Intro to Data Science Presentation

Data Science Presentation for Association of Computing Machinery

Description: Introductory data science presentation and project walkthrough with Gettysburg College students

The Association for Computing Machinery (ACM) in Gettysburg College is a student-led organization where students from different disciplines and fields with interest in computer science gather to learn from each other and to form a community on campus. For this week’s meeting, gave a presentation on “Into to Data Science”, demonstrating the data science and data analytics workflow, from data exploration, hypothesis testing, and modelling processes useful to answer data-driven questions. This presentation is adapted from Kaggle’s Titanic - Machine Learning from Disaster’ Machine Learning Competition.

Step 1: Loading the Data

import numpy as np 
import pandas as pd

# Loading Dataset

# Titanic survival data is divided into two datasets: Train and Test
train_data = pd.read_csv("/Users/gabrielguzman/School/Research/Data Science/titanic/train.csv")
test_data = pd.read_csv("/Users/gabrielguzman/School/Research/Data Science/titanic/train.csv")

Step 2: Exploratory Data Analysis

# Tabular Output of 'test_data'
test_data.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

# Women survived in train data 
women = train_data.loc[train_data.Sex == 'female']["Survived"]
rate_women = sum(women)/len(women)
print("% of women who survived:", rate_women)

# Men survived in train data 
men = train_data.loc[train_data.Sex == 'male']["Survived"]
rate_men = sum(men)/len(men)
print("% of men who survived:", rate_men)

% of women who survived: 0.7420382165605095
% of men who survived: 0.18890814558058924

# Machine Learning Model (Random Forest Algorithm)

from sklearn.ensemble import RandomForestClassifier

y = train_data["Survived"]

features = ["Pclass", "Sex", "SibSp", "Parch"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])

model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)

output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions, 'Sex': test_data.Sex})
output.to_csv('submission.csv', index=False)

# Female survived = 0.910828025 
# Male survived = 0.010398614

# % of women who survived: 0.7420382165605095
# % of men who survived: 0.18890814558058924