# coding: utf-8 # In[128]: get_ipython().magic(u'matplotlib inline') import pandas as pd from pandas import Series,DataFrame import seaborn as sns sns.set_style('whitegrid') pd.set_option('display.mpl_style', 'default') import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC, LinearSVC from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB train_df= pd.read_csv("/home/lpstudy/下载/train.csv") test_df = pd.read_csv("/home/lpstudy/下载/test.csv") train_df.head() test_df.head() # In[129]: train_df = train_df.drop(["Ticket","PassengerId","Name"],axis = 1) test_df = test_df.drop(["Name","Ticket"],axis =1) # In[130]: train_df.head() # In[131]: train_df["Embarked"] = train_df["Embarked"].fillna("S") #plot sns.factorplot("Embarked","Survived",data = train_df,size = 6,aspect = 2) fig,(axis1,axis2,axis3) = plt.subplots(1,3,figsize = (15,5)) sns.countplot(x='Embarked', data=train_df, ax=axis1) sns.countplot(x='Survived', hue="Embarked", data=train_df, order=[1,0], ax=axis2) embark_perc = train_df[["Embarked", "Survived"]].groupby(['Embarked'],as_index=False).mean() sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3) embark_dummies_train = pd.get_dummies(train_df['Embarked']) embark_dummies_train.drop(['S'], axis=1, inplace=True) embark_dummies_test = pd.get_dummies(test_df['Embarked']) embark_dummies_test.drop(['S'], axis=1, inplace=True) train_df = train_df.join(embark_dummies_train) test_df = test_df.join(embark_dummies_test) train_df.drop(['Embarked'], axis=1,inplace=True) test_df.drop(['Embarked'], axis=1,inplace=True) # In[132]: test_df["Fare"].fillna(test_df["Fare"].median(), inplace=True) train_df['Fare'] = train_df['Fare'].astype(int) test_df['Fare'] = test_df['Fare'].astype(int) fare_not_survived = train_df["Fare"][train_df["Survived"] == 0] fare_survived = train_df["Fare"][train_df["Survived"] == 1] avgerage_fare = DataFrame([fare_not_survived.mean(), fare_survived.mean()]) std_fare = DataFrame([fare_not_survived.std(), fare_survived.std()]) #plot train_df['Fare'].plot(kind='hist', figsize=(15,3),bins=100, xlim=(0,50)) avgerage_fare.index.names = std_fare.index.names = ["Survived"] avgerage_fare.plot(yerr=std_fare,kind='bar',legend=False) # In[133]: # Age fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4)) axis1.set_title('Original Age values - Titanic') axis2.set_title('New Age values - Titanic') average_age_titanic = train_df["Age"].mean() std_age_titanic = train_df["Age"].std() count_nan_age_titanic = train_df["Age"].isnull().sum() # get average, std, and number of NaN values in test_df average_age_test = test_df["Age"].mean() std_age_test = test_df["Age"].std() count_nan_age_test = test_df["Age"].isnull().sum() # generate random numbers between (mean - std) & (mean + std) rand_1 = np.random.randint(average_age_titanic - std_age_titanic, average_age_titanic + std_age_titanic, size = count_nan_age_titanic) rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test) # plot original Age values # NOTE: drop all null values, and convert to int train_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1) # test_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1) # fill NaN values in Age column with random values generated train_df["Age"][np.isnan(train_df["Age"])] = rand_1 test_df["Age"][np.isnan(test_df["Age"])] = rand_2 # convert from float to int train_df['Age'] = train_df['Age'].astype(int) test_df['Age'] = test_df['Age'].astype(int) # plot new Age Values train_df['Age'].hist(bins=70, ax=axis2) # test_df['Age'].hist(bins=70, ax=axis4) # In[134]: # .... continue with plot Age column # peaks for survived/not survived passengers by their age facet = sns.FacetGrid(train_df, hue="Survived",aspect=4) facet.map(sns.kdeplot,'Age',shade= True) facet.set(xlim=(0, train_df['Age'].max())) facet.add_legend() # average survived passengers by age fig, axis1 = plt.subplots(1,1,figsize=(18,4)) average_age = train_df[["Age", "Survived"]].groupby(['Age'],as_index=False).mean() sns.barplot(x='Age', y='Survived', data=average_age) # In[135]: # Cabin # It has a lot of NaN values, so it won't cause a remarkable impact on prediction train_df.drop("Cabin",axis=1,inplace=True) test_df.drop("Cabin",axis=1,inplace=True) # Family # Instead of having two columns Parch & SibSp, # we can have only one column represent if the passenger