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2022-074
Commit 50bc0a3f authored by Anuththara18's avatar Anuththara18
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Deployment Files Added

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HerokuDeployment/.ipynb_checkpoints/Alternating Attention-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/BarPlots-Mean-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/BoxPlots - Grouped-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/Clustering - Mean Reaction Time - Ages 4 & 5-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/Clustering - Mean Reaction Time - Ages 6 & 7-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/Clustering - OE - Divided - Age 4-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/Clustering - OE - Selective - Age 4-checkpoint.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"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": null,
"id": "f448f999",
"metadata": {},
"outputs": [],
"source": [
"# Importing the dataset \n",
"dataset = pd.read_csv('A4.csv') \n",
"dataset.drop(dataset.index[dataset['game'] == 'Focused'], inplace = True)\n",
"dataset.drop(dataset.index[dataset['game'] == 'Sustained'], inplace = True)\n",
"\n",
"dataset.drop(dataset.index[dataset['game'] == 'Alternating'], inplace = True)\n",
"dataset.drop(dataset.index[dataset['game'] == 'Selective'], inplace = True)\n",
"display(dataset)\n",
"# statistics of the data\n",
"dataset.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12841129",
"metadata": {},
"outputs": [],
"source": [
"# extracting only 11-comission & 12-omission\n",
"x = dataset.iloc[:, [18, 19]].values \n",
"display(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d569e05b",
"metadata": {},
"outputs": [],
"source": [
"# standardizing the data\n",
"from sklearn.preprocessing import StandardScaler\n",
"scaler = StandardScaler()\n",
"x = scaler.fit_transform(x)\n",
"\n",
"# statistics of scaled data\n",
"pd.DataFrame(x).describe()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58284e31",
"metadata": {},
"outputs": [],
"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": null,
"id": "5d1c61bf",
"metadata": {},
"outputs": [],
"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",
"\n",
"#visulaizing the clusters \n",
"mtp.scatter(x[y_predict == 0, 0], x[y_predict == 0, 1], s = 100, c = 'blue', label = 'Cluster 1') #for first cluster \n",
"mtp.scatter(x[y_predict == 1, 0], x[y_predict == 1, 1], s = 100, c = 'green', label = 'Cluster 2') #for second cluster \n",
"mtp.scatter(x[y_predict== 2, 0], x[y_predict == 2, 1], s = 100, c = 'red', label = 'Cluster 3') #for third cluster \n",
"mtp.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroid') \n",
"mtp.title('Clusters of children') \n",
"mtp.xlabel('Commission Errors') \n",
"mtp.ylabel('Omission Errors') \n",
"mtp.legend() \n",
"mtp.show() "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e691585",
"metadata": {},
"outputs": [],
"source": [
"new_df = dataset.iloc[:, [18, 19]].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": null,
"id": "ba8fef3b",
"metadata": {},
"outputs": [],
"source": [
"len(new_df[new_df[\"clusters\"] == 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['CER'].max()\n",
"minVal = cluster_0['CER'].min()\n",
"\n",
"print(\"CER min - \", minVal)\n",
"print(\"CER 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": "code",
"execution_count": null,
"id": "6c5b7397",
"metadata": {},
"outputs": [],
"source": [
"cluster_0 = new_df[new_df[\"clusters\"] == 0 ]\n",
"display(cluster_0)\n",
"cluster_0.boxplot(column =['CER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c98d3f7e",
"metadata": {},
"outputs": [],
"source": [
"cluster_0.boxplot(column =['OER'], grid = False)"
]
},
{
"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": "2ab1bc45",
"metadata": {},
"outputs": [],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n",
"\n",
"maxVal = cluster_1['CER'].max()\n",
"minVal = cluster_1['CER'].min()\n",
"\n",
"print(\"CER min - \", minVal)\n",
"print(\"CER 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": "e3eeb500",
"metadata": {},
"outputs": [],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 1 ]\n",
"display(cluster_1)\n",
"cluster_1.boxplot(column =['CER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10ff99a7",
"metadata": {},
"outputs": [],
"source": [
"cluster_1.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "markdown",
"id": "e62b9a30",
"metadata": {},
"source": [
"## Cluster 3"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "105ff3ad",
"metadata": {},
"outputs": [],
"source": [
"len(new_df[new_df[\"clusters\"] == 2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09b1596d",
"metadata": {},
"outputs": [],
"source": [
"cluster_2 = new_df[new_df[\"clusters\"] == 2 ]\n",
"\n",
"maxVal = cluster_2['CER'].max()\n",
"minVal = cluster_2['CER'].min()\n",
"\n",
"print(\"CER min - \", minVal)\n",
"print(\"CER 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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c9ac4a6",
"metadata": {},
"outputs": [],
"source": [
"cluster_2 = new_df[new_df[\"clusters\"] == 2 ]\n",
"display(cluster_2)\n",
"cluster_2.boxplot(column =['CER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ee51b0b",
"metadata": {},
"outputs": [],
"source": [
"cluster_2.boxplot(column =['OER'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c25bc1bd",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"# Pandas dataframe\n",
"data = pd.DataFrame({\"Cluster1\": cluster_0['CER'], \"Cluster2\": cluster_1['CER'], \"Cluster3\": cluster_2['CER']})\n",
"\n",
"# Plot the dataframe\n",
"ax = data[['Cluster1', 'Cluster2', 'Cluster3']].plot(kind='box', title='boxplot')\n",
"\n",
"# Display the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c24315eb",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"# Pandas dataframe\n",
"data = pd.DataFrame({\"Cluster1\": cluster_0['OER'], \"Cluster2\": cluster_1['OER'], \"Cluster3\": cluster_2['OER']})\n",
"\n",
"# Plot the dataframe\n",
"ax = data[['Cluster1', 'Cluster2', 'Cluster3']].plot(kind='box', title='boxplot')\n",
"\n",
"# Display the plot\n",
"plt.show()"
]
}
],
"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": "3.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/.ipynb_checkpoints/Clustering - Percentage of Correct Responses-checkpoint.ipynb 0 → 100644
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/.ipynb_checkpoints/Clustering - Total Duration - Selective-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"id": "65bc68df",
"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": 20,
"id": "7f3124a2",
"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>total_correct_responses</th>\n",
" <th>correct_responses</th>\n",
" <th>commission_errors</th>\n",
" <th>omission_errors</th>\n",
" <th>child_age</th>\n",
" <th>mean_reaction_time</th>\n",
" <th>total_duration</th>\n",
" <th>percentage_no_of_correct_responses</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" <td>109.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>89.266055</td>\n",
" <td>1.587156</td>\n",
" <td>12.449541</td>\n",
" <td>11.348624</td>\n",
" <td>1.027523</td>\n",
" <td>1.110092</td>\n",
" <td>4.486239</td>\n",
" <td>1325.733945</td>\n",
" <td>74760.513761</td>\n",
" <td>89.806919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>62.855749</td>\n",
" <td>0.494619</td>\n",
" <td>5.182746</td>\n",
" <td>5.203945</td>\n",
" <td>2.056871</td>\n",
" <td>1.461477</td>\n",
" <td>0.502119</td>\n",
" <td>274.517747</td>\n",
" <td>26714.660146</td>\n",
" <td>14.756363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>481.000000</td>\n",
" <td>8137.000000</td>\n",
" <td>12.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>28.000000</td>\n",
" <td>1.000000</td>\n",
" <td>10.000000</td>\n",
" <td>8.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1150.000000</td>\n",
" <td>57000.000000</td>\n",
" <td>84.210526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>110.000000</td>\n",
" <td>2.000000</td>\n",
" <td>12.000000</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1296.000000</td>\n",
" <td>72000.000000</td>\n",
" <td>94.736842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>137.000000</td>\n",
" <td>2.000000</td>\n",
" <td>17.000000</td>\n",
" <td>13.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1499.000000</td>\n",
" <td>86529.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>219.000000</td>\n",
" <td>2.000000</td>\n",
" <td>34.000000</td>\n",
" <td>33.000000</td>\n",
" <td>11.000000</td>\n",
" <td>7.000000</td>\n",
" <td>5.000000</td>\n",
" <td>2032.000000</td>\n",
" <td>220070.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id child_gender total_correct_responses correct_responses \\\n",
"count 109.000000 109.000000 109.000000 109.000000 \n",
"mean 89.266055 1.587156 12.449541 11.348624 \n",
"std 62.855749 0.494619 5.182746 5.203945 \n",
"min 1.000000 1.000000 1.000000 1.000000 \n",
"25% 28.000000 1.000000 10.000000 8.000000 \n",
"50% 110.000000 2.000000 12.000000 10.000000 \n",
"75% 137.000000 2.000000 17.000000 13.000000 \n",
"max 219.000000 2.000000 34.000000 33.000000 \n",
"\n",
" commission_errors omission_errors child_age mean_reaction_time \\\n",
"count 109.000000 109.000000 109.000000 109.000000 \n",
"mean 1.027523 1.110092 4.486239 1325.733945 \n",
"std 2.056871 1.461477 0.502119 274.517747 \n",
"min 0.000000 0.000000 4.000000 481.000000 \n",
"25% 0.000000 0.000000 4.000000 1150.000000 \n",
"50% 0.000000 1.000000 4.000000 1296.000000 \n",
"75% 2.000000 2.000000 5.000000 1499.000000 \n",
