Listar Regression Machine Learning Formula Using Scikit-learn & Pandas in Python that Training thirty

In this tutorial on Python for Data Science, You will learn about Multiple linear regression Model using Scikit learn and pandas in Python. You will learn about how to check missing data and Correlation.

This is the 30th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist „the sexiest job of the 21st century.“ Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We’ll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.

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Resourceful Methods to Spend money on some money,500 (Other than in Carries & Buildings)

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Python: Real-time Automated Long Short Term Memory (LSTM) Short-term Load Forecasting & Plotting

Introduction 00:00:00
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Details of short-term load forecasting problem 00:43:02
– Data Preparation 00:44:00
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Spend money on Japan Carries?【ENGLISH & 日本語❗ 】Dan Takahashi 高橋ダン


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Cornell University, Honors Magna Cum Laude

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東京生まれ、日本国籍のハーフ 。6ヵ国に住み、60ヵ国以上は旅行。

Started investing at 12 years old, began Wall Street when 19, created hedge fund when 26, and sold company stake at 30 years old.

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In this episode, Warren Buffett and Charlie Munger were asked on whether it would make sense to invest in a basket or index fund of Japanese stocks when its price-to-book value is low. Moreover, they were also asked on why Berkshire made an investment in the airline industry that seems to go against their usual investment principles.

In this episode, you’ll learn:
– What truly determine the price of a stock.
– Why price-to-book ratio means nothing to Buffett and Munger.
– Why earnings matter.
– What does high or low price-to-book ratio shows.
– Why Warren Buffett made an investment in USAir.
– What unpleasant experience did Buffett and Munger felt in USAir.
– What is Munger’s thoughts on corporate culture.

#WarrenBuffett #CharlieMunger #BerkshireHathaway

~ Please visit the site above for full video of Berkshire Hathaway Annual Meeting.

Good afternoon, Mr. Buffett and Mr. Munger. My name is Jack Sutton (PH) from New York City. I have two questions.

The Japanese stock market has been likened to the U.S. market in 1974. With Japanese stocks selling at very low price-to-book values, as compared to U.S. stocks, would it not make sense to invest in a basket of Japanese stocks or an index fund of Japanese stocks?

Question number two: Berkshire Hathaway tends to invest in companies with high margins and high return on common equity. Berkshire’s investment in the airline business seems to have digressed widely from those principles.

Could you elaborate on why Berkshire invested in the airline industry, and would Berkshire consider new investments in the industry in the future?

I’m going to the first question. The reason that — and I don’t know the exact figures — that Japanese stocks would sell at a lower price-book ratio than U.S. stocks is simply because Japanese companies are earning far less on book than American companies.

And earnings are what determine value, not book value. Book value is not a factor we consider. Future earnings are a factor we consider. And as we mentioned earlier this morning, earnings have been poor for a great many Japanese companies.

Now, if you think that the return on equity of Japanese business is going to increase dramatically, then you’re going to make a lot of — I mean, and you’re correct, you’re going to make a lot of money in Japanese stocks.

But the return on equity for Japanese businesses has been quite low, and that makes a low price-to-book ratio very appropriate because earnings are measured against book. And if a company’s earning 5 percent on book value, I don’t want to buy it at book value if I think it’s going to keep earning 5 percent on book value. So a low price-book ratio means nothing to us. It does not intrigue us.

In fact, if anything, we are less likely to look at something that sells at a low relationship to book than something that sells at a high relationship to book, because the chances are we’re looking at a poor business in the first case and a good business in the second case.

What was the other question on, Charlie?

Buying — airlines.

Airlines. Yeah, I always repress everything on airlines. I don’t want to — (Laughter)

No, we’ve never bought an airline common stock that I can remember. So what we did was we lent money to USAir for a 10-year period and we had a conversion privilege there.

It looked like it — it was a terrible mistake. I made the mistake. But we got bailed out. But we — we never made the determination — when we bought our stock, USAir was selling at a share or thereabouts, the common. And we didn’t have an interest in buying USAir at 50, or 40, or 30, or 20. And we got a chance to as things went along — (laughter) — all the way down to 4. (Laughter)

And we never bought it. And we’ve never bought American, or United, or Delta, or any other airline. It is not a business that intrigues us.

We did think it was intriguing to lend money to them with a conversion privilege and it’s worked out now because we got lucky, and because Steve Wolf came along and really rescued the company from right at the brink of bankruptcy.

But we’re unlikely to be in airlines, although again, we wouldn’t mind lending money to a lot of businesses that we wouldn’t buy common equity in. I mean, that could happen again in various industries, including the airline industry.

Charlie, do you have anything to say on either the airlines or the Japanese market?








Developing a The right time Series Prediction Product by having parsnip & XGBoost | two (2) of two

The 2nd part of a tutorial from the #Shiny Web Apps Course – This video is from the Demand #Forecast section where our students build a predictive model to forecast sales demand with #parsnip & #XGBoost. Learn more:

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Benefit Computing Using Python & Ml

Stock Price Prediction Using Python & Machine Learning (LSTM).
In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock.

NOTE: In the video to calculate the RMSE I put the following statement:
rmse=np.sqrt(np.mean((predictions- y_test)**2))

When in fact I meant to put :
rmse=np.sqrt(np.mean(((predictions- y_test)**2)))

You can use the following statements to calculate RMSE:
1. rmse =np.sqrt(np.mean(((predictions- y_test)**2)))
2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)-np.array(predictions)),2)))
3. rmse = np.sqrt(((predictions – y_test) ** 2).mean())

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