{"id":53078,"date":"2025-08-11T23:59:35","date_gmt":"2025-08-11T15:59:35","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/53078.html"},"modified":"2025-08-11T23:59:35","modified_gmt":"2025-08-11T15:59:35","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0-kaggle%e9%a1%b9%e7%9b%ae%e5%ae%9e%e8%b7%b5%ef%bc%881%ef%bc%89titanic","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/53078.html","title":{"rendered":"\u673a\u5668\u5b66\u4e60 - Kaggle\u9879\u76ee\u5b9e\u8df5\uff081\uff09Titanic"},"content":{"rendered":"

Titanic – Machine Learning from Disaster | Kaggle \u9898\u76ee<\/p>\n

Titanic Data Science Solutions | Kaggle \u53c2\u8003\u9898\u89e3<\/p>\n

notebookbe6ed1ba33 | Kaggle \u4e0b\u9762\u9879\u76ee\u6211\u5728Kaggle\u4e0a\u7684\u5448\u73b0<\/p>\n

\u76ee\u5f55<\/p>\n

1. \u6570\u636e\u5206\u6790<\/p>\n

2. \u6570\u636e\u6e05\u6d17<\/p>\n

3. \u6a21\u578b\u8c03\u7528\u4e0e\u9884\u6d4b<\/p>\n

\n

1. \u6570\u636e\u5206\u6790<\/h2>\n

Pandas \u4e2d\u7684\u5bfc\u5165\u4e3a DataFrame；\u00a0 \u00a0\u518d\u7528 .head()\u00a0 \u00a0.info()\u00a0 \u00a0.describe() \u5f97\u5230\u4e00\u4e9b\u57fa\u672c\u4fe1\u606f<\/p>\n

import pandas as pd<\/p>\n

#kaggle\u5bfc\u5165 \u590d\u5236\u53f3\u4fa7\u8def\u5f84
\ntrain_df = pd.read_csv('\/kaggle\/input\/titanic\/train.csv')
\ntest_df = pd.read_csv('\/kaggle\/input\/titanic\/test.csv')
\nprint(train_df.columns.values) #\u5217\u7684\u79cd\u7c7b<\/p>\n

train_df.head() #\u524d\u4e94\u4e2a
\ntrain_df.tail() #\u540e\u4e94\u4e2a<\/p>\n

train_df.info()
\nprint('_'*40)
\ntest_df.info() # \u5217\u540d\u3001\u975e\u7a7a\u503c\u6570\u91cf\u3001\u6570\u636e\u7c7b\u578b\u3001\u5185\u5b58\u5360\u7528 \u4fe1\u606f<\/p>\n

train_df.describe() # \u6570\u503c\u578b\u5217\u7684 \u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf<\/p>\n

train_df.describe(include=['O']) # \u975e\u6570\u503c\u578b\u5217（object\/\u5b57\u7b26\u4e32） \u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf <\/p>\n

\u5bf9\u4e8e\u6bcf\u4e00\u5217 \u5206\u4e3a\u6bcf\u4e00\u7c7b\u522b \u6c42\u5e73\u5747\u5b58\u6d3b\u7387 \u518d\u964d\u5e8f\u6392\u5217<\/p>\n

train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)<\/p>\n

train_df[["Sex", "Survived"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)<\/p>\n

train_df[["SibSp", "Survived"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)<\/p>\n

train_df[["Parch", "Survived"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False) <\/p>\n

\u4e09\u79cdPclass\u4e0b\u00a0 \u5e78\u5b58\/\u6b7b\u4ea1\u00a0 3*2\u79cd\u60c5\u51b5\u4e0b\u7684\u5e74\u9f84\u5206\u5e03\u56fe<\/p>\n

\u4e09\u79cdEmbarked\u4e0b \u4e0d\u540c\u6027\u522b\u5728\u4e09\u79cdPclass\u4e0b\u7684\u00a0\u5e78\u5b58\/\u6b7b\u4ea1\u7387\u56fe<\/p>\n

# \u4e24\u4e2a\u53d8\u91cf
\ngrid = sns.FacetGrid(train_df, col='Survived', row='Pclass', height=2.2, aspect=1.6)
\ngrid.map(plt.hist, 'Age', alpha=.5, bins=20)<\/p>\n

# \u4e09\u4e2a\u53d8\u91cf
\ngrid = sns.FacetGrid(train_df, row='Embarked', height=2.2, aspect=1.6)
\ngrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
\ngrid.add_legend() #\u56fe\u4f8b <\/p>\n<\/p>\n

2. \u6570\u636e\u6e05\u6d17<\/h2>\n

\u4e00\u4e9b\u5b57\u7b26\u4e32\u5217 \u6709\u7684\u6ca1\u4f5c\u7528 \u6709\u7684\u9700\u8981\u63d0\u53d6\u8f6c\u6362\u4e3a\u6570\u5b57\u5f62\u5f0f<\/p>\n

