Data Understanding: This step involves getting to know your data, understanding its structure, identifying the variables, their types, and their meanings.
Handling Missing Values: This step involves identifying missing values in the data and deciding how to handle them. Common methods include removing the rows or columns with missing values, imputing values using methods like mean, median, or mode, or using more advanced imputation methods like regression or k-nearest neighbors.
Handling Outliers: This step involves identifying and handling outliers in the data. Outliers can be removed, replaced with other values, or left in the data depending on the nature of the analysis.
Handling Duplicate Values: This step involves identifying and removing duplicate values in the data. Duplicate values can cause errors and distort the analysis results.
Handling Inconsistent Values: This step involves identifying and handling inconsistent values in the data. Inconsistent values can be corrected, removed, or replaced with other values to ensure that the data is consistent and accurate.
Handling Typos and Inaccurate Values: This step involves identifying and handling typos and inaccurate values in the data. These errors can be corrected, removed, or replaced with other values to ensure that the data is accurate and reliable.
Handling Irrelevant Variables: This step involves identifying and removing variables that are not relevant to the analysis or that may introduce bias into the analysis.
Data Transformation: This step involves transforming the data into a format that is suitable for the analysis. This may involve converting variables to different data types, creating new variables, or combining variables.
Data Integration: This step involves integrating data from multiple sources into a single dataset. This may involve merging datasets, appending datasets, or joining datasets.

Unable to do the cleaning process, since I do not know where to start. So, I would like to know the step by step process involved in data cleaning.