Machine Learning classification app

app.py

@@ -0,0 +1,162 @@
+import joblib
+import pandas as pd
+import streamlit as st
+
+
+# loading in the model to predict on the data
+scaler = joblib.load(r'scaler.pickle')
+
+# loading Logistic Regression model
+classifier_lr = joblib.load(r'model_lr.pickle')
+
+# Loading Decision Tree model
+classifier_dt = joblib.load(r'model_dt.pickle')
+
+
+# the font and background color, the padding and the text to be displayed
+html_temp = """
+	<div style ="background-color:black;padding:13px">
+	<h1 style ="color:white;text-align:center;">Titanic Survivors Prediction App</h1>
+	</div>
+	"""
+# this line allows us to display the front end aspects we have
+# defined in the above code
+st.markdown(html_temp, unsafe_allow_html = True)
+# Image
+st.image("https://pngimg.com/uploads/titanic/titanic_PNG36.png")
+
+# giving the webpage a title
+st.title("Machine Learning [ Classification ]")
+
+# WElcome Function
+def welcome():
+	return 'welcome all'
+
+# Features and labels
+features = ['sex_female', 'n_siblings_spouses_8', 'n_siblings_spouses_1',
+    'parch_6', 'n_siblings_spouses_4', 'parch_0', 'parch_5', 'n_siblings_spouses_0', 'parch_3',
+    'sex_male', 'Class_First', 'parch_2', 'alone_y', 'n_siblings_spouses_5', 'n_siblings_spouses_2',
+    'n_siblings_spouses_3', 'Class_Second', 'parch_1', 'alone_n', 'Class_Third', 'parch_4']
+labels = ['sex', 'age', 'n_siblings_spouses', 'parch', 'fare', 'Class', 'alone']
+
+# defining the function which will make the prediction{Logistic regression}using the user inputs
+def predict_lr(sex, age, n_siblings_spouses, parch, fare, Class, alone):
+    feature_names = [sex, age, n_siblings_spouses, parch, fare, Class, alone]
+    features_df = pd.DataFrame([feature_names], columns=labels)
+    categorical_features = ['sex', 'n_siblings_spouses', 'parch', 'Class', 'alone']
+    numeric_features = ['age', 'fare']
+    features_df[numeric_features] = scaler.transform(features_df[numeric_features])
+    features_df = pd.get_dummies(features_df,columns=categorical_features)
+    #setting aside and making up for the whole categorical features from our first model
+    c_engineering_features = set(features_df.columns) - set(numeric_features)
+    missing_features = list(set(features) - c_engineering_features)
+    for feature in missing_features:
+        #add zeroes
+        features_df[feature] = [0]*len(features_df)
+    result = classifier_lr.predict(features_df)
+    return result
+
+# defining the function which will make the prediction{Decision Tree}using the user inputs
+def predict_dt(sex, age, n_siblings_spouses, parch, fare, Class, alone):  
+    feature_names = [sex, age, n_siblings_spouses, parch, fare, Class, alone]
+    features_df = pd.DataFrame([feature_names], columns=labels)
+    categorical_features = ['sex', 'n_siblings_spouses', 'parch', 'Class', 'alone']
+    numeric_features = ['age', 'fare']
+    features_df[numeric_features] = scaler.transform(features_df[numeric_features])
+    features_df = pd.get_dummies(features_df,columns=categorical_features)
+    #setting aside and making up for the whole categorical features from our first model
+    c_engineering_features = set(features_df.columns) - set(numeric_features)
+    missing_features = list(set(features) - c_engineering_features)
+    for feature in missing_features:
+        #add zeroes
+        features_df[feature] = [0]*len(features_df)
+    result = classifier_dt.predict(features_df)
+    return result
+
+#The parameters and their input formats.
+
+# Gender
+st.write("Male / Female")
+sex = st.radio("Select gender", ('male', 'female'))
+
+# Age
+age = st.number_input("What is the age ?")
+
+# Spouses and siblings
+st.write("Number of spouses & siblings.")
+n_siblings_spouses = st.slider("Select the number of siblings or spouses", 0,5)
+
+# Parch
+st.write("Parch number ")
+parch = st.slider("Select parch number", 0, 6)
+
+# Fare
+st.write("Fare")
+fare = st.number_input("Thousand Dollars($)")
+
+# Class 
+st.write("First/Second/Third")
+Class = st.radio("Select Class", ('First', 'Second', 'Third')) 
+
+# Alone
+passenger_status = st.radio("Is the passenger alone ?", ('yes', 'no'))
+#conditionals for alone status
+if (passenger_status) == 'yes':
+	alone = 'y'
+else:
+	alone = 'n'
+
+
+
+# this is the main function in which is defined on the webpage
+def main():
+	#List of available models 
+	options = st.radio("Available Models:", ["Logistic Regression", "Decision Tree"])
+	result =""
+
+	# the below line ensures that when the button called 'Predict' is clicked,
+	# the prediction function defined above is called to make the prediction
+	# and store it in the variable result
+	if options == "Logistic Regression":
+		st.success("You picked {}".format(options))
+
+		if st.button('Predict'):
+			result = predict_lr(sex, age, n_siblings_spouses, parch, fare, Class, alone)
+			if result[0] == 0:
+				st.error('Not a Survivor')
+			else:
+				st.success('A Survivor')
+	else:
+		st.success("You picked {}".format(options))
+
+		if st.button('Predict'):
+			result = predict_dt(sex, age, n_siblings_spouses, parch, fare, Class, alone)
+			if result[0] == 0:
+				st.error('Not a Survivor')
+			else:
+				st.success('A Survivor')
+	
+# Links and Final Touches
+	html_git = """
+	<h3>Checkout my GitHub</h3>
+	<div style ="background-color:black;padding:13px">
+	<h1 style ="color:white;text-align:center;"><a href="https://github.com/Taoheed-O"> My GitHub link</h1>
+	</div>
+	"""
+	html_linkedIn = """
+	<h3>Connect with me on LinkedIn</h3>
+	<div style ="background-color:black;padding:13px">
+	<h1 style ="color:white;text-align:center;"><a href="https://www.linkedin.com/in/taoheed-oyeniyi"> My LinkedIn</h1>
+	</div>
+	"""
+	
+	# this line allows us to display the front end aspects we have
+	# defined in the above code
+	st.markdown(html_git, unsafe_allow_html = True)
+	st.markdown(html_linkedIn, unsafe_allow_html = True)
+
+			
+        
+	
+if __name__=='__main__':
+	main()

eval.csv

@@ -0,0 +1,265 @@
+survived,sex,age,n_siblings_spouses,parch,fare,class,deck,embark_town,alone
+0,male,35.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,54.0,0,0,51.8625,First,E,Southampton,y
+1,female,58.0,0,0,26.55,First,C,Southampton,y
+1,female,55.0,0,0,16.0,Second,unknown,Southampton,y
+1,male,34.0,0,0,13.0,Second,D,Southampton,y
+1,female,15.0,0,0,8.0292,Third,unknown,Queenstown,y
+0,female,8.0,3,1,21.075,Third,unknown,Southampton,n
+0,male,21.0,0,0,8.05,Third,unknown,Southampton,y
+0,female,18.0,2,0,18.0,Third,unknown,Southampton,n
+1,female,19.0,0,0,7.8792,Third,unknown,Queenstown,y
+1,female,28.0,0,0,7.75,Third,unknown,Queenstown,y
+0,male,21.0,0,0,7.8,Third,unknown,Southampton,y
+1,female,5.0,1,2,27.75,Second,unknown,Southampton,n
+0,male,28.0,0,0,27.7208,First,unknown,Cherbourg,y
+1,female,29.0,0,0,10.5,Second,F,Southampton,y
+0,female,16.0,5,2,46.9,Third,unknown,Southampton,n
+0,male,26.0,1,0,14.4542,Third,unknown,Cherbourg,n
+1,female,17.0,0,0,10.5,Second,unknown,Southampton,y
+1,female,33.0,3,0,15.85,Third,unknown,Southampton,n
+0,male,29.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,20.0,0,0,7.8542,Third,unknown,Southampton,y
+0,male,26.0,1,2,20.575,Third,unknown,Southampton,n
+1,female,21.0,0,0,7.65,Third,unknown,Southampton,y
+0,male,38.0,0,0,7.8958,Third,unknown,Southampton,y
+0,female,20.0,1,0,9.825,Third,unknown,Southampton,n
+0,female,2.0,4,2,31.275,Third,unknown,Southampton,n
+0,male,21.0,2,0,73.5,Second,unknown,Southampton,n
+0,male,54.0,0,1,77.2875,First,D,Southampton,n
+1,male,12.0,1,0,11.2417,Third,unknown,Cherbourg,n
+1,female,28.0,1,1,22.3583,Third,F,Cherbourg,n
+0,male,33.0,0,0,7.8958,Third,unknown,Cherbourg,y
+1,female,29.0,1,0,26.0,Second,unknown,Southampton,n
+0,male,25.0,0,0,13.0,Second,unknown,Southampton,y
+1,female,24.0,1,0,15.85,Third,unknown,Southampton,n
+0,male,19.0,1,1,36.75,Second,unknown,Southampton,n
+1,male,27.0,0,0,7.7958,Third,unknown,Southampton,y
+0,male,36.5,0,2,26.0,Second,F,Southampton,n
+0,male,42.0,0,0,13.0,Second,unknown,Southampton,y
+1,female,22.0,1,0,66.6,First,C,Southampton,n
+0,male,30.0,0,0,8.05,Third,unknown,Southampton,y
+1,female,40.0,0,0,15.75,Second,unknown,Southampton,y
+1,male,9.0,0,2,20.525,Third,unknown,Southampton,n
+0,female,45.0,1,4,27.9,Third,unknown,Southampton,n
+0,male,28.0,0,0,25.925,First,unknown,Southampton,y
+0,male,61.0,0,0,33.5,First,B,Southampton,y
+1,female,1.0,1,1,11.1333,Third,unknown,Southampton,n
+0,male,21.0,0,0,7.925,Third,unknown,Southampton,y
+0,male,56.0,0,0,30.6958,First,A,Cherbourg,y
+1,male,1.0,2,1,39.0,Second,F,Southampton,n
+0,male,28.0,0,0,50.0,First,A,Southampton,y
+0,male,40.0,1,1,15.5,Third,unknown,Queenstown,n
+0,male,19.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,42.0,0,1,8.4042,Third,unknown,Southampton,n
+0,male,45.5,0,0,7.225,Third,unknown,Cherbourg,y
+1,male,18.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,24.0,0,0,7.05,Third,unknown,Southampton,y
+0,male,30.0,0,0,13.0,Second,unknown,Southampton,y
+1,female,32.0,0,0,76.2917,First,D,Cherbourg,y
+0,male,27.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,28.0,0,0,7.8958,Third,unknown,Southampton,y
+1,male,38.0,1,0,90.0,First,C,Southampton,n
+1,female,5.0,4,2,31.3875,Third,unknown,Southampton,n
+0,male,24.0,0,0,10.5,Second,unknown,Southampton,y
+1,female,8.0,0,2,26.25,Second,unknown,Southampton,n
+0,male,29.0,0,0,10.5,Second,unknown,Southampton,y
+0,female,25.0,0,0,7.775,Third,unknown,Southampton,y
+1,male,37.0,1,1,52.5542,First,D,Southampton,n
+0,male,54.0,1,0,26.0,Second,unknown,Southampton,n
+0,male,62.0,0,0,26.55,First,C,Southampton,y
+1,female,29.0,0,2,15.2458,Third,unknown,Cherbourg,n
+1,female,30.0,0,0,86.5,First,B,Southampton,y
+0,male,52.0,1,1,79.65,First,E,Southampton,n
+0,male,65.0,0,0,7.75,Third,unknown,Queenstown,y
