# 读取数据、合并

train_df = pd.read_csv('../input/train.csv')

test_df = pd.read_csv('../input/test.csv')

combine = [train_df, test_df]

# 检视数据1（观察是否混合类型【订单号】、是否错误值【名称拼写】）

train_df.head()

# 检视数据2（观察数据类型【整数、字符串】、空值数量）

train_df.info()

test_df.info()

# 检视数据3 （观察每个特征的分布情况）

train_df.describe() #数值型

train_df.describe(include=['O']) #对象型

2、Analyze data（分析数据）

Analyze by pivoting features（数据表格）

# 看各自变量与因变量的关系（分布-选出相关性强的若干种并作出假设-比如婴儿的存活率更高）

train_df[["Sex", "Survived"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)

Analyze by visualizing data （可视化-将数据表格的猜想可视化）

#关联数值特征（验证婴儿存活率的假设）

g = sns.FacetGrid(train_df, col='Survived')

g.map(plt.hist, 'Age', bins=20)

# 关联数值特征和序数特征

grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)

grid.map(plt.hist, 'Age', alpha=.5, bins=20)

grid.add_legend()

# 关联分类特征

grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)

grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')

grid.add_legend()

# 关联分类和数值特征

grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)

grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)

grid.add_legend()

3、Wrangle data（处理错误数据）

主要包括：删除特征、新增特征、特征变换（连续值分段、分类特征数值化）

Correcting by dropping features （删除部分特征-比如缺失值严重）

train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)

test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)

combine = [train_df, test_df]

Creating new feature extracting from existing（包括文本处理）

for dataset in combine:

dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\.', expand=False)

pd.crosstab(train_df['Title'], train_df['Sex']) #交叉表，按指定的行列统计分组频数；sum(sex) group by Title

# 将对象特征转换为序数特征

title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}

for dataset in combine:

dataset['Title'] = dataset['Title'].map(title_mapping)

dataset['Title'] = dataset['Title'].fillna(0)

Completing a numerical continuous feature (对连续值特征的处理)

# 填补空值的三种方法，此处选第二种

1、平均值+-方差之间的随机数

2、中值

3、中值+-方差之间的随机数

for dataset in combine:

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()

# Convert random age float to nearest .5 age

guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5

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)

# 连续数值特征分段

train_df['AgeBand'] = pd.cut(train_df['Age'], 5)# 按数值值等分，区别 qcut()按数值个数等分

train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)

for dataset in combine:

dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0

dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1

dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2

dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3

dataset.loc[ dataset['Age'] > 64, 'Age']

train_df.head()

Create new feature combining existing features（通过现有特征分析产生新特征（alone））

# 将两个数值特征相加合并

for dataset in combine:

dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1

Completing a categorical feature（对分类特征的处理）

#填补缺失值（只有两个，所以按最常用的填补）

freq_port = train_df.Embarked.dropna().mode()[0] # 最常见值

for dataset in combine:

dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)

train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)

Converting categorical feature to numeric（将字符分类改为数值形式）

for dataset in combine:

dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)

Quick completing and converting a numeric feature

test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)# 缺失较少，取中值

4、Model, predict and solve（建模、预测）

# 数据准备

X_train = train_df.drop("Survived", axis=1)

Y_train = train_df["Survived"]

X_test = test_df.drop("PassengerId", axis=1).copy()

X_train.shape, Y_train.shape, X_test.shape

# Logistic Regression

logreg = LogisticRegression()

logreg.fit(X_train, Y_train)

Y_pred = logreg.predict(X_test)

acc_log = round(logreg.score(X_train, Y_train) * 100, 2)

----acc_log =80.359999999999999

# 验证各自变量与因变量之间的相关性

coeff_df = pd.DataFrame(train_df.columns.delete(0))

coeff_df.columns = ['Feature']

coeff_df["Correlation"] = pd.Series(logreg.coef_[0])

coeff_df.sort_values(by='Correlation', ascending=False)

# Support Vector Machines

svc = SVC()

svc.fit(X_train, Y_train)

Y_pred = svc.predict(X_test)

acc_svc = round(svc.score(X_train, Y_train) * 100, 2)

----acc_svc =83.840000000000003

# KNN

knn = KNeighborsClassifier(n_neighbors = 3)

knn.fit(X_train, Y_train)

Y_pred = knn.predict(X_test)

acc_knn = round(knn.score(X_train, Y_train) * 100, 2)

----acc_knn =84.739999999999995

# Gaussian Naive Bayes

gaussian = GaussianNB()

gaussian.fit(X_train, Y_train)

Y_pred = gaussian.predict(X_test)

acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)

----acc_gaussian =72.280000000000001

# Perceptron

perceptron = Perceptron()

perceptron.fit(X_train, Y_train)

Y_pred = perceptron.predict(X_test)

acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)

----acc_perceptron =78.0

# Linear SVC

linear_svc = LinearSVC()

linear_svc.fit(X_train, Y_train)

Y_pred = linear_svc.predict(X_test)

acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)

----acc_linear_svc =79.120000000000005

# Stochastic Gradient Descent

sgd = SGDClassifier()

sgd.fit(X_train, Y_train)

Y_pred = sgd.predict(X_test)

acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)

acc_sgd =77.670000000000002

# Decision Tree

decision_tree = DecisionTreeClassifier()

decision_tree.fit(X_train, Y_train)

Y_pred = decision_tree.predict(X_test)

acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)

----acc_decision_tree =86.760000000000005

# Random Forest

random_forest = RandomForestClassifier(n_estimators=100)

random_forest.fit(X_train, Y_train)

Y_pred = random_forest.predict(X_test)

random_forest.score(X_train, Y_train)

acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)

----acc_random_forest =86.760000000000005

# Model evaluation

models = pd.DataFrame({

'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression',

'Random Forest', 'Naive Bayes', 'Perceptron',

'Stochastic Gradient Decent', 'Linear SVC',

'Decision Tree'],

'Score': [acc_svc, acc_knn, acc_log,

acc_random_forest, acc_gaussian, acc_perceptron,

acc_sgd, acc_linear_svc, acc_decision_tree]})

models.sort_values(by='Score', ascending=False)

# 提交

submission = pd.DataFrame({

"PassengerId": test_df["PassengerId"],

"Survived": Y_pred

})

# submission.to_csv('../output/submission.csv', index=False)

Kaggle—Titanic

目录

1、import python package and analysis data（导入和初步分析数据）

2、Analyze data（分析数据）

3、Wrangle data（处理错误数据）

Completing a numerical continuous feature (对连续值特征的处理)

4、Model, predict and solve（建模、预测）

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