On April 15, 1912, during its first voyage, the Titanic sank after hitting an iceberg, killing 1502 of 2224 passengers and crew members.The tragedy stirred up the international community.One of the reasons for the shipwreck was that there were not enough lifeboats for the passengers and crew.Although there was some luck in surviving this disaster, some people are more likely to survive than others, such as women, children and the upper classes.
survival - survival (0 = survival, 1 = Death)
pclass - Ticket type (1 = first class, 2 = second class, 3 = third class)
sex - Gender
Number of siblings on the sibsp-Titanic
parch-Titanic is good at the number of parents or children of this person
ticket - ticket number
Fare - Passenger fare
Cabin - cabin number
embarked - Port of departure (C = Cherbourg, Q = Queenstown, S = Southampton)
boat - lifeboat number (if survived)
Body - human number (if in distress and the body is found)
home.dest - Departure to destination
Calculations show that only about 38% of the passengers survived, and the tragedy occurred because the Titanic did not carry enough lifeboats, only 20, which was not enough for 1317 passengers and 885 crew members.
We can see that first-class has a 62% chance of survival for passengers, compared with third-class with a 25.5% chance of survival. The more luxurious the cabin, the older the passengers are, and the first-class fare is significantly higher than second-class and third-class fares.
From the above analysis, we can see that people tend to evacuate women and children first when the tragedy happens.In all strata, women are more likely to survive than men.
From the analysis of the chart above, it can be seen that the likelihood of children surviving the tragedy was relatively high.
Before building a machine learning model, we need to delete the fill-in missing values and divide the dataset into training and test sets.
Because boat, cabin, body are missing severely and can not provide enough information for subsequent analysis, delete the three fields boat, cabin, body
Because age also has a big impact on whether passengers can live or not, we chose to delete the missing part of the data in the age field.
Both sex and embarked are string values corresponding to categories (for example, sex has two values, male and female), so we can convert category strings to numeric data by LabelEncoder, such as converting "male" and "female" to 0 and 1, respectively.The name, ticket, home.dest fields cannot be coded into numeric data, so we remove them from the dataset.
Different dataset selections also result in different predictions.The average prediction accuracy of the above decision tree models is 79.61%, which can vary by about 2% depending on the data.
We can also use the random forest algorithm to get the weights of different features in the final result prediction
By comparing the figures above, we can see that the stochastic forest algorithm performs well in the current data prediction, with an optimal value of more than 86%.
Extraction Code: 1uey
#!/usr/bin/env python # coding: utf-8 # Import python trigonometric library functions import os import matplotlib.pyplot as plt get_ipython().run_line_magic('matplotlib', 'inline') import random import numpy as np import pandas as pd from sklearn import preprocessing data_path='Case data' titanic_df = pd.read_excel(os.path.join(data_path,'titanic3.xls'),'titanic3',index_col=None,na_values=['NA']) titanic_df.head() titanic_df['survived'].mean() titanic_df.groupby('pclass').mean() class_sex_grouping = titanic_df.groupby(['pclass','sex']).mean() class_sex_grouping class_sex_grouping['survived'].plot.bar(figsize=(12,7),fontsize=12) plt.xticks(rotation=45) group_by_age = pd.cut(titanic_df['age'],np.arange(0,90,10)) age_grouping = titanic_df.groupby(group_by_age).mean() age_grouping['survived'].plot.bar(figsize=(12, 7),colors=['r','y','b'],fontsize=12) plt.xticks(rotation=45) titanic_df.info() # axis = 1 Delete by column titanic_df = titanic_df.drop(['body','boat','cabin'],axis=1) titanic_df['home.dest'] = titanic_df['home.dest'].fillna('NA') titanic_df.head() titanic_df = titanic_df.dropna() titanic_df.info() def preprocess_titanic_df(df): preprocess_df = df.copy() le = preprocessing.LabelEncoder() preprocess_df.sex = le.fit_transform(preprocess_df.sex) preprocess_df.embarked = le.fit_transform(preprocess_df.embarked) preprocess_df = preprocess_df.drop(['name','ticket','home.dest'],axis=1) return preprocess_df preprocess_df = preprocess_titanic_df(titanic_df) preprocess_df.head() # Machine Learning Simple Prediction x = preprocess_df.drop(['survived'],axis=1).values y = preprocess_df['survived'].values from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=0.2) np.random.seed(42) # decision tree classifier from sklearn.tree import DecisionTreeClassifier clf_dt = DecisionTreeClassifier(max_depth = 5) clf_dt.fit(X_train,y_train) clf_dt.score(X_test,y_test) # Cross-validation measures model performance from sklearn.model_selection import ShuffleSplit,cross_val_score # Random Split Data shuff_split = ShuffleSplit(n_splits=20,test_size=0.2,random_state=0) def test_classifier_suf(clf): scores = cross_val_score(clf,x,y,cv=shuff_split) print ("Accuracy: %0.4f (+/- %0.2f)" % (scores.mean(), scores.std())) return scores clf_dt_scores = test_classifier_suf(clf_dt) # Random Forest # from sklearn.feature_selection import SelectFromModel np.random.seed(42) from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier(n_estimators=50) clf_rf_scores = test_classifier_suf(clf) np.random.seed(42) from sklearn.ensemble import GradientBoostingClassifier # from sklearn.feature_selection import SelectFromModel clf = GradientBoostingClassifier(n_estimators=50) clf_grad_scores = test_classifier_suf(clf) # Random Forest from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel np.random.seed(42) clf = RandomForestClassifier(n_estimators=50) clf = clf.fit(X_train,y_train) test_classifier_suf(clf) features = pd.DataFrame() features["feature"] = ["pclass","sex","age","sibsp","parch","fare","embarked"] features["importance"] = clf.feature_importances_ features.sort_values(by=["importance"],ascending=True,inplace=True) features.set_index('feature',inplace=True) features.plot(kind="barh",figsize=(12,7),fontsize=12) plt.show() x = np.linspace(0,1,20) plt.figure(figsize=(12,7)) #Similar to declaring a picture first, all settings after this figure work on this picture plt.plot(x,clf_dt_scores,label="DecisionTreeClassifier") #Drafting plt.plot(x,clf_grad_scores,color='r',linestyle='--',label="GradientBoostingClassifier") #Set the color and style of the function line plt.plot(x,clf_rf_scores,color='y',linestyle='--',label="RandomForestClassifier") #Set the color and style of the function line plt.legend(loc="upper right") plt.grid() plt.show()