Machine learning is an exciting field, but it's important to remember that data preparation is essential to achieving successful models. Data scientists often spend up to 80% of their time on data preparation tasks. To ensure that your data preparation journey runs smoothly before you start building your models, we have created a 20-step checklist that you can follow.
Understanding and defining the project is crucial for aligning the ML model with the end goals.
- Think like an end-user.Example: For a recommendation system, consider the preferences and behavior of the targeted audience.
- Brainstorm problems and solutions. Example: If users are not engaging with a platform, brainstorm whether a content recommendation system could enhance user interaction.
- Solidify the ML techniques and data requirements. Example: Decide whether to use a collaborative filtering approach and identify data like user-item interactions for the model.
- Summarize the scope and objectives. Example: Establish the goal to increase user engagement by 20% through personalized content recommendations.
Data fuels the ML engine, and getting the right kind can set the stage for robust modeling.
- Locate data from multiple sources. Example: Aggregate user interaction data from logs, databases, and third-party APIs.
- Read data into Pandas DataFrames.Example: Use pd.read_csv() or pd.read_sql() to ingest data into a workable format.
- Quickly explore the DataFrames.Example: Employ .head(), .describe(), and .info() to get a high-level overview of your data.
Ensuring data quality is paramount to avoid the classic "garbage in, garbage out" scenario in ML.
- Convert data to the correct data types. Example: Change date strings to date-time objects using pd.to_datetime().
- Identify and handle missing data. Example: Use .isna() to find and .fillna() or .dropna() to handle missing values.
- Identify and handle inconsistent text and typos. Example: Utilize string methods or regular expressions to standardize text data.
- Identify and handle duplicate data. Example: Leverage .duplicated() and .drop_duplicates() to remove redundant entries.
- Identify and handle outliers. Example: Employ IQR or Z-score methods to detect and handle anomalous data points.
- Create new fields from existing fields. Example: Extract the day of the week from a datetime column for temporal analysis.
EDA provides insights into trends and helps to refine data for further modeling.
- View the data from multiple angles. Example: Use .groupby() to observe data subsets and identify patterns or anomalies.
- Visualize the data to identify trends and patterns quickly. Example: Plot histograms or boxplots to understand data distributions and variations.
The final step before modeling is ensuring that data is in the right shape, size, and format.
- Create a single table. Example: Merge user profile and interaction data into one data frame for a comprehensive dataset.
- Set the correct row granularity. Example: Aggregate transaction data at a customer level if predictions are to be made for customers.
- Ensure each column is non-null and numeric. Example: Convert categorical data to numerical format using one-hot encoding.
- Engineer new features. Example: Create a 'user_active_hours' feature from timestamp data to enhance model performance potentially.
- Split the data into training, validation, and test sets. Example: Use train_test_split() from sci-kit-learn to create disjoint datasets for model training, validation, and testing.
Embarking on a machine learning project is an exhilarating journey. This 20-step checklist ensures you navigate the crucial data preparation stage effectively, laying a solid foundation for building impactful models. Begin with understanding and scoping, traverse through data gathering, cleaning, and exploration, and gear up through final preparations for modeling. Your ML model is set for a successful takeoff! 🚀
