61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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# Load data
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data = pd.read_csv('person_types.csv')
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# Select variables for the model
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features = ['HEIGHT', 'WEIGHT', 'ACTIVITY_LEVEL']
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# Select relevant columns
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df = data[features + ['SEX']]
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# Drop rows with missing values
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df = df.dropna()
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# Convert string values to numerical for 'ACTIVITY_LEVEL'
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le_activity = LabelEncoder()
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df['ACTIVITY_LEVEL'] = le_activity.fit_transform(df['ACTIVITY_LEVEL'])
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# Split into features and target variable
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X = df.drop('SEX', axis=1)
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y = df['SEX']
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# Split into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Standardize features
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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# Create and train MLPClassifier
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model = MLPClassifier(random_state=42)
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model.fit(X_train_scaled, y_train)
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# Predict on the test set
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y_pred = model.predict(X_test_scaled)
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# Evaluate the model
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accuracy = accuracy_score(y_test, y_pred)
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conf_matrix = confusion_matrix(y_test, y_pred)
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class_report = classification_report(y_test, y_pred)
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print(f'Accuracy: {accuracy}')
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print(f'Confusion Matrix:\n{conf_matrix}')
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print(f'Classification Report:\n{class_report}')
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# Visualize the results (e.g., a histogram)
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plt.hist(y_pred, bins=np.arange(3)-0.5, alpha=0.75, color='blue', label='Predicted')
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plt.hist(y_test, bins=np.arange(3)-0.5, alpha=0.5, color='green', label='Actual')
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plt.xlabel('Sex')
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plt.ylabel('Count')
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plt.xticks([0, 1], ['Female', 'Male'])
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plt.legend()
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plt.show()
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