Dataset

This dataset was created by Aziz ullah Khan

Contents

The Titanic dataset is a well-known dataset that provides information on the passengers who were onboard the fateful voyage of the RMS Titanic. The data includes details such as the passenger's name, age, gender, ticket class, fare paid, and information on their family members. The dataset also includes a column called "Survived" which indicates whether a passenger survived the disaster or not.

There are a total of 891 rows in the dataset, with 12 columns. Some of the key columns in the dataset include:

• PassengerId: a unique identifier for each passenger • Survived: a binary variable that indicates whether the passenger survived (1) or did not survive (0) the disaster • Pclass: the ticket class of the passenger (1 = first class, 2 = second class, 3 = third class) • Name: the name of the passenger • Sex: the gender of the passenger (male or female) • Age: the age of the passenger (some values are missing) • SibSp: the number of siblings or spouses the passenger had on board • Parch: the number of parents or children the passenger had on board • Ticket: the ticket number of the passenger • Fare: the fare paid by the passenger • Cabin: the cabin number of the passenger (some values are missing) • Embarked: the port at which the passenger embarked (C = Cherbourg, Q = Queenstown, S = Southampton)

Overall, the key challenges I encountered when working on the Titanic dataset were: how to handle missing values and imbalanced classes, encode categorical variables, reduce the dimensionality of the dataset, and identify and handle noise in the data.

Here are a few tips and resources that I found helpful when getting started in the Titanic dataset competition: 1. Get familiar with the dataset 2. Pre-process the data 3. Split the data into training and test sets 4. Try out a few different algorithms 5. Tune the hyper parameters 6. Evaluate the model

Here are a few resources that I found helpful as I started Working on the competition: • Kaggle's Titanic tutorial • scikit-learn documentation. • Pandas documentation

Analysis of ‘Titanic Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/titanic-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

https://raw.githubusercontent.com/Masterx-AI/Project_Titanic_Survival_Prediction_/main/titanic.jpg" alt="">

Description:

The sinking of the Titanic is one of the most infamous shipwrecks in history.

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew.

While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Acknowledgements:

This dataset has been referred from Kaggle: https://www.kaggle.com/c/titanic/data.

Objective:

• Understand the Dataset & cleanup (if required).
• Build a strong classification model to predict whether the passenger survives or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

--- Original source retains full ownership of the source dataset ---

https://raw.githubusercontent.com/Masterx-AI/Project_Titanic_Survival_Prediction_/main/titanic.jpg" alt="">

Description:

The sinking of the Titanic is one of the most infamous shipwrecks in history.

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew.

While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Acknowledgements:

This dataset has been referred from Kaggle: https://www.kaggle.com/c/titanic/data.

Objective:

• Understand the Dataset & cleanup (if required).
• Build a strong classification model to predict whether the passenger survives or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

The Titanic dataset is a popular dataset used for data analysis and machine learning tasks. It contains various information about passengers aboard the Titanic, including whether they survived or not. Here's a brief description of each of the columns:

PassengerId: A unique identifier for each passenger. Survived: Indicates whether the passenger survived or not. (0 = No, 1 = Yes) Pclass: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd) Name: Name of the passenger. Sex: Gender of the passenger. Age: Age of the passenger in years. (Fractional if less than 1) SibSp: Number of siblings or spouses aboard the Titanic. Parch: Number of parents or children aboard the Titanic. Ticket: Ticket number. Fare: Fare paid for the ticket. Cabin: Cabin number. Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton) This dataset is often used for tasks such as predicting survival based on various factors or analyzing demographics of passengers aboard the Titanic.

