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The previous article was about finding the best-performing machine learning algorithm for the given dataset.

These techniques are often the first step after exploratory data analysis to cross-check if the input features in a given dataset have enough prediction power or not. Also, it is an efficient way to explore…

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The human brain responds well and retains more information from simple diagrams or visual content than text or numbers. Therefore, representing a complex dataset in graphical format is an effective way to drive crucial insights and gain more information about the data. Furthermore, the popularity of data visualization techniques can…

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Classification algorithms are machine learning techniques that involve categorizing data into classes. It is one of the kinds of supervised machine learning, in which algorithms learn from labeled data. Since algorithms learn from the labeled data, hence the distribution of classes plays an important role. For example, training algorithms on…

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Imagine a situation where you want to test if the given dataset has sufficient features to train machine learning algorithms or to test different algorithms’ performance on the given dataset. Both cases are pretty common in the field of data science.

Usually, to test the features, one can train models…

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Data Scientists widely use EDA to understand datasets for decision-making and data cleaning processes. EDA reveals crucial information about the data, such as hidden patterns, outliers, variance, covariance, correlations between features. The information is essential for the hypothesis’s design and creating better-performing models.

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A successful Machine Learning (ML) project involves several steps such as gathering data, data preparation, data exploration, feature engineering, model building, and serving out predictions to the end-users. …

MACHINE LEARNING MODEL DEPLOYMENT

Investing a considerable amount of time optimizing the ML model is one of the most common misconceptions and pitfalls for an unsuccessful ML project. Instead, teams with successful ML project invests time in gathering data, building efficient data pipelines to avoid training-serving skew, and building reliable model serving infrastructure. …

A/B Testing

Studies conducted by big companies have shown that even changing a minor feature such as the response time by few milliseconds, the color of a button, welcome image, fonts, and many more can significantly affect website traffic. A relatable example could be posting a picture on social media. Why specific…

Rahul Pandey

Google Certified ML Engineer | Exploring possibilites of ML in Photovoltaics

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