• sahithya pasumarthi
• A.Adithya Srija
• K.Kedareswari
• N.Aswini
• Dr. Veera Ankalu.Vuyyuru

In the era of big data, data cleaning is essential for ensuring data integrity and reliability, significantly impacting downstream tasks such as machine learning and analytics. This review highlights the steps involved in data cleaning, including outlier detection, missing value imputation, deduplication, and normalization. Analyzing various techniques and methodologies, the paper demonstrates the effectiveness of data cleaning in enhancing predictive performance and decision-making. The results show that cleaned datasets lead to more accurate models and reliable analyses. Ultimately, the paper emphasizes the critical importance of data cleaning in the data science pipeline.

Data cleaning; Machine learning; Analytics; Data preprocessing; Downstream tasks; Data quality assurance.

"A Review of Data Cleaning and its Impact on Downstream Tasks in Machine Learning and Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.b165-b171, July-2024, Available :http://www.jetir.org/papers/JETIR2407120.pdf

"A Review of Data Cleaning and its Impact on Downstream Tasks in Machine Learning and Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppb165-b171, July-2024, Available at : http://www.jetir.org/papers/JETIR2407120.pdf