\u6211\u4eec\u5728describe\u4e2d\u77e5\u9053 Ticket\u662f\u7968\u7684\u7f16\u53f7（\u5bf9\u5206\u7c7b\u6ca1\u6709\u5e2e\u52a9）Cabin\u6709\u5f88\u591a\u7f3a\u5931\u503c\u00a0 \u00a0 \u9042\u8e22\u9664<\/p>\n

train_df = train_df.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1)
\ntest_df = test_df.drop(['Ticket', 'Cabin'], axis=1)
\ncombine = [train_df, test_df] <\/p>\n

Name\u90a3\u5217 \u53ea\u6709\u4e2d\u95f4\u7684 Mr Miss\u00a0 \u8fd9\u6837\u7684\u4e2d\u95f4\u7684\u4f4d\u7f6e\u5bf9\u5206\u7c7b\u6709\u5e2e\u52a9 \u63d0\u53d6\u51fa\u6765\u66f4\u6362\u6807\u7b7e<\/p>\n

for dataset in combine:
\n dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\\\.', expand=False)<\/p>\n

pd.crosstab(train_df['Title'], train_df['Sex']) <\/p>\n

\u628aTitle\u548cSex\u4e24\u4e24\u6bd4\u5bf9\u00a0 \u00a0\u53ef\u4ee5\u5206\u4e3aMaster Miss Mr Mrs \u548c Rare \u4e94\u7c7b\u5206\u522b\u5bf9\u5e941~5<\/p>\n

\"\"\"\"\"\"<\/p>\n

title_map = {
\n 'Mr': 1,
\n 'Miss': 2, 'Mlle': 2, 'Ms': 2,
\n 'Mrs': 3, 'Mme': 3,
\n 'Master': 4,
\n 'Lady': 5, 'Countess': 5, 'Capt': 5, 'Col': 5, 'Don': 5,
\n 'Dr': 5, 'Major': 5, 'Rev': 5, 'Sir': 5, 'Jonkheer': 5, 'Dona': 5
\n}<\/p>\n

for dataset in combine:
\n dataset['Title'] = dataset['Title'].map(title_map).astype('int8')
\ntrain_df.head() <\/p>\n

Name \u5217\u5df2\u7ecf\u8f6c\u6362\u4e3a Title\u5217 ；\u00a0 \u00a0 \u628aSex\u4e2d\u7684 female -> 1\u00a0 \u00a0male -> 0<\/p>\n

train_df = train_df.drop(['Name'], axis=1)
\ntest_df = test_df.drop(['Name'], axis=1)
\ncombine = [train_df, test_df]
\nfor dataset in combine:
\n dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int) <\/p>\n

\u7136\u540eAge\u4e2d\u8fd8\u6709\u7f3a\u5931\u503c； \u53ef\u4ee5\u62ff\u5168\u4f53\u4eba\u5458\u7684\u5e73\u5747\u6570\u586b\u5145； \u4e5f\u53ef\u4ee5\u901a\u8fc7\u5206\u7c7b\u522b\u586b\u5145\u4e2d\u4f4d\u6570\u3002<\/p>\n

\u6839\u636ePclass \u548c Sex 3*2=6\u7c7b\u522b \u5148\u5bf9\u4e0d\u7f3a\u5931\u7684\u7b97\u51fa\u4e2d\u4f4d\u6570 \u518d\u586b\u5145\u3002<\/p>\n

\u518d\u4ee5 16,32,48,64 \u4e3a\u754c\u9650\u628a\u8fde\u7eed\u578b\u7684\u5e74\u9f84\u5212\u5206\u4e3a5\u7c7b<\/p>\n

# 1. \u8ba1\u7b97\u5404\u5206\u7ec4\u7684\u5e74\u9f84\u4e2d\u4f4d\u6570
\nguess_ages = np.zeros((2, 3))
\nfor dataset in combine:
\n for i in range(2): # \u6027\u522b: 0=\u7537,1=\u5973
\n for j in range(3): # \u8231\u7b49: 1,2,3
\n guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j+1)]['Age'].dropna()
\n age_guess = guess_df.median()
\n guess_ages[i, j] = int(age_guess\/0.5 + 0.5) * 0.5<\/p>\n

# 2. \u5b9a\u4e49\u5206\u6bb5\u6761\u4ef6\u548c\u6807\u7b7e
\nage_bins = [0, 16, 32, 48, 64, np.inf]
\nage_labels = [0, 1, 2, 3, 4]<\/p>\n

