Выполнено задание для базовой нейросети
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.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb
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104
.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6af36011-9a8c-4dc1-85c5-910263c2d25e",
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"metadata": {},
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"source": [
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"Базовая нейросеть"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2115d6e8-d6d0-4025-9ee0-32c46b20fe45",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 10\n",
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" 1 1.00 1.00 1.00 9\n",
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" 2 1.00 1.00 1.00 11\n",
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"\n",
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" accuracy 1.00 30\n",
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" macro avg 1.00 1.00 1.00 30\n",
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"weighted avg 1.00 1.00 1.00 30\n",
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"\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"# Загрузка и разбиение данных\n",
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"X, y = load_iris(return_X_y=True)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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"\n",
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"# Нормализация данных\n",
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"scaler = StandardScaler()\n",
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"X_train = scaler.fit_transform(X_train)\n",
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"X_test = scaler.transform(X_test)\n",
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"\n",
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"# Модель MLP — многослойный перцептрон\n",
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"clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, learning_rate_init=0.001, random_state=42)\n",
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"clf.fit(X_train, y_train)\n",
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"\n",
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"# Отчёт о точности\n",
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"print(classification_report(y_test, clf.predict(X_test)))\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "125b3f46-cc81-4341-94f3-9de6dee8aff5",
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"metadata": {},
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"source": [
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"Модель работает очень хорошо и достигла 100% точности на тестовых данных, что является отличным результатом для этого набора данных."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d032afd-fc98-48cd-9ec8-7a742fcf8a50",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62c7892d-b296-4f0c-8886-514b4ee2bad6",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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104
week4_scikit_learn.ipynb.ipynb
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104
week4_scikit_learn.ipynb.ipynb
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@ -0,0 +1,104 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6af36011-9a8c-4dc1-85c5-910263c2d25e",
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"metadata": {},
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"source": [
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"Базовая нейросеть"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2115d6e8-d6d0-4025-9ee0-32c46b20fe45",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 10\n",
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" 1 1.00 1.00 1.00 9\n",
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" 2 1.00 1.00 1.00 11\n",
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"\n",
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" accuracy 1.00 30\n",
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" macro avg 1.00 1.00 1.00 30\n",
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"weighted avg 1.00 1.00 1.00 30\n",
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"\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"# Загрузка и разбиение данных\n",
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"X, y = load_iris(return_X_y=True)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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"\n",
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"# Нормализация данных\n",
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"scaler = StandardScaler()\n",
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"X_train = scaler.fit_transform(X_train)\n",
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"X_test = scaler.transform(X_test)\n",
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"\n",
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"# Модель MLP — многослойный перцептрон\n",
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"clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, learning_rate_init=0.001, random_state=42)\n",
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"clf.fit(X_train, y_train)\n",
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"\n",
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"# Отчёт о точности\n",
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"print(classification_report(y_test, clf.predict(X_test)))\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "125b3f46-cc81-4341-94f3-9de6dee8aff5",
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"metadata": {},
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"source": [
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"Модель работает очень хорошо и достигла 100% точности на тестовых данных, что является отличным результатом для этого набора данных."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d032afd-fc98-48cd-9ec8-7a742fcf8a50",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62c7892d-b296-4f0c-8886-514b4ee2bad6",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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