0 0.0 Divided \n",
"58 No 12.5 87.5 87.5 Divided \n",
"59 No 87.5 12.5 12.5 Divided \n",
"60 No 87.5 12.5 50.0 Divided \n",
"61 No 100.0 0.0 25.0 Divided \n",
"62 No 100.0 0.0 12.5 Divided \n",
"63 No 62.5 37.5 50.0 Divided \n",
"64 No 87.5 12.5 12.5 Divided \n",
"65 No 87.5 12.5 12.5 Divided \n",
"66 No 87.5 12.5 12.5 Divided \n",
"67 No 87.5 12.5 37.5 Divided \n",
"68 No 87.5 12.5 62.5 Divided \n",
"69 No 100.0 0.0 37.5 Divided \n",
"70 No 75.0 25.0 0.0 Divided \n",
"71 No 87.5 12.5 25.0 Divided \n",
"72 No 100.0 0.0 0.0 Divided \n",
"73 No 87.5 12.5 12.5 Divided \n",
"74 No 75.0 25.0 37.5 Divided \n",
"75 No 87.5 12.5 12.5 Divided \n",
"76 No 100.0 0.0 12.5 Divided \n",
"77 No 100.0 0.0 12.5 Divided \n",
"78 No 62.5 37.5 0.0 Divided "
]
},
"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'] == 'Focused'], inplace = True)\n",
"display(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "12841129",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1303. , 62.5, 37.5, 37.5],\n",
" [1384. , 62.5, 37.5, 25. ],\n",
" [1191. , 87.5, 12.5, 50. ],\n",
" [1335. , 50. , 50. , 25. ],\n",
" [1253. , 75. , 25. , 25. ],\n",
" [1239. , 100. , 0. , 25. ],\n",
" [1109. , 87.5, 12.5, 50. ],\n",
" [ 952. , 100. , 0. , 25. ],\n",
" [ 928. , 100. , 0. , 0. ],\n",
" [1428. , 87.5, 12.5, 37.5],\n",
" [1115. , 62.5, 37.5, 75. ],\n",
" [1157. , 100. , 0. , 25. ],\n",
" [1097. , 75. , 25. , 37.5],\n",
" [1160. , 100. , 0. , 0. ],\n",
" [1053. , 87.5, 12.5, 12.5],\n",
" [ 953. , 87.5, 12.5, 0. ],\n",
" [1303. , 75. , 25. , 37.5],\n",
" [1257. , 37.5, 62.5, 75. ],\n",
" [1008. , 100. , 0. , 0. ],\n",
" [1188. , 12.5, 87.5, 87.5],\n",
" [ 670. , 87.5, 12.5, 12.5],\n",
" [ 614. , 87.5, 12.5, 50. ],\n",
" [ 778. , 100. , 0. , 25. ],\n",
" [ 778. , 100. , 0. , 12.5],\n",
" [ 832. , 62.5, 37.5, 50. ],\n",
" [1173. , 87.5, 12.5, 12.5],\n",
" [1007. , 87.5, 12.5, 12.5],\n",
" [1025. , 87.5, 12.5, 12.5],\n",
" [1012. , 87.5, 12.5, 37.5],\n",
" [ 845. , 87.5, 12.5, 62.5],\n",
" [ 850. , 100. , 0. , 37.5],\n",
" [ 586. , 75. , 25. , 0. ],\n",
" [ 845. , 87.5, 12.5, 25. ],\n",
" [1033. , 100. , 0. , 0. ],\n",
" [ 867. , 87.5, 12.5, 12.5],\n",
" [ 901. , 75. , 25. , 37.5],\n",
" [ 955. , 87.5, 12.5, 12.5],\n",
" [ 780. , 100. , 0. , 12.5],\n",
" [ 694. , 100. , 0. , 12.5],\n",
" [ 719. , 62.5, 37.5, 0. ]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# extracting only 11-comission & 12-omission\n",
"x = dataset.iloc[:, [7, 10, 11, 12]].values \n",
"display(x)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d569e05b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.35119909, -1.05730976, 1.05730976, 0.46525043],\n",
" [ 1.72400715, -1.05730976, 1.05730976, -0.09868948],\n",
" [ 0.8357114 , 0.26432744, -0.26432744, 1.02919034],\n",
" [ 1.49848129, -1.71812836, 1.71812836, -0.09868948],\n",
" [ 1.12107066, -0.39649116, 0.39649116, -0.09868948],\n",
" [ 1.0566347 , 0.92514604, -0.92514604, -0.09868948],\n",
" [ 0.45830077, 0.26432744, -0.26432744, 1.02919034],\n",
" [-0.2643025 , 0.92514604, -0.92514604, -0.09868948],\n",
" [-0.37476415, 0.92514604, -0.92514604, -1.22656931],\n",
" [ 1.92652017, 0.26432744, -0.26432744, 0.46525043],\n",
" [ 0.48591619, -1.05730976, 1.05730976, 2.15707017],\n",
" [ 0.67922407, 0.92514604, -0.92514604, -0.09868948],\n",
" [ 0.40306995, -0.39649116, 0.39649116, 0.46525043],\n",
" [ 0.69303177, 0.92514604, -0.92514604, -1.22656931],\n",
" [ 0.20055693, 0.26432744, -0.26432744, -0.6626294 ],\n",
" [-0.25969994, 0.26432744, -0.26432744, -1.22656931],\n",
" [ 1.35119909, -0.39649116, 0.39649116, 0.46525043],\n",
" [ 1.13948093, -2.37894696, 2.37894696, 2.15707017],\n",
" [-0.00655866, 0.92514604, -0.92514604, -1.22656931],\n",
" [ 0.8219037 , -3.70058416, 3.70058416, 2.72101008],\n",
" [-1.56222686, 0.26432744, -0.26432744, -0.6626294 ],\n",
" [-1.81997071, 0.26432744, -0.26432744, 1.02919034],\n",
" [-1.06514945, 0.92514604, -0.92514604, -0.09868948],\n",
" [-1.06514945, 0.92514604, -0.92514604, -0.6626294 ],\n",
" [-0.81661074, -1.05730976, 1.05730976, 1.02919034],\n",
" [ 0.75286517, 0.26432744, -0.26432744, -0.6626294 ],\n",
" [-0.01116123, 0.26432744, -0.26432744, -0.6626294 ],\n",
" [ 0.07168501, 0.26432744, -0.26432744, -0.6626294 ],\n",
" [ 0.01185161, 0.26432744, -0.26432744, 0.46525043],\n",
" [-0.75677735, 0.26432744, -0.26432744, 1.59313025],\n",
" [-0.73376451, 0.92514604, -0.92514604, 0.46525043],\n",
" [-1.94884263, -0.39649116, 0.39649116, -1.22656931],\n",
" [-0.75677735, 0.26432744, -0.26432744, -0.09868948],\n",
" [ 0.10850556, 0.92514604, -0.92514604, -1.22656931],\n",
" [-0.65552084, 0.26432744, -0.26432744, -0.6626294 ],\n",
" [-0.49903351, -0.39649116, 0.39649116, 0.46525043],\n",
" [-0.2504948 , 0.26432744, -0.26432744, -0.6626294 ],\n",
" [-1.05594431, 0.92514604, -0.92514604, -0.6626294 ],\n",
" [-1.45176522, 0.92514604, -0.92514604, -0.6626294 ],\n",
" [-1.336701 , -1.05730976, 1.05730976, -1.22656931]])"
]
},
"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([[ 4.00000000e+00, 1.35119909e+00, -1.05730976e+00,\n",
" 1.05730976e+00, 4.65250428e-01],\n",
" [ 4.00000000e+00, 1.72400715e+00, -1.05730976e+00,\n",
" 1.05730976e+00, -9.86894847e-02],\n",
" [ 4.00000000e+00, 8.35711402e-01, 2.64327440e-01,\n",
" -2.64327440e-01, 1.02919034e+00],\n",
" [ 4.00000000e+00, 1.49848129e+00, -1.71812836e+00,\n",
" 1.71812836e+00, -9.86894847e-02],\n",
" [ 4.00000000e+00, 1.12107066e+00, -3.96491160e-01,\n",
" 3.96491160e-01, -9.86894847e-02],\n",
" [ 4.00000000e+00, 1.05663470e+00, 9.25146041e-01,\n",
" -9.25146041e-01, -9.86894847e-02],\n",
" [ 4.00000000e+00, 4.58300773e-01, 2.64327440e-01,\n",
" -2.64327440e-01, 1.02919034e+00],\n",
" [ 4.00000000e+00, -2.64302505e-01, 9.25146041e-01,\n",
" -9.25146041e-01, -9.86894847e-02],\n",
" [ 4.00000000e+00, -3.74764152e-01, 9.25146041e-01,\n",
" -9.25146041e-01, -1.22656931e+00],\n",
" [ 4.00000000e+00, 1.92652017e+00, 2.64327440e-01,\n",
" -2.64327440e-01, 4.65250428e-01],\n",
" [ 4.00000000e+00, 4.85916185e-01, -1.05730976e+00,\n",
" 1.05730976e+00, 2.15707017e+00],\n",
" [ 5.00000000e+00, 6.79224068e-01, 9.25146041e-01,\n",
" -9.25146041e-01, -9.86894847e-02],\n",
" [ 5.00000000e+00, 4.03069949e-01, -3.96491160e-01,\n",
" 3.96491160e-01, 4.65250428e-01],\n",
" [ 5.00000000e+00, 6.93031774e-01, 9.25146041e-01,\n",
" -9.25146041e-01, -1.22656931e+00],\n",
" [ 5.00000000e+00, 2.00556929e-01, 2.64327440e-01,\n",
" -2.64327440e-01, -6.62629397e-01],\n",
" [ 5.00000000e+00, -2.59699936e-01, 2.64327440e-01,\n",
" -2.64327440e-01, -1.22656931e+00],\n",
" [ 5.00000000e+00, 1.35119909e+00, -3.96491160e-01,\n",
" 3.96491160e-01, 4.65250428e-01],\n",
" [ 5.00000000e+00, 1.13948093e+00, -2.37894696e+00,\n",
" 2.37894696e+00, 2.15707017e+00],\n",
" [ 5.00000000e+00, -6.55866032e-03, 9.25146041e-01,\n",
" -9.25146041e-01, -1.22656931e+00],\n",
" [ 5.00000000e+00, 8.21903696e-01, -3.70058416e+00,\n",
" 3.70058416e+00, 2.72101008e+00],\n",
" [ 6.00000000e+00, -1.56222686e+00, 2.64327440e-01,\n",
" -2.64327440e-01, -6.62629397e-01],\n",
" [ 6.00000000e+00, -1.81997071e+00, 2.64327440e-01,\n",
" -2.64327440e-01, 1.02919034e+00],\n",
" [ 6.00000000e+00, -1.06514945e+00, 9.25146041e-01,\n",
" -9.25146041e-01, -9.86894847e-02],\n",
" [ 6.00000000e+00, -1.06514945e+00, 9.25146041e-01,\n",
" -9.25146041e-01, -6.62629397e-01],\n",
" [ 6.00000000e+00, -8.16610742e-01, -1.05730976e+00,\n",
" 1.05730976e+00, 1.02919034e+00],\n",
" [ 6.00000000e+00, 7.52865167e-01, 2.64327440e-01,\n",
" -2.64327440e-01, -6.62629397e-01],\n",
" [ 6.00000000e+00, -1.11612290e-02, 2.64327440e-01,\n",
" -2.64327440e-01, -6.62629397e-01],\n",
" [ 6.00000000e+00, 7.16850067e-02, 2.64327440e-01,\n",
" -2.64327440e-01, -6.62629397e-01],\n",
" [ 6.00000000e+00, 1.18516143e-02, 2.64327440e-01,\n",
" -2.64327440e-01, 4.65250428e-01],\n",
" [ 6.00000000e+00, -7.56777350e-01, 2.64327440e-01,\n",
" -2.64327440e-01, 1.59313025e+00],\n",
" [ 7.00000000e+00, -7.33764507e-01, 9.25146041e-01,\n",
" -9.25146041e-01, 4.65250428e-01],\n",
" [ 7.00000000e+00, -1.94884263e+00, -3.96491160e-01,\n",
" 3.96491160e-01, -1.22656931e+00],\n",
" [ 7.00000000e+00, -7.56777350e-01, 2.64327440e-01,\n",
" -2.64327440e-01, -9.86894847e-02],\n",
" [ 7.00000000e+00, 1.08505556e-01, 9.25146041e-01,\n",
" -9.25146041e-01, -1.22656931e+00],\n",
" [ 7.00000000e+00, -6.55520840e-01, 2.64327440e-01,\n",
" -2.64327440e-01, -6.62629397e-01],\n",
" [ 7.00000000e+00, -4.99033506e-01, -3.96491160e-01,\n",
" 3.96491160e-01, 4.65250428e-01],\n",
" [ 7.00000000e+00, -2.50494799e-01, 2.64327440e-01,\n",
" -2.64327440e-01, -6.62629397e-01],\n",
" [ 7.00000000e+00, -1.05594431e+00, 9.25146041e-01,\n",
" -9.25146041e-01, -6.62629397e-01],\n",
" [ 7.00000000e+00, -1.45176522e+00, 9.25146041e-01,\n",
" -9.25146041e-01, -6.62629397e-01],\n",
" [ 7.00000000e+00, -1.33670100e+00, -1.05730976e+00,\n",
" 1.05730976e+00, -1.22656931e+00]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = dataset.iloc[:, [2, 7, 10, 11, 12]].copy()\n",
"x[['mean_reaction_time', 'percentage_no_of_correct_responses', 'oer', 'cer']] = new_df\n",
"x.head()\n",
"x = x.to_numpy()\n",
"display(x)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "58284e31",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Finding the optimal number of clusters using the elbow method\n",
"from sklearn.cluster import KMeans \n",
"wcss_list= [] #Initializing the list for the values of WCSS \n",
" \n",
"#Using for loop for iterations from 1 to 10. \n",
"for i in range(1, 11): \n",
" kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42) \n",
" kmeans.fit(x) \n",
" wcss_list.append(kmeans.inertia_) \n",
"mtp.plot(range(1, 11), wcss_list) \n",
"mtp.title('The Elobw Method Graph') \n",
