Scribd
Upload
11 views1 page

Reflection

The exploration of Titanic data records revealed successful insights into passenger survival based on factors like class, age, and gender, but also highlighted failures in processing biased data. Future improvements include better data preprocessing, feature engineering, and advanced data cleaning to enhance analysis accuracy. The experience underscored the importance of data visualization and iterative analysis in uncovering complex relationships within the data.

Uploaded by

alex
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
11 views1 page

Reflection

The exploration of Titanic data records revealed successful insights into passenger survival based on factors like class, age, and gender, but also highlighted failures in processing biased data. Future improvements include better data preprocessing, feature engineering, and advanced data cleaning to enhance analysis accuracy. The experience underscored the importance of data visualization and iterative analysis in uncovering complex relationships within the data.

Uploaded by

alex
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
You are on page 1/ 1

reflection

1. success and failures

In the exploration of P2 and P3 (Titanic) data records, successful discovery and correlation of patterns
within the data included insights into passenger survival and other factors such as class, age, and
gender. However, we failed to draw false conclusions from biased data and fail to effectively process
consistent or inconsistent data that could lead to inaccurate modeling.

2. what I needed to change and why?

When exploring P2 and P3 (Titanic) data records in Python, effective analysis requires some frequent
changes. This includes lack of data, transformation of category features, and creating new features. The
main reasons for these changes are to improve the accuracy of the analysis, promote the quality of the
dataframe, and gain insight into the structure of the data.

3. what would you do differently next time?

Will Perform data preprocessing and feature engineering such as Handling


missing values Creating new features as well as Encoding categorical
variables

4. How would you improve it if you had more time?

With more time, the process of P2 and P3 (Titanic) data exploration can be significantly

improved by focusing on more complex functional engineering and investigating interactions

between properties, implementing more advanced data cleaning, and implementing thorough

hypothesis testing and verification. This involves clear complex relationships and patterns within

the data, which may not be recognized immediately, ultimately leading to a more robust and

useful analysis.

5. what did you learn from the experience?

The P2 and P3 (Titanic) Data highlighted several important factors that influence passenger

survival, such as gender, passenger rank and age. It also demonstrated how data visualization is

important for providing insights. Additionally, the experience provided valuable insight into the

use of data visualization techniques and the iterative process of data analysis.

You might also like