Learn From Data
15/06/2021
Accuracy, Recall, Precision, F-Score & Specificity, which to optimize on? Based on your project, which performance metric to improve on?
07/06/2021
https://medium.com/pythoneers/30-basic-machine-learning-questions-answered-692acd10841f
30 Basic Machine Learning Questions Answered To Check and Increase Your Knowledge of ML 🤔
11/05/2021
Log Transform:-
Logarithm transformation (or log transform) is one of the most commonly used mathematical transformations in feature engineering. What are the benefits of log transform:
->It helps to handle skewed data and after transformation, the distribution becomes more approximate to normal.
->In most of the cases the magnitude order of the data changes within the range of the data. For instance, the difference between ages 15 and 20 is not equal to the ages 65 and 70. In terms of years, yes, they are identical, but for all other aspects, 5 years of difference in young ages mean a higher magnitude difference. This type of data comes from a multiplicative process and log transform normalizes the magnitude differences like that.
->It also decreases the effect of the outliers, due to the normalization of magnitude differences and the model become more robust.
*A critical note: The data you apply log transform must have only positive values, otherwise you receive an error. Also, you can add 1 to your data before transform it. Thus, you ensure the output of the transformation to be positive.
05/05/2021
https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/? #
A comprehensive beginner's guide to create a Time Series Forecast (with Codes in Python and R) Overview Learn the steps to create a Time Series forecast Additional focus on Dickey-Fuller test & ARIMA (Autoregressive, moving average) models Learn the concepts theoretically as well as with their implementation in python  Introduction Time Series (referred as TS from now) is considered to be o...
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