+1,male,19.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,33.0,0,0,8.6625,Third,unknown,Cherbourg,y
+1,male,30.0,0,0,9.5,Third,unknown,Southampton,y
+0,male,22.0,0,0,7.8958,Third,unknown,Southampton,y
+1,male,42.0,0,0,13.0,Second,unknown,Southampton,y
+1,female,22.0,0,0,7.75,Third,unknown,Queenstown,y
+1,female,19.0,1,0,91.0792,First,B,Cherbourg,n
+0,female,24.0,0,0,8.85,Third,unknown,Southampton,y
+0,male,28.0,0,0,27.7208,First,unknown,Cherbourg,y
+0,male,23.5,0,0,7.2292,Third,unknown,Cherbourg,y
+0,female,2.0,1,2,151.55,First,C,Southampton,n
+1,male,28.0,2,0,23.25,Third,unknown,Queenstown,n
+1,male,0.92,1,2,151.55,First,C,Southampton,n
+1,female,26.0,0,0,7.8542,Third,unknown,Southampton,y
+0,male,54.0,0,0,14.0,Second,unknown,Southampton,y
+1,female,30.0,0,0,12.35,Second,unknown,Queenstown,y
+1,female,36.0,0,0,13.0,Second,D,Southampton,y
+1,female,16.0,0,1,57.9792,First,B,Cherbourg,n
+0,male,45.5,0,0,28.5,First,C,Southampton,y
+0,male,16.0,2,0,18.0,Third,unknown,Southampton,n
+1,female,41.0,0,0,134.5,First,E,Cherbourg,y
+1,male,2.0,1,1,26.0,Second,F,Southampton,n
+1,female,24.0,3,2,263.0,First,C,Southampton,n
+1,male,3.0,1,1,15.9,Third,unknown,Southampton,n
+0,male,42.0,0,0,8.6625,Third,unknown,Southampton,y
+0,male,23.0,0,0,9.225,Third,unknown,Southampton,y
+0,male,28.0,0,0,35.0,First,C,Southampton,y
+0,male,30.0,0,0,7.25,Third,unknown,Southampton,y
+1,male,25.0,1,0,55.4417,First,E,Cherbourg,n
+0,male,22.0,0,0,135.6333,First,unknown,Cherbourg,y
+0,female,3.0,3,1,21.075,Third,unknown,Southampton,n
+1,female,22.0,0,0,7.25,Third,unknown,Southampton,y
+0,male,20.0,0,0,4.0125,Third,unknown,Cherbourg,y
+1,female,1.0,0,2,15.7417,Third,unknown,Cherbourg,n
+1,female,35.0,1,0,52.0,First,unknown,Southampton,n
+1,female,36.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,28.0,2,0,7.925,Third,unknown,Southampton,n
+1,female,28.0,0,0,12.65,Second,unknown,Southampton,y
+0,female,21.0,1,0,9.825,Third,unknown,Southampton,n
+0,female,20.0,0,0,8.6625,Third,unknown,Southampton,y
+0,male,51.0,0,0,7.75,Third,unknown,Southampton,y
+1,female,33.0,1,0,90.0,First,C,Queenstown,n
+0,female,10.0,0,2,24.15,Third,unknown,Southampton,n
+0,male,29.0,0,0,7.875,Third,unknown,Southampton,y
+0,female,28.0,1,1,14.4,Third,unknown,Southampton,n
+0,male,28.0,0,0,7.25,Third,unknown,Southampton,y
+1,female,28.0,1,0,26.0,Second,unknown,Southampton,n
+0,female,21.0,2,2,34.375,Third,unknown,Southampton,n
+0,male,25.0,1,0,7.775,Third,unknown,Southampton,n
+1,male,4.0,0,2,81.8583,First,A,Southampton,n
+1,female,13.0,0,1,19.5,Second,unknown,Southampton,n
+0,male,28.0,1,0,19.9667,Third,unknown,Southampton,n
+0,male,28.0,0,0,8.05,Third,unknown,Southampton,y
+1,female,50.0,0,0,10.5,Second,unknown,Southampton,y
+0,male,28.0,0,0,7.75,Third,unknown,Queenstown,y
+0,male,48.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,28.0,0,0,0.0,Second,unknown,Southampton,y
+0,male,38.0,0,0,8.6625,Third,unknown,Southampton,y
+0,female,22.0,0,0,9.8375,Third,unknown,Southampton,y
+0,male,22.0,0,0,7.5208,Third,unknown,Southampton,y
+0,male,50.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,30.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,21.0,0,0,7.25,Third,unknown,Southampton,y
+0,male,55.0,0,0,30.5,First,C,Southampton,y
+0,male,28.0,0,0,14.4583,Third,unknown,Cherbourg,y
+1,female,54.0,1,0,78.2667,First,D,Cherbourg,n
+0,male,28.0,0,0,15.1,Third,unknown,Southampton,y
+0,male,24.0,0,0,7.7958,Third,unknown,Southampton,y
+0,female,28.0,0,0,7.6292,Third,unknown,Queenstown,y
+1,female,16.0,0,0,86.5,First,B,Southampton,y
+0,male,28.0,0,0,22.525,Third,unknown,Southampton,y
+1,male,29.0,0,0,7.75,Third,unknown,Queenstown,y
+0,male,28.0,0,0,8.05,Third,unknown,Southampton,y
+1,male,36.0,0,0,26.2875,First,E,Southampton,y
+0,male,28.0,0,0,24.15,Third,unknown,Queenstown,y
+0,male,28.0,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,39.0,0,0,7.925,Third,unknown,Southampton,y
+0,male,28.0,0,0,7.2292,Third,unknown,Cherbourg,y
+0,male,28.0,0,0,14.5,Third,unknown,Southampton,y
+1,male,32.0,1,0,26.0,Second,unknown,Southampton,n
+0,male,64.0,0,0,26.0,First,unknown,Southampton,y
+1,male,8.0,1,1,36.75,Second,unknown,Southampton,n
+1,male,22.0,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,62.0,0,0,26.55,First,unknown,Southampton,y
+0,male,28.0,0,0,13.5,Second,unknown,Southampton,y
+0,female,28.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,28.0,0,0,7.2292,Third,unknown,Cherbourg,y
+1,female,28.0,0,0,7.75,Third,unknown,Queenstown,y
+0,male,19.0,0,0,14.5,Third,unknown,Southampton,y
+1,female,39.0,1,0,55.9,First,E,Southampton,n
+0,female,28.0,1,0,14.4583,Third,unknown,Cherbourg,n
+1,male,32.0,0,0,7.925,Third,unknown,Southampton,y
+1,female,39.0,1,1,110.8833,First,C,Cherbourg,n
+0,male,54.0,0,0,26.0,Second,unknown,Southampton,y
+0,male,28.0,0,0,8.7125,Third,unknown,Cherbourg,y
+1,female,18.0,0,2,79.65,First,E,Southampton,n
+0,male,22.0,0,0,8.05,Third,unknown,Southampton,y
+1,female,24.0,2,1,27.0,Second,unknown,Southampton,n
+0,male,28.0,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28.0,0,0,42.4,First,unknown,Southampton,y
+0,male,44.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,28.0,0,0,7.75,Third,unknown,Queenstown,y
+0,female,26.0,1,0,16.1,Third,unknown,Southampton,n
+1,female,4.0,2,1,39.0,Second,F,Southampton,n
+0,male,27.0,1,0,14.4542,Third,unknown,Cherbourg,n
+1,male,42.0,1,0,52.5542,First,D,Southampton,n
+0,male,21.0,0,0,7.8542,Third,unknown,Southampton,y
+0,male,21.0,0,0,16.1,Third,unknown,Southampton,y
+0,male,28.0,0,0,0.0,First,unknown,Southampton,y
+0,male,31.0,1,1,26.25,Second,unknown,Southampton,n
+1,female,24.0,0,0,69.3,First,B,Cherbourg,y
+0,female,2.0,3,2,27.9,Third,unknown,Southampton,n
+0,male,19.0,0,0,7.8958,Third,unknown,Southampton,y
+1,male,56.0,0,0,35.5,First,A,Cherbourg,y
+1,female,28.0,0,0,7.8292,Third,unknown,Queenstown,y
+0,female,18.0,0,0,6.75,Third,unknown,Queenstown,y
+0,male,23.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,58.0,0,2,113.275,First,D,Cherbourg,n
+0,male,40.0,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,32.0,2,0,73.5,Second,unknown,Southampton,n
+1,female,28.0,1,0,52.0,First,C,Southampton,n
+0,male,70.0,0,0,10.5,Second,unknown,Southampton,y
+0,male,24.5,0,0,8.05,Third,unknown,Southampton,y
+0,female,43.0,1,6,46.9,Third,unknown,Southampton,n
+0,female,28.0,0,0,8.1375,Third,unknown,Queenstown,y
+0,male,25.0,1,2,41.5792,Second,unknown,Cherbourg,n
+0,male,14.0,4,1,39.6875,Third,unknown,Southampton,n
+0,male,25.0,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,52.0,0,0,13.5,Second,unknown,Southampton,y
+0,male,44.0,0,0,8.05,Third,unknown,Southampton,y
+0,male,42.0,0,0,7.65,Third,F,Southampton,y
+1,female,18.0,1,0,227.525,First,C,Cherbourg,n
+0,female,18.0,0,1,14.4542,Third,unknown,Cherbourg,n
+0,male,26.0,1,0,7.8542,Third,unknown,Southampton,n
+1,female,45.0,0,0,13.5,Second,unknown,Southampton,y
+1,male,42.0,0,0,26.2875,First,E,Southampton,y
+0,male,52.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,17.0,1,0,7.0542,Third,unknown,Southampton,n
+0,male,34.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,20.0,0,0,8.6625,Third,unknown,Southampton,y
+1,female,28.0,0,0,7.7375,Third,unknown,Queenstown,y
+0,male,28.5,0,0,16.1,Third,unknown,Southampton,y
+0,female,48.0,1,3,34.375,Third,unknown,Southampton,n
+1,male,28.0,0,0,30.0,First,D,Southampton,y
+1,male,31.0,0,0,7.925,Third,unknown,Southampton,y
+0,male,16.0,1,1,20.25,Third,unknown,Southampton,n
+1,female,30.0,0,0,13.0,Second,unknown,Southampton,y
+0,male,19.0,1,0,53.1,First,D,Southampton,n
+0,male,31.0,0,0,7.75,Third,unknown,Queenstown,y
+1,male,0.67,1,1,14.5,Second,unknown,Southampton,n
+0,male,18.0,0,0,11.5,Second,unknown,Southampton,y
+1,female,33.0,0,0,86.5,First,B,Southampton,y
+1,female,36.0,1,2,120.0,First,B,Southampton,n
+0,male,16.0,0,0,7.775,Third,unknown,Southampton,y
+0,female,30.5,0,0,7.75,Third,unknown,Queenstown,y
+0,male,24.0,0,0,9.5,Third,unknown,Southampton,y
+0,male,28.0,0,0,7.225,Third,unknown,Cherbourg,y
+1,female,54.0,1,3,23.0,Second,unknown,Southampton,n
+0,male,28.0,0,0,7.7375,Third,unknown,Queenstown,y
+1,female,43.0,0,1,211.3375,First,B,Southampton,n
+1,female,13.0,0,0,7.2292,Third,unknown,Cherbourg,y
+0,male,25.0,0,0,7.25,Third,unknown,Southampton,y
+1,female,18.0,0,0,7.4958,Third,unknown,Southampton,y
+0,male,8.0,4,1,29.125,Third,unknown,Queenstown,n
+1,male,0.42,0,1,8.5167,Third,unknown,Cherbourg,n
+1,male,27.0,0,0,6.975,Third,unknown,Southampton,y
+0,female,18.0,0,0,7.775,Third,unknown,Southampton,y
+0,female,6.0,4,2,31.275,Third,unknown,Southampton,n
+0,male,43.0,0,0,6.45,Third,unknown,Southampton,y
+0,male,38.0,0,0,0.0,First,unknown,Southampton,y
+1,female,27.0,0,1,12.475,Third,E,Southampton,n
+1,male,1.0,0,2,37.0042,Second,unknown,Cherbourg,n
+1,female,62.0,0,0,80.0,First,B,unknown,y
+1,male,0.83,1,1,18.75,Second,unknown,Southampton,n
+1,male,32.0,0,0,56.4958,Third,unknown,Southampton,y
+0,male,20.0,0,0,7.925,Third,unknown,Southampton,y
+0,male,17.0,0,0,8.6625,Third,unknown,Southampton,y
+0,male,74.0,0,0,7.775,Third,unknown,Southampton,y
+0,female,9.0,1,1,15.2458,Third,unknown,Cherbourg,n
+0,female,44.0,1,0,26.0,Second,unknown,Southampton,n
+0,male,28.0,0,0,7.2292,Third,unknown,Cherbourg,y
+0,male,21.0,1,0,11.5,Second,unknown,Southampton,n
+1,female,48.0,0,0,25.9292,First,D,Southampton,y
+0,female,28.0,8,2,69.55,Third,unknown,Southampton,n
+0,male,28.0,0,0,9.5,Third,unknown,Southampton,y
+1,female,56.0,0,1,83.1583,First,C,Cherbourg,n
+1,female,25.0,0,1,26.0,Second,unknown,Southampton,n
+0,male,33.0,0,0,7.8958,Third,unknown,Southampton,y
+0,female,39.0,0,5,29.125,Third,unknown,Queenstown,n
+0,male,27.0,0,0,13.0,Second,unknown,Southampton,y
+1,male,26.0,0,0,30.0,First,C,Cherbourg,y

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+pandas
+sklearn