"max 11.000000 7.000000 5.000000 2032.000000 \n",
"\n",
" total_duration percentage_no_of_correct_responses \n",
"count 109.000000 109.000000 \n",
"mean 74760.513761 89.806919 \n",
"std 26714.660146 14.756363 \n",
"min 8137.000000 12.500000 \n",
"25% 57000.000000 84.210526 \n",
"50% 72000.000000 94.736842 \n",
"75% 86529.000000 100.000000 \n",
"max 220070.000000 100.000000 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Importing the dataset \n",
"dataset = pd.read_csv('All.csv') \n",
"dataset = dataset[dataset.mean_reaction_time != 0]\n",
"dataset.head()\n",
"# statistics of the data\n",
"dataset.describe()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7516307f",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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" [ 5, 1462],\n",
" [ 5, 1069],\n",
" [ 5, 1221],\n",
" [ 5, 1775],\n",
" [ 5, 1852],\n",
" [ 5, 1598],\n",
" [ 5, 1785],\n",
" [ 5, 1628],\n",
" [ 5, 1758],\n",
" [ 5, 1215],\n",
" [ 5, 1134],\n",
" [ 5, 1364],\n",
" [ 5, 1499],\n",
" [ 5, 1998],\n",
" [ 5, 1916],\n",
" [ 5, 1152],\n",
" [ 5, 1086],\n",
" [ 5, 1207],\n",
" [ 5, 1047],\n",
" [ 5, 1162],\n",
" [ 5, 1278],\n",
" [ 5, 1296],\n",
" [ 5, 1173],\n",
" [ 5, 851],\n",
" [ 5, 1029],\n",
" [ 5, 1056],\n",
" [ 5, 741],\n",
" [ 5, 481]], dtype=int64)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = dataset.iloc[:, [12, 13]].values \n",
"display(x)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d926c7d9",
"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>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.090000e+02</td>\n",
" <td>1.090000e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-8.596589e-16</td>\n",
" <td>-3.259370e-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.004619e+00</td>\n",
" <td>1.004619e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-9.728456e-01</td>\n",
" <td>-3.091369e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-9.728456e-01</td>\n",
" <td>-6.431120e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-9.728456e-01</td>\n",
" <td>-1.088137e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.027912e+00</td>\n",
" <td>6.340806e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.027912e+00</td>\n",
" <td>2.584635e+00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"count 1.090000e+02 1.090000e+02\n",
"mean -8.596589e-16 -3.259370e-17\n",
"std 1.004619e+00 1.004619e+00\n",
"min -9.728456e-01 -3.091369e+00\n",
"25% -9.728456e-01 -6.431120e-01\n",
"50% -9.728456e-01 -1.088137e-01\n",
"75% 1.027912e+00 6.340806e-01\n",
"max 1.027912e+00 2.584635e+00"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# standardizing the data\n",
"from sklearn.preprocessing import StandardScaler\n",
"scaler = StandardScaler()\n",
"data_scaled = scaler.fit_transform(x)\n",
"\n",
"# statistics of scaled data\n",
"pd.DataFrame(data_scaled).describe()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ea8688fa",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": 24,
"id": "330980a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0\n",
" 0 0 1 0 0 0 0 0 1 1 1 1 1 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0\n",
" 0 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"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)\n",
"\n",
"#visulaizing the clusters \n",
"mtp.scatter(x[y_predict == 0, 0], x[y_predict == 0, 1], s = 100, c = 'blue', label = 'Cluster 1') #for first cluster \n",
"mtp.scatter(x[y_predict == 1, 0], x[y_predict == 1, 1], s = 100, c = 'green', label = 'Cluster 2') #for second cluster \n",
"mtp.scatter(x[y_predict== 2, 0], x[y_predict == 2, 1], s = 100, c = 'red', label = 'Cluster 3') #for third cluster \n",
"mtp.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroid') \n",
"mtp.title('Clusters of children') \n",
"mtp.xlabel('Mean Reaction Time') \n",
"mtp.ylabel('Age') \n",
"mtp.legend() \n",
"mtp.show() "
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "32052143",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .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>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>1605</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>1404</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1782</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>1258</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>213</th>\n",
" <td>5</td>\n",
" <td>851</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>214</th>\n",
" <td>5</td>\n",
" <td>1029</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>215</th>\n",
" <td>5</td>\n",
" <td>1056</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>217</th>\n",
" <td>5</td>\n",
" <td>741</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>218</th>\n",
" <td>5</td>\n",
" <td>481</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>109 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" child_age mean_reaction_time clusters\n",
"0 4 1479 1\n",
"1 4 1605 1\n",
"2 4 1404 1\n",
"3 4 1782 1\n",
"4 4 1258 0\n",
".. ... ... ...\n",
"213 5 851 0\n",
"214 5 1029 0\n",
"215 5 1056 0\n",
"217 5 741 0\n",
"218 5 481 0\n",
"\n",
"[109 rows x 3 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"new_df = dataset.iloc[:, [12, 13]].copy()\n",
"new_df['clusters'] = y_predict\n",
"new_df.head()\n",
"display(new_df)"
]
},
{
"cell_type": "markdown",
"id": "66aa30cf",
"metadata": {},
"source": [
"# Cluster Analysis"
]
},
{
"cell_type": "markdown",
"id": "d34394fc",
"metadata": {},
"source": [
"## Cluster 1"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "56332554",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"70"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 0])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "1757175b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"481\n",
"1384\n"
]
}
],
"source": [
"cluster_1 = new_df[new_df[\"clusters\"] == 0 ]\n",
"\n",
"maxVal = cluster_1['mean_reaction_time'].max()\n",
"minVal = cluster_1['mean_reaction_time'].min()\n",
"\n",
"print(minVal)\n",
"print(maxVal)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "febd13f6",
"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>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>1258</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4</td>\n",
" <td>1043</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4</td>\n",
" <td>1267</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>4</td>\n",
" <td>1076</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>4</td>\n",
" <td>1303</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>5</td>\n",
" <td>851</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>214</th>\n",
" <td>5</td>\n",
" <td>1029</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>215</th>\n",
" <td>5</td>\n",
" <td>1056</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>217</th>\n",
" <td>5</td>\n",
" <td>741</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>218</th>\n",
" <td>5</td>\n",
" <td>481</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>70 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" child_age mean_reaction_time clusters\n",
"4 4 1258 0\n",
"5 4 1043 0\n",
"6 4 1267 0\n",
"10 4 1076 0\n",
"11 4 1303 0\n",
".. ... ... ...\n",
"213 5 851 0\n",
"214 5 1029 0\n",
"215 5 1056 0\n",
"217 5 741 0\n",
"218 5 481 0\n",
"\n",
"[70 rows x 3 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"display(cluster_1)\n",
"cluster_1.boxplot(column =['mean_reaction_time'], grid = False)"
]
},
{
"cell_type": "markdown",
"id": "3f8254b4",
"metadata": {},
"source": [
"## Cluster 2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "fcfedc59",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"39"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df[\"clusters\"] == 1])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "328c6c45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1396\n",
"2032\n"
]
}
],
"source": [
"cluster_2 = new_df[new_df[\"clusters\"] == 1 ]\n",
"\n",
"maxVal = cluster_2['mean_reaction_time'].max()\n",
"minVal = cluster_2['mean_reaction_time'].min()\n",
"\n",
"print(minVal)\n",
"print(maxVal)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2e9a2dfe",
"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>clusters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>1479</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>1605</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>1404</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1782</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4</td>\n",
" <td>1439</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4</td>\n",
" <td>1614</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4</td>\n",
" <td>1540</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>4</td>\n",
" <td>1428</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>4</td>\n",
" <td>1448</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>4</td>\n",
" <td>1426</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>4</td>\n",
" <td>1632</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>4</td>\n",
" <td>1564</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>4</td>\n",
" <td>2032</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>4</td>\n",
" <td>1789</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>4</td>\n",
" <td>1680</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>4</td>\n",
" <td>1457</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>4</td>\n",
" <td>1830</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>4</td>\n",
" <td>1657</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>4</td>\n",
