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#Basic data science questions how to#

Lastly, you can improve the balance of classes by oversampling the minority class or by undersampling the majority class.

By increasing the penalty of such, the model should classify the minority class more accurately.

Another method to improve unbalanced binary classification is by increasing the cost of misclassifying the minority class.

If your model simply classified every instance as “not fraudulent”, it would have an accuracy of 99%! Therefore, you may want to consider using metrics like precision and recall. Let’s say 99 bank withdrawals were not fraudulent and 1 withdrawal was. The accuracy of your model might not be the best metric to look at because and I’ll use an example to explain why. First, you want to reconsider the metrics that you’d use to evaluate your model.There are a number of ways to handle unbalanced binary classification (assuming that you want to identify the minority class):

#Basic data science questions how to#

Q: How to deal with unbalanced binary classification?

Other things include: removing irrelevant data, removing duplicates, and type conversion.

unknown), predicting the values, or using machine learning models that can deal with null values.

Handling null values: There are a number of ways to handle null values including deleting rows with null values altogether, replacing null values with the mean/median/mode, replacing null values with a new category (eg.

Standardization or normalization: Depending on the dataset your working with and the machine learning method you decide to use, it may be useful to standardize or normalize your data so that different scales of different variables don’t negatively impact the performance of your model.

Syntax error: This includes making sure there’s no white space, making sure letter casing is consistent, and checking for typos.

Data visualizations: Sometimes, it’s useful to visualize your data with histograms, boxplots, and scatterplots to better understand the relationships between variables and also to identify potential outliers.shape and a description of your numerical variables with. More specifically, you can look at the shape of the dataset with. Data profiling: Almost everyone starts off by getting an understanding of their dataset.Some of the most common steps are listed below: There are many steps that can be taken when data wrangling and data cleaning. Machine Learning Fundamentals Q: What are some of the steps for data wrangling and data cleaning before applying machine learning algorithms?