47 lines
2.0 KiB
Python
47 lines
2.0 KiB
Python
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import numpy as np
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import pandas as pb
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression, Perceptron
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from sklearn.neural_network import MLPClassifier, MLPRegressor
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from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler
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from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
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df = pb.read_csv("StudentsPerformance.csv", sep=",", encoding="windows-1251")
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df1 = df
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print("Данные без подготовки:")
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with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
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print(df[:5])
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def prepareStringData(columnName):
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uniq = df[columnName].unique()
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mp = {}
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for i in uniq:
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mp[i] = len(mp)
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df[columnName] = df[columnName].map(mp)
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print()
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print("Данные после подготовки:")
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prepareStringData("gender")
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prepareStringData("race/ethnicity")
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prepareStringData("parental level of education")
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prepareStringData("lunch")
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prepareStringData("test preparation course")
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with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
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print(df[:5])
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X = df[["gender", "race/ethnicity", "lunch", "test preparation course", "math score", "reading score", "writing score"]]
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y = df["parental level of education"]
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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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dtc = DecisionTreeClassifier()
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dtc = dtc.fit(X_train, y_train)
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dtr = DecisionTreeRegressor()
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dtr = dtr.fit(X_train, y_train)
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print()
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print("Результат дерева класификации на учебных данных: ", dtc.score(X_train, y_train))
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print("Результат дерева класификации на тестовых данных: ", dtc.score(X_Test, y_test))
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print()
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print("Результат дерева регрессии на учебных данных: ", dtr.score(X_train, y_train))
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print("Результат дерева регрессии на тестовых данных: ", dtr.score(X_Test, y_test))
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