"mtp.xlabel('Number of clusters(k)') \n",
"mtp.ylabel('wcss_list') \n",
"mtp.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5d1c61bf",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import Birch\n",
"\n",
"# define the model\n",
"model = Birch(threshold=0.01, n_clusters=4)\n",
"# fit the model\n",
"model.fit(x)\n",
"# assign a cluster to each example\n",
"yhat = model.predict(x)\n",
"# retrieve unique clusters\n",
"clusters = unique(yhat)\n"
]
},
{
"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>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>4</td>\n",
" <td>1303</td>\n",
" <td>62.5</td>\n",
" <td>37.5</td>\n",
" <td>37.5</td>\n",
" <td>1</td>\n",
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" <tr>\n",
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" <td>4</td>\n",
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" <td>4</td>\n",
" <td>1253</td>\n",
" <td>75.0</td>\n",
" <td>25.0</td>\n",
" <td>25.0</td>\n",
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" <th>44</th>\n",
" <td>4</td>\n",
" <td>1239</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>3</td>\n",
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" <th>45</th>\n",
" <td>4</td>\n",
" <td>1109</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>50.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>4</td>\n",
" <td>952</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>4</td>\n",
" <td>928</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
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" <td>12.5</td>\n",
" <td>37.5</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>49</th>\n",
" <td>4</td>\n",
" <td>1115</td>\n",
" <td>62.5</td>\n",
" <td>37.5</td>\n",
" <td>75.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>5</td>\n",
" <td>1157</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>5</td>\n",
" <td>1097</td>\n",
" <td>75.0</td>\n",
" <td>25.0</td>\n",
" <td>37.5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>5</td>\n",
" <td>1160</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>5</td>\n",
" <td>1053</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>54</th>\n",
" <td>5</td>\n",
" <td>953</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>5</td>\n",
" <td>1303</td>\n",
" <td>75.0</td>\n",
" <td>25.0</td>\n",
" <td>37.5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>5</td>\n",
" <td>1257</td>\n",
" <td>37.5</td>\n",
" <td>62.5</td>\n",
" <td>75.0</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>57</th>\n",
" <td>5</td>\n",
" <td>1008</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>87.5</td>\n",
" <td>87.5</td>\n",
" <td>2</td>\n",
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" <th>59</th>\n",
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" <td>50.0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>61</th>\n",
" <td>6</td>\n",
" <td>778</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>6</td>\n",
" <td>778</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>12.5</td>\n",
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" <td>62.5</td>\n",
" <td>37.5</td>\n",
" <td>50.0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>64</th>\n",
" <td>6</td>\n",
" <td>1173</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>65</th>\n",
" <td>6</td>\n",
" <td>1007</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>6</td>\n",
" <td>1025</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>6</td>\n",
" <td>1012</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>37.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>6</td>\n",
" <td>845</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>62.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>7</td>\n",
" <td>850</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>37.5</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>70</th>\n",
" <td>7</td>\n",
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" <td>75.0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>7</td>\n",
" <td>845</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>7</td>\n",
" <td>1033</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>7</td>\n",
" <td>867</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>7</td>\n",
" <td>901</td>\n",
" <td>75.0</td>\n",
" <td>25.0</td>\n",
" <td>37.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>7</td>\n",
" <td>955</td>\n",
" <td>87.5</td>\n",
" <td>12.5</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>7</td>\n",
" <td>780</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>7</td>\n",
" <td>694</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>12.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>7</td>\n",
" <td>719</td>\n",
" <td>62.5</td>\n",
" <td>37.5</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",
"39 4 1303 62.5 37.5 \n",
"40 4 1384 62.5 37.5 \n",
"41 4 1191 87.5 12.5 \n",
"42 4 1335 50.0 50.0 \n",
"43 4 1253 75.0 25.0 \n",
"44 4 1239 100.0 0.0 \n",
"45 4 1109 87.5 12.5 \n",
"46 4 952 100.0 0.0 \n",
"47 4 928 100.0 0.0 \n",
"48 4 1428 87.5 12.5 \n",
"49 4 1115 62.5 37.5 \n",
"50 5 1157 100.0 0.0 \n",
"51 5 1097 75.0 25.0 \n",
"52 5 1160 100.0 0.0 \n",
"53 5 1053 87.5 12.5 \n",
"54 5 953 87.5 12.5 \n",
"55 5 1303 75.0 25.0 \n",
"56 5 1257 37.5 62.5 \n",
"57 5 1008 100.0 0.0 \n",
"58 5 1188 12.5 87.5 \n",
"59 6 670 87.5 12.5 \n",
"60 6 614 87.5 12.5 \n",
"61 6 778 100.0 0.0 \n",
"62 6 778 100.0 0.0 \n",
"63 6 832 62.5 37.5 \n",
"64 6 1173 87.5 12.5 \n",
"65 6 1007 87.5 12.5 \n",
"66 6 1025 87.5 12.5 \n",
"67 6 1012 87.5 12.5 \n",
"68 6 845 87.5 12.5 \n",
"69 7 850 100.0 0.0 \n",
"70 7 586 75.0 25.0 \n",
"71 7 845 87.5 12.5 \n",
"72 7 1033 100.0 0.0 \n",
"73 7 867 87.5 12.5 \n",
"74 7 901 75.0 25.0 \n",
"75 7 955 87.5 12.5 \n",
"76 7 780 100.0 0.0 \n",
"77 7 694 100.0 0.0 \n",
"78 7 719 62.5 37.5 \n",
"\n",
" cer clusters \n",
"39 37.5 1 \n",
"40 25.0 1 \n",
"41 50.0 1 \n",
"42 25.0 1 \n",
"43 25.0 1 \n",
"44 25.0 3 \n",
"45 50.0 1 \n",
"46 25.0 3 \n",
"47 0.0 3 \n",
"48 37.5 1 \n",
"49 75.0 1 \n",
"50 25.0 3 \n",
"51 37.5 1 \n",
"52 0.0 3 \n",
"53 12.5 3 \n",
"54 0.0 3 \n",
"55 37.5 1 \n",
"56 75.0 2 \n",
"57 0.0 3 \n",
"58 87.5 2 \n",
"59 12.5 0 \n",
"60 50.0 0 \n",
"61 25.0 0 \n",
"62 12.5 0 \n",
"63 50.0 0 \n",
"64 12.5 0 \n",
"65 12.5 0 \n",
"66 12.5 0 \n",
"67 37.5 0 \n",
"68 62.5 0 \n",
"69 37.5 0 \n",
"70 0.0 0 \n",
"71 25.0 0 \n",
"72 0.0 0 \n",
"73 12.5 0 \n",
"74 37.5 0 \n",
"75 12.5 0 \n",
"76 12.5 0 \n",
"77 12.5 0 \n",
"78 0.0 0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"new_df = dataset.iloc[:, [2, 7, 10, 11, 12]].copy()\n",
"new_df['clusters'] = yhat\n",
"new_df.head()\n",
"display(new_df)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "dd477754",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Silhouette Coefficient: 0.319\n",
"Calinski-Harabasz Index: 25.712\n",
"Davies-Bouldin Index: 0.865\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": null,
"id": "ba8fef3b",
"metadata": {},
"outputs": [],
"source": [
"len(new_df[new_df[\"clusters\"] == 0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c5b7397",
"metadata": {},
"outputs": [],
"source": [
"cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n",
"display(cluster_0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "849d9447",
"metadata": {},
"outputs": [],
"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)\n",
"print()\n",
"\n",
"maxVal = cluster_0['cer'].max()\n",
"minVal = cluster_0['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "markdown",
"id": "dd8d7e4f",
"metadata": {},
"source": [
"## Cluster 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9ed816e",
"metadata": {},
"outputs": [],
"source": [
"len(new_df[new_df[\"clusters\"] == 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3eeb500",
"metadata": {},
"outputs": [],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n",
"display(cluster_1)\n",
"#cluster_1.boxplot(column =['CER'], grid = False)\n",
"#cluster_1.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ab1bc45",
"metadata": {},
"outputs": [],
"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)\n",
"\n",
"print()\n",
"\n",
"maxVal = cluster_1['cer'].max()\n",
"minVal = cluster_1['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61fc2f95",
"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
}
HerokuDeployment/Divided Attention - KMeans.ipynb 0 → 100644
View file @ 8ceb3408
This source diff could not be displayed because it is too large. You can view the blob instead.
HerokuDeployment/Focused Attention - BIRCH - Age 4 & 5.ipynb 0 → 100644
View file @ 8ceb3408
{
"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": {
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" <td>0</td>\n",
" <td>1564</td>\n",
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" <td>0</td>\n",
" <td>1142</td>\n",
" <td>75500</td>\n",
" <td>No</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
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" <td>4</td>\n",
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" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1270</td>\n",
" <td>76000</td>\n",
" <td>No</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>96</th>\n",
" <td>97</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1457</td>\n",
" <td>73000</td>\n",
" <td>No</td>\n",
" <td>90.000000</td>\n",
" <td>10.000000</td>\n",
" <td>0.0</td>\n",
" <td>Focused</td>\n",
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" <tr>\n",
" <th>97</th>\n",
" <td>98</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1180</td>\n",
" <td>72500</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>98</th>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1261</td>\n",
