Preparing data for a machine learning algorithm is an important step in the process of building a successful model. In particular, dealing with missing data can be a challenge. Here are some steps to follow when preparing your data for a machine learning algorithm:
- Identify any missing values in your dataset. This can be done by using the
isnull()function in Pandas, for example.
- Decide how to handle the missing values. You have a few options here:
- If the missing values are a small percentage of the overall dataset, you can simply remove them. This can be done using the
dropna()function in Pandas.
- If the missing values are a significant portion of the dataset, removing them may not be the best option. Instead, you can try to impute the missing values using the mean, median, or mode of the rest of the data. This can be done using the
fillna()function in Pandas.
- Another option is to use a more advanced method of imputation, such as using a machine learning algorithm to predict the missing values based on the rest of the data.
- Once you have handled the missing values, it’s a good idea to double-check your dataset to make sure that there are no more missing values.
- Finally, you may want to scale your data so that all of the features are on a similar scale. This can be done using the
StandardScalerclass in scikit-learn.
Conclusion, if their is missing values in categorical column, how to deal with ?
Overall, preparing data for a machine learning algorithm involves identifying and handling missing values, and possibly scaling the data to ensure that all features are on a similar scale. This process can be challenging, but it is an important step in building a successful model.