Namaste
Sure, I would be happy to help you with your small dataset cleaning project.
Firstly, it is important to thoroughly understand the dataset and its variables. This will help in determining the appropriate cleaning and preprocessing techniques.
Next, we can begin with handling missing values. Depending on the amount of missing data, we can either drop the rows or impute the missing values using appropriate techniques such as mean, median or mode.
For text data, we can perform techniques such as removing stopwords, punctuation, and converting all the text to lower case. We can also perform tokenization and stemming to reduce the number of unique words.
For numerical data, we can check for outliers and handle them using techniques like Winsorization or capping. We can also perform feature scaling to standardize the range of values and make the dataset more manageable.
Additionally, we can check for any duplicates in the dataset and remove them to avoid any bias in the analysis.
Once the cleaning process is complete, we can perform exploratory data analysis to identify any patterns or insights in the data. This can help in identifying any further cleaning steps that may be required.
In conclusion, a meticulous approach with a combination of different techniques for handling text and numerical data will ensure a comprehensive and effective cleaning process for your small dataset. I am confident that my experience and expertise in data cleaning will be beneficial for this project. I am looking forward to working with you.
Best regards,
Giáp Văn Hưng