Data cleaning and preprocessing is a crucial step in data analytics. It involves the process of identifying and handling errors, inconsistencies, and missing data in the dataset before analysis. This step ensures that the data is accurate, reliable, and ready for further analysis. Let's explore some important techniques used in data cleaning.

• Handling Missing Data: Missing data can often impact the analysis results. Techniques like imputation (mean, median, mode) or deletion (row or column deletion) can be used based on the situation.

• Outlier Detection: Outliers are data points that deviate significantly from the normal distribution. Detecting and handling outliers is important to prevent them from skewing the analysis results. Techniques like z-score, modified z-score or using interquartile range (IQR) can be used for outlier detection.

• Feature Scaling: When analysing datasets with different units or scales, feature scaling helps to normalize the data. Techniques like min-max scaling or standardization (mean and standard deviation) can be used for feature scaling.

Remember, data cleaning and preprocessing ensure that our analysis is based on a clean and accurate dataset, leading to more reliable insights.

Example: Let’s say we are analysing a dataset on customer feedback ratings. We observe missing values in the 'rating' column. We decide to impute the missing values using the mean rating of the dataset. By doing this, we can ensure that our analysis is not biased due to missing data.

Tags: data analytics, data cleaning, preprocessing