In any data science project, data cleaning and data transformation are crucial steps to ensure that your dataset is ready for analysis. These steps involve fixing errors and reshaping data into a usable format. Let’s look at some commonly used techniques for both data cleaning and transformation, explained in simple terms.

Data cleaning and transformation



1. Handling Missing Values

Sometimes, data will have gaps or missing values. There are different ways to deal with these gaps to avoid skewed (adverse) results.

  • Mean Imputation: Replace missing values with the mean (average) of the data in that column.
  • Forward Filling: Use the previous value to fill in the missing data.
  • Removing Rows: If there are too many missing values, you might remove those rows completely.

Example: If you're analyzing a dataset of temperatures but some days are missing values, you might replace those with the average temperature from the dataset.


2. Outlier Detection

Outliers are data points that are significantly different from others and can throw off your analysis. You can either remove these outliers or adjust them depending on their relevance.

  • Example: In a dataset of people's heights, if most people are around 5-6 feet tall but one entry shows 10 feet, you might remove this as an error or investigate further.


3. Normalization

Normalization ensures that all your data is on the same scale, especially if you're comparing variables with different units or ranges. It rescales data to a consistent range, usually between 0 and 1.

  • Example: In a dataset that includes both age (in years) and income (in thousands), you might normalize the data so that both variables fall between 0 and 1, making them easier to compare.


4. Encoding Categorical Data

Data often includes categorical values like "Yes" and "No" or "Red," "Blue," "Green." Statistical models usually require numbers, so we convert these categories into numeric values

  • Example: In a customer survey, answers like "Strongly Agree," "Agree," "Neutral," and "Disagree" can be transformed into numeric values like 4, 3, 2, and 1.