你相亲成功的几率有多高？机器学习硬核预测

print("欢迎来到DSW👏👏👏 你可以把这里当作一个自由自在的实验室😝，做各种你想尝试的数据实验 ")

# 计算第 N 个斐波那契数列 的函数def Fibonacci(n):     if n<0:         print("异常输入，请确认输入一个正整数")     # 第一个斐波那契数是 0     elif n==1:         return 0    # 第二个斐波那契数是 1     elif n==2:         return 1    else:         return Fibonacci(n-1)+Fibonacci(n-2) 
# 计算第10个斐波那契数并打印出来print('第10个斐波那契数是:',Fibonacci(10)) 

import pandas as pddf = pd.read_csv('Speed Dating Data.csv', encoding='gbk')print(df.shape)

percent_missing = df.isnull().sum() * 100 / len(df)missing_value_df = pd.DataFrame({    'column_name': df.columns,    'percent_missing': percent_missing})missing_value_df.sort_values(by='percent_missing')

# 多少人通过Speed Dating找到了对象plt.subplots(figsize=(3,3), dpi=110,)# 构造数据size_of_groups=df.match.value_counts().values
single_percentage = round(size_of_groups[0]/sum(size_of_groups) * 100,2) matched_percentage = round(size_of_groups[1]/sum(size_of_groups)* 100,2) names = [    'Single:' + str(single_percentage) + '%',    'Matched' + str(matched_percentage) + '%']
# 创建饼图plt.pie(    size_of_groups,     labels=names,     labeldistance=1.2,     colors=Pastel1_3.hex_colors)plt.show()

df[df.gender == 0]

# 多少女生通过Speed Dating找到了对象plt.subplots(figsize=(3,3), dpi=110,)# 构造数据size_of_groups=df[df.gender == 0].match.value_counts().values # 男生只需要吧0替换成1即可
single_percentage = round(size_of_groups[0]/sum(size_of_groups) * 100,2) matched_percentage = round(size_of_groups[1]/sum(size_of_groups)* 100,2) names = [    'Single:' + str(single_percentage) + '%',    'Matched' + str(matched_percentage) + '%']
# 创建饼图plt.pie(    size_of_groups,     labels=names,     labeldistance=1.2,     colors=Pastel1_3.hex_colors)plt.show()

# 年龄分布age = df[np.isfinite(df['age'])]['age']plt.hist(age,bins=35)plt.xlabel('Age')plt.ylabel('Frequency')

date_df = df[[    'iid', 'gender', 'pid', 'match', 'int_corr', 'samerace', 'age_o',       'race_o', 'pf_o_att', 'pf_o_sin', 'pf_o_int', 'pf_o_fun', 'pf_o_amb',       'pf_o_sha', 'dec_o', 'attr_o', 'sinc_o', 'intel_o', 'fun_o', 'like_o',       'prob_o', 'met_o', 'age', 'race', 'imprace', 'imprelig', 'goal', 'date',       'go_out', 'career_c', 'sports', 'tvsports', 'exercise', 'dining',       'museums', 'art', 'hiking', 'gaming', 'clubbing', 'reading', 'tv',       'theater', 'movies', 'concerts', 'music', 'shopping', 'yoga', 'attr1_1',       'sinc1_1', 'intel1_1', 'fun1_1', 'amb1_1', 'attr3_1', 'sinc3_1',       'fun3_1', 'intel3_1', 'dec', 'attr', 'sinc', 'intel', 'fun', 'like',       'prob', 'met']]
# heatmapplt.subplots(figsize=(20,15))ax = plt.axes()ax.set_title("Correlation Heatmap")corr = date_df.corr()sns.heatmap(corr,             xticklabels=corr.columns.values,            yticklabels=corr.columns.values)

'attr_o','sinc_o','intel_o','fun_o','amb_o','shar_o'

# preparing the dataclean_df = df[['attr_o','sinc_o','intel_o','fun_o','amb_o','shar_o','match']]clean_df.dropna(inplace=True)X=clean_df[['attr_o','sinc_o','intel_o','fun_o','amb_o','shar_o',]]y=clean_df['match']
oversample = imblearn.over_sampling.SVMSMOTE()X, y = oversample.fit_resample(X, y)
# 做训练集和测试集分割X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, stratify=y)

# logistic regression classification modelmodel = LogisticRegression(C=1, random_state=0)lrc = model.fit(X_train, y_train)predict_train_lrc = lrc.predict(X_train)predict_test_lrc = lrc.predict(X_test)print('Training Accuracy:', metrics.accuracy_score(y_train, predict_train_lrc))print('Validation Accuracy:', metrics.accuracy_score(y_test, predict_test_lrc))