" <td>73500</td>\n",
" <td>No</td>\n",
" <td>90.000000</td>\n",
" <td>10.000000</td>\n",
" <td>0.0</td>\n",
" <td>Focused</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1234</td>\n",
" <td>71500</td>\n",
" <td>No</td>\n",
" <td>70.000000</td>\n",
" <td>30.000000</td>\n",
" <td>0.0</td>\n",
" <td>Focused</td>\n",
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" <tr>\n",
" <th>100</th>\n",
" <td>101</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1165</td>\n",
" <td>73000</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>101</th>\n",
" <td>102</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1238</td>\n",
" <td>71000</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",
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" <tr>\n",
" <th>102</th>\n",
" <td>103</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1830</td>\n",
" <td>71000</td>\n",
" <td>No</td>\n",
" <td>90.000000</td>\n",
" <td>10.000000</td>\n",
" <td>0.0</td>\n",
" <td>Focused</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>104</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1657</td>\n",
" <td>78000</td>\n",
" <td>No</td>\n",
" <td>80.000000</td>\n",
" <td>20.000000</td>\n",
" <td>0.0</td>\n",
" <td>Focused</td>\n",
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" <tr>\n",
" <th>104</th>\n",
" <td>105</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1817</td>\n",
" <td>74000</td>\n",
" <td>No</td>\n",
" <td>70.000000</td>\n",
" <td>30.000000</td>\n",
" <td>0.0</td>\n",
" <td>Focused</td>\n",
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" <tr>\n",
" <th>105</th>\n",
" <td>106</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>1600</td>\n",
" <td>84500</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",
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" <tr>\n",
" <th>106</th>\n",
" <td>107</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>1396</td>\n",
" <td>86500</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",
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" <tr>\n",
" <th>107</th>\n",
" <td>108</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>1380</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",
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" <tr>\n",
" <th>108</th>\n",
" <td>109</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>1350</td>\n",
" <td>90000</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",
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" <tr>\n",
" <th>109</th>\n",
" <td>110</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>1310</td>\n",
" <td>87000</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",
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" <tr>\n",
" <th>110</th>\n",
" <td>111</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>1462</td>\n",
" <td>94000</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",
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" <tr>\n",
" <th>111</th>\n",
" <td>112</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1069</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",
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" <tr>\n",
" <th>112</th>\n",
" <td>113</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1221</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",
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" <td>12</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1852</td>\n",
" <td>89500</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",
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" <tr>\n",
" <th>115</th>\n",
" <td>116</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>1598</td>\n",
" <td>92000</td>\n",
" <td>No</td>\n",
" <td>91.666667</td>\n",
" <td>8.333333</td>\n",
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" <td>Focused</td>\n",
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" <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": [
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" [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": {
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" [-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": {
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" [ 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": 16,
"id": "5d1c61bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3]\n"
]
}
],
"source": [
"from sklearn.cluster import Birch\n",
"\n",
"# define the model\n",
"model = Birch(threshold=0.01, n_clusters=4)\n",
"# fit the model\n",
"model.fit(x)\n",
"# assign a cluster to each example\n",
"yhat = model.predict(x)\n",
"# retrieve unique clusters\n",
"clusters = unique(yhat)\n",
"print(clusters)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2e691585",
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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" <td>4</td>\n",
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" <td>0.000000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>4</td>\n",
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" <td>5</td>\n",
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"</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",
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"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 2 \n",
"80 0.000000 2 \n",
"81 0.000000 2 \n",
"82 0.000000 1 \n",
"83 10.000000 3 \n",
"84 0.000000 1 \n",
"85 20.000000 0 \n",
"86 0.000000 2 \n",
"87 20.000000 0 \n",
"88 10.000000 0 \n",
"89 0.000000 1 \n",
"90 10.000000 3 \n",
"91 0.000000 2 \n",
"92 0.000000 2 \n",
"93 0.000000 2 \n",
"94 0.000000 2 \n",
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"96 10.000000 3 \n",
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"99 30.000000 0 \n",
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"105 8.333333 3 \n",
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"111 0.000000 2 \n",
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"113 16.666667 0 \n",
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"115 8.333333 3 \n",
"116 0.000000 1 \n",
"117 0.000000 1 \n",
"118 0.000000 1 \n",
"119 0.000000 2 \n",
"120 0.000000 2 \n",
"121 8.333333 3 \n",
"122 0.000000 2 \n",
"123 0.000000 1 \n",
"124 16.666667 0 \n",
"125 0.000000 2 \n",
"126 0.000000 2 \n",
"127 0.000000 2 \n",
"128 0.000000 2 \n",
"129 16.666667 0 \n",
"130 0.000000 2 "
]
},
"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": 9,
"id": "50a9adbb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Silhouette Coefficient: 0.401\n",
"Calinski-Harabasz Index: 41.707\n",
"Davies-Bouldin Index: 0.932\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": 10,
"id": "ba8fef3b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6c5b7397",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe thead th {\n",
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"</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>85</th>\n",
" <td>4</td>\n",
" <td>1366</td>\n",
" <td>80.000000</td>\n",
" <td>20.000000</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>0</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>4</td>\n",
" <td>1234</td>\n",
" <td>70.000000</td>\n",
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" <th>102</th>\n",
" <td>4</td>\n",
" <td>1830</td>\n",
" <td>90.000000</td>\n",
" <td>10.000000</td>\n",
" <td>0</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>0</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>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>0</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>0</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>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>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" child_age mean_reaction_time percentage_no_of_correct_responses \\\n",
"85 4 1366 80.000000 \n",
"87 4 2032 80.000000 \n",
"88 4 1789 90.000000 \n",
"99 4 1234 70.000000 \n",
"102 4 1830 90.000000 \n",
"103 4 1657 80.000000 \n",
"104 4 1817 70.000000 \n",
"113 5 1775 83.333333 \n",
"114 5 1852 83.333333 \n",
"124 5 1916 83.333333 \n",
"129 5 1162 83.333333 \n",
"\n",
" oer clusters \n",
"85 20.000000 0 \n",
"87 20.000000 0 \n",
"88 10.000000 0 \n",
"99 30.000000 0 \n",
"102 10.000000 0 \n",
"103 20.000000 0 \n",
"104 30.000000 0 \n",
"113 16.666667 0 \n",
"114 16.666667 0 \n",
"124 16.666667 0 \n",
"129 16.666667 0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n",
"display(cluster_0)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "849d9447",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean_reaction_time min - 1162\n",
"mean_reaction_time max - 2032\n",
"\n",
"percentage_no_of_correct_responses min - 70.0\n",
"percentage_no_of_correct_responses max - 90.0\n",
"\n",
"oer min - 10.0\n",
"oer max - 30.0\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": 13,
"id": "f9ed816e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 1])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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",
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" 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",
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" <tr>\n",
" <th>82</th>\n",
" <td>4</td>\n",
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" <tr>\n",
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" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>4</td>\n",
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" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>1</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>1</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>1</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>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>5</td>\n",
