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variabel terbaik yang berhubungan dengan emansipasi wanita p6
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variabel terbaik yang berhubungan dengan emansipasi wanita p6
kode python nya
==========
import numpy as np
import pandas as pd
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
from sklearn.model_selection import train_test_split
# Assuming the data is in a pandas DataFrame
data = pd.DataFrame({
'Year': list(range(2003, 2024)),
'FLFPR': [46.6, 47.3, 48.1, 49.0, 49.8, 50.4, 50.1, 49.5, 50.0, 50.7, 51.2, 51.7, 52.1, 52.4, 52.8, 53.3, 53.7, 54.0, 52.7, 53.4, 53.1],
'FDI': [0.1, 0.8, 2.2, 2.3, 1.3, 1.8, 0.9, 1.9, 2.2, 2.2, 2.4, 2.1, 2.2, 2.0, 2.0, 1.8, 1.9, 1.6, 2.3, 2.5, 2.1],
'Manufacturing': [27.7, 27.3, 27.5, 27.4, 27.3, 27.0, 26.6, 26.5, 26.2, 25.7, 24.9, 24.5, 22.0, 21.7, 21.3, 20.3, 19.7, 19.8, 19.1, 19.2, 18.34],
'GPI': [0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.99, 0.99, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01],
'Literacy': [86.10, 86.80, 87.60, 88.79, 89.00, 89.10, 89.68, 90.00, 90.07, 90.50, 91.00, 93.45, 93.34, 93.59, 93.80, 93.99, 94.20, 94.55, 94.80, 95.00, 95.20],
'Exports': [21.7, 22.3, 23.7, 24.6, 25.9, 27.0, 23.5, 24.2, 24.8, 24.4, 23.3, 21.9, 19.9, 17.7, 18.1, 18.4, 18.3, 15.6, 17.4, 18.8, 19.2]
})
# Splitting the data
X = data.drop(columns=['FLFPR', 'Year'])
y = data['FLFPR']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Defining the model
nn_model = MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=1000, random_state=0)
# Training the model
nn_model.fit(X_train, y_train)
# Making predictions
y_pred = nn_model.predict(X_test)
# Calculating MSE and MAPE
mse_nn = mean_squared_error(y_test, y_pred)
mape_nn = mean_absolute_percentage_error(y_test, y_pred)
print(f'Neural Network - MSE: {mse_nn}, MAPE: {mape_nn}')
# Adding hypothetical future data for 2024 and 2025
future_data = pd.DataFrame({
'Year': [2024, 2025],
'FDI': [2.3, 2.4], # Hypothetical values
'Manufacturing': [18.0, 17.8], # Hypothetical values
'GPI': [1.01, 1.01], # Assuming no change
'Literacy': [95.40, 95.60], # Continuing the trend
'Exports': [19.3, 19.4] # Hypothetical values
})
==========
import numpy as np
import pandas as pd
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
from sklearn.model_selection import train_test_split
# Assuming the data is in a pandas DataFrame
data = pd.DataFrame({
'Year': list(range(2003, 2024)),
'FLFPR': [46.6, 47.3, 48.1, 49.0, 49.8, 50.4, 50.1, 49.5, 50.0, 50.7, 51.2, 51.7, 52.1, 52.4, 52.8, 53.3, 53.7, 54.0, 52.7, 53.4, 53.1],
'FDI': [0.1, 0.8, 2.2, 2.3, 1.3, 1.8, 0.9, 1.9, 2.2, 2.2, 2.4, 2.1, 2.2, 2.0, 2.0, 1.8, 1.9, 1.6, 2.3, 2.5, 2.1],
'Manufacturing': [27.7, 27.3, 27.5, 27.4, 27.3, 27.0, 26.6, 26.5, 26.2, 25.7, 24.9, 24.5, 22.0, 21.7, 21.3, 20.3, 19.7, 19.8, 19.1, 19.2, 18.34],
'GPI': [0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.99, 0.99, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01],
'Literacy': [86.10, 86.80, 87.60, 88.79, 89.00, 89.10, 89.68, 90.00, 90.07, 90.50, 91.00, 93.45, 93.34, 93.59, 93.80, 93.99, 94.20, 94.55, 94.80, 95.00, 95.20],
'Exports': [21.7, 22.3, 23.7, 24.6, 25.9, 27.0, 23.5, 24.2, 24.8, 24.4, 23.3, 21.9, 19.9, 17.7, 18.1, 18.4, 18.3, 15.6, 17.4, 18.8, 19.2]
})
# Splitting the data
X = data.drop(columns=['FLFPR', 'Year'])
y = data['FLFPR']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Defining the model
nn_model = MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=1000, random_state=0)
# Training the model
nn_model.fit(X_train, y_train)
# Making predictions
y_pred = nn_model.predict(X_test)
# Calculating MSE and MAPE
mse_nn = mean_squared_error(y_test, y_pred)
mape_nn = mean_absolute_percentage_error(y_test, y_pred)
print(f'Neural Network - MSE: {mse_nn}, MAPE: {mape_nn}')
# Adding hypothetical future data for 2024 and 2025
future_data = pd.DataFrame({
'Year': [2024, 2025],
'FDI': [2.3, 2.4], # Hypothetical values
'Manufacturing': [18.0, 17.8], # Hypothetical values
'GPI': [1.01, 1.01], # Assuming no change
'Literacy': [95.40, 95.60], # Continuing the trend
'Exports': [19.3, 19.4] # Hypothetical values
})
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