# 3. \u586b\u5145\u7f3a\u5931\u503c\u5e76\u5206\u6bb5
\nfor dataset in combine:
\n # \u586b\u5145\u7f3a\u5931\u503c
\n for i in range(2):
\n for j in range(3):
\n mask = (dataset['Age'].isnull()) & (dataset['Sex'] == i) & (dataset['Pclass'] == j+1)
\n dataset.loc[mask, 'Age'] = guess_ages[i, j]<\/p>\n

# \u5e74\u9f84\u5206\u6bb5
\n dataset['Age'] = pd.cut(dataset['Age'], bins=age_bins, labels=age_labels).astype(int)<\/p>\n

# \u9a8c\u8bc1\u7ed3\u679c
\nprint("\u5e74\u9f84\u5206\u5e03:")
\nprint(train_df['Age'].value_counts().sort_index())
\nprint("\\\\n\u5404\u5e74\u9f84\u6bb5\u751f\u5b58\u7387:")
\nprint(train_df.groupby('Age')['Survived'].mean()) <\/p>\n

SibSp \u548c Parch \u5144\u5f1f\u59d0\u59b9\u7236\u6bcd\u5b50\u5973\u7684\u4fe1\u606f\u00a0\u53ef\u4ee5\u7edf\u4e00\u5f52\u7c7b\u4e3a\u00a0 \u00a0 \u662f\u5426\u72ec\u81ea\u4e00\u4e2a\u4eba\u00a0'IsAlone'<\/p>\n

for dataset in combine:
\n dataset['IsAlone'] = 1
\n dataset.loc[dataset['SibSp'] + dataset['Parch'] > 0, 'IsAlone'] = 0
\ntrain_df = train_df.drop(['Parch', 'SibSp'], axis=1)
\ntest_df = test_df.drop(['Parch', 'SibSp'], axis=1) <\/p>\n

Embarked \u6e2f\u6709\u4e24\u4e2a\u7f3a\u5931\u503c \u6211\u4eec\u53ef\u4ee5\u7528.mode() \u5f97\u5230\u4f17\u6570\u586b\u5145，\u7136\u540e\u6709 S C Q\u4e09\u7c7b\u8f6c\u5316\u4e3a0 1 2\u3002<\/p>\n

freq_port = train_df.Embarked.dropna().mode()[0]
\ncombine = [train_df, test_df]
\nfor dataset in combine:
\n dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)<\/p>\n

train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)<\/p>\n

for dataset in combine:
\n dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int) <\/p>\n

Fare \u4e58\u5ba2\u82b1\u8d39\u00a0 \u00a0 \u00a0test\u4e2d\u6709\u4e00\u4e2a\u7f3a\u5931\u5747\u503c\u586b\u5145\u00a0 -> qcut \u8f6c\u5316\u4e3a\u533a\u95f4 \u5212\u5206\u4e3a4\u7c7b\u00a0 -> \u8f6c\u5316\u4e3a0~3<\/p>\n

\"\"<\/p>\n

train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)
\ntrain_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False, observed=True).mean().sort_values(by='FareBand', ascending=True)<\/p>\n

for dataset in combine:
\n # \u4e2d\u4f4d\u6570\u586b\u5145\u7f3a\u5931\u503c
\n dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].dropna().median())
\n # \u7136\u540e\u8fdb\u884c\u5206\u7bb1\u64cd\u4f5c
\n dataset.loc[dataset['Fare'] <= 7.91, 'Fare'] = 0
\n dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1
\n dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2
\n dataset.loc[dataset['Fare'] > 31, 'Fare'] = 3
\n # \u8f6c\u6362\u4e3a\u6574\u6570\u7c7b\u578b
\n dataset['Fare'] = dataset['Fare'].astype(int)<\/p>\n

train_df = train_df.drop(['FareBand'], axis=1)
\ncombine = [train_df, test_df]
\nprint(train_df.head(10)) <\/p>\n

\u6e05\u6d17\u540e\u7684\u6700\u7ec8\u6570\u636e\u53d8\u6210\u4e86\u8fd9\u6837 \u5747\u4e3a\u4e0d\u8d85\u8fc75\u4e2a\u7c7b\u522b\u7684 0~5\u7684\u6574\u6570<\/p>\n

\u5c0f\u7ed3\u4e4b\u524d\u7684\u64cd\u4f5c：\u53d8\u6210\u73b0\u5728\u76848\u7c7b<\/p>\n

Survived \u4fdd\u63010-1\u6b7b\u6d3b\u00a0 \u00a0 \u00a0 Pclass \u4e3a1 2 3\u7b49\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Sex\u7537\u5973\u6362\u4e3a0-1<\/p>\n

