59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
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# app.py
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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.linear_model import LogisticRegression
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app = Flask(__name__)
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# Load data from the CSV file
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data = pd.read_csv("student-mat.csv")
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# Map the 'health' variable to three classes
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data['health_class'] = pd.cut(data['health'], bins=[-1, 2, 3, 5], labels=['плохое', 'среднее', 'хорошее'])
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# Define features and target variable
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features = ['Pstatus', 'guardian', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'absences']
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X = pd.get_dummies(data[features])
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y = data['health_class']
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# Split the data into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42)
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# Create and train the logistic regression model
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model = LogisticRegression(max_iter=1000)
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model.fit(X_train, y_train)
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@app.route("/", methods=["GET", "POST"])
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def index():
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if request.method == "POST":
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# Get data from the form
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form_data = request.form
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input_data = pd.DataFrame({
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'Pstatus': [form_data['Pstatus']],
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'guardian': [form_data['guardian']],
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'internet': [form_data['internet']],
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'romantic': [form_data['romantic']],
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'famrel': [int(form_data['famrel'])],
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'freetime': [int(form_data['freetime'])],
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'goout': [int(form_data['goout'])],
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'Dalc': [int(form_data['Dalc'])],
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'Walc': [int(form_data['Walc'])],
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'absences': [int(form_data['absences'])]
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})
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# One-hot encode the categorical variables consistently
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input_data = pd.get_dummies(input_data)
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# Ensure the input features match the features seen during training
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input_data = input_data.reindex(columns=X.columns, fill_value=0)
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# Make prediction
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prediction = model.predict(input_data)[0]
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return render_template("index.html", prediction_result=prediction)
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return render_template("index.html")
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if __name__ == "__main__":
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app.run(host="localhost", port=5000)
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