Nigeria is located in West Africa (Nigeria).
To acquire well-rounded learning on my research topic, I decided to learn the demography of the type of countries that is different from the two past posts I have written for my independent study.
In this blog, I am going to share with you my initial exploration of the data that I collected last month. I am also experimenting with visualizing the data with R, which is a statistical programming language, but we will talk about that another time.
This time, I first looked at the data collected from two sites, US and Chinese, separately. I graphed the data as a line graph. The graphs contained data from all categories of dates – next-day, next-week, next-month, 6/4 (a date far in the future), and 11/1 (a date in the very near future) – as 5 series of lines of different colors. Continue reading
This past week marks the end of the data collection period of my project. After I figured out how to scrape data generally on websites with simple structures in the last blog post, I had been experimenting with pulling data down from the Expedia website which was way more complex. However, as I tried to do this, I encountered some difficulties. At first, I decided to start experimenting with data that should be easily pulled to see if the code would indeed even work for this site. Therefore, I picked the date of the flight shown on the website. It had the tag class=”title-date-rtv“. I put this value into the code. Continue reading
If you remembered from my last blog, my focus in this project has recently been on figuring out a way to scrape data off of the travel websites using code instead of doing it manually since it is indeed a tedious job. Of course, while working on the code, I have also kept with the primitive collecting method since data collection is the objective of this month’s work in my project. So here’s an updated version of my data table: Continue reading
After the introduction to my website last time, in this blog, I am going to redirect the focus back to the topic of plane tickets prices and the analysis of it through data science approach. Continue reading
I changed the name of my nonprofit from Project G.I.R.L to The Girl Narrative this previous summer 2018, and I don’t think I could have made a better decision for my business.
For the first 4 months of being Project G.I.R.L, things were going as any new business was going; we were beginning to build our foundation, our audience, and our marketing plan for the future. Later on, there were several issues that presented themselves that caused me to seriously reconsider our name.
1. Project G.I.R.L is great…but it’s generic
a. When I really got to thinking about it, When you hear the name Project G.I.R.L, you have no idea what we do, who we are, or what our goals are. It is beautiful and fantastic but is overall too generic. When you are building the foundation of your business there are two incredibly important things you need to have: consistency and clarity.
2. There were other organizations that had similar names
a. I remember when I was interviewing Gauri Kapoor, CEO and founder of the after-school program The Girl and I (read our article about her daughter in the program here!), she got confused because she tried to look us up, but found another nonprofit organization. This was also not a one-time-incident. We were constantly confused with other organizations that sounded similar or had similar elements to Project G.I.R.L
3. Our message wasn’t clear
a. This is the biggest reason that we changed our name. Project G.I.R.L tells you nothing about what we want to achieve or why we are important. You would be able to guess that we had something to do with young female empowerment, but other than that, you would have no idea what we do.
The Girl Narrative is unique, our own, and clearly states what we do. We TELL the Girl Narrative. We tell the stories of strong young women to inspire girls to be limitless. Now, with our new name, our partners like March Against Revenge Porn, Nonprofit Jenni, Live Girl (and more!), our audience, and our future customers (HINT! HINT!) will be able to find us, know us, and connect with us better. It doesn’t make it effortless, running a business is never effortless, but it does make things a little easier.
Citations:
Kapoor, Gauri. “Home.” The Girl And I, http://www.thegirlandi.com/.
Schoenborn, Sabrina “Mahika Chopra.” The Girl Narrative,
https://thegirlnarrative.com/mahika-chopra/
Juliett, Leah. “#MARCH AGAINST REVENGE PORN.” March Against Revenge Porn,
marchagainstrevengeporn.org/.
Hargrove, Jenni. “Home.” Nonprofit Jenni, http://www.nonprofitjenni.com/.
West, Sheri. “Home.” LiveGirl, golivegirl.org/.
Schoenborn, Sabrina “Home.” The Girl Narrative, http://www.thegirlnarrative.com/.
Running a business has truly been a test of patience. Being told no all day every day is exhausting, there is no way around that. It feels as if there is only a descent. As someone who was expecting some ups and downs, it was a harsh reality to realize that, for most of my process, it has been a straight descent. Continue reading
For last week’s blog post, I wrote a short tutorial for training a custom object detection model using TensorFlow Object Detection API. Due to the limited space and time constraints, my tutorial was not quite finished. Therefore, in this week’s blog, I will continue my tutorial and include additional steps such as the usage of a tool to test your model’s accuracy.
Have you ever heard of Tesla’s Model S sedan? It is one of the few cars capable of fully autonomous driving. Although U.S. laws currently do not permit this, the Model S can pick you up at your house and drop you off at school, all without you even touching the steering wheel. To create a self-driving vehicle, Tesla engineers had to employ many machine learning techniques, including an object detector that recognizes and classifies objects around the car. For example, the on-board camera is able to recognize pedestrians and instructs the car to stop. Another example is that the object detector recognizes other vehicles on the road, keeping the Tesla from colliding into them.
With the use of the TensorFlow Object Detection API, creating such a model (though probably not as accurate as the one Tesla developed) can now be done with consumer-grade equipment such as a personal computer. As promised in last week’s blog, I will discuss how to create a customized object detector with the TensorFlow API.
In last week’s blog, I wrote about the installation process of TensorFlow on a PC. In this blog, I will continue my tutorial on TensorFlow installation.
Step 3: Installing TensorFlow
At the time of writing, there are two supported ways of installing TensorFlow:
While native pip installs TensorFlow directly onto your computer, Anaconda allows you to create a virtual environment and install TensorFlow into that environment. The benefit of this is to help you avoid unwanted interference with other packages. However, if you do chose to use Anaconda, you will not be able to access the TensorFlow package globally (from any directory on your computer). The following chart compares the two different installation methods:
Independent Seminar Blog 