" <td>1998</td>\n",
" <td>100.0</td>\n",
" <td>0.0</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 oer \\\n",
"82 4 1632 100.0 0.0 \n",
"84 4 1564 100.0 0.0 \n",
"89 4 1680 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",
"123 5 1998 100.0 0.0 \n",
"\n",
" clusters \n",
"82 1 \n",
"84 1 \n",
"89 1 \n",
"116 1 \n",
"117 1 \n",
"118 1 \n",
"123 1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n",
"display(cluster_1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bb910e6a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean_reaction_time min - 1564\n",
"mean_reaction_time max - 1998\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_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": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c84eacb",
"metadata": {},
"outputs": [],
"source": []
}
],
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"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
}
HerokuDeployment/Focused Attention - KMeans - Age 4 & 5.ipynb 0 → 100644
View file @ 8ceb3408
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HerokuDeployment/Focused Attention - KMeans - Age 6 & 7.ipynb 0 → 100644
View file @ 8ceb3408
{
"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": {
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" <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",
" <th>correct_responses</th>\n",
" <th>commission_errors</th>\n",
" <th>omission_errors</th>\n",
" <th>mean_reaction_time</th>\n",
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" <th>diagnosis</th>\n",
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" <td>783</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>139</th>\n",
" <td>140</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>901</td>\n",
" <td>85000</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>140</th>\n",
" <td>141</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>976</td>\n",
" <td>88500</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>141</th>\n",
" <td>142</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>826</td>\n",
" <td>89500</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>142</th>\n",
" <td>143</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>855</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>143</th>\n",
" <td>144</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>885</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>144</th>\n",
" <td>145</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1031</td>\n",
" <td>91000</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>145</th>\n",
" <td>146</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1082</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>146</th>\n",
" <td>147</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1061</td>\n",
" <td>83000</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>147</th>\n",
" <td>148</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1374</td>\n",
" <td>85000</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>148</th>\n",
" <td>149</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>940</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>149</th>\n",
" <td>150</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1071</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>150</th>\n",
" <td>151</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1102</td>\n",
" <td>85500</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>151</th>\n",
" <td>152</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1146</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>152</th>\n",
" <td>153</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>792</td>\n",
" <td>87000</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>153</th>\n",
" <td>154</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1070</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>154</th>\n",
" <td>155</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1087</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>155</th>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1230</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>156</th>\n",
" <td>157</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1128</td>\n",
" <td>90000</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>157</th>\n",
" <td>158</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>814</td>\n",
" <td>87000</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>158</th>\n",
" <td>159</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1326</td>\n",
" <td>85000</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>159</th>\n",
" <td>160</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1298</td>\n",
" <td>91000</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",
"131 132 2 6 12 12 \n",
"132 133 2 6 12 12 \n",
"133 134 2 6 12 12 \n",
"134 135 1 6 12 12 \n",
"135 136 1 6 12 12 \n",
"136 137 1 6 12 12 \n",
"137 138 2 6 12 12 \n",
"138 139 2 6 12 12 \n",
"139 140 2 6 12 12 \n",
"140 141 2 6 12 12 \n",
"141 142 2 6 12 11 \n",
"142 143 2 6 12 12 \n",
"143 144 2 6 12 12 \n",
"144 145 2 6 12 11 \n",
"145 146 2 7 12 12 \n",
"146 147 2 7 12 12 \n",
"147 148 2 7 12 12 \n",
"148 149 2 7 12 12 \n",
"149 150 2 7 12 12 \n",
"150 151 2 7 12 11 \n",
"151 152 2 7 12 12 \n",
"152 153 2 7 12 10 \n",
"153 154 1 7 12 12 \n",
"154 155 1 7 12 12 \n",
"155 156 1 7 12 12 \n",
"156 157 1 7 12 12 \n",
"157 158 1 7 12 12 \n",
"158 159 1 7 12 12 \n",
"159 160 1 7 12 12 \n",
"\n",
" commission_errors omission_errors mean_reaction_time total_duration \\\n",
"131 0 0 1041 89000 \n",
"132 0 0 1298 87000 \n",
"133 0 0 1080 86500 \n",
"134 0 0 1284 88000 \n",
"135 0 0 1140 88000 \n",
"136 0 0 1125 90000 \n",
"137 0 0 819 84000 \n",
"138 0 0 783 89500 \n",
"139 0 0 901 85000 \n",
"140 0 0 976 88500 \n",
"141 0 1 826 89500 \n",
"142 0 0 855 89500 \n",
"143 0 0 885 86500 \n",
"144 0 1 1031 91000 \n",
"145 0 0 1082 86000 \n",
"146 0 0 1061 83000 \n",
"147 0 0 1374 85000 \n",
"148 0 0 940 86000 \n",
"149 0 0 1071 86500 \n",
"150 0 1 1102 85500 \n",
"151 0 0 1146 86500 \n",
"152 0 2 792 87000 \n",
"153 0 0 1070 89500 \n",
"154 0 0 1087 86000 \n",
"155 0 0 1230 89500 \n",
"156 0 0 1128 90000 \n",
"157 0 0 814 87000 \n",
"158 0 0 1326 85000 \n",
"159 0 0 1298 91000 \n",
"\n",
" diagnosis percentage_no_of_correct_responses oer cer game \n",
"131 No 100.000000 0.000000 0.0 Focused \n",
"132 No 100.000000 0.000000 0.0 Focused \n",
"133 No 100.000000 0.000000 0.0 Focused \n",
"134 No 100.000000 0.000000 0.0 Focused \n",
"135 No 100.000000 0.000000 0.0 Focused \n",
"136 No 100.000000 0.000000 0.0 Focused \n",
"137 No 100.000000 0.000000 0.0 Focused \n",
"138 No 100.000000 0.000000 0.0 Focused \n",
"139 No 100.000000 0.000000 0.0 Focused \n",
"140 No 100.000000 0.000000 0.0 Focused \n",
"141 No 91.666667 8.333333 0.0 Focused \n",
"142 No 100.000000 0.000000 0.0 Focused \n",
"143 No 100.000000 0.000000 0.0 Focused \n",
"144 No 91.666667 8.333333 0.0 Focused \n",
"145 No 100.000000 0.000000 0.0 Focused \n",
"146 No 100.000000 0.000000 0.0 Focused \n",
"147 No 100.000000 0.000000 0.0 Focused \n",
"148 No 100.000000 0.000000 0.0 Focused \n",
"149 No 100.000000 0.000000 0.0 Focused \n",
"150 No 91.666667 8.333333 0.0 Focused \n",
"151 No 100.000000 0.000000 0.0 Focused \n",
"152 No 83.333333 16.666667 0.0 Focused \n",
"153 No 100.000000 0.000000 0.0 Focused \n",
"154 No 100.000000 0.000000 0.0 Focused \n",
"155 No 100.000000 0.000000 0.0 Focused \n",
"156 No 100.000000 0.000000 0.0 Focused \n",
"157 No 100.000000 0.000000 0.0 Focused \n",
"158 No 100.000000 0.000000 0.0 Focused \n",
"159 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'] == 4], inplace = True)\n",
"dataset.drop(dataset.index[dataset['child_age'] == 5], inplace = True)\n",
"\n",
"display(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "12841129",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1041. , 100. , 0. ],\n",
" [1298. , 100. , 0. ],\n",
" [1080. , 100. , 0. ],\n",
" [1284. , 100. , 0. ],\n",
" [1140. , 100. , 0. ],\n",
" [1125. , 100. , 0. ],\n",
" [ 819. , 100. , 0. ],\n",
" [ 783. , 100. , 0. ],\n",
" [ 901. , 100. , 0. ],\n",
" [ 976. , 100. , 0. ],\n",
" [ 826. , 91.66666667, 8.33333333],\n",
" [ 855. , 100. , 0. ],\n",
" [ 885. , 100. , 0. ],\n",
" [1031. , 91.66666667, 8.33333333],\n",
" [1082. , 100. , 0. ],\n",
" [1061. , 100. , 0. ],\n",
" [1374. , 100. , 0. ],\n",
" [ 940. , 100. , 0. ],\n",
" [1071. , 100. , 0. ],\n",
" [1102. , 91.66666667, 8.33333333],\n",
" [1146. , 100. , 0. ],\n",
" [ 792. , 83.33333333, 16.66666667],\n",
" [1070. , 100. , 0. ],\n",
" [1087. , 100. , 0. ],\n",
" [1230. , 100. , 0. ],\n",
" [1128. , 100. , 0. ],\n",
" [ 814. , 100. , 0. ],\n",
" [1326. , 100. , 0. ],\n",
" [1298. , 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([[-0.07666676, 0.37476584, -0.37476584],\n",
" [ 1.44300705, 0.37476584, -0.37476584],\n",
" [ 0.15394522, 0.37476584, -0.37476584],\n",
" [ 1.36022326, 0.37476584, -0.37476584],\n",
" [ 0.50873288, 0.37476584, -0.37476584],\n",
" [ 0.42003596, 0.37476584, -0.37476584],\n",
" [-1.3893811 , 0.37476584, -0.37476584],\n",
" [-1.60225369, 0.37476584, -0.37476584],\n",
" [-0.90450463, 0.37476584, -0.37476584],\n",
" [-0.46102006, 0.37476584, -0.37476584],\n",
" [-1.3479892 , -1.79887605, 1.79887605],\n",
" [-1.1765085 , 0.37476584, -0.37476584],\n",
" [-0.99911467, 0.37476584, -0.37476584],\n",
" [-0.13579803, -1.79887605, 1.79887605],\n",
" [ 0.16577148, 0.37476584, -0.37476584],\n",
" [ 0.04159579, 0.37476584, -0.37476584],\n",
" [ 1.89240475, 0.37476584, -0.37476584],\n",
" [-0.67389265, 0.37476584, -0.37476584],\n",
" [ 0.10072707, 0.37476584, -0.37476584],\n",
" [ 0.28403403, -1.79887605, 1.79887605],\n",
" [ 0.54421164, 0.37476584, -0.37476584],\n",
" [-1.54903554, -3.97251795, 3.97251795],\n",
" [ 0.09481394, 0.37476584, -0.37476584],\n",
" [ 0.19533711, 0.37476584, -0.37476584],\n",
" [ 1.04091437, 0.37476584, -0.37476584],\n",
" [ 0.43777535, 0.37476584, -0.37476584],\n",
" [-1.41894673, 0.37476584, -0.37476584],\n",
" [ 1.60857462, 0.37476584, -0.37476584],\n",
" [ 1.44300705, 0.37476584, -0.37476584]])"
]
},
"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([[ 6. , -0.07666676, 0.37476584, -0.37476584],\n",
" [ 6. , 1.44300705, 0.37476584, -0.37476584],\n",
" [ 6. , 0.15394522, 0.37476584, -0.37476584],\n",
" [ 6. , 1.36022326, 0.37476584, -0.37476584],\n",
" [ 6. , 0.50873288, 0.37476584, -0.37476584],\n",
" [ 6. , 0.42003596, 0.37476584, -0.37476584],\n",
" [ 6. , -1.3893811 , 0.37476584, -0.37476584],\n",
" [ 6. , -1.60225369, 0.37476584, -0.37476584],\n",
" [ 6. , -0.90450463, 0.37476584, -0.37476584],\n",
" [ 6. , -0.46102006, 0.37476584, -0.37476584],\n",
" [ 6. , -1.3479892 , -1.79887605, 1.79887605],\n",
" [ 6. , -1.1765085 , 0.37476584, -0.37476584],\n",
" [ 6. , -0.99911467, 0.37476584, -0.37476584],\n",
" [ 6. , -0.13579803, -1.79887605, 1.79887605],\n",
" [ 7. , 0.16577148, 0.37476584, -0.37476584],\n",
" [ 7. , 0.04159579, 0.37476584, -0.37476584],\n",
" [ 7. , 1.89240475, 0.37476584, -0.37476584],\n",
" [ 7. , -0.67389265, 0.37476584, -0.37476584],\n",
" [ 7. , 0.10072707, 0.37476584, -0.37476584],\n",
" [ 7. , 0.28403403, -1.79887605, 1.79887605],\n",
" [ 7. , 0.54421164, 0.37476584, -0.37476584],\n",
" [ 7. , -1.54903554, -3.97251795, 3.97251795],\n",
" [ 7. , 0.09481394, 0.37476584, -0.37476584],\n",
" [ 7. , 0.19533711, 0.37476584, -0.37476584],\n",
" [ 7. , 1.04091437, 0.37476584, -0.37476584],\n",
" [ 7. , 0.43777535, 0.37476584, -0.37476584],\n",
" [ 7. , -1.41894673, 0.37476584, -0.37476584],\n",
" [ 7. , 1.60857462, 0.37476584, -0.37476584],\n",
" [ 7. , 1.44300705, 0.37476584, -0.37476584]])"
]
},
"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": "58284e31",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Finding the optimal number of clusters using the elbow method\n",
"from sklearn.cluster import KMeans \n",
"wcss_list= [] #Initializing the list for the values of WCSS \n",
" \n",
"#Using for loop for iterations from 1 to 10. \n",
"for i in range(1, 11): \n",
" kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42) \n",
" kmeans.fit(x) \n",
" wcss_list.append(kmeans.inertia_) \n",
"mtp.plot(range(1, 11), wcss_list) \n",
"mtp.title('The Elobw Method Graph') \n",
"mtp.xlabel('Number of clusters(k)') \n",
"mtp.ylabel('wcss_list') \n",
"mtp.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5d1c61bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0]\n"
]
}
],
"source": [
"#training the K-means model on a dataset \n",
"kmeans = KMeans(n_clusters=2, init='k-means++', random_state= 42) \n",
"y_predict= kmeans.fit_predict(x) \n",
"print(y_predict)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2e691585",
"metadata": {},
"outputs": [
{
"data": {
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"<style scoped>\n",
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"</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>131</th>\n",
" <td>6</td>\n",
" <td>1041</td>\n",
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" </tr>\n",
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" <td>1080</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>6</td>\n",
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" </tr>\n",
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" <th>136</th>\n",
" <td>6</td>\n",
" <td>1125</td>\n",
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" </tr>\n",
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" <td>819</td>\n",
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" <tr>\n",
" <th>139</th>\n",
" <td>6</td>\n",
" <td>901</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>6</td>\n",
" <td>976</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>6</td>\n",
" <td>826</td>\n",
" <td>91.666667</td>\n",
" <td>8.333333</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td>6</td>\n",
" <td>855</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>6</td>\n",
" <td>885</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>6</td>\n",
" <td>1031</td>\n",
" <td>91.666667</td>\n",
" <td>8.333333</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>145</th>\n",
" <td>7</td>\n",
" <td>1082</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>146</th>\n",
" <td>7</td>\n",
" <td>1061</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>7</td>\n",
" <td>1374</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>7</td>\n",
" <td>940</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>7</td>\n",
" <td>1071</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>7</td>\n",
" <td>1102</td>\n",
" <td>91.666667</td>\n",
" <td>8.333333</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>7</td>\n",
" <td>1146</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>7</td>\n",
" <td>792</td>\n",
" <td>83.333333</td>\n",
" <td>16.666667</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>153</th>\n",
" <td>7</td>\n",
" <td>1070</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154</th>\n",
" <td>7</td>\n",
" <td>1087</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>155</th>\n",
" <td>7</td>\n",
" <td>1230</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>7</td>\n",
" <td>1128</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>157</th>\n",
" <td>7</td>\n",
" <td>814</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>7</td>\n",
" <td>1326</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>159</th>\n",
" <td>7</td>\n",
" <td>1298</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",
"131 6 1041 100.000000 \n",
"132 6 1298 100.000000 \n",
"133 6 1080 100.000000 \n",
"134 6 1284 100.000000 \n",
"135 6 1140 100.000000 \n",
"136 6 1125 100.000000 \n",
"137 6 819 100.000000 \n",
"138 6 783 100.000000 \n",
"139 6 901 100.000000 \n",
"140 6 976 100.000000 \n",
"141 6 826 91.666667 \n",
"142 6 855 100.000000 \n",
"143 6 885 100.000000 \n",
"144 6 1031 91.666667 \n",
"145 7 1082 100.000000 \n",
"146 7 1061 100.000000 \n",
"147 7 1374 100.000000 \n",
"148 7 940 100.000000 \n",
"149 7 1071 100.000000 \n",
"150 7 1102 91.666667 \n",
"151 7 1146 100.000000 \n",
"152 7 792 83.333333 \n",
"153 7 1070 100.000000 \n",
"154 7 1087 100.000000 \n",
"155 7 1230 100.000000 \n",
"156 7 1128 100.000000 \n",
"157 7 814 100.000000 \n",
"158 7 1326 100.000000 \n",
"159 7 1298 100.000000 \n",
"\n",
" oer clusters \n",
"131 0.000000 0 \n",
"132 0.000000 0 \n",
"133 0.000000 0 \n",
"134 0.000000 0 \n",
"135 0.000000 0 \n",
"136 0.000000 0 \n",
"137 0.000000 0 \n",
"138 0.000000 0 \n",
"139 0.000000 0 \n",
"140 0.000000 0 \n",
"141 8.333333 1 \n",
"142 0.000000 0 \n",
"143 0.000000 0 \n",
"144 8.333333 1 \n",
"145 0.000000 0 \n",
"146 0.000000 0 \n",
"147 0.000000 0 \n",
"148 0.000000 0 \n",
"149 0.000000 0 \n",
"150 8.333333 1 \n",
"151 0.000000 0 \n",
"152 16.666667 1 \n",
"153 0.000000 0 \n",
"154 0.000000 0 \n",
"155 0.000000 0 \n",
"156 0.000000 0 \n",
"157 0.000000 0 \n",
"158 0.000000 0 \n",
"159 0.000000 0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"new_df = dataset.iloc[:, [2, 7, 10, 11]].copy()\n",
"new_df['clusters'] = y_predict\n",
"new_df.head()\n",
"display(new_df)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "22e58322",
"metadata": {},
"outputs": [],
"source": [
"# save the model to disk\n",
"import pickle\n",
"filename = 'modelfa2.sav'\n",
"pickle.dump(kmeans, open(filename, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "358178a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Silhouette Coefficient: 0.628\n",
"Calinski-Harabasz Index: 34.856\n",
"Davies-Bouldin Index: 0.641\n"
]
}
],
"source": [
"from sklearn.metrics import silhouette_score,calinski_harabasz_score,davies_bouldin_score\n",
"\n",
"print(\"Silhouette Coefficient: %0.3f\" % silhouette_score(x, y_predict))\n",
"print(\"Calinski-Harabasz Index: %0.3f\" % calinski_harabasz_score(x, y_predict))\n",
"print(\"Davies-Bouldin Index: %0.3f\" % davies_bouldin_score(x, y_predict))"
]
},
{
"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": [
"25"
]
},
"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",
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" }\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",
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" <tr>\n",
" <th>138</th>\n",
" <td>6</td>\n",
" <td>783</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>6</td>\n",
" <td>901</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>6</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>7</td>\n",
" <td>1061</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>7</td>\n",
" <td>1374</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>148</th>\n",
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" <tr>\n",
" <th>149</th>\n",
" <td>7</td>\n",
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" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>151</th>\n",
" <td>7</td>\n",
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" <tr>\n",
" <th>153</th>\n",
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" <td>0.0</td>\n",
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" <tr>\n",
" <th>154</th>\n",
" <td>7</td>\n",
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" <td>100.0</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>156</th>\n",
" <td>7</td>\n",