titanic_model_project.ipynb

@@ -0,0 +1,1713 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report,accuracy_score,f1_score,confusion_matrix,precision_recall_fscore_support,recall_score\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import pandas\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "titanic dataset\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survived</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>n_siblings_spouses</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>class</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>First</td>\n",
+       "      <td>E</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>First</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16.0000</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>D</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived     sex   age  n_siblings_spouses  parch     fare   class  \\\n",
+       "0         0    male  35.0                   0      0   8.0500   Third   \n",
+       "1         0    male  54.0                   0      0  51.8625   First   \n",
+       "2         1  female  58.0                   0      0  26.5500   First   \n",
+       "3         1  female  55.0                   0      0  16.0000  Second   \n",
+       "4         1    male  34.0                   0      0  13.0000  Second   \n",
+       "\n",
+       "      deck  embark_town alone  \n",
+       "0  unknown  Southampton     y  \n",
+       "1        E  Southampton     y  \n",
+       "2        C  Southampton     y  \n",
+       "3  unknown  Southampton     y  \n",
+       "4        D  Southampton     y  "
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval = pd.read_csv('training.csv')\n",
+    "train = pd.read_csv('eval.csv')\n",
+    "print('titanic dataset')\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training features\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>n_siblings_spouses</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>class</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>First</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>First</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>female</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16.0000</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      sex   age  n_siblings_spouses  parch     fare   class alone\n",
+       "0    male  35.0                   0      0   8.0500   Third     y\n",
+       "1    male  54.0                   0      0  51.8625   First     y\n",
+       "2  female  58.0                   0      0  26.5500   First     y\n",
+       "3  female  55.0                   0      0  16.0000  Second     y\n",
+       "4    male  34.0                   0      0  13.0000  Second     y"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_names = ['sex','age','n_siblings_spouses','parch','fare','class','alone']\n",
+    "training_features = train[feature_names]\n",
+    "outcome_feature = ['survived']\n",
+    "outcome_label = train[outcome_feature]\n",
+    "categorical_features = ['sex','n_siblings_spouses','parch','class','alone']\n",
+    "numeric_features = ['age','fare']\n",
+    "print('training features')\n",
+    "training_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fitted_training features:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-122-84de2d3665e0>:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  training_features[numeric_features] = ss.transform(training_features[numeric_features])\n",
+      "/home/prince_tesla/.local/lib/python3.8/site-packages/pandas/core/indexing.py:1738: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(loc, value[:, i].tolist(), pi)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>n_siblings_spouses</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>class</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>male</td>\n",
+       "      <td>0.444353</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.543558</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male</td>\n",
+       "      <td>1.788943</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.711569</td>\n",
+       "      <td>First</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>2.072015</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.013576</td>\n",
+       "      <td>First</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>female</td>\n",
+       "      <td>1.859711</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.315809</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>0.373585</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.401752</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.263326</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.029332</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>male</td>\n",
+       "      <td>0.302817</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.547976</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>female</td>\n",
+       "      <td>0.727424</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.060192</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.121790</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.401752</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>male</td>\n",
+       "      <td>-0.192558</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.085259</td>\n",
+       "      <td>First</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>264 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        sex       age  n_siblings_spouses  parch      fare   class alone\n",
+       "0      male  0.444353                   0      0 -0.543558   Third     y\n",
+       "1      male  1.788943                   0      0  0.711569   First     y\n",
+       "2    female  2.072015                   0      0 -0.013576   First     y\n",
+       "3    female  1.859711                   0      0 -0.315809  Second     y\n",
+       "4      male  0.373585                   0      0 -0.401752  Second     y\n",
+       "..      ...       ...                 ...    ...       ...     ...   ...\n",
+       "259  female -0.263326                   0      1 -0.029332  Second     n\n",
+       "260    male  0.302817                   0      0 -0.547976   Third     y\n",
+       "261  female  0.727424                   0      5  0.060192   Third     n\n",
+       "262    male -0.121790                   0      0 -0.401752  Second     y\n",
+       "263    male -0.192558                   0      0  0.085259   First     y\n",
+       "\n",
+       "[264 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss = StandardScaler()\n",
+    "ss.fit(training_features[numeric_features])\n",
+    "training_features[numeric_features] = ss.transform(training_features[numeric_features])\n",
+    "print('fitted_training features:')\n",
+    "training_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>sex_female</th>\n",
+       "      <th>sex_male</th>\n",
+       "      <th>n_siblings_spouses_0</th>\n",
+       "      <th>n_siblings_spouses_1</th>\n",
+       "      <th>n_siblings_spouses_2</th>\n",
+       "      <th>n_siblings_spouses_3</th>\n",
+       "      <th>n_siblings_spouses_4</th>\n",
+       "      <th>n_siblings_spouses_5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>parch_2</th>\n",
+       "      <th>parch_3</th>\n",
+       "      <th>parch_4</th>\n",
+       "      <th>parch_5</th>\n",
+       "      <th>parch_6</th>\n",
+       "      <th>class_First</th>\n",
+       "      <th>class_Second</th>\n",
+       "      <th>class_Third</th>\n",
+       "      <th>alone_n</th>\n",
+       "      <th>alone_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.444353</td>\n",
+       "      <td>-0.543558</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.788943</td>\n",
+       "      <td>0.711569</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.072015</td>\n",
+       "      <td>-0.013576</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.859711</td>\n",
+       "      <td>-0.315809</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.373585</td>\n",
+       "      <td>-0.401752</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age      fare  sex_female  sex_male  n_siblings_spouses_0  \\\n",
+       "0  0.444353 -0.543558           0         1                     1   \n",
+       "1  1.788943  0.711569           0         1                     1   \n",
+       "2  2.072015 -0.013576           1         0                     1   \n",
+       "3  1.859711 -0.315809           1         0                     1   \n",
+       "4  0.373585 -0.401752           0         1                     1   \n",
+       "\n",
+       "   n_siblings_spouses_1  n_siblings_spouses_2  n_siblings_spouses_3  \\\n",
+       "0                     0                     0                     0   \n",
+       "1                     0                     0                     0   \n",
+       "2                     0                     0                     0   \n",
+       "3                     0                     0                     0   \n",
+       "4                     0                     0                     0   \n",
+       "\n",
+       "   n_siblings_spouses_4  n_siblings_spouses_5  ...  parch_2  parch_3  parch_4  \\\n",
+       "0                     0                     0  ...        0        0        0   \n",
+       "1                     0                     0  ...        0        0        0   \n",
+       "2                     0                     0  ...        0        0        0   \n",
+       "3                     0                     0  ...        0        0        0   \n",
+       "4                     0                     0  ...        0        0        0   \n",
+       "\n",