had any family member aboard or not, # Meaning, if having any family member(whether parent, brother, ...etc) will increase chances of Survival or not. train_df['Family'] = train_df["Parch"] + train_df["SibSp"] train_df['Family'].loc[train_df['Family'] > 0] = 1 train_df['Family'].loc[train_df['Family'] == 0] = 0 test_df['Family'] = test_df["Parch"] + test_df["SibSp"] test_df['Family'].loc[test_df['Family'] > 0] = 1 test_df['Family'].loc[test_df['Family'] == 0] = 0 # drop Parch & SibSp train_df = train_df.drop(['SibSp','Parch'], axis=1) test_df = test_df.drop(['SibSp','Parch'], axis=1) # plot fig, (axis1,axis2) = plt.subplots(1,2,sharex=True,figsize=(10,5)) # sns.factorplot('Family',data=train_df,kind='count',ax=axis1) sns.countplot(x='Family', data=train_df, order=[1,0], ax=axis1) # average of survived for those who had/didn't have any family member family_perc = train_df[["Family", "Survived"]].groupby(['Family'],as_index=False).mean() sns.barplot(x='Family', y='Survived', data=family_perc, order=[1,0], ax=axis2) axis1.set_xticklabels(["With Family","Alone"], rotation=0) # In[136]: # Sex # As we see, children(age < ~16) on aboard seem to have a high chances for Survival. # So, we can classify passengers as males, females, and child def get_person(passenger): age,sex = passenger return 'child' if age < 16 else sex train_df['Person'] = train_df[['Age','Sex']].apply(get_person,axis=1) test_df['Person'] = test_df[['Age','Sex']].apply(get_person,axis=1) # No need to use Sex column since we created Person column train_df.drop(['Sex'],axis=1,inplace=True) test_df.drop(['Sex'],axis=1,inplace=True) # create dummy variables for Person column, & drop Male as it has the lowest average of survived passengers person_dummies_titanic = pd.get_dummies(train_df['Person']) person_dummies_titanic.columns = ['Child','Female','Male'] person_dummies_titanic.drop(['Male'], axis=1, inplace=True) person_dummies_test = pd.get_dummies(test_df['Person']) person_dummies_test.columns = ['Child','Female','Male'] person_dummies_test.drop(['Male'], axis=1, inplace=True) train_df = train_df.join(person_dummies_titanic) test_df = test_df.join(person_dummies_test) fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5)) # sns.factorplot('Person',data=train_df,kind='count',ax=axis1) sns.countplot(x='Person', data=train_df, ax=axis1) # average of survived for each Person(male, female, or child) person_perc = train_df[["Person", "Survived"]].groupby(['Person'],as_index=False).mean() sns.barplot(x='Person', y='Survived', data=person_perc, ax=axis2, order=['male','female','child']) train_df.drop(['Person'],axis=1,inplace=True) test_df.drop(['Person'],axis=1,inplace=True) # In[137]: # Pclass # sns.factorplot('Pclass',data=train_df,kind='count',order=[1,2,3]) sns.factorplot('Pclass','Survived',order=[1,2,3], data=train_df,size=5) # create dummy variables for Pclass column, & drop 3rd class as it has the lowest average of survived passengers pclass_dummies_titanic = pd.get_dummies(train_df['Pclass']) pclass_dummies_titanic.columns = ['Class_1','Class_2','Class_3'] pclass_dummies_titanic.drop(['Class_3'], axis=1, inplace=True) pclass_dummies_test = pd.get_dummies(test_df['Pclass']) pclass_dummies_test.columns = ['Class_1','Class_2','Class_3'] pclass_dummies_test.drop(['Class_3'], axis=1, inplace=True) train_df.drop(['Pclass'],axis=1,inplace=True) test_df.drop(['Pclass'],axis=1,inplace=True) train_df = train_df.join(pclass_dummies_titanic) test_df = test_df.join(pclass_dummies_test) # In[139]: # define training and testing sets X_train = train_df.drop("Survived",axis=1) Y_train = train_df["Survived"] X_test = test_df.drop("PassengerId",axis=1).copy() # In[140]: # Logistic Regression logreg = LogisticRegression() logreg.fit(X_train, Y_train) Y_pred = logreg.predict(X_test) logreg.score(X_train, Y_train) # In[141]: # Support Vector Machines svc = SVC() svc.fit(X_train, Y_train) Y_pred = svc.predict(X_test) svc.score(X_train, Y_train) # In[142]: # Random Forests 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) # In[143]: # get Correlation Coefficient for each feature using Logistic Regression coeff_df = DataFrame(train_df.columns.delete(0)) coeff_df.columns = ['Features'] coeff_df["Coefficient Estimate"] = pd.Series(logreg.coef_[0]) # preview coeff_df # In[ ]: