10.Write a program to build predictive model using classification techniques

import pandas as pd

import numpy as np

import seaborn as sns

%matplotlib inline

df= sns.load_dataset('titanic')

df

df.isnull().sum()

cols_to_drop = ['who','adult_male','deck','embark_town','alive','alone']

df = df.drop(cols_to_drop, axis=1)

df

sns.heatmap(df.isnull())

# replace missing values with interpolated values

df['age'] = df['age'].interpolate()

sns.heatmap(df.isnull())

df.info()

cols_to_drop = ['class']

df = df.drop(cols_to_drop, axis=1)

df.info()

# conver categorical columns to binary

# to do that create dummy columns for the you want to convert concatenate with the dataframe, then drop existinc columns

embarkedcolumndummy = pd.get_dummies(df['embarked'])

sexcolumndummy = pd.get_dummies(df['sex'])

df = pd.concat((df,embarkedcolumndummy,sexcolumndummy),axis=1)

df.head(10)

# drop the redundant columns thus converted

df = df.drop(['sex','embarked'],axis=1)

df.head(10)

#seperate dataframe int x and y values

x = df.values

y = df['survived'].values

# delete survived colums from x

x= np.delete(x,0,axis=1)

df

#Split the dataset

from sklearn.model_selection import train_test_split

x_train, x_test,y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=0)

# Buid Decision tree classifier

from sklearn import tree

df_clf =tree.DecisionTreeClassifier(max_depth=5) #build

df_clf.fit(x_train, y_train) #train

df_clf.score(x_test,y_test) # make prediction

y_pred = df_clf.predict(x_test)

df_clf.score(x_test,y_test)

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test,y_pred)

# build randomForest classifier

from sklearn import ensemble

rf_clf = ensemble.RandomForestClassifier(n_estimators=100)

rf_clf.fit(x_train, y_train)

rf_clf.score(x_test,y_test)

# build gradient boosting classifier

gb_clf = ensemble.GradientBoostingClassifier()

gb_clf.fit(x_train, y_train)

gb_clf.score(x_test,y_test)

# naive bayes classifier

from sklearn.naive_bayes import GaussianNB

nb_clf = GaussianNB()

nb_clf.fit(x_train, y_train)

nb_clf.score(x_test,y_test)

#K-nearest neighbor classifier

from sklearn.neighbors import KNeighborsClassifier

knn_clf = KNeighborsClassifier(n_neighbors=3)

knn_clf.fit(x_train, y_train)

knn_clf.score(x_test,y_test)

from sklearn.linear_model import LogisticRegression

lr_clf = LogisticRegression()

lr_clf.fit(x_train, y_train)

lr_clf.score(x_test,y_test)

# SVM classifier

from sklearn.svm import SVC

sv_clf = SVC(probability = True)

sv_clf.fit(x_train, y_train)

sv_clf.score(x_test,y_test)