" <td>1817</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>4</td>\n",
" <td>1500</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>4</td>\n",
" <td>1472</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>4</td>\n",
" <td>1523</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>4</td>\n",
" <td>1501</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>4</td>\n",
" <td>1655</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>5</td>\n",
" <td>1525</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>5</td>\n",
" <td>1506</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>5</td>\n",
" <td>1489</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>5</td>\n",
" <td>1600</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>5</td>\n",
" <td>1396</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>5</td>\n",
" <td>1462</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>5</td>\n",
" <td>1775</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>5</td>\n",
" <td>1852</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>5</td>\n",
" <td>1598</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td>5</td>\n",
" <td>1785</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>5</td>\n",
" <td>1628</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>5</td>\n",
" <td>1758</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>5</td>\n",
" <td>1499</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5</td>\n",
" <td>1998</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>5</td>\n",
" <td>1916</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" child_age mean_reaction_time clusters\n",
"0 4 1479 1\n",
"1 4 1605 1\n",
"2 4 1404 1\n",
"3 4 1782 1\n",
"7 4 1439 1\n",
"8 4 1614 1\n",
"9 4 1540 1\n",
"20 4 1428 1\n",
"22 4 1448 1\n",
"24 4 1426 1\n",
"25 4 1632 1\n",
"27 4 1564 1\n",
"30 4 2032 1\n",
"31 4 1789 1\n",
"32 4 1680 1\n",
"39 4 1457 1\n",
"45 4 1830 1\n",
"46 4 1657 1\n",
"47 4 1817 1\n",
"103 4 1500 1\n",
"104 4 1472 1\n",
"105 4 1523 1\n",
"107 4 1501 1\n",
"110 4 1655 1\n",
"113 5 1525 1\n",
"120 5 1506 1\n",
"121 5 1489 1\n",
"131 5 1600 1\n",
"132 5 1396 1\n",
"136 5 1462 1\n",
"139 5 1775 1\n",
"140 5 1852 1\n",
"141 5 1598 1\n",
"142 5 1785 1\n",
"143 5 1628 1\n",
"144 5 1758 1\n",
"148 5 1499 1\n",
"149 5 1998 1\n",
"150 5 1916 1"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"display(cluster_2)\n",
"cluster_2.sort_values('mean_reaction_time')\n",
"cluster_2.boxplot(column =['mean_reaction_time'], grid = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48b1a860",
"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": "3.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/.ipynb_checkpoints/Clustering - Total Duration - Sustained-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
This source diff could not be displayed because it is too large. You can view the blob instead.
HerokuDeployment/.ipynb_checkpoints/DataVisualization-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
{
"cells": [
{
"cell_type": "markdown",
"id": "aba9a81b",
"metadata": {},
"source": [
"## Focused Attention\n",
">__Test Variables__ \n",
">1. Age\n",
">2. % No Of Correct Responses\n",
">3. Commission Errors\n",
">4. Mean Reaction Time\n",
"\n",
"__Graphs__\n",
"\n",
"1. Variation of the No of Correct Responses as a Percentage\n",
"***\n",
"2. Variation of the Commission Errors\n",
"***\n",
"3. Variation of the Mean Reaction Time"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27781ab1",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'Age']\n",
"\n",
"df = pd.read_csv(\"FocusedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "761448cc",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['CommissionErrors', 'Age']\n",
"\n",
"df = pd.read_csv(\"FocusedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07f1e1c6",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['MeanReactionTime', 'Age']\n",
"\n",
"df = pd.read_csv(\"FocusedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9042cdf3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'CommissionErrors', 'TotalDuration', 'Age']\n",
"\n",
"df = pd.read_csv(\"FocusedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f146b568",
"metadata": {},
"source": [
"## Divided Attention\n",
">__Test Variables__ \n",
">1. Age\n",
">2. % No Of Correct Responses\n",
">3. Commission Errors\n",
">4. Mean Reaction Time\n",
"\n",
"__Graphs__\n",
"\n",
"1. Variation of the No of Correct Responses as a Percentage\n",
"***\n",
"2. Variation of the Commission Errors\n",
"***\n",
"3. Variation of the Mean Reaction Time"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a9511b3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'Age']\n",
"\n",
"df = pd.read_csv(\"DividedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b1ef810",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['CommissionErrors', 'Age']\n",
"\n",
"df = pd.read_csv(\"DividedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c37f5044",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['MeanReactionTime', 'Age']\n",
"\n",
"df = pd.read_csv(\"DividedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9898b943",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'CommissionErrors', 'TotalDuration', 'Age']\n",
"\n",
"df = pd.read_csv(\"DividedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "57416007",
"metadata": {},
"source": [
"## Alternating Attention\n",
">__Test Variables__ \n",
">1. Age\n",
">2. % No Of Correct Responses\n",
">3. Commission Errors\n",
">4. Mean Reaction Time\n",
"\n",
"__Graphs__\n",
"\n",
"1. Variation of the No of Correct Responses as a Percentage\n",
"***\n",
"2. Variation of the Commission Errors\n",
"***\n",
"3. Variation of the Mean Reaction Time"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c5a363f",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'Age']\n",
"\n",
"df = pd.read_csv(\"AlternatingAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b01c1bd",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['CommissionErrors', 'Age']\n",
"\n",
"df = pd.read_csv(\"AlternatingAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4376d8b9",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['MeanReactionTime', 'Age']\n",
"\n",
"df = pd.read_csv(\"AlternatingAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d39ee1b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'CommissionErrors', 'TotalDuration', 'Age']\n",
"\n",
"df = pd.read_csv(\"AlternatingAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b266f918",
"metadata": {},
"source": [
"## Selective Attention\n",
">__Test Variables__ \n",
">1. Age\n",
">2. % No Of Correct Responses\n",
">3. Commission Errors\n",
">4. Total Duration\n",
"\n",
"__Graphs__\n",
"\n",
"1. Variation of the No of Correct Responses as a Percentage\n",
"***\n",
"2. Variation of the Commission Errors\n",
"***\n",
"3. Variation of the Total Duration"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15abe785",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'Age']\n",
"\n",
"df = pd.read_csv(\"SelectiveAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62564019",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['CommissionErrors', 'Age']\n",
"\n",
"df = pd.read_csv(\"SelectiveAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cbd7c13",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['TotalDuration', 'Age']\n",
"\n",
"df = pd.read_csv(\"SelectiveAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb09fb15",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'CommissionErrors', 'TotalDuration', 'Age']\n",
"\n",
"df = pd.read_csv(\"SelectiveAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "96bf6447",
"metadata": {},
"source": [
"## Sustained Attention\n",
">__Test Variables__ \n",
">1. Age\n",
">2. % No Of Correct Responses\n",
">3. Commission Errors\n",
">4. Mean Reaction Time\n",
">5. Total Duration\n",
"\n",
"__Graphs__\n",
"\n",
"1. Variation of the No of Correct Responses as a Percentage\n",
"***\n",
"2. Variation of the Commission Errors\n",
"***\n",
"3. Variation of the Mean Reaction Time\n",
"***\n",
"4. Variation of the Total Duration"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7db0fabe",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'Age']\n",
"\n",
"df = pd.read_csv(\"SustainedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "336e0965",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['CommissionErrors', 'Age']\n",
"\n",
"df = pd.read_csv(\"SustainedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f09af06",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['TotalDuration', 'Age']\n",
"\n",
"df = pd.read_csv(\"SustainedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "106c294f",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['MeanReactionTime', 'Age']\n",
"\n",
"df = pd.read_csv(\"SustainedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01ae0aed",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [15.00, 7.00]\n",
"plt.rcParams[\"figure.autolayout\"] = True\n",
"\n",
"columns = ['%NoOfCorrectResponses', 'CommissionErrors', 'TotalDuration', 'Age']\n",
"\n",
"df = pd.read_csv(\"SustainedAttention.csv\", usecols=columns)\n",
"\n",
"df.plot()\n",
"\n",
"plt.title('Variation of the No. of Correct Responses')\n",
"plt.xlabel('Child')\n",
"plt.ylabel('Value')\n",
"\n",
"plt.show()"
]
}
],
"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": "3.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/.ipynb_checkpoints/Divided Attention-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
This source diff could not be displayed because it is too large. You can view the blob instead.
