python学习DAY22打卡

作业：

自行学习参考如何使用kaggle平台，写下使用注意点，并对下述比赛提交代码

kaggle泰坦尼克号人员生还预测

import warningswarnings.filterwarnings(\"ignore\") #忽略警告信息# 数据处理清洗包import pandas as pdimport numpy as npimport random as rnd# 可视化包import seaborn as snsimport matplotlib.pyplot as plt# 机器学习算法相关包from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier# 设置中文字体（解决中文显示问题）plt.rcParams[\'font.sans-serif\'] = [\'SimHei\'] # Windows系统常用黑体字体plt.rcParams[\'axes.unicode_minus\'] = False # 正常显示负号data_train = pd.read_csv(r\'train.csv\') #读取训练集数据data_test = pd.read_csv(r\'test.csv\') #读取测试集数据combine = [data_train, data_test] # 合并数据#这只是放到一个列表，以便后续合并，现在还没合并print(data_train.isnull().sum())print(data_test.isnull().sum())#删除无用特征 Ticket print(\"Before\", data_train.shape, data_test.shape, combine[0].shape, combine[1].shape)data_train = data_train.drop([\'Ticket\'], axis=1)data_test = data_test.drop([\'Ticket\'], axis=1)combine = [data_train, data_test]print(\"After\", data_train.shape, data_test.shape, combine[0].shape, combine[1].shape)# 转换分类特征Sexfor dataset in combine: dataset[\'Sex\'] = dataset[\'Sex\'].map( {\'female\': 1, \'male\': 0} ).astype(int) #男性赋值为0，女性赋值为1，并转换为整型数据data_test.head()# 创建空数组guess_ages = np.zeros((2,3))guess_ages# 遍历 Sex (0 或 1) 和 Pclass (1, 2, 3) 来计算六种组合的 Age 猜测值for dataset in combine: # 第一个for循环计算每一个分组的Age预测值 for i in range(0, 2): for j in range(0, 3): guess_df = dataset[(dataset[\'Sex\'] == i) & \\ (dataset[\'Pclass\'] == j+1)][\'Age\'].dropna() age_guess = guess_df.median() # 将随机年龄浮点数转换为最接近的 0.5 年龄（四舍五入） guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5 # 第二个for循环对空值进行赋值 for i in range(0, 2): for j in range(0, 3): dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\  \'Age\'] = guess_ages[i,j] dataset[\'Age\'] = dataset[\'Age\'].astype(int)data_train.head()# Embarked: 仅2个缺失，用众数填充data_train[\'Embarked\'].fillna(data_train[\'Embarked\'].mode()[0], inplace=True)data_test[\'Embarked\'].fillna(data_test[\'Embarked\'].mode()[0], inplace=True)print(data_train.isnull().sum())# Fare: 极少数缺失，用中位数填充data_test[\'Fare\'].fillna(data_test[\'Fare\'].median(), inplace=True)# 姓名对预测生存率的影响较小，除非从中提取出有用的信息（如头衔、家庭关系等）data_train[\'Title\'] = data_train[\'Name\'].str.extract(\' ([A-Za-z]+)\\.\', expand=False)data_train[\'Title\'] = data_train[\'Title\'].replace([\'Lady\', \'Countess\', \'Dr\', \'Rev\', \'Sir\', \'Jonkheer\', \'Dona\'], \'Rare\')data_train[\'Title\'] = data_train[\'Title\'].replace(\'Mlle\', \'Miss\').replace(\'Ms\', \'Miss\').replace(\'Mme\', \'Mrs\')data_test[\'Title\'] = data_test[\'Name\'].str.extract(\' ([A-Za-z]+)\\.\', expand=False)data_test[\'Title\'] = data_test[\'Title\'].replace([\'Lady\', \'Countess\', \'Dr\', \'Rev\', \'Sir\', \'Jonkheer\', \'Dona\'], \'Rare\')data_test[\'Title\'] = data_test[\'Title\'].replace(\'Mlle\', \'Miss\').replace(\'Ms\', \'Miss\').replace(\'Mme\', \'Mrs\')# 计算家庭成员数量，家庭规模可能影响生存率data_train[\'FamilySize\'] = data_train[\'SibSp\'] + data_train[\'Parch\'] + 1data_test[\'FamilySize\'] = data_test[\'SibSp\'] + data_test[\'Parch\'] + 1# 结合 FamilySize 创建新特征（如是否独自乘船）：data_train[\'IsAlone\'] = (data_train[\'FamilySize\'] == 1).astype(int)data_test[\'IsAlone\'] = (data_test[\'FamilySize\'] == 1).astype(int)# 客舱甲板# 提取客舱首字母（如果有的话）data_train[\'Deck\'] = data_train[\'Cabin\'].str[0]data_train[\'Deck\'] = data_train[\'Deck\'].fillna(\'Unknown\') # 填充缺失值data_test[\'Deck\'] = data_test[\'Cabin\'].str[0]data_test[\'Deck\'] = data_test[\'Deck\'].fillna(\'Unknown\') # 填充缺失值# 删除冗余特征data_train.drop([ \'Name\', \'Cabin\'], axis=1, inplace=True)data_test.drop([\'Name\', \'Cabin\'], axis=1, inplace=True)# 先筛选字符串变量discrete_features = data_train.select_dtypes(include=[\'object\']).columns.tolist()print(\"离散变量:\", discrete_features) # 打印离散变量列名data_train.rename(columns={\'Sex\': \'isFemale\'}, inplace=True) # 重命名列名 -> 是否女性data_test.rename(columns={\'Sex\': \'isFemale\'}, inplace=True) # 重命名列名print(data_train[\'isFemale\'].value_counts()) # 打印Sex列的取值分布# 对embarked和deck进行独热编码（无序）data_train = pd.get_dummies(data_train, columns=[\'Embarked\', \'Deck\'], dtype=int, drop_first=True)data_test = pd.get_dummies(data_test, columns=[\'Embarked\', \'Deck\'], dtype=int, drop_first=True) # 确保训练集和测试集的列顺序一致# 排除标签列\'Survived\'，仅比较特征列feature_columns = [col for col in data_train.columns if col != \'Survived\']missing_cols = set(feature_columns) - set(data_test.columns)for col in missing_cols: data_test[col] = 0# 按照训练集特征列的顺序排序测试集列data_test = data_test[feature_columns] print(data_train.head())title_mapping = { \'Mr\': 0, \'Rare\': 1, \'Master\': 2, \'Miss\': 3, \'Mrs\': 4}data_train[\'Title\'] = data_train[\'Title\'].map(title_mapping)data_test[\'Title\'] = data_test[\'Title\'].map(title_mapping)print(data_train[\'Title\'].value_counts()) # 打印Title列的取值分布# 对Age, Fare 进行标准化（均值为0，方差为1）from sklearn.preprocessing import StandardScaler, MinMaxScalerscaler = StandardScaler()data_train[[\'Age\', \'Fare\']] = scaler.fit_transform(data_train[[\'Age\', \'Fare\']]) print(data_train.head()) # 打印前几行数据print(data_train.info()) # 划分一下测试集from sklearn.model_selection import train_test_splitX = data_train.drop([\'Survived\'], axis=1) # 特征，axis=1表示按列删除y = data_train[\'Survived\'] # 标签# 按照8:2划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 80%训练集，20%测试集# --- 1. 默认参数的随机森林 ---from sklearn.ensemble import RandomForestClassifier #随机森林分类器from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # 用于评估分类器性能的指标from sklearn.metrics import classification_report, confusion_matrix #用于生成分类报告和混淆矩阵import warnings #用于忽略警告信息warnings.filterwarnings(\"ignore\") # 忽略所有警告信息import timeprint(\"--- 1. 默认参数随机森林 (训练集 -> 测试集) ---\")start_time = time.time() # 记录开始时间rf_model = RandomForestClassifier(random_state=42)rf_model.fit(X_train, y_train) # 在训练集上训练rf_pred = rf_model.predict(X_test) # 在测试集上预测end_time = time.time() # 记录结束时间print(f\"训练与预测耗时: {end_time - start_time:.4f} 秒\")print(\"\\n默认随机森林 在测试集上的分类报告：\")print(classification_report(y_test, rf_pred))print(\"默认随机森林 在测试集上的混淆矩阵：\")print(confusion_matrix(y_test, rf_pred))

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