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Comparing anomaly detection algorithms for Outlier detection

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A real-world dataset often contains anomalies or outlier data points. The cause of anomalies may be data corruption, experimental or human errors. The presence of anomalies may impact the performance of the model, hence to train a robust data science model, the dataset should be free from anomalies.

In this…

Techniques to detect data drift

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Model Monitoring is an important component of the end-to-end data science model development pipeline. The robustness of the model not only depends upon the training of the feature engineered data but also depends on how well the model is monitored after deployment.

Typically a machine learning model's performance degrades over…

Essential guide to detect and handle multicollinearity in the dataset

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Exploratory data analysis and statistical analysis are important components of a data science model development pipeline to generate insights about the data. Before fitting a machine learning model, a data scientist needs to perform various feature engineering and data preprocessing techniques to train a robust model. …

Deep dive analysis on predicting coupon redemption status to develop more precise and targeted coupons and marketing strategies

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E-commerce companies employ various marketing strategies to sell their products by running campaigns, advertisements, free product distribution, discount marketing, and many more. Coupons are one of the famous marketing strategies that various companies provide to increase their revenue. …

Satyam Kumar

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