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How to Prepare Your Data for Machine Learning

Good data preparation is the single biggest factor in machine learning success. Clean, well-structured data with relevant features produces accurate models. Messy data with missing values, irrelevant columns, and inconsistent formatting produces models that make bad predictions. Most ML projects spend more time preparing data than training models.

What ML-Ready Data Looks Like

Machine learning algorithms expect structured data in rows and columns. Each row is one example (one customer, one transaction, one product). Each column is one feature (age, amount, category, date). The data should be in a format like CSV, a spreadsheet, or a database table export.

For supervised learning (classification and regression), you also need a target column that contains the outcome you want to predict. This is the "answer key" the model learns from. For example, a "churned" column with yes/no values, or a "revenue" column with dollar amounts.

For unsupervised learning (clustering and anomaly detection), you do not need a target column. The algorithm works with the features alone to find patterns and outliers.

Step-by-Step Data Preparation

Step 1: Gather your data into one table.
Pull together all the information you want the model to use. If your customer data lives in multiple systems (CRM, billing, support, website analytics), export from each and combine into a single spreadsheet where each row is one customer and each column is one metric. The more relevant features you include, the more patterns the model can find.

Step 2: Remove irrelevant columns.
Drop columns that cannot possibly help predict the outcome. Internal IDs, randomly generated codes, row numbers, and auto-increment fields add noise without adding signal. Column names like "record_id," "uuid," or "row_number" are almost always safe to remove. Keep anything that describes the entity (customer, transaction, product) in a meaningful way.

Step 3: Handle missing values.
Most datasets have some blank cells. You have three options for each column with missing values:

Remove rows where the value is missing. Best when only a small percentage of rows are affected.
Fill in a default like the column average (for numbers) or the most common value (for categories). Best when the column is important and you do not want to lose the rows.
Remove the column entirely if more than 30-40% of its values are missing. A column that is mostly blank does not give the model enough information to learn from.

Step 4: Fix inconsistent values.
Look for columns where the same thing is spelled different ways: "USA," "US," "United States," "us" should all be one value. Category columns with dozens of rare values should be simplified. If a "state" column has 50 values but some states have only 1-2 records, consider grouping rare states into "Other." Inconsistent formatting confuses algorithms and splits what should be one pattern into many.

Step 5: Handle extreme outliers.
A few extreme values can distort your model. One customer who spent $500,000 when the average is $200 will pull the model's attention toward an unrepresentative case. Consider capping extreme values (replace anything over the 99th percentile with the 99th percentile value) or removing the most extreme rows if they represent data errors rather than real behavior.

Step 6: Convert dates to useful features.
Raw dates (2026-03-18) are not useful to most algorithms because they are unique values, not patterns. Convert dates into features the model can learn from: day of week (Tuesday), month (March), days since signup (47), days since last purchase (12), or time-based flags (is_weekend, is_holiday). These derived features expose the patterns hidden in timestamps.

Step 7: Check your target column.
For classification, make sure your target column has enough examples of each category. If you have 9,500 "stayed" customers and only 500 "churned" customers, the model might just predict "stayed" for everyone and still be 95% accurate. Aim for at least 50-100 examples of each category. For regression, check that your target values have reasonable variation and no impossible numbers.

Common Data Mistakes

Data Leakage

This is the most dangerous mistake. Data leakage happens when your training data accidentally includes information that reveals the answer. For example, if you are predicting customer churn and include a "cancellation_date" column, the model will learn that having a cancellation date means the customer churned. That is not a prediction, it is reading the answer. Remove any column that would not be available at the time you need to make the prediction.

Too Few Examples

Machine learning needs enough examples to find reliable patterns. With 20 rows, the model cannot tell the difference between a real pattern and random coincidence. Most algorithms need at least a few hundred rows to produce useful results, and more is almost always better. See How Much Data Do You Need for Machine Learning.

Too Many Features Relative to Rows

If you have 50 columns but only 100 rows, the model has too many dimensions to work with and not enough examples to learn from. This leads to overfitting, where the model memorizes the training data instead of learning generalizable patterns. As a rule of thumb, aim for at least 10 rows per feature column.

Upload format: On this platform, you upload training data as CSV files or from S3 buckets through the Data Aggregator app. CSV files should use comma separators, have column headers in the first row, and use UTF-8 encoding. See How to Upload Training Data From CSV or S3.

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