HerokuDeployment/.ipynb_checkpoints/Focused Attention-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "bcdc5589",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.decomposition import FactorAnalysis, PCA\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.datasets import load_iris"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f448f999",
"metadata": {},
"outputs": [
{
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" <td>22</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>7821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>23</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>10828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" <td>20371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>26</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>11142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>9874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>10696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>7476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>11</td>\n",
" <td>17314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>31</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>17215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>10265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>34</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>12375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>11</td>\n",
" <td>13754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>13273</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id child_gender child_age level total_correct_responses \\\n",
"0 4 1 4 1 7 \n",
"1 5 1 4 2 6 \n",
"2 6 1 4 3 6 \n",
"3 7 1 4 4 6 \n",
"4 8 1 4 5 6 \n",
"5 9 1 4 1 6 \n",
"6 10 1 4 2 6 \n",
"7 11 1 4 3 8 \n",
"8 12 1 4 4 7 \n",
"9 13 1 4 5 8 \n",
"10 14 1 4 1 7 \n",
"11 15 1 4 2 6 \n",
"12 16 1 4 3 6 \n",
"13 17 2 4 1 6 \n",
"14 18 2 4 2 6 \n",
"15 19 2 4 3 6 \n",
"16 20 2 4 4 6 \n",
"17 21 2 4 5 6 \n",
"18 22 2 4 1 6 \n",
"19 23 2 4 2 6 \n",
"20 24 2 4 3 7 \n",
"21 25 2 4 4 9 \n",
"22 26 2 4 5 8 \n",
"23 27 1 4 1 6 \n",
"24 28 1 4 2 7 \n",
"25 29 1 4 3 7 \n",
"26 30 1 4 4 9 \n",
"27 31 1 4 5 10 \n",
"28 32 1 4 1 7 \n",
"29 33 1 4 2 6 \n",
"30 34 1 4 3 9 \n",
"31 35 1 4 4 9 \n",
"32 36 1 4 5 8 \n",
"\n",
" correct_responses total_duration \n",
"0 7 50680 \n",
"1 6 20251 \n",
"2 6 17833 \n",
"3 5 12742 \n",
"4 6 10185 \n",
"5 6 10906 \n",
"6 7 17589 \n",
"7 7 16523 \n",
"8 8 19378 \n",
"9 8 20784 \n",
"10 7 20538 \n",
"11 6 42105 \n",
"12 5 28675 \n",
"13 6 12639 \n",
"14 6 11978 \n",
"15 6 12227 \n",
"16 6 11602 \n",
"17 6 12929 \n",
"18 6 7821 \n",
"19 6 8191 \n",
"20 8 10828 \n",
"21 7 20371 \n",
"22 7 11142 \n",
"23 7 9874 \n",
"24 7 10696 \n",
"25 8 7476 \n",
"26 11 17314 \n",
"27 10 17215 \n",
"28 7 10265 \n",
"29 6 9727 \n",
"30 10 12375 \n",
"31 11 13754 \n",
"32 6 13273 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"selective.csv\")\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "12841129",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'data'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\users\\anuththara\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[1;32mc:\\users\\anuththara\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mc:\\users\\anuththara\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'data'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X \u001b[38;5;241m=\u001b[39m StandardScaler()\u001b[38;5;241m.\u001b[39mfit_transform(\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[0;32m 2\u001b[0m feature_names \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
"File \u001b[1;32mc:\\users\\anuththara\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
"File \u001b[1;32mc:\\users\\anuththara\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3628\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
"\u001b[1;31mKeyError\u001b[0m: 'data'"
]
}
],
"source": [
"X = StandardScaler().fit_transform(data[\"data\"])\n",
"feature_names = data[\"feature_names\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58284e31",
"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": "3.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/.ipynb_checkpoints/Mean Calculations & Box Plots-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
This source diff could not be displayed because it is too large. You can view the blob instead.
HerokuDeployment/.ipynb_checkpoints/ScatterPlots-Group-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
{
"cells": [
{
"cell_type": "markdown",
"id": "a2dda982",
"metadata": {},
"source": [
"## Alternating Attention, Divided Attention\n",
">__Test Variables__ \n",
">1. Percentage Number of Correct Responses\n",
">2. Commission Errors\n",
">3. Omission Errors\n",
">4. Mean Reaction Time\n",
"\n",
"## Sustained Attention, Focused Attention\n",
">__Test Variables__ \n",
">1. Percentage Number of Correct Responses\n",
">2. Omission Errors \n",
">3. Mean Reaction Time\n",
">4. Total Duration\n",
"\n",
"## Selective Attention\n",
">__Test Variables__ \n",
">1. Percentage Number of Correct Responses\n",
">2. Commission Errors\n",
">3. Omission Errors\n",
">4. Total Duration"
]
},
{
"cell_type": "markdown",
"id": "3b2a0d3e",
"metadata": {},
"source": [
"## Age 4"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1dd8b910",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Commission & Omission Errors')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"df = pd.read_csv(\"A4.csv\")\n",
"\n",
"# Sustained Attention, Focused Attention -> 0 *\n",
"x = df[['id']]\n",
"y = df[['commission_errors']]\n",
"plt.scatter(x, y, c =\"blue\")\n",
"\n",
"x2 = df[['id']]\n",
"y2 = df[['omission_errors']]\n",
"plt.scatter(x2, y2, c =\"red\")\n",
"\n",
"plt.xlabel(\"ID\")\n",
"plt.ylabel(\"Blue = CE, Red = OE\")\n",
"plt.title(\"Commission & Omission Errors\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5659fad0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Mean Reaction Time'}, xlabel='id', ylabel='mean_reaction_time'>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"df = pd.read_csv(\"A4.csv\")\n",
"\n",
"df.plot(kind='scatter',x='id',y='percentage_no_of_correct_responses', c =\"orange\", title=\"Percentage No. of Correct Responses\")\n",
"\n",
"# Mean Reaction Time\n",
"mrtdf = df.loc[df['mean_reaction_time'] != 0]\n",
"mrtdf.plot(kind='scatter',x='id',y='mean_reaction_time', c =\"green\", title=\"Mean Reaction Time\") \n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bbc235bd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Total Duration'}, xlabel='id', ylabel='total_duration'>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Total Duration \n",
"# Alternating Attention, Divided Attention -> Not Assessed *\n",
"df = pd.read_csv(\"A4.csv\")\n",
"df.plot(kind='scatter',x='id',y='total_duration', c =\"purple\", title=\"Total Duration\")"
]
},
{
"cell_type": "markdown",
"id": "8697dcf1",
"metadata": {},
"source": [
"## Age 5"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "111d78c6",
"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": "3.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/.ipynb_checkpoints/ScatterPlots-Individual-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/Selective Attention-checkpoint.ipynb 0 → 100644
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This source diff could not be displayed because it is too large. You can view the blob instead.