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" <td>0.0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>157</th>\n",
" <td>7</td>\n",
" <td>814</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" child_age mean_reaction_time percentage_no_of_correct_responses oer \\\n",
"131 6 1041 100.0 0.0 \n",
"132 6 1298 100.0 0.0 \n",
"133 6 1080 100.0 0.0 \n",
"134 6 1284 100.0 0.0 \n",
"135 6 1140 100.0 0.0 \n",
"136 6 1125 100.0 0.0 \n",
"137 6 819 100.0 0.0 \n",
"138 6 783 100.0 0.0 \n",
"139 6 901 100.0 0.0 \n",
"140 6 976 100.0 0.0 \n",
"142 6 855 100.0 0.0 \n",
"143 6 885 100.0 0.0 \n",
"145 7 1082 100.0 0.0 \n",
"146 7 1061 100.0 0.0 \n",
"147 7 1374 100.0 0.0 \n",
"148 7 940 100.0 0.0 \n",
"149 7 1071 100.0 0.0 \n",
"151 7 1146 100.0 0.0 \n",
"153 7 1070 100.0 0.0 \n",
"154 7 1087 100.0 0.0 \n",
"155 7 1230 100.0 0.0 \n",
"156 7 1128 100.0 0.0 \n",
"157 7 814 100.0 0.0 \n",
"158 7 1326 100.0 0.0 \n",
"159 7 1298 100.0 0.0 \n",
"\n",
" clusters \n",
"131 0 \n",
"132 0 \n",
"133 0 \n",
"134 0 \n",
"135 0 \n",
"136 0 \n",
"137 0 \n",
"138 0 \n",
"139 0 \n",
"140 0 \n",
"142 0 \n",
"143 0 \n",
"145 0 \n",
"146 0 \n",
"147 0 \n",
"148 0 \n",
"149 0 \n",
"151 0 \n",
"153 0 \n",
"154 0 \n",
"155 0 \n",
"156 0 \n",
"157 0 \n",
"158 0 \n",
"159 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 - 783\n",
"mean_reaction_time max - 1374\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": [
"4"
]
},
"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": {},
"outputs": [
{
"data": {
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" <th>152</th>\n",
" <td>7</td>\n",
" <td>792</td>\n",
" <td>83.333333</td>\n",
" <td>16.666667</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",
"141 6 826 91.666667 \n",
"144 6 1031 91.666667 \n",
"150 7 1102 91.666667 \n",
"152 7 792 83.333333 \n",
"\n",
" oer clusters \n",
"141 8.333333 1 \n",
"144 8.333333 1 \n",
"150 8.333333 1 \n",
"152 16.666667 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": "2ab1bc45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean_reaction_time min - 792\n",
"mean_reaction_time max - 1102\n",
"\n",
"percentage_no_of_correct_responses min - 83.33333333\n",
"percentage_no_of_correct_responses max - 91.66666667\n",
"\n",
"oer min - 8.333333333\n",
"oer max - 16.66666667\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)"
]
}
],
"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
}
HerokuDeployment/Focused Attention - MeanShift - Age 4 &.ipynb 0 → 100644
View file @ 8ceb3408
{
"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": [
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"</style>\n",
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" <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",
" <th>correct_responses</th>\n",
" <th>commission_errors</th>\n",
" <th>omission_errors</th>\n",
" <th>mean_reaction_time</th>\n",
" <th>total_duration</th>\n",
" <th>diagnosis</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>game</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
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" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1462</td>\n",
" <td>94000</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>111</th>\n",
" <td>112</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1069</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>112</th>\n",
" <td>113</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1221</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>113</th>\n",
" <td>114</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1775</td>\n",
" <td>90000</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>114</th>\n",
" <td>115</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>1852</td>\n",
" <td>89500</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>115</th>\n",
" <td>116</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>1598</td>\n",
" <td>92000</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>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": [
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" [ 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",
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" [ 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",
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" [ 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": {
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"</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",
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" <tr>\n",
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" <td>5</td>\n",
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" <tr>\n",
" <th>110</th>\n",
" <td>5</td>\n",
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" <td>5</td>\n",
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" <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",
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" 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",
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" <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",
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" <td>0.0</td>\n",
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" <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",
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" <td>0</td>\n",
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" <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",
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"110 0 \n",
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"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": {
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"<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",
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" <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
}
HerokuDeployment/Focused Attention - OPTICS - Age 4 & 5.ipynb 0 → 100644
View file @ 8ceb3408
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HerokuDeployment/Selective Attention - BIRCH.ipynb 0 → 100644
View file @ 8ceb3408
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HerokuDeployment/Selective Attention - KMeans.ipynb 0 → 100644
View file @ 8ceb3408
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HerokuDeployment/Selective Attention - MeanShift.ipynb 0 → 100644
View file @ 8ceb3408
{
"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",
" <th>correct_responses</th>\n",
" <th>commission_errors</th>\n",
" <th>omission_errors</th>\n",
" <th>mean_reaction_time</th>\n",
" <th>total_duration</th>\n",
" <th>diagnosis</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>game</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>161</td>\n",
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" <td>100.000000</td>\n",
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" <td>0.0</td>\n",
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" <td>0.000000</td>\n",
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" <td>Selective</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
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" <td>2</td>\n",
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" <td>No</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>Selective</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163</th>\n",
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" <td>2</td>\n",
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" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>165</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>9953</td>\n",
" <td>No</td>\n",
" <td>85.714286</td>\n",
" <td>14.285714</td>\n",
" <td>0.0</td>\n",
" <td>Selective</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>319</th>\n",
" <td>320</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12332</td>\n",
" <td>No</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>Selective</td>\n",
" </tr>\n",
" <tr>\n",
" <th>320</th>\n",
" <td>321</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
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" <td>8190</td>\n",
" <td>No</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>50.0</td>\n",
" <td>Selective</td>\n",
" </tr>\n",
" <tr>\n",
" <th>321</th>\n",
" <td>322</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>6749</td>\n",
" <td>No</td>\n",
" <td>71.428571</td>\n",
" <td>28.571429</td>\n",
" <td>0.0</td>\n",
" <td>Selective</td>\n",
" </tr>\n",
" <tr>\n",
" <th>322</th>\n",
" <td>323</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>No</td>\n",
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" <td>0.0</td>\n",
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" <th>323</th>\n",
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" <td>1</td>\n",
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" <td>No</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>Selective</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>164 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" id child_gender child_age total_correct_responses correct_responses \\\n",
"160 161 1 4 8 8 \n",
"161 162 2 4 6 6 \n",
"162 163 2 4 6 6 \n",
"163 164 2 4 6 6 \n",
"164 165 2 4 7 6 \n",
".. ... ... ... ... ... \n",
"319 320 1 7 8 8 \n",
"320 321 1 7 6 0 \n",
"321 322 1 7 7 5 \n",
"322 323 1 7 7 7 \n",
"323 324 1 7 6 6 \n",
"\n",
" commission_errors omission_errors mean_reaction_time total_duration \\\n",
"160 0 0 0 3459 \n",
"161 0 0 0 3000 \n",
"162 0 0 0 10888 \n",
"163 0 0 0 7081 \n",
"164 0 1 0 9953 \n",
".. ... ... ... ... \n",
"319 0 0 0 12332 \n",
"320 3 6 0 8190 \n",
"321 0 2 0 6749 \n",
"322 0 0 0 7795 \n",
"323 0 0 0 14779 \n",
"\n",