Age \u5212\u5206\u4e865\u4e2a\u5e74\u9f84\u6bb5\u00a0 \u00a0 \u00a0 Fare\u7968\u4ef7\u5206\u4e3a\u4e864\u6bb5\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Embarked\u7684Q S C\u4e09\u4e2a\u7c7b\u522b\u8f6c\u6362\u4e3a0 1 2<\/p>\n

Name\u4e2d\u79f0\u8c13\u4fe1\u606f\u8f6c\u6362\u4e3a5\u4e2aTitle\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Sibsp\u548cParch \u8f6c\u5316\u4e3a\u662f\u5426\u4e00\u4e2a\u4eba IsAlone<\/p>\n

\"\"<\/p>\n<\/p>\n

3. \u6a21\u578b\u8c03\u7528\u4e0e\u9884\u6d4b<\/h2>\n

\u5148\u51c6\u5907\u597d\u8bad\u7ec3\u96c6X_train\u00a0 Y_train \u548c\u6d4b\u8bd5\u96c6 X_test<\/p>\n

X_train = train_df.drop("Survived", axis=1)
\nY_train = train_df["Survived"]
\nX_test = test_df.drop("PassengerId", axis=1).copy()
\nX_train.shape, Y_train.shape, X_test.shape <\/p>\n

LogisticRegression \u903b\u8f91\u56de\u5f52<\/p>\n

from sklearn.linear_model import LogisticRegression
\nm1 = LogisticRegression()
\nm1.fit(X_train, Y_train)
\nY_pred = m1.predict(X_test)
\nprint(round(m1.score(X_train, Y_train) * 100, 2)) <\/p>\n

\u8fd8\u53ef\u4ee5\u8f93\u51fa\u56de\u5f52\u4e2d\u6bcf\u4e2a\u91cf\u7684\u7cfb\u6570<\/p>\n

coeff_df = pd.DataFrame(train_df.columns.delete(0))
\ncoeff_df.columns = ['Feature']
\ncoeff_df["Correlation"] = pd.Series(m1.coef_[0])<\/p>\n

coeff_df.sort_values(by='Correlation', ascending=False) <\/p>\n

\"\"<\/p>\n

\u4ee5\u4e0b\u4e3a\u4e00\u4e9b\u6a21\u578b\u7684\u9009\u7528<\/p>\n

from sklearn.linear_model import LogisticRegression
\nfrom sklearn.svm import SVC, LinearSVC
\nfrom sklearn.neighbors import KNeighborsClassifier
\nfrom sklearn.naive_bayes import GaussianNB
\nfrom sklearn.linear_model import Perceptron, SGDClassifier
\nfrom sklearn.tree import DecisionTreeClassifier
\nfrom sklearn.ensemble import RandomForestClassifier<\/p>\n

# \u521b\u5efa\u6a21\u578b\u5217\u8868，\u6bcf\u4e2a\u5143\u7d20\u662f（\u6a21\u578b\u540d\u79f0，\u6a21\u578b\u5b9e\u4f8b）\u7684\u5143\u7ec4
\nmodels = [
\n ("LogisticRegression", LogisticRegression()),
\n ("SVC", SVC()),
\n ("KNN", KNeighborsClassifier(n_neighbors=3)),
\n ("GaussianNB", GaussianNB()),
\n ("Perceptron", Perceptron()),
\n ("LinearSVC", LinearSVC()),
\n ("SGDClassifier", SGDClassifier()),
\n ("DecisionTree", DecisionTreeClassifier()),
\n ("RandomForest", RandomForestClassifier(n_estimators=100))
\n]<\/p>\n

results = []
\nfor name, model in models:
\n model.fit(X_train, Y_train)
\n acc = round(model.score(X_train, Y_train) * 100, 2)
\n results.append((name, acc))<\/p>\n

# \u8f93\u51fa\u7ed3\u679c：\u6a21\u578b\u540d\u79f0 + \u51c6\u786e\u7387
\nresults = sorted(results, key=lambda x: x[1], reverse=True)
\nfor name, acc in results:
\n print(f"{name}\u6a21\u578b\u51c6\u786e\u7387: {acc}%") <\/p>\n

\"\"<\/p>\n

\u6211\u4eec\u7528\u968f\u673a\u68ee\u6797\u9884\u6d4b\u4e4b\u540e \u8fdb\u884csubmission\u6587\u4ef6\u7684\u751f\u6210\u548c\u63d0\u4ea4<\/p>\n

model= RandomForestClassifier(n_estimators=100)
\nmodel.fit(X_train, Y_train)
\nY_pred = model.predict(X_test)
\nsubmission = pd.DataFrame({
\n "PassengerId": test_df["PassengerId"],
\n "Survived": Y_pred
\n })
\nsubmission.to_csv('submission.csv', index=False)<\/p>\n","protected":false},"excerpt":{"rendered":"

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