HerokuDeployment/.ipynb_checkpoints/StimulusAnalysis-checkpoint.ipynb 0 → 100644
View file @ 50bc0a3f
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/.ipynb_checkpoints/Sustained Attention-checkpoint.ipynb 0 → 100644
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HerokuDeployment/.ipynb_checkpoints/Untitled-checkpoint.ipynb 0 → 100644
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
HerokuDeployment/Alternating Attention.ipynb 0 → 100644
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HerokuDeployment/Procfile 0 → 100644
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web: uvicorn app:app --host=0.0.0.0 --port=${PORT:-5000}
\ No newline at end of file
HerokuDeployment/Selective Attention.ipynb 0 → 100644
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HerokuDeployment/app.py 0 → 100644
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from fastapi import FastAPI
import uvicorn
import pickle
from models import Performance
app=FastAPI()
model=pickle.load(open("selectivemodel.sav", "rb"))
@app.get("/{name}")
async def hello(name):
return {"Hello {} sss".format(name)}
@app.get("/")
async def greet():
return {"Hello World!"}
@app.post("/predict")
async def predict(req:Performance):
age=req.age
mrt=req.mrt
pcr=req.pcr
oer=req.oer
cer=req.cer
features=list([age,mrt,pcr,oer,cer])
predict=model.predict([features])
if(predict==1):
return {"Ans 1 {}".format(predict)}
elif (predict==2):
return {"Ans 2 {}".format(predict)}
elif (predict==3):
return {"Ans 3 {}".format(predict)}
else:
return {"Ans 4 {}".format(predict)}
if __name__=="__main__":
uvicorn.run(app, host="127.0.0.1", port=5049)
\ No newline at end of file
HerokuDeployment/data.csv 0 → 100644
View file @ 50bc0a3f
id,child_gender,child_age,total_correct_responses,correct_responses,commission_errors,omission_errors,mean_reaction_time,total_duration,diagnosis,percentage_no_of_correct_responses,oer,cer,game
1,2,4,19,18,0,1,1479,57000, No,94.73684211,5.263157895,0,Alternating
2,1,4,19,19,0,0,1605,57000, No,100,0,0,Alternating
3,2,4,19,18,3,1,1404,57000, No,94.73684211,5.263157895,15.78947368,Alternating
4,2,4,19,19,4,0,1782,57000, No,100,0,21.05263158,Alternating
5,2,4,19,19,6,0,1258,57000, No,100,0,31.57894737,Alternating
6,1,4,19,15,5,4,1043,57000, No,78.94736842,21.05263158,26.31578947,Alternating
7,1,4,19,19,3,0,1267,57000, No,100,0,15.78947368,Alternating
8,1,4,19,15,0,4,1439,57000, No,78.94736842,21.05263158,0,Alternating
9,1,4,19,18,0,1,1614,57000, No,94.73684211,5.263157895,0,Alternating
10,1,4,19,13,2,6,1540,57000, No,68.42105263,31.57894737,10.52631579,Alternating
11,2,4,19,19,0,0,1076,57000, No,100,0,0,Alternating
12,2,5,19,19,2,0,1144,57000, No,100,0,10.52631579,Alternating
13,2,5,19,18,0,1,1366,57000, No,94.73684211,5.263157895,0,Alternating
14,2,5,19,17,2,2,1525,57000, No,89.47368421,10.52631579,10.52631579,Alternating
15,2,5,19,19,0,0,1146,57000, No,100,0,0,Alternating
16,2,5,19,19,0,0,1135,57000, No,100,0,0,Alternating
17,2,5,19,18,4,1,1185,57000, No,94.73684211,5.263157895,21.05263158,Alternating
18,2,5,19,18,2,1,1375,57000, No,94.73684211,5.263157895,10.52631579,Alternating
19,2,5,19,19,5,0,912,57000, No,100,0,26.31578947,Alternating
20,1,5,19,19,11,0,823,57000, No,100,0,57.89473684,Alternating
21,1,5,19,16,2,3,1506,57000, No,84.21052632,15.78947368,10.52631579,Alternating
22,2,5,19,13,9,6,1489,57000, No,68.42105263,31.57894737,47.36842105,Alternating
23,2,6,19,19,0,0,1023,57000, No,100,0,0,Alternating
24,1,6,19,19,0,0,1397,57000, No,100,0,0,Alternating
25,1,6,19,19,10,0,961,57000, No,100,0,52.63157895,Alternating
26,1,6,19,19,8,0,804,57000, No,100,0,42.10526316,Alternating
27,1,6,19,19,11,0,1180,57000, No,100,0,57.89473684,Alternating
28,1,6,19,18,2,1,994,57000, No,94.73684211,5.263157895,10.52631579,Alternating
29,2,6,19,18,3,1,448,57000, No,94.73684211,5.263157895,15.78947368,Alternating
29,2,6,19,18,7,1,733,57000, No,94.73684211,5.263157895,36.84210526,Alternating
29,2,6,19,18,5,1,1083,57000, No,94.73684211,5.263157895,26.31578947,Alternating
29,1,7,19,19,15,0,771,57000, No,100,0,78.94736842,Alternating
30,1,7,19,18,9,1,668,57000, No,94.73684211,5.263157895,47.36842105,Alternating
31,1,7,19,17,9,2,838,57000, No,89.47368421,10.52631579,47.36842105,Alternating
32,1,7,19,19,0,0,1338,57000, No,100,0,0,Alternating
33,1,7,19,19,0,0,1106,57000, No,100,0,0,Alternating
37,2,7,19,18,1,1,987,57000, No,94.73684211,5.263157895,5.263157895,Alternating
38,2,7,19,17,2,2,1181,57000, No,89.47368421,10.52631579,10.52631579,Alternating
39,2,7,19,19,0,0,1179,57000, No,100,0,0,Alternating
40,2,4,8,5,3,3,1303,70000, Yes,62.5,37.5,37.5,Divided
41,1,4,8,5,2,3,1384,70000, No,62.5,37.5,25,Divided
42,2,4,8,7,4,1,1191,70000, No,87.5,12.5,50,Divided
43,2,4,8,4,2,4,1335,70000, No,50,50,25,Divided
44,1,4,8,6,2,2,1253,70000, No,75,25,25,Divided
45,1,4,8,8,2,0,1239,70000, No,100,0,25,Divided
46,1,4,8,7,4,1,1109,70000, No,87.5,12.5,50,Divided
47,1,4,8,8,2,0,952,70000, No,100,0,25,Divided
48,1,4,8,8,0,0,928,70000, No,100,0,0,Divided
49,1,4,8,7,3,1,1428,70000, No,87.5,12.5,37.5,Divided
50,1,4,8,5,6,3,1115,70000, No,62.5,37.5,75,Divided
51,2,5,8,8,2,0,1157,60000, No,100,0,25,Divided
52,2,5,8,6,3,2,1097,60000, No,75,25,37.5,Divided
53,2,5,8,8,0,0,1160,60000, No,100,0,0,Divided
54,2,5,8,7,1,1,1053,60000, No,87.5,12.5,12.5,Divided
55,2,5,8,7,0,1,953,60000, No,87.5,12.5,0,Divided
56,2,5,8,6,3,2,1303,60000, No,75,25,37.5,Divided
57,2,5,8,3,6,5,1257,60000, No,37.5,62.5,75,Divided
58,1,5,8,8,0,0,1008,60000, No,100,0,0,Divided
59,1,5,8,1,7,7,1188,60000, No,12.5,87.5,87.5,Divided
60,2,6,8,7,1,1,670,60000, No,87.5,12.5,12.5,Divided
61,2,6,8,7,4,1,614,60000, No,87.5,12.5,50,Divided
62,2,6,8,8,2,0,778,60000, No,100,0,25,Divided
63,2,6,8,8,1,0,778,60000, No,100,0,12.5,Divided