+       "   parch_5  parch_6  class_First  class_Second  class_Third  alone_n  alone_y  \n",
+       "0        0        0            0             0            1        0        1  \n",
+       "1        0        0            1             0            0        0        1  \n",
+       "2        0        0            1             0            0        0        1  \n",
+       "3        0        0            0             1            0        0        1  \n",
+       "4        0        0            0             1            0        0        1  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_features = pd.get_dummies(training_features,columns=categorical_features)\n",
+    "training_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "engineering features:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['sex_female',\n",
+       " 'n_siblings_spouses_8',\n",
+       " 'n_siblings_spouses_1',\n",
+       " 'parch_6',\n",
+       " 'n_siblings_spouses_4',\n",
+       " 'parch_0',\n",
+       " 'parch_5',\n",
+       " 'n_siblings_spouses_0',\n",
+       " 'parch_3',\n",
+       " 'sex_male',\n",
+       " 'class_First',\n",
+       " 'parch_2',\n",
+       " 'alone_y',\n",
+       " 'n_siblings_spouses_5',\n",
+       " 'n_siblings_spouses_2',\n",
+       " 'n_siblings_spouses_3',\n",
+       " 'class_Second',\n",
+       " 'parch_1',\n",
+       " 'alone_n',\n",
+       " 'class_Third',\n",
+       " 'parch_4']"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "engineering_features = list(set(training_features.columns) - set(numeric_features))\n",
+    "print('engineering features:')\n",
+    "engineering_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression()\n",
+    "model_lr = lr.fit(training_features,np.array(outcome_label['survived']))\n",
+    "model_lr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy score: 0.803030303030303\n",
+      "classification report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.81      0.89      0.85       165\n",
+      "           1       0.78      0.66      0.71        99\n",
+      "\n",
+      "    accuracy                           0.80       264\n",
+      "   macro avg       0.80      0.77      0.78       264\n",
+      "weighted avg       0.80      0.80      0.80       264\n",
+      "\n",
+      "confusion matrix:\n",
+      " [[147  18]\n",
+      " [ 34  65]]\n",
+      "precison,recall,fscore,support ARRAYS:\n",
+      " (array([0.8121547 , 0.78313253]), array([0.89090909, 0.65656566]), array([0.84971098, 0.71428571]), array([165,  99]))\n",
+      "sensitivity score:\n",
+      " 0.6565656565656566\n",
+      "specificity score:\n",
+      " 0.8909090909090909\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "264"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predicted_label  = model_lr.predict(training_features)\n",
+    "actual_label = np.array(outcome_label['survived'])\n",
+    "print('accuracy score:',accuracy_score(actual_label,predicted_label))\n",
+    "print('classification report:\\n',classification_report(actual_label,predicted_label))\n",
+    "print('confusion matrix:\\n',confusion_matrix(actual_label,predicted_label))\n",
+    "print('precison,recall,fscore,support ARRAYS:\\n',precision_recall_fscore_support(actual_label,predicted_label))\n",
+    "def specificity(y_true , y_pred):\n",
+    "    w,x,y,z = precision_recall_fscore_support(actual_label,predicted_label)\n",
+    "    return(x[0])\n",
+    "print('sensitivity score:\\n',recall_score(actual_label,predicted_label))\n",
+    "print('specificity score:\\n',specificity(actual_label,predicted_label))\n",
+    "len(actual_label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.98979592 0.9939759 ]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dt = DecisionTreeClassifier()\n",
+    "model_dt = dt.fit(training_features,np.array(outcome_label['survived']))\n",
+    "actual_label_dt = np.array(outcome_label['survived'])\n",
+    "predicted_label_dt = model_dt.predict(training_features)\n",
+    "print(f1_score(actual_label_dt,predicted_label_dt,labels=(1,0),average=None))\n",
+    "cm = confusion_matrix(actual_label,predicted_label)\n",
+    "\n",
+    "sns.heatmap(cm,annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy score: 0.9924242424242424\n",
+      "classification report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.99      1.00      0.99       165\n",
+      "           1       1.00      0.98      0.99        99\n",
+      "\n",
+      "    accuracy                           0.99       264\n",
+      "   macro avg       0.99      0.99      0.99       264\n",
+      "weighted avg       0.99      0.99      0.99       264\n",
+      "\n",
+      "confusion matrix:\n",
+      " [[165   0]\n",
+      " [  2  97]]\n",
+      "precison,recall,fscore,support ARRAYS:\n",
+      " (array([0.98802395, 1.        ]), array([1.        , 0.97979798]), array([0.9939759 , 0.98979592]), array([165,  99]))\n",
+      "sensitivity score:\n",
+      " 0.9797979797979798\n",
+      "specificity score:\n",
+      " 1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "264"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predicted_label  = model_dt.predict(training_features)\n",
+    "actual_label = np.array(outcome_label['survived'])\n",
+    "print('accuracy score:',accuracy_score(actual_label,predicted_label))\n",
+    "print('classification report:\\n',classification_report(actual_label,predicted_label))\n",
+    "print('confusion matrix:\\n',confusion_matrix(actual_label,predicted_label))\n",
+    "print('precison,recall,fscore,support ARRAYS:\\n',precision_recall_fscore_support(actual_label,predicted_label))\n",
+    "def specificity(y_true , y_pred):\n",
+    "    w,x,y,z = precision_recall_fscore_support(actual_label,predicted_label)\n",
+    "    return(x[0])\n",
+    "print('sensitivity score:\\n',recall_score(actual_label,predicted_label))\n",
+    "print('specificity score:\\n',specificity(actual_label,predicted_label))\n",
+    "len(actual_label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['model_dt.pickle']"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sklearn\n",
+    "import joblib\n",
+    "# save model[Logistic REgression] to be deployed on your server\n",
+    "joblib.dump(model_lr, r'model_lr.pickle')\n",
+    "joblib.dump(ss, r'scaler.pickle')\n",
+    "joblib.dump(model_dt, r'model_dt.pickle')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#LOADING.............\n",
+    "model_lr = joblib.load(r'model_lr.pickle')\n",
+    "model_dt = joblib.load(r'model_dt.pickle')\n",
+    "scaler = joblib.load(r'scaler.pickle')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>n_siblings_spouses</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>class</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>First</td>\n",
+       "      <td>n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>First</td>\n",
+       "      <td>n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      sex   age  n_siblings_spouses  parch     fare  class alone\n",
+       "0    male  22.0                   1      0   7.2500  Third     n\n",
+       "1  female  38.0                   1      0  71.2833  First     n\n",
+       "2  female  26.0                   0      0   7.9250  Third     y\n",
+       "3  female  35.0                   1      0  53.1000  First     n\n",
+       "4    male  28.0                   0      0   8.4583  Third     y"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_names = ['sex','age','n_siblings_spouses','parch','fare','class','alone']\n",
+    "prediction_features = eval[feature_names]\n",
+    "outcome_feature = ['survived']\n",
+    "outcome_label = eval[outcome_feature]\n",
+    "categorical_features = ['sex','n_siblings_spouses','parch','class','alone']\n",
+    "numeric_features = ['age','fare']\n",
+    "prediction_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-132-8f8c82c8febc>:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  prediction_features[numeric_features] = scaler.transform(prediction_features[numeric_features])\n",
+      "/home/prince_tesla/.local/lib/python3.8/site-packages/pandas/core/indexing.py:1738: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(loc, value[:, i].tolist(), pi)\n"
+     ]
+    }
+   ],
+   "source": [
+    "scaler.fit(prediction_features[numeric_features])\n",
+    "prediction_features[numeric_features] = scaler.transform(prediction_features[numeric_features])\n",
+    "prediction_features = pd.get_dummies(prediction_features,columns=categorical_features)\n",
+    "c_engineering_features = list(set(prediction_features.columns)-set(numeric_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "missing feature(s): ['parch_6']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>sex_female</th>\n",
+       "      <th>sex_male</th>\n",
+       "      <th>n_siblings_spouses_0</th>\n",
+       "      <th>n_siblings_spouses_1</th>\n",
+       "      <th>n_siblings_spouses_2</th>\n",
+       "      <th>n_siblings_spouses_3</th>\n",
+       "      <th>n_siblings_spouses_4</th>\n",
+       "      <th>n_siblings_spouses_5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>parch_2</th>\n",
+       "      <th>parch_3</th>\n",
+       "      <th>parch_4</th>\n",
+       "      <th>parch_5</th>\n",
+       "      <th>class_First</th>\n",
+       "      <th>class_Second</th>\n",
+       "      <th>class_Third</th>\n",
+       "      <th>alone_n</th>\n",
+       "      <th>alone_y</th>\n",
+       "      <th>parch_6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.610415</td>\n",
+       "      <td>-0.497403</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.669397</td>\n",
+       "      <td>0.676353</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.290462</td>\n",