" diagnosis percentage_no_of_correct_responses oer cer game \n",
"160 No 100.000000 0.000000 0.0 Selective \n",
"161 No 100.000000 0.000000 0.0 Selective \n",
"162 No 100.000000 0.000000 0.0 Selective \n",
"163 No 100.000000 0.000000 0.0 Selective \n",
"164 No 85.714286 14.285714 0.0 Selective \n",
".. ... ... ... ... ... \n",
"319 No 100.000000 0.000000 0.0 Selective \n",
"320 No 0.000000 100.000000 50.0 Selective \n",
"321 No 71.428571 28.571429 0.0 Selective \n",
"322 No 100.000000 0.000000 0.0 Selective \n",
"323 No 100.000000 0.000000 0.0 Selective \n",
"\n",
"[164 rows x 14 columns]"
]
},
"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'] == 'Focused'], inplace = True)\n",
"dataset.drop(dataset.index[dataset['game'] == 'Divided'], inplace = True)\n",
"display(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "12841129",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3.45900000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
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" [1.06880000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.46340000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.38610000e+04, 1.00000000e+02, 0.00000000e+00, 1.25000000e+01],\n",
" [1.21830000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.54290000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [9.03700000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.42630000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.36920000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.11330000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.16450000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
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" [9.13800000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [2.01770000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.17300000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.30270000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [9.28600000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [8.97000000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.24430000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.43890000e+04, 8.75000000e+01, 1.25000000e+01, 0.00000000e+00],\n",
" [8.37000000e+03, 1.66666667e+01, 8.33333333e+01, 1.66666667e+01],\n",
" [1.40110000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [2.36890000e+04, 8.33333333e+01, 1.66666667e+01, 3.33333333e+01],\n",
" [1.61690000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [9.12600000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.44260000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.01810000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [4.78290000e+04, 1.00000000e+02, 0.00000000e+00, 5.71428571e+01],\n",
" [1.45930000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [7.15200000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [7.93500000e+03, 8.57142857e+01, 1.42857143e+01, 0.00000000e+00],\n",
" [1.15260000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [9.09000000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [8.32500000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.13240000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.48250000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.65900000e+04, 8.75000000e+01, 1.25000000e+01, 0.00000000e+00],\n",
" [1.82770000e+04, 8.57142857e+01, 1.42857143e+01, 0.00000000e+00],\n",
" [6.94200000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [7.15400000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [8.86600000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [9.01900000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [5.56800000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [7.34900000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.44060000e+04, 1.66666667e+01, 8.33333333e+01, 0.00000000e+00],\n",
" [9.16600000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.23320000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [8.19000000e+03, 0.00000000e+00, 1.00000000e+02, 5.00000000e+01],\n",
" [6.74900000e+03, 7.14285714e+01, 2.85714286e+01, 0.00000000e+00],\n",
" [7.79500000e+03, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00],\n",
" [1.47790000e+04, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# extracting only 11-comission & 12-omission\n",
"x = dataset.iloc[:, [8, 10, 11, 12]].values \n",
"display(x)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d569e05b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
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" -3.16765400e-01],\n",
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" -3.16765400e-01],\n",
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" -3.16765400e-01],\n",
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" -3.16765400e-01],\n",
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" -3.16765400e-01],\n",
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" -3.16765400e-01],\n",
" [-6.64534678e-01, -4.21670152e+00, 4.21670152e+00,\n",
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" 2.29861377e+00],\n",
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" -3.16765400e-01],\n",
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" -3.16765400e-01],\n",
" [-8.84783821e-01, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
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" -3.16765400e-01],\n",
" [-5.88033575e-01, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
" [-5.64435452e-01, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
" [-1.09670421e+00, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
" [-8.22009730e-01, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
" [ 2.66434389e-01, -4.21670152e+00, 4.21670152e+00,\n",
" -3.16765400e-01],\n",
" [-5.41762746e-01, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
" [-5.34512709e-02, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
" [-6.92297175e-01, -5.13364491e+00, 5.13364491e+00,\n",
" 3.60630336e+00],\n",
" [-9.14551387e-01, -1.20388755e+00, 1.20388755e+00,\n",
" -3.16765400e-01],\n",
" [-7.53220432e-01, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01],\n",
" [ 3.23964452e-01, 3.68015386e-01, -3.68015386e-01,\n",
" -3.16765400e-01]])"
]
},
"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": "82afaeca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" -3.68015386e-01, -3.16765400e-01]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = dataset.iloc[:, [2, 8, 10, 11, 12]].copy()\n",
"x[['total_duration', 'percentage_no_of_correct_responses', 'oer', 'cer']] = 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": "ac062128",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 4.79279279 -0.17014686 0.27567199 -0.27567199 -0.19713228]\n",
" [ 4.4 -0.60311786 -3.52899399 3.52899399 -0.3167654 ]\n",
" [ 4. 1.11920576 0.36801539 -0.36801539 5.10080575]\n",
" [ 7. 5.42146738 0.36801539 -0.36801539 4.16674176]\n",
" [ 7. 1.69820806 -0.548928 0.548928 2.29861377]\n",
" [ 7. 0.26643439 -4.21670152 4.21670152 -0.3167654 ]\n",
" [ 7. -0.69229717 -5.13364491 5.13364491 3.60630336]\n",
" [ 6. -0.66453468 -4.21670152 4.21670152 0.99092419]\n",
" [ 6. -0.99891853 -1.46587138 1.46587138 2.29861377]\n",
" [ 5. 5.82741678 0.36801539 -0.36801539 -0.3167654 ]\n",
" [ 5. 4.55311817 0.36801539 -0.36801539 6.22168254]]\n",
"Estimated clusters: 11\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": 8,
"id": "2e691585",
"metadata": {},
"outputs": [
{
"data": {
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"</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>total_duration</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>clusters</th>\n",
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" <tbody>\n",
" <tr>\n",
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"</table>\n",
"<p>164 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" child_age total_duration percentage_no_of_correct_responses \\\n",
"160 4 3459 100.000000 \n",
"161 4 3000 100.000000 \n",
"162 4 10888 100.000000 \n",
"163 4 7081 100.000000 \n",
"164 4 9953 85.714286 \n",
".. ... ... ... \n",
"319 7 12332 100.000000 \n",
"320 7 8190 0.000000 \n",
"321 7 6749 71.428571 \n",
"322 7 7795 100.000000 \n",
"323 7 14779 100.000000 \n",
"\n",
" oer cer clusters \n",
"160 0.000000 0.0 0 \n",
"161 0.000000 0.0 0 \n",
"162 0.000000 0.0 0 \n",
"163 0.000000 0.0 0 \n",
"164 14.285714 0.0 0 \n",
".. ... ... ... \n",
"319 0.000000 0.0 0 \n",
"320 100.000000 50.0 6 \n",
"321 28.571429 0.0 8 \n",
"322 0.000000 0.0 0 \n",
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"\n",
"[164 rows x 6 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"new_df = dataset.iloc[:, [2, 8, 10, 11, 12]].copy()\n",
"new_df['clusters'] = yhat\n",
"new_df.head()\n",
"display(new_df)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "017f8397",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Silhouette Coefficient: 0.416\n",
"Calinski-Harabasz Index: 27.590\n",
"Davies-Bouldin Index: 0.519\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": 10,
"id": "ba8fef3b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"145"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>total_duration</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>4</td>\n",
" <td>3459</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>4</td>\n",
" <td>3000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>4</td>\n",
" <td>10888</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163</th>\n",
" <td>4</td>\n",
" <td>7081</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>4</td>\n",
" <td>9953</td>\n",
" <td>85.714286</td>\n",
" <td>14.285714</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>316</th>\n",
