54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
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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from scipy.cluster.hierarchy import dendrogram, linkage
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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[:15]
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X = X[["math score", "reading score", "writing score"]].values
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labelList = []
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for i in X:
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st = ""
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for j in i:
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st += str(j)
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st += ","
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st = "(" + st[:len(st) - 1] + ")"
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labelList.append(st)
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linked = linkage(X, 'single')
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plt.figure(figsize=(10, 7))
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dendrogram(linked,
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orientation='top',
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labels=labelList,
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distance_sort='descending',
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show_leaf_counts=True)
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plt.show() |