+       "      <td>-0.485030</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.429432</td>\n",
+       "      <td>0.343046</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.130485</td>\n",
+       "      <td>-0.475254</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age      fare  sex_female  sex_male  n_siblings_spouses_0  \\\n",
+       "0 -0.610415 -0.497403           0         1                     0   \n",
+       "1  0.669397  0.676353           1         0                     0   \n",
+       "2 -0.290462 -0.485030           1         0                     1   \n",
+       "3  0.429432  0.343046           1         0                     0   \n",
+       "4 -0.130485 -0.475254           0         1                     1   \n",
+       "\n",
+       "   n_siblings_spouses_1  n_siblings_spouses_2  n_siblings_spouses_3  \\\n",
+       "0                     1                     0                     0   \n",
+       "1                     1                     0                     0   \n",
+       "2                     0                     0                     0   \n",
+       "3                     1                     0                     0   \n",
+       "4                     0                     0                     0   \n",
+       "\n",
+       "   n_siblings_spouses_4  n_siblings_spouses_5  ...  parch_2  parch_3  parch_4  \\\n",
+       "0                     0                     0  ...        0        0        0   \n",
+       "1                     0                     0  ...        0        0        0   \n",
+       "2                     0                     0  ...        0        0        0   \n",
+       "3                     0                     0  ...        0        0        0   \n",
+       "4                     0                     0  ...        0        0        0   \n",
+       "\n",
+       "   parch_5  class_First  class_Second  class_Third  alone_n  alone_y  parch_6  \n",
+       "0        0            0             0            1        1        0        0  \n",
+       "1        0            1             0            0        1        0        0  \n",
+       "2        0            0             0            1        0        1        0  \n",
+       "3        0            1             0            0        1        0        0  \n",
+       "4        0            0             0            1        0        1        0  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#setting aside and making up for the whole categorical features from our first model\n",
+    "c_engineering_features = set(prediction_features.columns) - set(numeric_features)\n",
+    "missing_features = list(set(engineering_features) - c_engineering_features)\n",
+    "for feature in missing_features:\n",
+    "    #add zeroes\n",
+    "    prediction_features[feature] = [0]*len(prediction_features)\n",
+    "print('missing feature(s):',missing_features)    \n",
+    "prediction_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survived</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>n_siblings_spouses</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>class</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alone</th>\n",
+       "      <th>survived[Logistic Regression]</th>\n",
+       "      <th>survived[Decision Tree]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>n</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>First</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>n</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>y</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>First</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>n</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>Queenstown</td>\n",
+       "      <td>y</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived     sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
+       "0         0    male  22.0                   1      0   7.2500  Third  unknown   \n",
+       "1         1  female  38.0                   1      0  71.2833  First        C   \n",
+       "2         1  female  26.0                   0      0   7.9250  Third  unknown   \n",
+       "3         1  female  35.0                   1      0  53.1000  First        C   \n",
+       "4         0    male  28.0                   0      0   8.4583  Third  unknown   \n",
+       "\n",
+       "   embark_town alone  survived[Logistic Regression]  survived[Decision Tree]  \n",
+       "0  Southampton     n                              0                        0  \n",
+       "1    Cherbourg     n                              0                        0  \n",
+       "2  Southampton     y                              1                        1  \n",
+       "3  Southampton     n                              0                        0  \n",
+       "4   Queenstown     y                              0                        0  "
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction_lr = model_lr.predict(prediction_features)\n",
+    "prediction_dt = model_dt.predict(prediction_features)\n",
+    "\n",
+    "# Making a copy of the eval dataset\n",
+    "eval_2 = eval.copy()\n",
+    "eval_2['survived[Logistic Regression]'] = prediction_lr\n",
+    "eval_2['survived[Decision Tree]'] = prediction_dt\n",
+    "eval_2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.98979592 0.9939759 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "print(f1_score(actual_label,predicted_label,labels=(1,0),average= None))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "survived    243\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outcome_label[outcome_label==1].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(sex, age, n_siblings_spouses, parch, fare, Class, alone):\n",
+    "    features = ['sex_female', 'n_siblings_spouses_8', 'n_siblings_spouses_1',\n",
+    "    'parch_6', 'n_siblings_spouses_4', 'parch_0', 'parch_5', 'n_siblings_spouses_0', 'parch_3',\n",
+    "    'sex_male', 'Class_First', 'parch_2', 'alone_y', 'n_siblings_spouses_5', 'n_siblings_spouses_2',\n",
+    "    'n_siblings_spouses_3', 'Class_Second', 'parch_1', 'alone_n', 'Class_Third', 'parch_4']\n",
+    "    labels = ['sex', 'age', 'n_siblings_spouses', 'parch', 'fare', 'Class', 'alone']\n",
+    "    feature_names = [sex, age, n_siblings_spouses, parch, fare, Class, alone]\n",
+    "    features_df = pd.DataFrame([feature_names], columns=labels)\n",
+    "    categorical_features = ['sex', 'n_siblings_spouses', 'parch', 'Class', 'alone']\n",
+    "    numeric_features = ['age', 'fare']\n",
+    "    features_df[numeric_features] = scaler.transform(features_df[numeric_features])\n",
+    "    features_df = pd.get_dummies(features_df,columns=categorical_features)\n",
+    "    #setting aside and making up for the whole categorical features from our first model\n",
+    "    c_engineering_features = set(features_df.columns) - set(numeric_features)\n",
+    "    missing_features = list(set(features) - c_engineering_features)\n",
+    "    for feature in missing_features:\n",
+    "        #add zeroes\n",
+    "        features_df[feature] = [0]*len(features_df)\n",
+    "    result = model_lr.predict(features_df)\n",
+    "    print(features_df)\n",
+    "    return result\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        age      fare  sex_male  n_siblings_spouses_1  parch_5  Class_First  \\\n",
+      "0 -0.770391  0.320363         1                     1        1            1   \n",
+      "\n",
+      "   alone_n  sex_female  n_siblings_spouses_8  alone_y  ...  \\\n",
+      "0        1           0                     0        0  ...   \n",
+      "\n",
+      "   n_siblings_spouses_2  Class_Second  n_siblings_spouses_3  \\\n",
+      "0                     0             0                     0   \n",
+      "\n",
+      "   n_siblings_spouses_0  parch_1  parch_3  n_siblings_spouses_4  Class_Third  \\\n",
+      "0                     0        0        0                     0            0   \n",
+      "\n",
+      "   parch_2  parch_4  \n",
+      "0        0        0  \n",
+      "\n",
+      "[1 rows x 23 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1])"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict('male', 20, 1, 5, 51.86255, 'First', 'n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>n_siblings_spouses</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>Class</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>male</td>\n",
+       "      <td>-1.410297</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.202742</td>\n",
+       "      <td>First</td>\n",
+       "      <td>y</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sex       age  n_siblings_spouses  parch      fare  Class alone\n",
+       "0  male -1.410297                   3      4  1.202742  First     y"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = ['male',12, 3,4,100,'First','y']\n",
+    "a = ['sex', 'age', 'n_siblings_spouses', 'parch', 'fare', 'Class', 'alone']\n",
+    "c = pd.DataFrame([b], columns=a)\n",
+    "x = ['age', 'fare']\n",
+    "c[x] = scaler.transform(c[x])\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training.csv

@@ -0,0 +1,628 @@
+survived,sex,age,n_siblings_spouses,parch,fare,class,deck,embark_town,alone
+0,male,22,1,0,7.25,Third,unknown,Southampton,n
+1,female,38,1,0,71.2833,First,C,Cherbourg,n
+1,female,26,0,0,7.925,Third,unknown,Southampton,y
+1,female,35,1,0,53.1,First,C,Southampton,n
+0,male,28,0,0,8.4583,Third,unknown,Queenstown,y
+0,male,2,3,1,21.075,Third,unknown,Southampton,n
+1,female,27,0,2,11.1333,Third,unknown,Southampton,n
+1,female,14,1,0,30.0708,Second,unknown,Cherbourg,n
+1,female,4,1,1,16.7,Third,G,Southampton,n
+0,male,20,0,0,8.05,Third,unknown,Southampton,y
