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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 various models, and later one can choose the top 10% high performing models for further studies. Once we have few models in our bag which are the plausible candidate to perform well on the dataset, then hyperparameter tuning of these models is done to make them even better. One should…

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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 be estimated through the number of visualization tools available now in the market. Many online platforms and businesses use data visualization techniques to present data as visual content (infographics), which helps them deliver crucial information quickly. …


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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 the severely skewed dataset, also known as imbalanced datasets, can result in algorithms that perform poorly on minority classes. Fraud detection, churn prediction, spam detection are real-world examples of the imbalanced dataset. …


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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 with no regularization and verify if the loss function is close to zero or not. This test can quickly tell if the model has enough parameters to memorize the dataset or not.

Which algorithm to use?

The answer to the question is similar to the process of Exploratory Data Analysis…


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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.


This article shows how to create fantastic art using artificial neural networks.

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The convolution neural network may contain several stacked layers, images fed as an input to neural network travel through subsequent layers, and the final decision made by the output layer. But, there exist several questions, such as

  • How layers communicate with one another?
  • What does each layer see?
  • What kind of information passes from one layer to another?

Visualizing the output of the layer of interest by enhancing the input image helps to understand what is happening at each neural network layer. A trained convolution neural network progressively…


Pandas is a Python Data Analysis Library that has cemented its place in the Data Science world. Articles on the internet about top Python libraries for Data Science include Pandas as one of its favorites. Pandas library offers several functions that can speed up data wrangling and exploratory data analysis processes. However, the first step for any Data Science project is to import data, and here also Pandas library has some great functions to offer. This article shows ways to import data into Pandas from different data sources.

In 2008, Wes McKinney started developing Pandas library to fulfill the need…


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. The following picture shows steps involved in the ML development phases.

Figure showing ML development processes

This article focuses on the ML model deployment step using Flask and Heroku. Flask is a micro web framework used for web application development, and it is a perfect choice for simple web applications. …

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 picture gets more likes than others? Why posting at a particular time leads to more engagement? Why logos of Facebook, Samsung, Paypal, IBM, and many more of the color blue? Is it a coincidence? Or are there any plausible reasons behind it?

A/B testing is widely used in marketing industries…


Forecasting stock price is an exciting topic. The number of articles published on the internet shows the popularity of this topic. However, many of them suffer from a fundamental error. This article offers some of the common pitfalls to avoid when creating a multi-step prediction model for stock prices.

Pitfall 1: Shuffling time-series data

Time-series data is sequential data measured at consistent time intervals. Each data point in the series is highly dependent on previous data points and also telling a story. Shuffling of time-series data should be avoided while training to retain the time dependency. …

Rahul Pandey

Google Certified ML Engineer | Exploring possibilites of ML in Photovoltaics

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