Models on Local Environment

The process of creating a finely tuned machine learning model consists of loading the data source, data preparation, understanding and its analysis and model development followed by hyperparameter tuning. All of this is present only on the developer side of the story. Generally in a typical scenario, the machine learning models tend to exist on our machines forever. But in order to make it available and experienceable for the end-users, we deploy our Machine Learning model. Henceforth, Flask comes into picture.

Contents

  • Flask

Flask

Flask is a web framework originally…


Recommendation systems are the most successful and widespread applications of Machine Learning. These systems filter out products to the users based on the previous data involved with them. If a user doesn’t have any previous history then, in this case, the popular products are recommended. These systems are integrated with most of the tech giants we see today including Amazon, Netflix, LinkedIn to help discover users new products, movies and shows, jobs etc.

Source : Google Images

Recommendation systems are generally classified into Content Based Filtering and Collaborative based Filtering. Both these techniques are unique in themselves and are based on the user-product interactive…


Exploratory Data Analysis is often used to uncover various patterns present in your data and to draw conclusions from it. EDA is the core part when it comes to developing a Machine Learning model. This takes place through analysis and visualization of the data which will be fed to the Machine Learning Model. In this blog, we will see on how to get started with Exploratory Data Analysis for Machine learning with Python.

As a beginner I struggled a lot while exploring and understanding different patterns present in the dataset, also the Exploratory Data Analysis part cannot be skipped while…

Aryan Kenchappagol

Machine Learning Enthusiast | Python Developer

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