+0,male,39,1,5,31.275,Third,unknown,Southampton,n
+0,female,14,0,0,7.8542,Third,unknown,Southampton,y
+0,male,2,4,1,29.125,Third,unknown,Queenstown,n
+1,male,28,0,0,13,Second,unknown,Southampton,y
+0,female,31,1,0,18,Third,unknown,Southampton,n
+1,female,28,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,35,0,0,26,Second,unknown,Southampton,y
+1,male,28,0,0,35.5,First,A,Southampton,y
+1,female,38,1,5,31.3875,Third,unknown,Southampton,n
+0,male,28,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,19,3,2,263,First,C,Southampton,n
+1,female,28,0,0,7.8792,Third,unknown,Queenstown,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,40,0,0,27.7208,First,unknown,Cherbourg,y
+1,female,28,1,0,146.5208,First,B,Cherbourg,n
+1,female,28,0,0,7.75,Third,unknown,Queenstown,y
+0,male,66,0,0,10.5,Second,unknown,Southampton,y
+0,male,28,1,0,82.1708,First,unknown,Cherbourg,n
+0,male,42,1,0,52,First,unknown,Southampton,n
+1,male,28,0,0,7.2292,Third,unknown,Cherbourg,y
+1,female,14,1,0,11.2417,Third,unknown,Cherbourg,n
+0,female,40,1,0,9.475,Third,unknown,Southampton,n
+0,female,27,1,0,21,Second,unknown,Southampton,n
+0,male,28,0,0,7.8958,Third,unknown,Cherbourg,y
+1,female,3,1,2,41.5792,Second,unknown,Cherbourg,n
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+0,male,28,1,0,15.5,Third,unknown,Queenstown,n
+0,male,28,2,0,21.6792,Third,unknown,Cherbourg,n
+0,female,18,1,0,17.8,Third,unknown,Southampton,n
+0,male,7,4,1,39.6875,Third,unknown,Southampton,n
+1,female,49,1,0,76.7292,First,D,Cherbourg,n
+1,female,29,1,0,26,Second,unknown,Southampton,n
+0,male,65,0,1,61.9792,First,B,Cherbourg,n
+1,male,28,0,0,35.5,First,C,Southampton,y
+1,female,21,0,0,10.5,Second,unknown,Southampton,y
+0,male,28.5,0,0,7.2292,Third,unknown,Cherbourg,y
+0,male,11,5,2,46.9,Third,unknown,Southampton,n
+0,male,22,0,0,7.2292,Third,unknown,Cherbourg,y
+1,female,38,0,0,80,First,B,unknown,y
+0,male,45,1,0,83.475,First,C,Southampton,n
+0,male,4,3,2,27.9,Third,unknown,Southampton,n
+1,male,28,1,1,15.2458,Third,unknown,Cherbourg,n
+0,male,19,0,0,8.1583,Third,unknown,Southampton,y
+1,female,17,4,2,7.925,Third,unknown,Southampton,n
+0,male,26,2,0,8.6625,Third,unknown,Southampton,n
+0,male,32,0,0,10.5,Second,unknown,Southampton,y
+0,male,21,0,0,73.5,Second,unknown,Southampton,y
+1,male,32,0,0,56.4958,Third,unknown,Southampton,y
+0,male,25,0,0,7.65,Third,F,Southampton,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+1,male,0.83,0,2,29,Second,unknown,Southampton,n
+1,female,30,0,0,12.475,Third,unknown,Southampton,y
+0,male,22,0,0,9,Third,unknown,Southampton,y
+1,male,29,0,0,9.5,Third,unknown,Southampton,y
+1,female,28,0,0,7.7875,Third,unknown,Queenstown,y
+0,male,28,0,0,47.1,First,unknown,Southampton,y
+0,male,16,1,3,34.375,Third,unknown,Southampton,n
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+1,female,23,3,2,263,First,C,Southampton,n
+0,male,24,0,0,8.05,Third,unknown,Southampton,y
+0,male,46,1,0,61.175,First,E,Southampton,n
+0,male,59,0,0,7.25,Third,unknown,Southampton,y
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+0,male,71,0,0,34.6542,First,A,Cherbourg,y
+1,male,23,0,1,63.3583,First,D,Cherbourg,n
+1,female,34,0,1,23,Second,unknown,Southampton,n
+0,male,34,1,0,26,Second,unknown,Southampton,n
+0,female,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,21,0,1,77.2875,First,D,Southampton,n
+0,male,33,0,0,8.6542,Third,unknown,Southampton,y
+0,male,37,2,0,7.925,Third,unknown,Southampton,n
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+1,male,28,0,0,7.775,Third,unknown,Southampton,y
+1,female,28,1,0,24.15,Third,unknown,Queenstown,n
+0,male,47,0,0,52,First,C,Southampton,y
+0,female,14.5,1,0,14.4542,Third,unknown,Cherbourg,n
+0,male,22,0,0,8.05,Third,unknown,Southampton,y
+0,female,17,0,0,14.4583,Third,unknown,Cherbourg,y
+0,male,21,0,0,7.925,Third,unknown,Southampton,y
+0,male,70.5,0,0,7.75,Third,unknown,Queenstown,y
+0,male,29,1,0,21,Second,unknown,Southampton,n
+0,male,24,0,1,247.5208,First,B,Cherbourg,n
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+0,male,32.5,1,0,30.0708,Second,unknown,Cherbourg,n
+1,female,32.5,0,0,13,Second,E,Southampton,y
+0,male,28,0,0,7.75,Third,unknown,Queenstown,y
+1,male,24,0,0,7.1417,Third,unknown,Southampton,y
+0,male,45,0,0,6.975,Third,unknown,Southampton,y
+0,male,20,0,0,7.05,Third,unknown,Southampton,y
+0,female,47,1,0,14.5,Third,unknown,Southampton,n
+0,male,23,0,0,15.0458,Second,unknown,Cherbourg,y
+1,female,19,0,2,26.2833,First,D,Southampton,n
+0,male,37,1,0,53.1,First,C,Southampton,n
+0,male,16,0,0,9.2167,Third,unknown,Southampton,y
+0,male,24,0,0,79.2,First,B,Cherbourg,y
+0,female,28,0,2,15.2458,Third,unknown,Cherbourg,n
+1,female,22,0,0,7.75,Third,unknown,Southampton,y
+0,male,19,0,0,6.75,Third,unknown,Queenstown,y
+0,male,18,0,0,11.5,Second,unknown,Southampton,y
+0,female,9,2,2,34.375,Third,unknown,Southampton,n
+0,male,51,0,0,12.525,Second,unknown,Southampton,y
+0,male,55.5,0,0,8.05,Third,unknown,Southampton,y
+0,male,40.5,0,2,14.5,Third,unknown,Southampton,n
+0,male,28,0,0,7.3125,Third,unknown,Southampton,y
+0,male,51,0,1,61.3792,First,unknown,Cherbourg,n
+1,female,16,0,0,7.7333,Third,unknown,Queenstown,y
+0,male,28,0,0,8.6625,Third,unknown,Southampton,y
+0,male,28,8,2,69.55,Third,unknown,Southampton,n
+0,male,44,0,1,16.1,Third,unknown,Southampton,n
+0,male,26,0,0,7.775,Third,unknown,Southampton,y
+0,male,17,0,0,8.6625,Third,unknown,Southampton,y
+0,male,1,4,1,39.6875,Third,unknown,Southampton,n
+1,female,28,0,1,55,First,E,Southampton,n
+0,male,28,0,0,56.4958,Third,unknown,Southampton,y
+0,male,4,4,1,29.125,Third,unknown,Queenstown,n
+0,male,18,1,1,7.8542,Third,unknown,Southampton,n
+0,male,28,3,1,25.4667,Third,unknown,Southampton,n
+0,female,50,0,0,28.7125,First,C,Cherbourg,y
+0,male,30,0,0,13,Second,unknown,Southampton,y
+0,male,36,0,0,0,Third,unknown,Southampton,y
+0,female,28,8,2,69.55,Third,unknown,Southampton,n
+0,male,28,0,0,15.05,Second,unknown,Cherbourg,y
+0,male,9,4,2,31.3875,Third,unknown,Southampton,n
+1,female,4,0,2,22.025,Third,unknown,Southampton,n
+1,female,28,1,0,15.5,Third,unknown,Queenstown,n
+1,male,45,0,0,26.55,First,unknown,Southampton,y
+0,male,36,0,0,7.8958,Third,unknown,Southampton,y
+1,female,32,0,0,13,Second,unknown,Southampton,y
+1,female,19,1,0,7.8542,Third,unknown,Southampton,n
+1,male,3,1,1,26,Second,F,Southampton,n
+1,female,44,0,0,27.7208,First,B,Cherbourg,y
+1,female,58,0,0,146.5208,First,B,Cherbourg,y
+0,male,28,0,0,7.75,Third,unknown,Queenstown,y
+1,female,28,0,0,7.75,Third,unknown,Queenstown,y
+0,female,24,0,0,13,Second,unknown,Southampton,y
+0,male,28,0,0,9.5,Third,unknown,Southampton,y
+0,male,28,8,2,69.55,Third,unknown,Southampton,n
+0,male,34,0,0,6.4958,Third,unknown,Southampton,y
+0,female,2,0,1,10.4625,Third,G,Southampton,n
+0,male,32,1,0,15.85,Third,unknown,Southampton,n
+1,male,26,0,0,18.7875,Third,unknown,Cherbourg,y
+1,female,16,0,0,7.75,Third,unknown,Queenstown,y
+1,male,40,0,0,31,First,A,Cherbourg,y
+1,female,35,0,0,21,Second,unknown,Southampton,y
+0,male,22,0,0,7.25,Third,unknown,Southampton,y
+0,male,28,1,0,7.75,Third,unknown,Queenstown,n
+1,female,31,1,0,113.275,First,D,Cherbourg,n
+1,female,27,0,0,7.925,Third,unknown,Southampton,y
+0,male,42,1,0,27,Second,unknown,Southampton,n
+0,male,30,0,0,10.5,Second,unknown,Southampton,y
+1,male,16,0,0,8.05,Third,unknown,Southampton,y
+0,male,51,0,0,8.05,Third,unknown,Southampton,y
+0,male,22,0,0,9.35,Third,unknown,Southampton,y
+1,male,19,0,0,10.5,Second,unknown,Southampton,y
+0,male,20.5,0,0,7.25,Third,unknown,Southampton,y
+0,male,18,0,0,13,Second,unknown,Southampton,y
+0,female,28,3,1,25.4667,Third,unknown,Southampton,n
+1,female,35,1,0,83.475,First,C,Southampton,n
+0,male,29,0,0,7.775,Third,unknown,Southampton,y
+0,male,59,0,0,13.5,Second,unknown,Southampton,y
+0,female,28,0,0,7.55,Third,unknown,Southampton,y
+0,male,44,1,0,26,Second,unknown,Southampton,n
+0,male,19,0,0,10.5,Second,unknown,Southampton,y
+0,male,33,0,0,12.275,Second,unknown,Southampton,y
+0,female,28,1,0,14.4542,Third,unknown,Cherbourg,n
+1,female,28,1,0,15.5,Third,unknown,Queenstown,n
+0,male,22,0,0,7.125,Third,unknown,Southampton,y
+0,male,30,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,44,2,0,90,First,C,Queenstown,n
+1,female,24,0,2,14.5,Second,unknown,Southampton,n
+0,male,28,0,0,7.25,Third,unknown,Southampton,y
+0,female,29,1,1,10.4625,Third,G,Southampton,n
+0,male,30,1,0,16.1,Third,unknown,Southampton,n
+0,female,41,0,2,20.2125,Third,unknown,Southampton,n
+1,female,28,0,0,79.2,First,unknown,Cherbourg,y
+1,female,35,0,0,512.3292,First,unknown,Cherbourg,y
+1,female,50,0,1,26,Second,unknown,Southampton,n
+0,male,28,0,0,7.75,Third,unknown,Queenstown,y
+1,male,3,4,2,31.3875,Third,unknown,Southampton,n
+0,male,40,0,0,0,First,B,Southampton,y
+0,female,28,0,0,7.75,Third,unknown,Queenstown,y
+0,male,36,0,0,10.5,Second,unknown,Southampton,y
+0,male,16,4,1,39.6875,Third,unknown,Southampton,n
+1,male,25,1,0,7.775,Third,unknown,Southampton,n
+1,female,58,0,1,153.4625,First,C,Southampton,n
+1,female,35,0,0,135.6333,First,C,Southampton,y
+0,male,28,0,0,31,First,unknown,Southampton,y
+1,male,25,0,0,0,Third,unknown,Southampton,y
+1,female,41,0,1,19.5,Second,unknown,Southampton,n
+0,male,37,0,1,29.7,First,C,Cherbourg,n
+1,female,28,0,0,7.75,Third,unknown,Queenstown,y
+1,female,63,1,0,77.9583,First,D,Southampton,n
+0,female,45,0,0,7.75,Third,unknown,Southampton,y
+0,male,28,0,0,0,Second,unknown,Southampton,y
+0,male,7,4,1,29.125,Third,unknown,Queenstown,n
+1,female,35,1,1,20.25,Third,unknown,Southampton,n
+0,male,28,0,0,7.8542,Third,unknown,Southampton,y
+0,male,16,0,0,9.5,Third,unknown,Southampton,y
+0,male,28,0,0,26,First,A,Southampton,y
+1,female,26,0,0,78.85,First,unknown,Southampton,y
+0,male,36,0,0,12.875,Second,D,Cherbourg,y
+0,male,24,0,0,7.8958,Third,unknown,Southampton,y
+1,male,28,0,0,30.5,First,C,Southampton,y
