Another helpful method we will call is the tail method. It displays the last

6 min readDec 2, 2020

That doesn’t mean everything is smooth sailing. Significant financial decisions like buying a house or making a big investment become even more complicated when one person earns more — and thus contributes more. Big outlays come with big stress, and not being aligned only adds to that stress.First things first, we have to install some libraries so that our program works. Here is a list of the libraries we will install: pandas, numpy, keras, and tensorflow. Tensorflow has to be installed so that keras can work.You know your partner is frustrated with carrying the load financially. And you know there’s pressure for you to contribute. Consider work that puts something on the table. Paying one bill consistently is better than paying no bills. If your partner truly doesn’t want you to, then don’t. But it’s worth a try. The effort has value, too.

If you’re out of work and your job hunt hasn’t been fruitful, talk to your partner about pausing the search, if possible. One or two months of building a better résumé, taking a class toward a certificate, and doing some additional training in your field could make the search less stressful — and likely work out better for your professional future in the long run.
This one isn’t for everyone. But if you can pull it off, more power to you. If you and your partner are in similar fields, think about establishing an LLC or an S Corp and having all earnings combined. When a singular entity like a corporation is bringing in the money, it’s not as critical to itemize where it’s coming from. Again, it’s the rare couple that can pull this one off (*waves at fiancée*). Not only must the jobs be complementary, each person has to be ready (and qualified) to fully support the other in their projects. My partner is way more valuable for our company as a producer of three of our podcasts and three for other folks than he would be at a 10-to-6 job similar to mine.
Disclosing your salary with your partner can be a big step in the relationship. But at some point, it needs to happen (especially if you’re planning to shack up). That conversation can open the door to determining each person’s monetary contributions. The only way you can truly work as a team is if you’re both on the same page — which requires both parties knowing what’s on the page in the first place.
As mentioned earlier, for this exercise we will be using historical data of Apple. I thought Apple would be a good one to go with. After walking through with me on this project, you will learn some skills that will give you the ability to practice yourself using different datasets.
In this post, I will show you how to build a program that can predict the price of a specific stock. This is a great project of using machine learning in finance. If we want a machine to make predictions for us, we should definitely train it well with some data. First, for those who are new to python, I will introduce it to you. Then, we will start working on our prediction model. As mentioned in the subtitle, we will be using Apple Stock Data. If you are wondering is it free to get that data, the answer is absolutely yes. The stock data is available on NASDAQ official website. The NASDAQ (National Association of Securities Dealers Automated Quotations) is an electronic stock exchange with more than 3,300 company listings.

Secondly, we will start loading the data into a dataframe, it is a good practice to take a look at it before we start manipulating it. This helps us to understand that we have the right data and to get some insights about it.

The first thing we’ll do to get some understanding of the data is using the head method. When you call the head method on the dataframe, it displays the first five rows of the dataframe. After running this method, we can also see that our data is sorted by the date index.

You know your partner is frustrated with carrying the load financially. And you know there’s pressure for you to contribute. Consider work that puts something on the table. Paying one bill consistently is better than paying no bills. If your partner truly doesn’t want you to, then don’t. But it’s worth a try. The effort has value, too.

Python is a general-purpose programming language that is becoming ever more popular for analyzing data. Python also lets you work quickly and integrate systems more effectively. Companies from all around the world are utilizing Python to gather bits of knowledge from their data. The official Python page if you want to learn more.

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.

The Apple stock data can be downloaded from here. On this website, you can also find stock data for different companies and practice your skills using different datasets. I can’t wait to see our prediction accuracy results, let’s get started!

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Another helpful method we will call is the tail method. It displays the last

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