From August to October 2022, I undertook a significant data cleaning project in Melbourne, utilizing Python as the primary tool for data processing and preparation.
The initial challenge encountered was the inconsistency in date formats across various data sets. Recognizing the need for uniformity, I employed robust techniques in Python to standardize these formats. This not only improved data coherence but also made downstream tasks such as analysis and modeling more efficient.
In data cleaning, handling outliers is a crucial step. For this task, I used the Interquartile Range (IQR) method, a statistical technique effective in outlier detection. By calculating the IQR for each variable in our data sets, I could identify and handle outliers, thus enhancing the accuracy and reliability of the data.
Another significant issue in any data project is handling missing data. Ignoring them or mishandling could lead to skewed or inaccurate results. Therefore, I implemented linear regression models to impute or estimate the missing values in the data sets. This technique proved effective in ensuring the completeness of our data, allowing for more accurate and meaningful analysis.
The project was successful, resulting in clean, reliable data ready for downstream use in various business analyses and modeling tasks. This effort highlighted the importance of careful data cleaning and preparation in any data-driven decision-making process.
I'm a Data analyst
- Bob Mai
Install my-project with npm
Jupyter notebook