+1,female,50,0,1,247.5208,First,B,Cherbourg,n
+1,female,28,0,0,7.75,Third,unknown,Queenstown,y
+0,male,19,0,0,0,Third,unknown,Southampton,y
+1,female,28,0,0,12.35,Second,E,Queenstown,y
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+1,female,28,0,0,110.8833,First,unknown,Cherbourg,y
+1,female,17,1,0,108.9,First,C,Cherbourg,n
+0,male,30,1,0,24,Second,unknown,Cherbourg,n
+1,female,30,0,0,56.9292,First,E,Cherbourg,y
+1,female,24,0,0,83.1583,First,C,Cherbourg,y
+1,female,18,2,2,262.375,First,B,Cherbourg,n
+0,female,26,1,1,26,Second,unknown,Southampton,n
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,43,1,1,26.25,Second,unknown,Southampton,n
+1,female,24,1,0,26,Second,unknown,Southampton,n
+1,female,31,0,2,164.8667,First,C,Southampton,n
+1,female,40,1,1,134.5,First,E,Cherbourg,n
+0,male,22,0,0,7.25,Third,unknown,Southampton,y
+0,male,27,0,0,7.8958,Third,unknown,Southampton,y
+1,female,22,1,1,29,Second,unknown,Southampton,n
+0,male,28,8,2,69.55,Third,unknown,Southampton,n
+1,female,36,0,0,135.6333,First,C,Cherbourg,y
+0,male,61,0,0,6.2375,Third,unknown,Southampton,y
+1,female,31,1,1,20.525,Third,unknown,Southampton,n
+1,female,28,2,0,23.25,Third,unknown,Queenstown,n
+0,male,38,0,1,153.4625,First,C,Southampton,n
+1,female,28,1,0,133.65,First,unknown,Southampton,n
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,29,1,0,66.6,First,C,Southampton,n
+1,male,45,0,0,8.05,Third,unknown,Southampton,y
+0,male,45,0,0,35.5,First,unknown,Southampton,y
+0,male,28,0,0,13,Second,unknown,Southampton,y
+0,male,25,0,0,13,Second,unknown,Southampton,y
+0,male,36,0,0,13,Second,unknown,Southampton,y
+1,female,24,0,0,13,Second,F,Southampton,y
+1,female,40,0,0,13,Second,unknown,Southampton,y
+1,female,28,1,0,16.1,Third,unknown,Southampton,n
+0,male,15,1,1,7.2292,Third,unknown,Cherbourg,n
+0,male,25,1,0,17.8,Third,unknown,Southampton,n
+0,male,28,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,28,0,0,9.5,Third,unknown,Southampton,y
+1,female,22,0,1,55,First,E,Southampton,n
+0,female,38,0,0,13,Second,unknown,Southampton,y
+1,female,28,0,0,7.8792,Third,unknown,Queenstown,y
+1,female,28,0,0,7.8792,Third,unknown,Queenstown,y
+0,male,40,1,4,27.9,Third,unknown,Southampton,n
+0,male,29,1,0,27.7208,Second,unknown,Cherbourg,n
+0,female,45,0,1,14.4542,Third,unknown,Cherbourg,n
+0,male,35,0,0,7.05,Third,unknown,Southampton,y
+0,male,28,1,0,15.5,Third,unknown,Queenstown,n
+1,female,60,1,0,75.25,First,D,Cherbourg,n
+1,female,28,0,0,7.2292,Third,unknown,Cherbourg,y
+1,female,28,0,0,7.75,Third,unknown,Queenstown,y
+1,female,24,0,0,69.3,First,B,Cherbourg,y
+0,male,18,1,0,6.4958,Third,unknown,Southampton,n
+0,male,19,0,0,8.05,Third,unknown,Southampton,y
+1,female,28,1,0,82.1708,First,unknown,Cherbourg,n
+0,male,27,0,2,211.5,First,C,Cherbourg,n
+0,male,19,0,0,7.775,Third,unknown,Southampton,y
+1,female,42,0,0,227.525,First,unknown,Cherbourg,y
+0,male,32,0,0,7.925,Third,unknown,Southampton,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,18,0,0,73.5,Second,unknown,Southampton,y
+0,male,1,5,2,46.9,Third,unknown,Southampton,n
+0,male,28,0,0,7.7292,Third,unknown,Queenstown,y
+1,female,17,0,0,12,Second,unknown,Cherbourg,y
+1,male,36,1,2,120,First,B,Southampton,n
+1,male,21,0,0,7.7958,Third,unknown,Southampton,y
+1,female,23,1,0,113.275,First,D,Cherbourg,n
+1,female,24,0,2,16.7,Third,G,Southampton,n
+0,male,22,0,0,7.7958,Third,unknown,Southampton,y
+0,female,31,0,0,7.8542,Third,unknown,Southampton,y
+0,male,46,0,0,26,Second,unknown,Southampton,y
+0,male,23,0,0,10.5,Second,unknown,Southampton,y
+1,male,39,0,0,7.925,Third,unknown,Southampton,y
+0,male,26,0,0,8.05,Third,unknown,Southampton,y
+0,male,28,1,0,15.85,Third,unknown,Southampton,n
+0,male,34,1,0,21,Second,unknown,Southampton,n
+1,male,3,1,1,18.75,Second,unknown,Southampton,n
+0,male,21,0,0,7.775,Third,unknown,Southampton,y
+0,female,28,3,1,25.4667,Third,unknown,Southampton,n
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28,0,0,6.8583,Third,unknown,Queenstown,y
+0,male,28,0,0,0,Second,unknown,Southampton,y
+1,male,44,0,0,7.925,Third,unknown,Southampton,y
+0,female,28,0,0,8.05,Third,unknown,Southampton,y
+1,female,34,1,1,32.5,Second,unknown,Southampton,n
+1,female,18,0,2,13,Second,unknown,Southampton,n
+0,male,30,0,0,13,Second,unknown,Southampton,y
+0,male,28,0,0,7.8958,Third,unknown,Cherbourg,y
+0,male,21,0,0,7.7333,Third,unknown,Queenstown,y
+0,male,18,1,1,20.2125,Third,unknown,Southampton,n
+1,female,19,0,0,26,Second,unknown,Southampton,y
+0,male,28,0,0,7.75,Third,unknown,Queenstown,y
+1,male,32,0,0,8.05,Third,E,Southampton,y
+1,male,28,0,0,26.55,First,C,Southampton,y
+1,female,28,1,0,16.1,Third,unknown,Southampton,n
+1,female,42,1,0,26,Second,unknown,Southampton,n
+0,male,17,0,0,7.125,Third,unknown,Southampton,y
+0,male,50,1,0,55.9,First,E,Southampton,n
+1,female,14,1,2,120,First,B,Southampton,n
+1,female,24,2,3,18.75,Second,unknown,Southampton,n
+0,male,64,1,4,263,First,C,Southampton,n
+0,male,31,0,0,10.5,Second,unknown,Southampton,y
+1,female,45,1,1,26.25,Second,unknown,Southampton,n
+0,male,20,0,0,9.5,Third,unknown,Southampton,y
+1,female,28,0,0,13,Second,unknown,Southampton,y
+1,male,28,0,0,8.1125,Third,unknown,Southampton,y
+1,male,34,0,0,26.55,First,unknown,Southampton,y
+1,female,5,2,1,19.2583,Third,unknown,Cherbourg,n
+1,male,52,0,0,30.5,First,C,Southampton,y
+0,male,36,1,2,27.75,Second,unknown,Southampton,n
+0,male,30,0,0,27.75,First,C,Cherbourg,y
+1,male,49,1,0,89.1042,First,C,Cherbourg,n
+1,male,29,0,0,7.8958,Third,unknown,Cherbourg,y
+0,male,65,0,0,26.55,First,E,Southampton,y
+1,female,28,1,0,51.8625,First,D,Southampton,n
+1,male,48,0,0,26.55,First,E,Southampton,y
+0,male,34,0,0,8.05,Third,unknown,Southampton,y
+0,male,47,0,0,38.5,First,E,Southampton,y
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+0,male,38,0,0,7.05,Third,unknown,Southampton,y
+0,male,56,0,0,26.55,First,unknown,Southampton,y
+0,male,28,0,0,7.725,Third,unknown,Queenstown,y
+1,female,0.75,2,1,19.2583,Third,unknown,Cherbourg,n
+0,male,28,0,0,7.25,Third,unknown,Southampton,y
+1,female,33,1,2,27.75,Second,unknown,Southampton,n
+1,female,23,0,0,13.7917,Second,D,Cherbourg,y
+0,male,28,0,0,52,First,A,Southampton,y
+0,male,34,1,0,21,Second,unknown,Southampton,n
+0,male,29,1,0,7.0458,Third,unknown,Southampton,n
+1,female,2,0,1,12.2875,Third,unknown,Southampton,n
+0,male,9,5,2,46.9,Third,unknown,Southampton,n
+0,male,28,0,0,0,Second,unknown,Southampton,y
+1,female,63,0,0,9.5875,Third,unknown,Southampton,y
+1,male,25,1,0,91.0792,First,B,Cherbourg,n
+0,female,28,3,1,25.4667,Third,unknown,Southampton,n
+1,female,35,1,0,90,First,C,Southampton,n
+0,male,58,0,0,29.7,First,B,Cherbourg,y
+1,male,9,1,1,15.9,Third,unknown,Southampton,n
+0,male,28,1,0,19.9667,Third,unknown,Southampton,n
+0,male,71,0,0,49.5042,First,unknown,Cherbourg,y
+0,male,21,0,0,8.05,Third,unknown,Southampton,y
+0,female,25,1,2,151.55,First,C,Southampton,n
+0,male,17,0,0,8.6625,Third,unknown,Southampton,y
+0,female,21,0,0,7.75,Third,unknown,Queenstown,y
+0,female,37,0,0,9.5875,Third,unknown,Southampton,y
+0,male,18,1,0,108.9,First,C,Cherbourg,n
+1,female,33,0,2,26,Second,unknown,Southampton,n
+1,male,28,0,0,26.55,First,unknown,Southampton,y
+1,male,26,0,0,56.4958,Third,unknown,Southampton,y
+1,female,54,1,0,59.4,First,unknown,Cherbourg,n
+0,male,24,0,0,7.4958,Third,unknown,Southampton,y
+0,male,47,0,0,34.0208,First,D,Southampton,y
+1,female,34,0,0,10.5,Second,F,Southampton,y
+1,female,36,1,0,26,Second,unknown,Southampton,n
+0,male,32,0,0,7.8958,Third,unknown,Southampton,y
+1,female,30,0,0,93.5,First,B,Southampton,y
+0,male,22,0,0,7.8958,Third,unknown,Southampton,y
+1,female,44,0,1,57.9792,First,B,Cherbourg,n
+0,male,28,0,0,7.2292,Third,unknown,Cherbourg,y
+0,male,40.5,0,0,7.75,Third,unknown,Queenstown,y
+1,female,50,0,0,10.5,Second,unknown,Southampton,y
+0,male,28,0,0,221.7792,First,C,Southampton,y
+0,male,23,2,1,11.5,Second,unknown,Southampton,n
+1,female,2,1,1,26,Second,unknown,Southampton,n
+0,male,17,1,1,7.2292,Third,unknown,Cherbourg,n
+1,female,28,0,2,22.3583,Third,unknown,Cherbourg,n
+0,female,30,0,0,8.6625,Third,unknown,Southampton,y
+1,female,7,0,2,26.25,Second,unknown,Southampton,n
+0,male,45,0,0,26.55,First,B,Southampton,y
+1,female,30,0,0,106.425,First,unknown,Cherbourg,y
+1,female,22,0,2,49.5,First,B,Cherbourg,n
+1,female,36,0,2,71,First,B,Southampton,n
+0,female,9,4,2,31.275,Third,unknown,Southampton,n
+0,female,11,4,2,31.275,Third,unknown,Southampton,n
+0,male,50,1,0,106.425,First,C,Cherbourg,n
+1,female,19,1,0,26,Second,unknown,Southampton,n
+1,male,28,0,0,13.8625,Second,unknown,Cherbourg,y
+0,male,33,1,1,20.525,Third,unknown,Southampton,n
+1,male,17,0,2,110.8833,First,C,Cherbourg,n
+0,male,27,0,0,26,Second,unknown,Southampton,y
+0,male,28,0,0,7.8292,Third,unknown,Queenstown,y
+1,female,22,0,0,7.775,Third,unknown,Southampton,y
+1,female,48,1,0,39.6,First,A,Cherbourg,n
+0,male,28,0,0,227.525,First,unknown,Cherbourg,y
+1,female,39,1,1,79.65,First,E,Southampton,n
+1,female,36,1,0,17.4,Third,unknown,Southampton,n
+0,male,28,0,0,7.75,Third,unknown,Queenstown,y
+0,male,40,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+0,male,24,2,0,24.15,Third,unknown,Southampton,n
+0,male,19,0,0,7.8958,Third,unknown,Southampton,y
+0,female,29,0,4,21.075,Third,unknown,Southampton,n
+1,male,32,0,0,7.8542,Third,unknown,Southampton,y
+1,male,62,0,0,10.5,Second,unknown,Southampton,y
+1,female,53,2,0,51.4792,First,C,Southampton,n
+1,male,36,0,0,26.3875,First,E,Southampton,y
+0,male,16,0,0,8.05,Third,unknown,Southampton,y
+1,female,34,0,0,13,Second,unknown,Southampton,y
+1,female,25,1,1,30,Second,unknown,Southampton,n
+0,male,36,0,0,40.125,First,A,Cherbourg,y
+0,male,47,0,0,15,Second,unknown,Southampton,y
+1,male,60,1,1,79.2,First,B,Cherbourg,n
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+0,male,35,0,0,7.125,Third,unknown,Southampton,y
