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### Pragmatic Data Science | 7, linear combination, implementation

In a linear combination it is easy to create arbitrarily long sequences of pairs of weights (thetas) and input values (x). But first, just a quick recap of how a simple linear combination might look like. score = theta_1 * x_1 + theta_2 * x_2 + theta_3 * x_3 Since each x value is multiplied …

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### Pragmatic Data Science | 6, linear combination theory

In the previous examples we have been doing a naive prediction from the linear dependency between temperature and staff. However, the temperature, by itself, is a too simplistic model. In our csv file with weather data we have more information available, such as humidity, wind speed etc. In order to do this we are going to …

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### Pragmatic Data Science | 5, Naive prediction

Once you have performed feature scaling on your data all the fun stuff can begin. When the temperature data is plotted over time in fahrenheit, on the y-axis we can see how the temperature is fluctuating over the year. When this data is scaled this internal structure is kept. Before scaling, this is how our data …

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