78 lines
3.0 KiB
Python
78 lines
3.0 KiB
Python
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from flask import Flask, render_template, request
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.neural_network import MLPRegressor
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from sklearn.pipeline import Pipeline
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import joblib
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app = Flask(__name__)
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# Загрузка данных
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data_bgg = pd.read_csv("bgg_dataset.csv", delimiter=";")
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# Выбор нужных столбцов
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selected_columns_bgg = ['Year Published', 'Users Rated', 'Rating Average', 'BGG Rank', 'Owned Users', 'Complexity Average']
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features = data_bgg[selected_columns_bgg]
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# Замена запятых на точки в столбцах 'Rating Average' и 'Complexity Average'
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features['Rating Average'] = features['Rating Average'].str.replace(',', '.').astype(float)
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features['Complexity Average'] = features['Complexity Average'].str.replace(',', '.').astype(float)
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# Замена пропущенных значений средними значениями по столбцам
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features = features.fillna(features.mean())
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# Разделение данных
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X_bgg = features.drop('Rating Average', axis=1)
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y_bgg = features['Rating Average']
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X_train_bgg, X_test_bgg, y_train_bgg, y_test_bgg = train_test_split(X_bgg, y_bgg, test_size=0.2, random_state=42)
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# Масштабирование данных
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scaler = StandardScaler()
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X_train_bgg_scaled = scaler.fit_transform(X_train_bgg)
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X_test_bgg_scaled = scaler.transform(X_test_bgg)
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# Обучение модели нейронной сети
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mlp_regressor_model = Pipeline([
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('scaler', StandardScaler()),
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('mlp_regressor', MLPRegressor(hidden_layer_sizes=(100, 50), max_iter=2000, random_state=42))
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])
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mlp_regressor_model.fit(X_train_bgg_scaled, y_train_bgg)
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# Сохранение модели
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joblib.dump(mlp_regressor_model, 'mlp_regressor_model.joblib')
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# Загрузка модели
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mlp_regressor_model = joblib.load('mlp_regressor_model.joblib')
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# Обновление маршрута для предсказания
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/predict', methods=['POST'])
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def predict():
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if request.method == 'POST':
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# Получение данных из формы
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input_data_bgg = {
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'Year Published': int(request.form['Year Published']),
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'Users Rated': int(request.form['Users Rated']),
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'BGG Rank': int(request.form['BGG Rank']),
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'Owned Users': int(request.form['Owned Users']),
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'Complexity Average': float(request.form['Complexity Average'])
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}
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# Преобразование данных в DataFrame
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input_df_bgg = pd.DataFrame([input_data_bgg])
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# Масштабирование входных данных
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input_data_scaled = scaler.transform(input_df_bgg)
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# Предсказание
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prediction_bgg = mlp_regressor_model.predict(input_data_scaled)[0]
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return render_template('index.html', prediction_bgg=prediction_bgg)
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if __name__ == '__main__':
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app.run(debug=True)
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