Boring or Amazing? Life of a Stay at Home Wife (Childless)

Huh? Don't you have a job? What do you do all day at home? Aren’t you getting bored? — Those are the questions that flood your mind when you mention that you don't have a job, no kids, and you're a stay-at-home wife. These drowning criticisms will make you question whether resigning from your career was a good move or not.

Defining the Stay at Home Wife:

Ever seen WandaVision? Think of it as that or the classic 'housewife' for the 80s crowd. A childless woman devoted to caring for her husband and managing the household while the husband takes care of finances.

How It Started:

In 2020, with the pandemic looming, my "essential" job demanded office presence. Concerned for our safety, my husband encouraged me to resign. The scary part? Transitioning from a working mom to a 'simple' housewife felt daunting. Questions about finances and my identity flooded my thoughts.

But fear not; we navigated this new chapter together. Monthly savings remained steady, and strategic financial moves eased the transition. 

Benefits of Stay at Home Wife:

Time - A precious commodity in any relationship, being a stay-at-home wife grants me more quality moments with my husband.

Intimacy - With more time on our hands, there's a playful wink-wink to exploring deeper connections.

Minimalism - Decluttering became an art form, and yes, even Abbey's piano had to find a new home!

Organization - The luxury of time allowed me to transform chaos into order, impressing even myself with improved documentations and excel sheets.

Boring or Amazing? Life of a Stay at Home Wife (Childless)

Isn't Boring?
Far from it! Embracing this phase meant seizing opportunities for personal growth. Despite initial skepticism, there's more to life than meets the eye.

Upskilling - From a novice in Excel to diving into data analytics, continuous learning became a fulfilling part of my routine.

Creating NEW Goals - As we achieved our 40x40 rental apartment dream ahead of schedule, we're laser-focused on the 10x50 House of Willmah, building a team for an uncharted market.

Health and Skincare - Fasting routines and skincare rituals became daily practices, prioritizing well-being.

Volunteering - Paying it forward and gaining diverse perspectives through volunteering for various causes.

My Day-to-Day Routine
Weekdays follow a rhythm:
Morning: Tea, email check, and social media updates.
Afternoon: Exercise, content creation, studying, connecting with family, cooking, and self-care.
Night: Dinner, movie marathon, and reading.
Weekends bring grocery runs or budget-friendly dates.

A Proverbs 31 Aspiration:
If your goal aligns with mine—a biblical representation of a good wife while stabilizing finances—start your savings journey with this FREE template (https://docs.google.com/spreadsheets/d/1UDfCET9z1Xgv9d-Hc2fMmjeVkV0UIDEsa0hFt7ePt4o/edit?usp=sharing). For detailed finance tracking, explore our Personal Finance Dashboard.

The path may be unconventional, but its rewards are extraordinary.

We love to travel, so here are some links I used for us to pretty save some on our international travels, local staycation, and activities. This is where I am affiliated at:

Expedia -- https://expedia.com/affiliate/wUj4hWB

KKDAY -- https://www.kkday.com/en-sg/i/UWHMYD?cid=10106&p=link

kris+ -- https://sqkrisplus.page.link/yS3NF9jkCJiW8CXq7

Zalora -- https://goto.zalora.com/c/5055389/1115734/14118

May ALMIGHTY bless you on your journey to be STAY-at-HOME-Wife! 

Project SPARTA PH's SP901 FINAL CAPSTONE: Data Science and Machine Learning Using Python | Week 5: Model Evaluation Improvement

This is it! The SP901 - Data Science and Machine Learning Using Python FINALS - the capstone project! A hearty congratulations to you for reaching this milestone. You've achieved a remarkable feat, and it's a testament to your dedication and hard work.

I am particularly delighted that SPARTA, in its inaugural launch, provides the flexibility of a self-paced learning journey. This privilege allowed me to immerse myself in this Python-focused course and dedicate the necessary time to master the content. It's worth noting that this comprehensive journey took me a span of two months to complete. The sense of accomplishment is truly overwhelming, and I can't help but look back on this with a smile and a few happy tears. (Umiiyak sa banyo para di makita ng asawa ko!) Haha! 

Without further ado, I am thrilled to present my capstone project for SP901 - Data Science and Machine Learning Using Python. 

I hope this project will serve as an inspiration for your own learning!

Happy Learning SPARTANs! 

Project SPARTA PH's SP901: Data Science and Machine Learning Using Python | Week 4: Representing Data and Engineering Features

I am only retrieving files from Coursebank's SPARTA's archived course. It is bit interesting that 4.6 Exit Assessment is locked. After years, I can't remember the assessment question too. I am sorry for that.

Anyways, here are the snapshots of my Jupyter notebook.

Sa mga nahihirapan po sa SP901: Data Science and Machine Learning Using Python check nyo po itong libro na ito, andyan po ung mga sinasabi ni teacher.. medyo verbatim 

https://www.oreilly.com/.../introduction.../9781449369880/ 

Introduction to Machine Learning with Python

by Andreas C. Müller, Sarah Guido

Happy Learning SPARTANs! 

Project SPARTA PH's SP901: Data Science and Machine Learning Using Python | 3.3.1 Unsupervised Learning

Imagine you're teaching a computer to do cool things.. like categorising ;) So what is the difference of Supervised and Unsupervised Machine Learning? 

Supervised Machine Learning:

Think of this like a teacher guiding a student. In supervised learning, we give the computer a bunch of data and also tell it what the correct answers should be. It's like having a teacher's answer key for a test. The computer then learns from this data and uses it to make predictions or decisions.

For example, if we want the computer to recognize cats and dogs in pictures, we'd show it lots of images of cats and dogs and tell it which is which. The computer learns to spot the differences, like pointy ears for dogs and whiskers for cats. This way, it can make predictions when it sees new pictures, like telling us if it's a cat or a dog.

Unsupervised Machine Learning:

Now, unsupervised learning is a bit different. Here, it's like giving the computer a bunch of stuff and letting it figure things out on its own. No teacher's answer key this time. The computer's job is to find patterns, group similar things together, or discover hidden structures in the data.

For example, imagine we have a bunch of customer data from an online store. In unsupervised learning, the computer might notice that some customers tend to buy similar items, and it groups those customers together. This can help the store recommend products to customers based on what others with similar tastes bought.

So for the Week 3, are you ready for Unsupervised Learning? Wink-wink! 

Again, I posted my Jupyter Notebook for a reason. But if you really need my notebook, you can send me a message on our Facebook page :) 

3.3.1 Unsupervised Learning

Guidelines:

* Using the IRIS dataset, create the following algorithms:

     - Kmeans

     - DBSCAN

     - Hierarchical

* After that, compare the results using ARI and Silhouette Score