1. Data collection: This is the initial phase where we gather data from various sources, such as databases, APIs, or even web scraping.
2. Data cleaning: This is where we deal with missing values, inconsistencies, and errors in the data. We can either remove or fill in missing values, correct erroneous data points, or even transform the data to a more usable format.
3. Data integration: Sometimes, we need to combine data from multiple sources. This step involves merging, concatenating, or joining datasets to create a comprehensive dataset for the project.
4. Data transformation: This step involves converting the data into a format that can be easily understood by the machine learning algorithms. Techniques like normalization, scaling, and encoding are common in this phase.
5. Feature engineering: In this step, we create new features from the existing data that can help improve the performance of the model. This can include creating interaction terms, aggregating data, or even applying domain knowledge to create meaningful features.
6. Data splitting: Finally, we split the dataset into training and testing sets, which allows us to evaluate the performance of the model on unseen data.