Filling Gaps in Time Series Data
Time Series data does not always come perfectly clean. Some days may have gaps and missing values. Machine learning models may require no data gaps, and you will need to fill missing values as part of the data analysis and cleaning process. This article walks through how to identify and fill those gaps using the pandas resample method.
ReadAutomated Exploratory Data Analysis
Exploratory data analysis is a critical initial step to building a machine learning model. Better understanding your data can make discovering outliers, feature engineering, and ultimately modeling more effective. Some pieces of exploratory data analysis such as reviewing feature histograms and missing values can be automated. This article walks through an open source library I created that runs some basic automated EDA processes.
ReadFavorite Places to Find Datasets
Interesting datasets can make personal machine learning projects more fun and exciting. Here are some of my favorite places to go looking for datasets to hone my data science and ML skills.
ReadDetecting Outliers Using Python
Detecting outliers can be important when exploring your data before building any type of machine learning model. Some causes of outliers include data collection issues, measurement errors, and data input errors. Detecting outliers is one step in analyzing data points for potential errors that may need to be removed prior to model training. This helps prevent a machine learning model from learning incorrect relationships and potentially lowering accuracy.
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