Dataset

This dataset was created by Simon

Released under Database: Open Database, Contents: Database Contents

Contents

The global Aluminium Titanic Boron Alloy market is experiencing robust growth, driven by increasing demand from the aluminum and casting industries. While the exact market size for 2025 is not provided, we can reasonably estimate it based on available information and industry trends. Assuming a moderately conservative market size of $500 million in 2025 and a Compound Annual Growth Rate (CAGR) of, let's say, 7% (a realistic figure given the growth potential of these industries and the alloy's properties), the market is projected to reach significant value by 2033. This growth is fueled by several factors, including the alloy's superior strength-to-weight ratio, enhanced corrosion resistance, and improved weldability compared to traditional aluminum alloys, making it particularly attractive for automotive, aerospace, and marine applications. The segmentation by type (varying boron and titanium content) reflects the diverse needs of different industries, with the 5% Boron variants potentially capturing a larger market share due to their enhanced properties. The key application segments, Aluminum and Casting industries, are expected to continue driving demand, with potential expansion into other sectors as the alloy's benefits become more widely recognized. The geographical distribution of the market reflects established industrial hubs and emerging economies. North America and Europe currently hold significant market shares, benefiting from established manufacturing bases and strong demand. However, the Asia-Pacific region, particularly China and India, is expected to exhibit faster growth due to rapid industrialization and increasing investment in infrastructure projects. Competitive dynamics in the market are shaped by a mix of established players and regional manufacturers, suggesting potential for both consolidation and new market entrants. Challenges such as fluctuating raw material prices and the need for specialized manufacturing processes could potentially constrain growth to some degree, but the overall market outlook remains optimistic, with significant growth projected throughout the forecast period (2025-2033). This comprehensive report provides an in-depth analysis of the burgeoning Aluminium Titanic Boron Alloy market, projected to reach $250 million by 2028. We delve into the concentration, characteristics, key trends, dominant segments, and future growth catalysts for this specialized alloy, offering invaluable insights for industry stakeholders, investors, and researchers. Keywords: Aluminium Titanic Boron Alloy, Boron Alloy, Titanium Alloy, Aluminum Alloys, Casting Alloys, Metal Alloys, Material Science, Market Analysis, Market Research, Industry Trends.

Dataset

This dataset was created by Jenkins Ruban

Contents

The sinking of the Titanic is one of the most infamous shipwrecks in history.

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.

While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Description The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Objective:

1. Survival Prediction: To build a logistic regression model that accurately predicts the survival of passengers based on features such as age, gender, passenger class, and number of siblings/spouses aboard.

2. Data Cleaning and Preprocessing:To perform data cleaning by handling missing values, removing unnecessary columns, and encoding categorical variables to prepare the dataset for analysis.

3. Exploratory Data Analysis (EDA): To conduct a thorough exploratory data analysis to visualize survival rates and identify patterns based on various factors like gender, passenger class, and embarked location.

4. Feature Importance Analysis: To analyze the correlation between different features and their impact on survival rates, identifying which factors are the most significant predictors of survival.

5. Model Evaluation: To evaluate the performance of the logistic regression model using accuracy scores and classification reports, ensuring that the model generalizes well to unseen data.

6. ROC Curve Analysis: To create a ROC curve to assess the trade-off between the true positive rate and false positive rate, providing insights into the model's ability to distinguish between survivors and non-survivors.

7. Insights and Recommendations: To derive insights from the analysis that could inform future safety measures or policies related to passenger safety in maritime travel.

The Aluminium Titanic Boron Alloy market is witnessing significant growth, driven by its unique properties that make it an ideal material for various industrial applications. This lightweight alloy, known for its excellent strength-to-weight ratio, corrosion resistance, and thermal stability, has established itself

Dataset

This dataset was created by leedoohwan

Contents

This dataset contains cleaned Titanic passenger data for EDA and machine learning tasks. Includes features like age, sex, class, fare, and family details. Ideal for survival prediction and beginner ML projects.

🚀 Great for:

Feature engineering

Data visualization

Classification modeling

🔄 Both train and test sets included.

💬 If you find this dataset helpful, please upvote and share your notebook!

Titanic Passenger Data

This dataset contains information about the passengers aboard the RMS Titanic, which tragically sank during its maiden voyage in April 1912. It provides detailed data points for survival analysis and predictive modeling, including demographic details, ticket class, fare, and survival outcomes.

Dataset Highlights:

• Passenger Details: Information such as Name, Age, Gender, and Embarked Port.
• Socioeconomic Status: Passenger Class (1st, 2nd, or 3rd) and Fare Price.
• Survival Information: Whether the passenger survived or perished.
• Family Relationships: Number of Siblings/Spouses and Parents/Children aboard.

This dataset is ideal for exploring patterns of survival, understanding social dynamics aboard the Titanic, and testing machine learning models for classification problems.

Suggested Use Cases:

• Survival rate analysis by age, gender, or class.
• Building classification models for survival prediction.
• Statistical tests and hypothesis exploration on historical data.

Dive in to analyze one of the most famous shipwrecks in history!