+1,female,52,1,0,78.2667,First,D,Cherbourg,n
+0,male,47,0,0,7.25,Third,unknown,Southampton,y
+0,female,28,0,2,7.75,Third,unknown,Queenstown,n
+0,male,37,1,0,26,Second,unknown,Southampton,n
+0,male,36,1,1,24.15,Third,unknown,Southampton,n
+1,female,28,0,0,33,Second,unknown,Southampton,y
+0,male,49,0,0,0,Third,unknown,Southampton,y
+0,male,28,0,0,7.225,Third,unknown,Cherbourg,y
+1,male,49,1,0,56.9292,First,A,Cherbourg,n
+1,male,35,0,0,26.55,First,unknown,Cherbourg,y
+0,male,36,1,0,15.55,Third,unknown,Southampton,n
+0,male,30,0,0,7.8958,Third,unknown,Southampton,y
+1,male,27,0,0,30.5,First,unknown,Southampton,y
+1,female,22,1,2,41.5792,Second,unknown,Cherbourg,n
+1,female,40,0,0,153.4625,First,C,Southampton,y
+0,female,39,1,5,31.275,Third,unknown,Southampton,n
+0,male,28,0,0,7.05,Third,unknown,Southampton,y
+1,female,28,1,0,15.5,Third,unknown,Queenstown,n
+0,male,35,0,0,8.05,Third,unknown,Southampton,y
+1,female,24,1,2,65,Second,unknown,Southampton,n
+0,male,34,1,1,14.4,Third,unknown,Southampton,n
+0,male,26,0,0,10.5,Second,unknown,Southampton,y
+1,male,20,1,1,15.7417,Third,unknown,Cherbourg,n
+0,male,61,0,0,32.3208,First,D,Southampton,y
+0,male,57,0,0,12.35,Second,unknown,Queenstown,y
+1,female,21,0,0,77.9583,First,D,Southampton,y
+0,male,26,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28,0,0,7.7333,Third,unknown,Queenstown,y
+1,male,80,0,0,30,First,A,Southampton,y
+0,male,51,0,0,7.0542,Third,unknown,Southampton,y
+1,male,32,0,0,30.5,First,B,Cherbourg,y
+0,female,9,3,2,27.9,Third,unknown,Southampton,n
+1,female,28,0,0,13,Second,unknown,Southampton,y
+0,male,32,0,0,7.925,Third,unknown,Southampton,y
+0,female,41,0,5,39.6875,Third,unknown,Southampton,n
+0,male,28,1,0,16.1,Third,unknown,Southampton,n
+0,male,20,0,0,7.8542,Third,unknown,Southampton,y
+1,male,28,0,0,56.4958,Third,unknown,Southampton,y
+1,female,0.75,2,1,19.2583,Third,unknown,Cherbourg,n
+1,male,48,1,0,76.7292,First,D,Cherbourg,n
+0,male,28,0,0,7.55,Third,unknown,Southampton,y
+1,female,23,0,0,7.55,Third,unknown,Southampton,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+1,female,18,0,1,23,Second,unknown,Southampton,n
+0,male,21,0,0,8.4333,Third,unknown,Southampton,y
+0,male,24,2,0,73.5,Second,unknown,Southampton,n
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,female,32,1,1,15.5,Third,unknown,Queenstown,n
+1,male,50,2,0,133.65,First,unknown,Southampton,n
+0,male,47,0,0,25.5875,First,E,Southampton,y
+0,male,36,0,0,7.4958,Third,unknown,Southampton,y
+1,male,20,1,0,7.925,Third,unknown,Southampton,n
+0,male,25,0,0,13,Second,unknown,Southampton,y
+0,male,28,0,0,7.775,Third,unknown,Southampton,y
+0,male,43,0,0,8.05,Third,unknown,Southampton,y
+1,female,40,1,1,39,Second,unknown,Southampton,n
+0,male,31,1,0,52,First,B,Southampton,n
+1,male,31,0,0,13,Second,unknown,Southampton,y
+0,male,28,0,0,0,Second,unknown,Southampton,y
+0,male,18,0,0,7.775,Third,unknown,Southampton,y
+1,female,18,0,0,9.8417,Third,unknown,Southampton,y
+1,male,36,0,1,512.3292,First,B,Cherbourg,n
+1,male,27,0,0,76.7292,First,D,Cherbourg,y
+0,male,20,0,0,9.225,Third,unknown,Southampton,y
+0,male,14,5,2,46.9,Third,unknown,Southampton,n
+0,male,60,1,1,39,Second,unknown,Southampton,n
+0,male,19,0,0,10.1708,Third,unknown,Southampton,y
+0,male,18,0,0,7.7958,Third,unknown,Southampton,y
+1,female,15,0,1,211.3375,First,B,Southampton,n
+1,male,31,1,0,57,First,B,Southampton,n
+1,female,4,0,1,13.4167,Third,unknown,Cherbourg,n
+1,male,28,0,0,56.4958,Third,unknown,Southampton,y
+0,male,60,0,0,26.55,First,unknown,Southampton,y
+1,female,28,0,0,7.7333,Third,unknown,Queenstown,y
+0,male,49,1,1,110.8833,First,C,Cherbourg,n
+1,male,35,0,0,26.2875,First,E,Southampton,y
+0,male,25,0,0,7.7417,Third,unknown,Queenstown,y
+0,male,39,0,0,26,Second,unknown,Southampton,y
+1,female,22,0,0,151.55,First,unknown,Southampton,y
+1,male,28,1,1,15.2458,Third,unknown,Cherbourg,n
+1,female,24,0,0,49.5042,First,C,Cherbourg,y
+0,male,28,0,0,26.55,First,C,Southampton,y
+1,male,48,1,0,52,First,C,Southampton,n
+0,male,29,0,0,9.4833,Third,unknown,Southampton,y
+0,male,19,0,0,7.65,Third,F,Southampton,y
+1,female,38,0,0,227.525,First,C,Cherbourg,y
+1,female,27,0,0,10.5,Second,E,Southampton,y
+0,male,28,0,0,15.5,Third,unknown,Queenstown,y
+0,male,33,0,0,7.775,Third,unknown,Southampton,y
+1,female,6,0,1,33,Second,unknown,Southampton,n
+0,male,50,0,0,13,Second,unknown,Southampton,y
+1,male,27,1,0,53.1,First,E,Southampton,n
+1,female,30,3,0,21,Second,unknown,Southampton,n
+0,male,25,1,0,26,Second,unknown,Southampton,n
+0,female,25,1,0,7.925,Third,unknown,Southampton,n
+1,female,29,0,0,211.3375,First,B,Southampton,y
+0,male,11,0,0,18.7875,Third,unknown,Cherbourg,y
+0,male,28,0,0,0,Second,unknown,Southampton,y
+0,male,23,0,0,13,Second,unknown,Southampton,y
+0,male,23,0,0,13,Second,unknown,Southampton,y
+1,male,35,0,0,512.3292,First,B,Cherbourg,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,male,36,1,0,78.85,First,C,Southampton,n
+1,female,21,2,2,262.375,First,B,Cherbourg,n
+0,male,24,1,0,16.1,Third,unknown,Southampton,n
+0,male,70,1,1,71,First,B,Southampton,n
+1,female,4,1,1,23,Second,unknown,Southampton,n
+1,male,6,0,1,12.475,Third,E,Southampton,n
+0,male,33,0,0,9.5,Third,unknown,Southampton,y
+0,male,23,0,0,7.8958,Third,unknown,Southampton,y
+1,female,48,1,2,65,Second,unknown,Southampton,n
+0,male,28,0,0,7.7958,Third,unknown,Southampton,y
+0,male,34,0,0,8.05,Third,unknown,Southampton,y
+0,male,28,0,0,14.5,Third,unknown,Southampton,y
+0,male,41,0,0,7.125,Third,unknown,Southampton,y
+1,male,20,0,0,7.2292,Third,unknown,Cherbourg,y
+1,female,51,1,0,77.9583,First,D,Southampton,n
+0,male,28,0,0,39.6,First,unknown,Cherbourg,y
+0,male,28,1,0,24.15,Third,unknown,Queenstown,n
+0,male,32,0,0,8.3625,Third,unknown,Southampton,y
+0,male,48,0,0,7.8542,Third,unknown,Southampton,y
+0,female,57,0,0,10.5,Second,E,Southampton,y
+0,male,18,0,0,7.75,Third,unknown,Southampton,y
+0,male,28,0,0,7.75,Third,F,Queenstown,y
+1,female,5,0,0,12.475,Third,unknown,Southampton,y
+1,female,17,1,0,57,First,B,Southampton,n
+0,male,29,0,0,30,First,D,Southampton,y
+0,male,28,1,2,23.45,Third,unknown,Southampton,n
+0,male,25,0,0,7.05,Third,unknown,Southampton,y
+1,male,1,1,2,20.575,Third,unknown,Southampton,n
+0,male,46,0,0,79.2,First,B,Cherbourg,y
+0,male,28,0,0,7.75,Third,unknown,Queenstown,y
+0,male,16,0,0,26,Second,unknown,Southampton,y
+0,female,28,8,2,69.55,Third,unknown,Southampton,n
+0,male,28,0,0,30.6958,First,unknown,Cherbourg,y
+0,male,25,0,0,7.8958,Third,unknown,Southampton,y
+0,male,39,0,0,13,Second,unknown,Southampton,y
+1,female,49,0,0,25.9292,First,D,Southampton,y
+1,female,31,0,0,8.6833,Third,unknown,Southampton,y
+0,male,30,0,0,7.2292,Third,unknown,Cherbourg,y
+0,female,30,1,1,24.15,Third,unknown,Southampton,n
+0,male,34,0,0,13,Second,unknown,Southampton,y
+1,female,31,1,1,26.25,Second,unknown,Southampton,n
+1,male,11,1,2,120,First,B,Southampton,n
+0,male,31,0,0,7.775,Third,unknown,Southampton,y
+0,male,39,0,0,0,First,A,Southampton,y
+0,male,39,0,0,13,Second,unknown,Southampton,y
+1,female,33,1,0,53.1,First,E,Southampton,n
+0,male,26,0,0,7.8875,Third,unknown,Southampton,y
+0,male,39,0,0,24.15,Third,unknown,Southampton,y
+0,male,35,0,0,10.5,Second,unknown,Southampton,y
+0,male,30.5,0,0,8.05,Third,unknown,Southampton,y
+0,male,28,0,0,0,First,B,Southampton,y
+0,female,23,0,0,7.925,Third,unknown,Southampton,y
+0,male,31,1,1,37.0042,Second,unknown,Cherbourg,n
+0,male,10,3,2,27.9,Third,unknown,Southampton,n
+1,female,52,1,1,93.5,First,B,Southampton,n
+1,male,27,0,0,8.6625,Third,unknown,Southampton,y
+0,male,2,4,1,39.6875,Third,unknown,Southampton,n
+0,male,28,0,0,6.95,Third,unknown,Queenstown,y
+0,male,28,0,0,56.4958,Third,unknown,Southampton,y
+1,male,28,0,0,7.75,Third,unknown,Queenstown,y
+1,female,15,1,0,14.4542,Third,unknown,Cherbourg,n
+0,male,28,0,0,7.2292,Third,unknown,Cherbourg,y
+0,male,23,0,0,7.8542,Third,unknown,Southampton,y
+0,male,18,0,0,8.3,Third,unknown,Southampton,y
+1,female,39,1,1,83.1583,First,E,Cherbourg,n
+0,male,21,0,0,8.6625,Third,unknown,Southampton,y
+0,male,28,0,0,8.05,Third,unknown,Southampton,y
+1,male,28,0,0,29.7,First,C,Cherbourg,y
+0,male,16,0,0,10.5,Second,unknown,Southampton,y
+1,female,30,0,0,31,First,unknown,Cherbourg,y
+0,male,34.5,0,0,6.4375,Third,unknown,Cherbourg,y
+0,male,42,0,0,7.55,Third,unknown,Southampton,y
+0,male,28,8,2,69.55,Third,unknown,Southampton,n
+0,male,35,0,0,7.8958,Third,unknown,Cherbourg,y
+0,male,28,0,1,33,Second,unknown,Southampton,n
+1,female,28,1,0,89.1042,First,C,Cherbourg,n
+0,male,4,4,2,31.275,Third,unknown,Southampton,n
+1,female,16,0,1,39.4,First,D,Southampton,n
+1,female,18,0,1,9.35,Third,unknown,Southampton,n
+1,female,45,1,1,164.8667,First,unknown,Southampton,n
+1,male,51,0,0,26.55,First,E,Southampton,y
+1,female,24,0,3,19.2583,Third,unknown,Cherbourg,n
+0,male,41,2,0,14.1083,Third,unknown,Southampton,n
+0,male,24,0,0,13,Second,unknown,Southampton,y
+1,female,42,0,0,13,Second,unknown,Southampton,y
+1,female,27,1,0,13.8583,Second,unknown,Cherbourg,n
+0,male,31,0,0,50.4958,First,A,Southampton,y
+1,male,4,1,1,11.1333,Third,unknown,Southampton,n
+0,male,26,0,0,7.8958,Third,unknown,Southampton,y
+1,female,47,1,1,52.5542,First,D,Southampton,n
+0,male,33,0,0,5,First,B,Southampton,y
+0,male,47,0,0,9,Third,unknown,Southampton,y
+1,female,28,1,0,24,Second,unknown,Cherbourg,n
+1,female,15,0,0,7.225,Third,unknown,Cherbourg,y
+0,male,20,0,0,9.8458,Third,unknown,Southampton,y
+0,male,19,0,0,7.8958,Third,unknown,Southampton,y
+0,male,28,0,0,7.8958,Third,unknown,Southampton,y
+0,female,22,0,0,10.5167,Third,unknown,Southampton,y
+0,male,28,0,0,10.5,Second,unknown,Southampton,y
+0,male,25,0,0,7.05,Third,unknown,Southampton,y
+1,female,19,0,0,30,First,B,Southampton,y
+0,female,28,1,2,23.45,Third,unknown,Southampton,n
+0,male,32,0,0,7.75,Third,unknown,Queenstown,y