DEVELOPED BY : K.KAVYA
REG NO : 212222230065
importing library:
import pandas as pd
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
data loading :
data = pd.read_csv('/content/titanic_dataset.csv')
data
data.tail()
data.isnull().sum()
data.describe()
now, we are checking start with a pairplot, and check for missing values :
sns.heatmap(data.isnull(),cbar=False)
Data Cleaning and Data Drop Process :
data['Fare'] = data['Fare'].fillna(data['Fare'].dropna().median())
data['Age'] = data['Age'].fillna(data['Age'].dropna().median())
Change to categoric column to numeric :
data.loc[data['Sex']=='male','Sex']=0
data.loc[data['Sex']=='female','Sex']=1
instead of nan values :
data['Embarked']=data['Embarked'].fillna('S')
Change to categoric column to numeric :
data.loc[data['Embarked']=='S','Embarked']=0
data.loc[data['Embarked']=='C','Embarked']=1
data.loc[data['Embarked']=='Q','Embarked']=2
Drop unnecessary columns :
drop_elements = ['Name','Cabin','Ticket']
data = data.drop(drop_elements, axis=1)

data.head(11)
heatmap for train dataset :
f,ax = plt.subplots(figsize=(5, 5))
sns.heatmap(data.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
Now, data is clean and read to a analyze :
sns.heatmap(data.isnull(),cbar=False)
how many people survived or not... %60 percent died %40 percent survived :
fig = plt.figure(figsize=(18,6))
data.Survived.value_counts(normalize=True).plot(kind='bar',alpha=0.5)
plt.show()
Age with survived :
plt.scatter(data.Survived, data.Age, alpha=0.1)
plt.title("Age with Survived")
plt.show()
Count the pessenger class :
fig = plt.figure(figsize=(18,6))
data.Pclass.value_counts(normalize=True).plot(kind='bar',alpha=0.5)
plt.show()

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

X = data.drop("Survived",axis=1)
y = data["Survived"]

mdlsel = SelectKBest(chi2, k=5)
mdlsel.fit(X,y)
ix = mdlsel.get_support()
data2 = pd.DataFrame(mdlsel.transform(X), columns = X.columns.values[ix]) # en iyi leri aldi... 7 tane...

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

target = data['Survived'].values
data_features_names = ['Pclass','Sex','SibSp','Parch','Fare','Embarked','Age']
features = data[data_features_names].values
Build test and training test :
X_train,X_test,y_train,y_test = train_test_split(features,target,test_size=0.3,random_state=42)

my_forest = RandomForestClassifier(max_depth=5, min_samples_split=10, n_estimators=500, random_state=5,criterion = 'entropy')


my_forest_ = my_forest.fit(X_train,y_train)
target_predict=my_forest_.predict(X_test)

print("Random forest score: ",accuracy_score(y_test,target_predict))

from sklearn.metrics import mean_squared_error, r2_score
print ("MSE    :",mean_squared_error(y_test,target_predict))
print ("R2     :",r2_score(y_test,target_predict))