Dataset

This dataset was created by Python

Contents

It contains the following files:

Dataset

This dataset was created by matin nb

Contents

Analysis of ‘Big Data Certification KR’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/agileteam/bigdatacertificationkr on 12 November 2021.

--- Dataset description provided by original source is as follows ---

빅데이터 분석기사 실기 준비 놀이터

함께 놀아볼까요? 무궁화 꽃이 피었습니다 😜 빅데이터 분석기사 실기 준비를 위한 데이터 셋입니다. (예상 문제에 따라 업데이트 될 수 있음) 더 좋은 코드를 만든다면 많은 공유 부탁드려요🎉 (Python과 R모두 환영합니다.)

👋 Tasks👋

• 모의 문제에 대한 풀이를 제출(submit)해 공유하고 있어요!

작업형1 문제별 키워드

• T1. Exam Question (2nd round) / 기출 (2회차)
• T1. Exercise / 예시문제 (dataq 공식 예제)
• T1-1.Outlier(IQR) / #이상치 #IQR
• T1-2.Outlier(age) / #이상치 #소수점나이
• T1-3. Missing data / #결측치 #삭제 #중앙 #평균
• T1-4. Skewness and Kurtosis (Log Scale) / #왜도 #첨도 #로그스케일
• T1-5. Standard deviation / #표준편차
• T1-6. Groupby Sum / #결측치 #조건
• T1-7. Replace / #값변경 #조건 #최대값
• T1-8. Cumulative Sum / #누적합 #결측치 #보간
• T1-9. Standardization / #표준화 #중앙값
• T1-10. Yeo-Johnson and Box–Cox / #여존슨 #박스-콕스 #결측치 #최빈값
• T1-11. min-max scaling / #스케일링 #상하위값
• T1-12. top10-bottom10 / #그룹핑 #정렬 #상하위값
• T1-13. Correlation / #상관관계
• T1-14. Multi Index & Groupby / #멀티인덱스 #정렬 #인덱스리셋 #상위값
• T1-15. Slicing & Condition / #슬라이싱 #결측치 #중앙값 #조건
• T1-16. Variance / #분산 #결측치전후값차이

작업형2 데이터

• T2. Exam Question (2nd round) / 기출 (2회차) : E-Commerce Shipping Data
• T2. Exercise / 예시문제 : 백화점고객의 1년간 데이터 (dataq 공식 예제)
• T2-1. Titanic (classification) / 타이타닉
• T2-2. Pima Indians Diabetes (classification) / 당뇨병

6 주 완성 코스 (아래 표 참고)

👋 Code👋

• 활용방법 : 노트북(코드) 클릭 후 우측 상단에 'copy & edit' 하면 사용한 데이터 셋과 함께 노트북이 열려요!!
• 예시 문제 및 기출 유형 Tutorial
• 모의문제 출제 및 풀이 ("kim tae heon" 검색)
• 작업형1 : 'T1' 을 검색해주세요!
• 작업형2 : 'T2'를 검색해주세요!

👋 Discussion👋

• 필답형 예상문제 제출
• 작업형 예상문제 제출

응시환경 체험

https://goor.me/EvH8T

빅분기 실기 준비 가이드 (파이썬)

🔥 파이썬 🔥

• 파이썬 입문 강의 : https://youtu.be/dpwTOQri42s
• 파이썬 전자책 : https://wikidocs.net/book/6708

🔥 판다스 🔥

• 판다스 전자책 : https://wikidocs.net/book/4639

🔥 모의 문제 풀이 🔥

• 모의문제 풀이 강의 : https://youtube.com/playlist?list=PLSlDi2AkDv82Qv7B3WiWypQSFmOCb-G_-

⚡️필답형 준비⚡️

1. 예상문제 : https://www.kaggle.com/agileteam/bigdatacertificationkr/discussion/277013
2. 개념학습 : https://www.hira.or.kr/ebooksc/ebook_659/ebook_659_202109300534201190.pdf

함께 공부하며 성장했으면 해요!!!:)

안내 링크가 아닌 복사로 블로그 포스팅 또는 출판물 등에 사용하시면 안 됩니다. 본 자료에 대한 허가되지 않은 배포를 금지합니다.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by LuisaAPF

Released under Database: Open Database, Contents: © Original Authors

Contents

Dataset

This dataset was created by S Sandeep Sagar

Contents

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?