64,2,6,8,5,4,3,832,60000, No,62.5,37.5,50,Divided
65,1,6,8,7,1,1,1173,60000, No,87.5,12.5,12.5,Divided
66,1,6,8,7,1,1,1007,60000, No,87.5,12.5,12.5,Divided
67,1,6,8,7,1,1,1025,60000, No,87.5,12.5,12.5,Divided
68,1,6,8,7,3,1,1012,60000, No,87.5,12.5,37.5,Divided
69,1,6,8,7,5,1,845,60000, No,87.5,12.5,62.5,Divided
70,1,7,8,8,3,0,850,60000, No,100,0,37.5,Divided
71,1,7,8,6,0,2,586,60000, No,75,25,0,Divided
72,1,7,8,7,2,1,845,60000, No,87.5,12.5,25,Divided
73,1,7,8,8,0,0,1033,60000, No,100,0,0,Divided
74,1,7,8,7,1,1,867,60000, No,87.5,12.5,12.5,Divided
75,1,7,8,6,3,2,901,60000, No,75,25,37.5,Divided
76,2,7,8,7,1,1,955,60000, No,87.5,12.5,12.5,Divided
77,2,7,8,8,1,0,780,60000, No,100,0,12.5,Divided
78,2,7,8,8,1,0,694,60000, No,100,0,12.5,Divided
79,2,7,8,5,0,3,719,60000, No,62.5,37.5,0,Divided
80,1,4,10,10,0,0,1448,74000, No,100,0,0,Focused
81,1,4,10,10,0,0,1331,78000, No,100,0,0,Focused
82,1,4,10,10,0,0,1426,74500, No,100,0,0,Focused
83,2,4,10,10,0,0,1632,76000, No,100,0,0,Focused
84,2,4,10,9,0,1,1340,72000, No,90,10,0,Focused
85,2,4,10,10,0,0,1564,76000, No,100,0,0,Focused
86,2,4,10,8,0,2,1366,76000, No,80,20,0,Focused
87,2,4,10,10,0,0,1291,74500, No,100,0,0,Focused
88,2,4,10,8,0,2,2032,71500, No,80,20,0,Focused
89,2,4,10,9,0,1,1789,74000, No,90,10,0,Focused
90,2,4,10,10,0,0,1680,73500, No,100,0,0,Focused
91,1,4,10,9,0,1,1317,67500, No,90,10,0,Focused
92,1,4,10,10,0,0,1040,70500, No,100,0,0,Focused
93,1,4,10,10,0,0,1142,75500, No,100,0,0,Focused
94,1,4,10,10,0,0,1168,75000, No,100,0,0,Focused
95,1,4,10,10,0,0,1150,77000, No,100,0,0,Focused
96,1,4,10,10,0,0,1270,76000, No,100,0,0,Focused
97,1,4,10,9,0,1,1457,73000, No,90,10,0,Focused
98,1,4,10,10,0,0,1180,72500, No,100,0,0,Focused
99,1,4,10,9,0,1,1261,73500, No,90,10,0,Focused
100,1,4,10,7,0,3,1234,71500, No,70,30,0,Focused
101,1,4,10,10,0,0,1165,73000, No,100,0,0,Focused
102,1,4,10,10,0,0,1238,71000, No,100,0,0,Focused
103,1,4,10,9,0,1,1830,71000, No,90,10,0,Focused
104,1,4,10,8,0,2,1657,78000, No,80,20,0,Focused
105,1,4,10,7,0,3,1817,74000, No,70,30,0,Focused
106,2,5,12,11,0,1,1600,84500, No,91.66666667,8.333333333,0,Focused
107,2,5,12,11,0,1,1396,86500, No,91.66666667,8.333333333,0,Focused
108,2,5,12,12,0,0,1380,89000, No,100,0,0,Focused
109,2,5,12,12,0,0,1350,90000, No,100,0,0,Focused
110,2,5,12,12,0,0,1310,87000, No,100,0,0,Focused
111,2,5,12,12,0,0,1462,94000, No,100,0,0,Focused
112,1,5,12,12,0,0,1069,89000, No,100,0,0,Focused
113,1,5,12,12,0,0,1221,92000, No,100,0,0,Focused
114,1,5,12,10,0,2,1775,90000, No,83.33333333,16.66666667,0,Focused
115,2,5,12,10,0,2,1852,89500, No,83.33333333,16.66666667,0,Focused
116,2,5,12,11,0,1,1598,92000, No,91.66666667,8.333333333,0,Focused
117,2,5,12,12,0,0,1785,86000, No,100,0,0,Focused
118,2,5,12,12,0,0,1628,92000, No,100,0,0,Focused
119,2,5,12,12,0,0,1758,86500, No,100,0,0,Focused
120,2,5,12,12,0,0,1215,92000, No,100,0,0,Focused
121,2,5,12,12,0,0,1134,89000, No,100,0,0,Focused
122,2,5,12,11,0,1,1364,89000, No,91.66666667,8.333333333,0,Focused
123,2,5,12,12,0,0,1499,89000, No,100,0,0,Focused
124,2,5,12,12,0,0,1998,88000, No,100,0,0,Focused
125,2,5,12,10,0,2,1916,85500, No,83.33333333,16.66666667,0,Focused
126,2,5,12,12,0,0,1152,89500, No,100,0,0,Focused
127,2,5,12,12,0,0,1086,92500, No,100,0,0,Focused
128,2,5,12,12,0,0,1207,86500, No,100,0,0,Focused
129,2,5,12,12,0,0,1047,92000, No,100,0,0,Focused
130,2,5,12,10,0,2,1162,88500, No,83.33333333,16.66666667,0,Focused
131,2,5,12,12,0,0,1278,89000, No,100,0,0,Focused
132,2,6,12,12,0,0,1041,89000, No,100,0,0,Focused
133,2,6,12,12,0,0,1298,87000, No,100,0,0,Focused
134,2,6,12,12,0,0,1080,86500, No,100,0,0,Focused
135,1,6,12,12,0,0,1284,88000, No,100,0,0,Focused
136,1,6,12,12,0,0,1140,88000, No,100,0,0,Focused
137,1,6,12,12,0,0,1125,90000, No,100,0,0,Focused
138,2,6,12,12,0,0,819,84000, No,100,0,0,Focused
139,2,6,12,12,0,0,783,89500, No,100,0,0,Focused
140,2,6,12,12,0,0,901,85000, No,100,0,0,Focused
141,2,6,12,12,0,0,976,88500, No,100,0,0,Focused
142,2,6,12,11,0,1,826,89500, No,91.66666667,8.333333333,0,Focused
143,2,6,12,12,0,0,855,89500, No,100,0,0,Focused
144,2,6,12,12,0,0,885,86500, No,100,0,0,Focused
145,2,6,12,11,0,1,1031,91000, No,91.66666667,8.333333333,0,Focused
146,2,7,12,12,0,0,1082,86000, No,100,0,0,Focused
147,2,7,12,12,0,0,1061,83000, No,100,0,0,Focused
148,2,7,12,12,0,0,1374,85000, No,100,0,0,Focused
149,2,7,12,12,0,0,940,86000, No,100,0,0,Focused
150,2,7,12,12,0,0,1071,86500, No,100,0,0,Focused
151,2,7,12,11,0,1,1102,85500, No,91.66666667,8.333333333,0,Focused
152,2,7,12,12,0,0,1146,86500, No,100,0,0,Focused
153,2,7,12,10,0,2,792,87000, No,83.33333333,16.66666667,0,Focused
154,1,7,12,12,0,0,1070,89500, No,100,0,0,Focused
155,1,7,12,12,0,0,1087,86000, No,100,0,0,Focused
156,1,7,12,12,0,0,1230,89500, No,100,0,0,Focused
157,1,7,12,12,0,0,1128,90000, No,100,0,0,Focused
158,1,7,12,12,0,0,814,87000, No,100,0,0,Focused
159,1,7,12,12,0,0,1326,85000, No,100,0,0,Focused
160,1,7,12,12,0,0,1298,91000, No,100,0,0,Focused
161,1,4,8,8,0,0,0,3459, No,100,0,0,Selective
162,2,4,6,6,0,0,0,3000, No,100,0,0,Selective
163,2,4,6,6,0,0,0,10888, No,100,0,0,Selective
164,2,4,6,6,0,0,0,7081, No,100,0,0,Selective
165,2,4,7,6,0,1,0,9953, No,85.71428571,14.28571429,0,Selective
166,2,4,9,9,2,0,0,14207, No,100,0,22.22222222,Selective
167,2,4,8,8,0,0,0,14036, No,100,0,0,Selective
168,1,4,6,6,0,0,0,12646, No,100,0,0,Selective
169,1,4,6,6,0,0,0,7251, No,100,0,0,Selective
170,1,4,7,7,0,0,0,10026, No,100,0,0,Selective
171,1,4,7,7,0,0,0,11482, No,100,0,0,Selective
172,1,4,10,10,0,0,0,12086, No,100,0,0,Selective
173,2,4,6,6,0,0,0,10816, No,100,0,0,Selective
174,2,4,6,6,0,0,0,6345, No,100,0,0,Selective
175,2,4,7,7,0,0,0,11201, No,100,0,0,Selective
176,2,4,8,7,0,1,0,10236, No,87.5,12.5,0,Selective
177,2,4,7,7,0,0,0,10830, No,100,0,0,Selective
178,2,4,8,1,0,7,0,10348, No,12.5,87.5,0,Selective
179,2,4,7,7,0,0,0,16222, No,100,0,0,Selective
180,2,4,6,6,0,0,0,10595, No,100,0,0,Selective
181,2,4,8,6,0,2,0,13987, No,75,25,0,Selective
182,2,4,7,6,0,1,0,15440, No,85.71428571,14.28571429,0,Selective
183,2,4,6,6,2,0,0,22296, No,100,0,33.33333333,Selective
184,2,4,6,2,0,4,0,11724, No,33.33333333,66.66666667,0,Selective
185,2,4,6,6,0,0,0,12248, No,100,0,0,Selective
186,2,4,7,6,0,1,0,11101, No,85.71428571,14.28571429,0,Selective
187,2,4,6,5,0,1,0,11191, No,83.33333333,16.66666667,0,Selective
188,2,4,7,7,0,0,0,13694, No,100,0,0,Selective
189,1,4,8,7,0,1,0,3231, No,87.5,12.5,0,Selective
190,1,4,6,6,0,0,0,9907, No,100,0,0,Selective
191,1,4,6,6,0,0,0,11292, No,100,0,0,Selective
192,1,4,7,7,5,0,0,17495, No,100,0,71.42857143,Selective
193,1,4,7,7,0,0,0,11627, No,100,0,0,Selective
194,1,4,10,10,0,0,0,23274, No,100,0,0,Selective
195,1,4,6,1,0,5,0,6276, No,16.66666667,83.33333333,0,Selective
196,1,4,6,6,0,0,0,7853, No,100,0,0,Selective
197,1,4,7,7,0,0,0,8574, No,100,0,0,Selective
198,1,4,6,6,0,0,0,7801, No,100,0,0,Selective
199,1,4,6,6,0,0,0,8413, No,100,0,0,Selective
200,1,4,6,6,0,0,0,8300, No,100,0,0,Selective
201,1,4,6,6,0,0,0,15645, No,100,0,0,Selective
202,1,4,6,6,0,0,0,14987, No,100,0,0,Selective
203,1,4,8,8,1,0,0,18248, No,100,0,12.5,Selective
204,1,4,8,8,0,0,0,13130, No,100,0,0,Selective
205,1,4,6,6,0,0,0,9934, No,100,0,0,Selective
206,1,4,6,6,1,0,0,30387, No,100,0,16.66666667,Selective
207,1,4,6,6,1,0,0,17464, No,100,0,16.66666667,Selective
208,1,4,6,6,0,0,0,23988, No,100,0,0,Selective
209,1,4,6,6,0,0,0,12807, No,100,0,0,Selective
210,1,4,7,7,0,0,0,25561, No,100,0,0,Selective
211,1,4,6,6,4,0,0,22375, No,100,0,66.66666667,Selective
212,1,4,6,6,0,0,0,11325, No,100,0,0,Selective
213,1,4,6,6,0,0,0,14820, No,100,0,0,Selective
214,1,4,8,7,0,1,0,16869, No,87.5,12.5,0,Selective
215,1,4,6,6,0,0,0,14130, No,100,0,0,Selective
216,2,5,7,5,0,2,0,12367, No,71.42857143,28.57142857,0,Selective
217,2,5,6,6,1,0,0,20664, No,100,0,16.66666667,Selective
218,2,5,6,6,1,0,0,23106, No,100,0,16.66666667,Selective
219,2,5,6,6,0,0,0,11978, No,100,0,0,Selective
220,2,5,6,6,0,0,0,10358, No,100,0,0,Selective
221,2,5,6,6,0,0,0,11941, No,100,0,0,Selective
222,2,5,7,7,0,0,0,13415, No,100,0,0,Selective
223,2,5,6,6,0,0,0,9074, No,100,0,0,Selective
224,2,5,8,8,0,0,0,8005, No,100,0,0,Selective
225,2,5,6,6,0,0,0,15879, No,100,0,0,Selective
226,2,5,8,8,0,0,0,12677, No,100,0,0,Selective
227,2,5,8,8,0,0,0,7744, No,100,0,0,Selective
228,2,5,6,6,0,0,0,10849, No,100,0,0,Selective
229,2,5,7,7,0,0,0,10577, No,100,0,0,Selective
230,2,5,8,8,1,0,0,10628, No,100,0,12.5,Selective
231,2,5,8,8,0,0,0,10764, No,100,0,0,Selective
232,2,5,6,4,0,2,0,12553, No,66.66666667,33.33333333,0,Selective
233,2,5,6,6,0,0,0,12413, No,100,0,0,Selective
234,2,5,6,6,0,0,0,7879, No,100,0,0,Selective
235,2,5,6,6,0,0,0,9200, No,100,0,0,Selective
236,2,5,6,6,0,0,0,7375, No,100,0,0,Selective
237,2,5,6,6,0,0,0,5437, No,100,0,0,Selective
238,2,5,7,7,0,0,0,14770, No,100,0,0,Selective
239,2,5,6,6,0,0,0,8935, No,100,0,0,Selective
240,2,5,7,7,1,0,0,13979, No,100,0,14.28571429,Selective
241,2,5,6,6,0,0,0,16502, No,100,0,0,Selective
242,2,5,6,6,0,0,0,12426, No,100,0,0,Selective
243,2,5,6,6,0,0,0,12219, No,100,0,0,Selective
244,2,5,7,7,0,0,0,8014, No,100,0,0,Selective
245,2,5,7,7,0,0,0,9373, No,100,0,0,Selective
246,2,5,6,5,1,1,0,9838, No,83.33333333,16.66666667,16.66666667,Selective
247,2,5,7,7,0,0,0,9299, No,100,0,0,Selective
248,2,5,7,6,0,1,0,15905, No,85.71428571,14.28571429,0,Selective
249,2,5,7,7,0,0,0,11342, No,100,0,0,Selective
250,2,5,6,6,1,0,0,10124, No,100,0,16.66666667,Selective
251,2,5,9,9,0,0,0,9889, No,100,0,0,Selective
252,2,5,6,6,0,0,0,13156, No,100,0,0,Selective
253,2,5,6,5,0,1,0,13786, No,83.33333333,16.66666667,0,Selective
254,1,5,6,6,0,0,0,14397, No,100,0,0,Selective
255,1,5,6,6,0,0,0,10058, No,100,0,0,Selective
256,1,5,7,7,0,0,0,10874, No,100,0,0,Selective
257,1,5,8,8,0,0,0,10874, No,100,0,0,Selective
258,1,5,6,2,0,4,0,5112, No,33.33333333,66.66666667,0,Selective
259,1,5,7,7,1,0,0,12410, No,100,0,14.28571429,Selective
260,1,5,6,5,0,1,0,13305, No,83.33333333,16.66666667,0,Selective
261,1,5,6,6,0,0,0,11075, No,100,0,0,Selective
262,1,5,6,6,0,0,0,9059, No,100,0,0,Selective
263,1,5,8,8,0,0,0,10000, No,100,0,0,Selective
264,1,5,6,6,0,0,0,12889, No,100,0,0,Selective
265,2,5,6,6,0,0,0,25719, No,100,0,0,Selective
266,2,5,6,6,0,0,0,50461, No,100,0,0,Selective
267,2,5,7,7,1,0,0,19886, No,100,0,14.28571429,Selective
268,2,5,6,6,5,0,0,42199, No,100,0,83.33333333,Selective
269,2,5,6,3,0,3,0,10381, No,50,50,0,Selective
270,2,6,6,4,2,2,0,6202, No,66.66666667,33.33333333,33.33333333,Selective
271,2,6,7,7,1,0,0,4780, Yes,100,0,14.28571429,Selective
272,2,6,6,3,0,3,0,19055, No,50,50,0,Selective
273,2,6,6,4,0,2,0,7026, No,66.66666667,33.33333333,0,Selective
274,2,6,8,7,0,1,0,11709, No,87.5,12.5,0,Selective
275,2,6,6,6,0,0,0,10688, No,100,0,0,Selective
276,2,6,7,7,0,0,0,14634, No,100,0,0,Selective
277,2,6,8,8,1,0,0,13861, No,100,0,12.5,Selective
278,1,6,6,6,0,0,0,12183, No,100,0,0,Selective
279,1,6,6,6,0,0,0,15429, No,100,0,0,Selective
280,1,6,6,6,0,0,0,9037, No,100,0,0,Selective
281,1,6,10,10,0,0,0,14263, No,100,0,0,Selective
282,1,6,7,7,0,0,0,13692, No,100,0,0,Selective
283,1,6,7,7,0,0,0,11133, No,100,0,0,Selective
284,1,6,6,6,0,0,0,11645, No,100,0,0,Selective
285,1,6,6,6,0,0,0,9792, No,100,0,0,Selective
286,2,6,6,6,0,0,0,9138, No,100,0,0,Selective
287,2,6,7,7,0,0,0,20177, No,100,0,0,Selective
288,2,6,10,10,0,0,0,11730, No,100,0,0,Selective
289,2,6,6,6,0,0,0,13027, No,100,0,0,Selective
290,2,6,6,6,0,0,0,9286, No,100,0,0,Selective
291,2,6,6,6,0,0,0,8970, No,100,0,0,Selective
292,2,6,7,7,0,0,0,12443, No,100,0,0,Selective
293,2,6,8,7,0,1,0,14389, No,87.5,12.5,0,Selective
294,2,6,6,1,1,5,0,8370, No,16.66666667,83.33333333,16.66666667,Selective
295,2,6,7,7,0,0,0,14011, No,100,0,0,Selective
296,2,7,6,5,2,1,0,23689, No,83.33333333,16.66666667,33.33333333,Selective
297,2,7,6,6,0,0,0,16169, No,100,0,0,Selective
298,2,7,7,7,0,0,0,9126, No,100,0,0,Selective
299,2,7,7,7,0,0,0,14426, No,100,0,0,Selective
300,2,7,7,7,0,0,0,10181, No,100,0,0,Selective
301,2,7,7,7,4,0,0,47829, No,100,0,57.14285714,Selective
302,2,7,6,6,0,0,0,14593, No,100,0,0,Selective
303,2,7,6,6,0,0,0,7152, No,100,0,0,Selective
304,2,7,7,6,0,1,0,7935, No,85.71428571,14.28571429,0,Selective
305,2,7,8,8,0,0,0,11526, No,100,0,0,Selective
306,1,7,6,6,0,0,0,9090, No,100,0,0,Selective
307,1,7,6,6,0,0,0,8325, No,100,0,0,Selective
308,1,7,7,7,0,0,0,11324, No,100,0,0,Selective
309,1,7,6,6,0,0,0,14825, No,100,0,0,Selective
310,1,7,8,7,0,1,0,16590, No,87.5,12.5,0,Selective
311,1,7,7,6,0,1,0,18277, No,85.71428571,14.28571429,0,Selective
312,1,7,6,6,0,0,0,6942, No,100,0,0,Selective
313,1,7,6,6,0,0,0,7154, No,100,0,0,Selective
314,1,7,6,6,0,0,0,8866, No,100,0,0,Selective
315,1,7,8,8,0,0,0,9019, No,100,0,0,Selective
316,1,7,6,6,0,0,0,5568, No,100,0,0,Selective
317,1,7,7,7,0,0,0,7349, No,100,0,0,Selective
318,1,7,6,1,0,5,0,14406, No,16.66666667,83.33333333,0,Selective
319,1,7,6,6,0,0,0,9166, No,100,0,0,Selective
320,1,7,8,8,0,0,0,12332, No,100,0,0,Selective
321,1,7,6,0,3,6,0,8190, No,0,100,50,Selective
322,1,7,7,5,0,2,0,6749, No,71.42857143,28.57142857,0,Selective
323,1,7,7,7,0,0,0,7795, No,100,0,0,Selective
324,1,7,6,6,0,0,0,14779, No,100,0,0,Selective
325,2,4,20,16,0,4,1500,132928, No,80,20,0,Sustained
326,1,4,20,18,0,2,1472,133598, No,90,10,0,Sustained
327,2,4,31,28,0,3,1523,203243, No,90.32258065,9.677419355,0,Sustained
328,2,4,13,12,0,1,1267,86043, No,92.30769231,7.692307692,0,Sustained
329,2,4,7,7,0,0,1501,46708, No,100,0,0,Sustained
330,1,4,17,15,0,2,1369,114319, No,88.23529412,11.76470588,0,Sustained
331,1,4,15,13,0,2,998,95887, No,86.66666667,13.33333333,0,Sustained
332,1,4,4,3,0,1,1655,25196, No,75,25,0,Sustained
333,2,5,34,33,0,1,1296,220070, No,97.05882353,2.941176471,0,Sustained
334,2,5,7,5,0,2,1173,44919, No,71.42857143,28.57142857,0,Sustained
335,1,5,14,13,0,1,851,86529, No,92.85714286,7.142857143,0,Sustained
336,1,5,5,4,0,1,1029,33134, No,80,20,0,Sustained
337,2,5,17,15,0,2,1056,106353, No,88.23529412,11.76470588,0,Sustained
338,2,5,13,11,0,2,1079,85407, No,84.61538462,15.38461538,0,Sustained
339,2,5,1,1,0,1,1059,8137, No,100,100,0,Sustained
340,2,5,2,1,0,1,1155,10332, No,50,50,0,Sustained
341,2,5,10,10,0,0,1254,65582, No,100,0,0,Sustained
342,2,6,3,2,0,1,757,16803, No,66.66666667,33.33333333,0,Sustained
343,2,6,9,7,0,2,824,56454, No,77.77777778,22.22222222,0,Sustained
344,1,6,9,8,0,1,971,53730, No,88.88888889,11.11111111,0,Sustained
345,1,6,14,13,0,1,785,93980, No,92.85714286,7.142857143,0,Sustained
346,1,6,17,17,0,0,1324,107652, No,100,0,0,Sustained
347,1,6,11,10,0,1,1024,71768, No,90.90909091,9.090909091,0,Sustained
348,1,6,1,1,0,0,1056,2558, No,100,0,0,Sustained
349,2,6,2,2,0,0,1025,9306, No,100,0,0,Sustained
350,2,6,2,1,0,1,1077,9653, No,50,50,0,Sustained
351,2,6,4,4,0,0,1098,24272, No,100,0,0,Sustained
352,2,6,6,6,0,0,926,37485, No,100,0,0,Sustained
353,2,7,6,4,0,2,1037,35262, No,66.66666667,33.33333333,0,Sustained
354,2,7,12,12,0,0,1023,76797, No,100,0,0,Sustained
355,2,7,7,6,0,1,1069,45086, No,85.71428571,14.28571429,0,Sustained
356,1,7,13,12,0,1,1225,84117, No,92.30769231,7.692307692,0,Sustained
357,1,7,17,14,0,3,1062,114481, No,82.35294118,17.64705882,0,Sustained
358,1,7,16,16,0,0,719,105494, No,100,0,0,Sustained
359,1,7,15,12,0,3,1045,2192, No,80,20,0,Sustained
360,1,7,9,8,0,1,1159,2125, No,88.88888889,11.11111111,0,Sustained
361,1,7,28,23,0,5,1257,179629, No,82.14285714,17.85714286,0,Sustained
362,1,7,15,15,0,0,1044,96133, No,100,0,0,Sustained
363,1,7,16,15,0,1,1083,102242, No,93.75,6.25,0,Sustained
HerokuDeployment/models.py 0 → 100644
View file @ 50bc0a3f
from pydantic import BaseModel
class Performance(BaseModel):
age: int
mrt: float
pcr: float
oer: float
cer: float
HerokuDeployment/requirements.txt 0 → 100644
View file @ 50bc0a3f
anyio==3.6.1
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
asttokens==2.0.8
attrs==22.1.0
backcall==0.2.0
beautifulsoup4==4.11.1
bleach==5.0.1
cffi==1.15.1
click==8.1.3
colorama==0.4.5
contourpy==1.0.5
cycler==0.11.0
debugpy==1.6.3
decorator==5.1.1
defusedxml==0.7.1
entrypoints==0.4
executing==1.1.0
fastapi==0.85.0
fastjsonschema==2.16.2
fonttools==4.37.4
h11==0.14.0
idna==3.4
ipykernel==6.16.0
ipython==8.5.0
ipython-genutils==0.2.0
jedi==0.18.1
Jinja2==3.1.2
joblib==1.2.0
jsonschema==4.16.0
jupyter-core==4.11.1
jupyter_client==7.3.5
jupyterlab-pygments==0.2.2
kiwisolver==1.4.4
MarkupSafe==2.1.1
matplotlib==3.6.0
matplotlib-inline==0.1.6
mistune==2.0.4
nbclient==0.6.8
nbconvert==7.1.0
nbformat==5.6.1
nest-asyncio==1.5.6
notebook==6.4.12
numpy==1.23.3
packaging==21.3
pandas==1.5.0
pandocfilters==1.5.0
parso==0.8.3
pickleshare==0.7.5
Pillow==9.2.0
playsound==1.3.0
prometheus-client==0.14.1
prompt-toolkit==3.0.31
psutil==5.9.2
pure-eval==0.2.2
pycparser==2.21
pydantic==1.10.2
Pygments==2.13.0
pyparsing==3.0.9
pyrsistent==0.18.1
python-dateutil==2.8.2
pytz==2022.4
pywin32==304;platform_system == "Windows"
pyzmq==24.0.1
scikit-learn==1.1.2
scipy==1.9.1
Send2Trash==1.8.0
six==1.16.0
sklearn==0.0
sniffio==1.3.0
soupsieve==2.3.2.post1
stack-data==0.5.1
starlette==0.20.4
terminado==0.16.0
threadpoolctl==3.1.0
tinycss2==1.1.1
tornado==6.2
traitlets==5.4.0
typing_extensions==4.3.0
uvicorn==0.18.3
wcwidth==0.2.5
webencodings==0.5.1
HerokuDeployment/runtime.txt 0 → 100644
View file @ 50bc0a3f
python-3.10.7
\ No newline at end of file
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