From Notebook to Production: Running ML in the Real World (Part 4)
Author(s): Raj kumar Originally published on Towards AI. Part 4 of a 4-part series: From Data to Decisions Most machine learning projects look successful right up to the moment they are deployed. The notebook runs. The metrics look good. Stakeholders sign off. …
Part 19: Data Manipulation in Statistical Profiling
Author(s): Raj kumar Originally published on Towards AI. Statistical profiling sits at the intersection of data validation and analytical insight. In banking operations, descriptive statistics are not academic exercises. They are diagnostic tools that surface anomalies in payment flows, quantify credit portfolio …
Part 16: Data Manipulation in Data Validation and Quality Control
Author(s): Raj kumar Originally published on Towards AI. Data quality issues are the silent killers of production systems. A single malformed record can crash your pipeline. A gradual drift in data distributions can slowly degrade model performance. Missing values that sneak through …
Part 9: Data Manipulation in Data Merging and Joins
Author(s): Raj kumar Originally published on Towards AI. Every analysis that combines data from multiple sources faces the same fundamental question: how should these datasets align? Which records match? What happens when they don’t? These aren’t just technical decisions. They shape what …
Part 8: Data Manipulation in Grouping and Aggregation
Author(s): Raj kumar Originally published on Towards AI. Every business decision starts with a question. What are our total sales by region? Which product categories generate the most revenue? How do customer segments compare in profitability? These questions all share something in …
Part 7: Data Manipulation in Date and Time Handling
Author(s): Raj kumar Originally published on Towards AI. Time is the invisible thread that runs through almost every dataset you’ll encounter. Sales happen on specific dates. Transactions occur at precise moments. Events unfold across hours, days, and years. Yet despite how fundamental …
Part 6: Data Manipulation in String and Text Processing
Author(s): Raj kumar Originally published on Towards AI. If you’ve ever worked with real-world data, you know the struggle. Names come in all caps when they should be title case. Email addresses have trailing spaces. Phone numbers show up in a dozen …
Part 5: Data Manipulation in Data Transformation
Author(s): Raj kumar Originally published on Towards AI. By the time we reach transformation in a data pipeline, the dataset usually appears stable. It has been imported with structure, inspected with skepticism, selected with intent, and cleaned through deliberate intervention. At this …
Part 4: Data Manipulation in Data Cleaning
Author(s): Raj kumar Originally published on Towards AI. There is an assumption many teams carry without fully examining it. Data cleaning feels responsible.It feels corrective.It feels like a necessary step to improve data quality before analysis or machine learning begins. But data …
Essential Python Libraries for Data Science
Author(s): Raj kumar Originally published on Towards AI. If you look closely at real-world tabular machine learning systems, a clear pattern emerges. Across industries, datasets, and problem domains, the same class of models keeps appearing in production environments. It is not deep …
Essential Python Libraries for Data Science
Author(s): Raj kumar Originally published on Towards AI. In Part 1, we focused on how data is represented, transformed, and computed using NumPy and Pandas. By the end of that part, the dataset was clean, structured, and numerically stable. In Part 2, …
Essential Python Libraries for Data Science
Author(s): Raj kumar Originally published on Towards AI. In Part 1, we built the foundations of the data pipeline. We loaded a real dataset, structured it using Pandas, selected relevant features, and performed numerical transformations using NumPy. By the end of Step …
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