" <td>7</td>\n",
" <td>7349</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>318</th>\n",
" <td>7</td>\n",
" <td>9166</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>319</th>\n",
" <td>7</td>\n",
" <td>12332</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>322</th>\n",
" <td>7</td>\n",
" <td>7795</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>323</th>\n",
" <td>7</td>\n",
" <td>14779</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>145 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" child_age total_duration percentage_no_of_correct_responses oer \\\n",
"160 4 3459 100.000000 0.000000 \n",
"161 4 3000 100.000000 0.000000 \n",
"162 4 10888 100.000000 0.000000 \n",
"163 4 7081 100.000000 0.000000 \n",
"164 4 9953 85.714286 14.285714 \n",
".. ... ... ... ... \n",
"316 7 7349 100.000000 0.000000 \n",
"318 7 9166 100.000000 0.000000 \n",
"319 7 12332 100.000000 0.000000 \n",
"322 7 7795 100.000000 0.000000 \n",
"323 7 14779 100.000000 0.000000 \n",
"\n",
" cer clusters \n",
"160 0.0 0 \n",
"161 0.0 0 \n",
"162 0.0 0 \n",
"163 0.0 0 \n",
"164 0.0 0 \n",
".. ... ... \n",
"316 0.0 0 \n",
"318 0.0 0 \n",
"319 0.0 0 \n",
"322 0.0 0 \n",
"323 0.0 0 \n",
"\n",
"[145 rows x 6 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n",
"display(cluster_0)\n",
"# cluster_0.boxplot(column =['CER'], grid = False)\n",
"# cluster_0.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "849d9447",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total_duration min - 3000\n",
"total_duration max - 30387\n",
"\n",
"percentage_no_of_correct_responses min - 66.66666667\n",
"percentage_no_of_correct_responses max - 100.0\n",
"\n",
"oer min - 0.0\n",
"oer max - 33.33333333\n",
"\n",
"cer min - 0.0\n",
"cer max - 22.22222222\n"
]
}
],
"source": [
"cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n",
"\n",
"maxVal = cluster_0['total_duration'].max()\n",
"minVal = cluster_0['total_duration'].min()\n",
"\n",
"print(\"total_duration min - \", minVal)\n",
"print(\"total_duration 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)\n",
"print()\n",
"\n",
"maxVal = cluster_0['cer'].max()\n",
"minVal = cluster_0['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "markdown",
"id": "dd8d7e4f",
"metadata": {},
"source": [
"## Cluster 2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f9ed816e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 1])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e3eeb500",
"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>total_duration</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>4</td>\n",
" <td>10348</td>\n",
" <td>12.500000</td>\n",
" <td>87.500000</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>4</td>\n",
" <td>11724</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>4</td>\n",
" <td>6276</td>\n",
" <td>16.666667</td>\n",
" <td>83.333333</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257</th>\n",
" <td>5</td>\n",
" <td>5112</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>5</td>\n",
" <td>10381</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>6</td>\n",
" <td>19055</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" child_age total_duration percentage_no_of_correct_responses oer \\\n",
"177 4 10348 12.500000 87.500000 \n",
"183 4 11724 33.333333 66.666667 \n",
"194 4 6276 16.666667 83.333333 \n",
"257 5 5112 33.333333 66.666667 \n",
"268 5 10381 50.000000 50.000000 \n",
"271 6 19055 50.000000 50.000000 \n",
"\n",
" cer clusters \n",
"177 0.0 1 \n",
"183 0.0 1 \n",
"194 0.0 1 \n",
"257 0.0 1 \n",
"268 0.0 1 \n",
"271 0.0 1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n",
"display(cluster_1)\n",
"#cluster_1.boxplot(column =['CER'], grid = False)\n",
"#cluster_1.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "2ab1bc45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total_duration min - 5112\n",
"total_duration max - 19055\n",
"\n",
"percentage_no_of_correct_responses min - 12.5\n",
"percentage_no_of_correct_responses max - 50.0\n",
"\n",
"oer min - 50.0\n",
"oer max - 87.5\n",
"\n",
"cer min - 0.0\n",
"cer max - 0.0\n"
]
}
],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n",
"\n",
"maxVal = cluster_1['total_duration'].max()\n",
"minVal = cluster_1['total_duration'].min()\n",
"\n",
"print(\"total_duration min - \", minVal)\n",
"print(\"total_duration 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)\n",
"print()\n",
"\n",
"maxVal = cluster_1['cer'].max()\n",
"minVal = cluster_1['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "markdown",
"id": "e62b9a30",
"metadata": {},
"source": [
"## Cluster 3"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "105ff3ad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 2])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "9c9ac4a6",
"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>total_duration</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>4</td>\n",
" <td>22296</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>33.333333</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>4</td>\n",
" <td>17495</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>71.428571</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>4</td>\n",
" <td>22375</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>66.666667</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" child_age total_duration percentage_no_of_correct_responses oer \\\n",
"182 4 22296 100.0 0.0 \n",
"191 4 17495 100.0 0.0 \n",
"210 4 22375 100.0 0.0 \n",
"\n",
" cer clusters \n",
"182 33.333333 2 \n",
"191 71.428571 2 \n",
"210 66.666667 2 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_2 = new_df[new_df[\"clusters\"] == 2 ]\n",
"display(cluster_2)\n",
"#cluster_2.boxplot(column =['CER'], grid = False)\n",
"#cluster_2.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "09b1596d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total_duration min - 17495\n",
"total_duration max - 22375\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",
"\n",
"cer min - 33.33333333\n",
"cer max - 71.42857143\n"
]
}
],
"source": [
"cluster_2 = new_df[new_df[\"clusters\"] == 2 ]\n",
"\n",
"maxVal = cluster_2['total_duration'].max()\n",
"minVal = cluster_2['total_duration'].min()\n",
"\n",
"print(\"total_duration min - \", minVal)\n",
"print(\"total_duration max - \", maxVal)\n",
"print()\n",
"\n",
"maxVal = cluster_2['percentage_no_of_correct_responses'].max()\n",
"minVal = cluster_2['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_2['oer'].max()\n",
"minVal = cluster_2['oer'].min()\n",
"\n",
"print(\"oer min - \", minVal)\n",
"print(\"oer max - \", maxVal)\n",
"print()\n",
"\n",
"maxVal = cluster_2['cer'].max()\n",
"minVal = cluster_2['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "markdown",
"id": "f5704cf3",
"metadata": {},
"source": [
"## Cluster 3"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a38aef5c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 3])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "764b57a9",
"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>total_duration</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>7</td>\n",
" <td>47829</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>57.142857</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" child_age total_duration percentage_no_of_correct_responses oer \\\n",
"300 7 47829 100.0 0.0 \n",
"\n",
" cer clusters \n",
"300 57.142857 3 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster_3 = new_df[new_df[\"clusters\"] == 3 ]\n",
"display(cluster_3)\n",
"#cluster_2.boxplot(column =['CER'], grid = False)\n",
"#cluster_2.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c8d202ee",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total_duration min - 47829\n",
"total_duration max - 47829\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",
"\n",
"cer min - 57.14285714\n",
"cer max - 57.14285714\n"
]
}
],
"source": [
"cluster_3 = new_df[new_df[\"clusters\"] == 3 ]\n",
"\n",
"maxVal = cluster_3['total_duration'].max()\n",
"minVal = cluster_3['total_duration'].min()\n",
"\n",
"print(\"total_duration min - \", minVal)\n",
"print(\"total_duration max - \", maxVal)\n",
"print()\n",
"\n",
"maxVal = cluster_3['percentage_no_of_correct_responses'].max()\n",
"minVal = cluster_3['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_3['oer'].max()\n",
"minVal = cluster_3['oer'].min()\n",
"\n",
"print(\"oer min - \", minVal)\n",
"print(\"oer max - \", maxVal)\n",
"print()\n",
"\n",
"maxVal = cluster_3['cer'].max()\n",
"minVal = cluster_3['cer'].min()\n",
"\n",
"print(\"cer min - \", minVal)\n",
"print(\"cer max - \", maxVal)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74a86c20",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c3a6eb79",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == -1])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "92f1117a",
"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>total_duration</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" <th>oer</th>\n",
" <th>cer</th>\n",
" <th>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [child_age, total_duration, percentage_no_of_correct_responses, oer, cer, clusters]\n",
"Index: []"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"outliers = new_df[new_df[\"clusters\"] == -1 ]\n",
"display(outliers)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f37f6f4f",
"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
}
HerokuDeployment/Selective Attention - OPTICS.ipynb 0 → 100644
View file @ 8ceb3408
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HerokuDeployment/Sustained Attention - KMeans.ipynb 0 → 100644
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