KNN算法泰坦尼克号生存概率预测Python 泰坦尼克号python数据分析

1.项目背景：

Titanic大概是kaggle上最受欢迎的项目，该项目主要是让参赛选手根据训练集中的乘客数据和存活情况进行建模，进而使用模型预测测试集中的乘客是否会存活。乘客特征总共有11个，如下：

PassengerId = 乘客ID
Pclass = 客舱等级(1/2/3等舱位)
Name = 乘客姓名
Sex = 性别
Age = 年龄
SibSp = 兄弟姐妹数/配偶数
Parch = 父母数/子女数
Ticket = 船票编号
Fare = 船票价格
Cabin = 客舱号
Embarked => 登船港口

总体来说Titanic和其他比赛比起来数据量算是很小的了，训练集合测试集加起来总共891+418=1309个。因为数据少，所以很容易过拟合(overfitting)，一些算法如GradientBoostingTree的树的数量就不能太多，需要在调参的时候多加注意。

2.分析步骤：

1. 数据清洗（Data Cleaning）

2. 探索性可视化（Exploratory Visualization）

3. 特征工程（Feature Engineering）

4. 基本建模&评估（Basic Modeling& Evaluation）

5. 参数调整（Hyperparameters Tuning）

6. 集成方法（EnsembleMethods）

3.分析结果：

# In[1]:
import pandas
titanic = pandas.read_csv("titanic_train.csv") 
titanic.head(5)
# print (titanic.describe()) # 查看数据基本统计参数
# print(titanic.info()) # 查看数据基本类型和大小
# In[2]:
titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
# print(titanic.describe()) # 用中位数来处理缺失值
# In[3]:
# print(titanic["Sex"].unique()) # 当年没有第三类人，否则会打印出NAN
# 将性别0,1化，男人0，女人1；在用pandas作统计或者后续的数据分析时，文本型数据要预处理。
titanic.loc[titanic["Sex"] == "male", "Sex"] = 0
titanic.loc[titanic["Sex"] == "female", "Sex"] = 1
# In[4]:
print(titanic["Embarked"].unique()) # 登船港口有未知的，说明当年偷渡已经是常态，套票哪里都有。
titanic["Embarked"] = titanic["Embarked"].fillna('S')
titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0
titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1
titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2
# In[22]:
# Import 线性回归类
from sklearn.linear_model import LinearRegression
# 交叉验证走起
from sklearn.cross_validation import KFold
# 自选特征量，船票本身和获救关系不大所以就没有入选
predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
# 实例化一个分类器
alg = LinearRegression()
# 生成一个交叉验证实例，titanic.shape[0]：数据集行数；n_splits：表示划分几等份；random_state：随机种子数
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
# 训练集
train_predictors = (titanic[predictors].iloc[train,:])
# 目标集（标签）
train_target = titanic["Survived"].iloc[train]
# 开始训练走起
alg.fit(train_predictors, train_target)
# 测试集
test_predictions = alg.predict(titanic[predictors].iloc[test,:])
# 记录测试结果
predictions.append(test_predictions)
# print(predictions)
# In[4]:
import numpy as np
# 测试结果是3个独立的矩阵（三份测试数据），接下来进行合并
predictions = np.concatenate(predictions, axis=0)
# 预测存货概率大于0.5生，小于等于0.5死（也是一瞬间）
predictions[predictions > .5] = 1
predictions[predictions <=.5] = 0
accuracy = sum(predictions[predictions == titanic["Survived"]]) / len(predictions)
# print(accuracy) # 精度
# In[5]:
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
alg = LogisticRegression(random_state=1)
# 直接计算交叉验证的结果，结果略有差异，下方法对三个分组的精度进行了平均
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)
# print(scores.mean())
# #### 预测
# In[6]:
titanic_test = pandas.read_csv("test.csv")
titanic_test["Age"] = titanic_test["Age"].fillna(titanic["Age"].median())
titanic_test["Fare"] = titanic_test["Fare"].fillna(titanic_test["Fare"].median())
titanic_test.loc[titanic_test["Sex"] == "male", "Sex"] = 0
titanic_test.loc[titanic_test["Sex"] == "female", "Sex"] = 1
titanic_test["Embarked"] = titanic_test["Embarked"].fillna("S")
titanic_test.loc[titanic_test["Embarked"] == "S", "Embarked"] = 0
titanic_test.loc[titanic_test["Embarked"] == "C", "Embarked"] = 1
titanic_test.loc[titanic_test["Embarked"] == "Q", "Embarked"] = 2
# In[7]:
from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier # 随机森林
predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
# random_state：随机数种子；子模型数:n_estimators;min_samples_split: 内部节点再划分所需最小样本数；min_samples_leaf:叶子节点最少样本数
alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2, min_samples_leaf=1)
kf = cross_validation.KFold(titanic.shape[0], n_folds=3, random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)
# 平均预测结果
print(scores.mean())
# In[8]:
# 调整参数
alg = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)
kf = cross_validation.KFold(titanic.shape[0], 3, random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)
print(scores.mean())
# In[9]:
# 加入家庭成员数作为特征
titanic["FamilySize"] = titanic["SibSp"] + titanic["Parch"]
titanic["NameLength"] = titanic["Name"].apply(lambda x: len(x))
# titanic["NameLength"].head()
# In[10]:
import re
# 获取名字的title
def get_title(name):
title_search = re.search(' ([A-Za-z]+)\.', name)
if title_search:
return title_search.group(1)
return ""
# 获取title的词频
titles = titanic["Name"].apply(get_title)
print(pandas.value_counts(titles)) # 打印词频
# 将主要title数字化
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2}
for k,v in title_mapping.items():
titles[titles == k] = v
# 验证假定
print(pandas.value_counts(titles))
# 增加一个title列
titanic["Title"] = titles
# In[11]:
import numpy as np
from sklearn.feature_selection import SelectKBest, f_classif
predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked", "FamilySize", "Title", "NameLength"]
# 选择K个最好的特征，返回选择特征后的数据
selector = SelectKBest(f_classif, k=5)
selector.fit(titanic[predictors], titanic["Survived"])
# 获取每个特征的p-values, 然后将其转化为得分
scores = -np.log10(selector.pvalues_)
# 选择四个最佳的特征
# predictors = ["Pclass", "Sex", "Fare", "Title"]
# alg = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)
# Bokeh
# In[12]:
# 看看哪个特征获救的几率最大?
from bokeh.io import output_notebook, show
output_notebook()
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, FactorRange
# In[13]:
source = ColumnDataSource({'predictors':predictors,'scores':scores})
source
# In[14]:
p = figure(title='泰坦尼克号乘客特征与存活率关系', y_range=FactorRange(factors=predictors), x_range=(0, 100), tools='save')
p.grid.grid_line_color = None
p.hbar(left=0, right='scores', y='predictors',height=0.5 ,color='seagreen', legend= None, source=source)
show(p)
# In[15]:
from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
# 迭代决策树
algorithms = [
[GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), ["Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Title",]],
[LogisticRegression(random_state=1), ["Pclass", "Sex", "Fare", "FamilySize", "Title", "Age", "Embarked"]]
]
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_target = titanic["Survived"].iloc[train]
full_test_predictions = []
for alg, predictors in algorithms:
# 训练集
alg.fit(titanic[predictors].iloc[train,:], train_target)
# 测试集
test_predictions = alg.predict_proba(titanic[predictors].iloc[test,:].astype(float))[:,1]
full_test_predictions.append(test_predictions)
# 测试准确率
test_predictions = (full_test_predictions[0] + full_test_predictions[1]) / 2
test_predictions[test_predictions <= .5] = 0
test_predictions[test_predictions > .5] = 1
predictions.append(test_predictions)
predictions = np.concatenate(predictions, axis=0)
accuracy = sum(predictions[predictions == titanic["Survived"]]) / len(predictions)
print(accuracy,len(predictions))