Blog-posts

Feature Engineering for machine learning

By Dr. Shripad Bhat, Data Scientist September 4, 2019 In this post, let us explore: What is the difference between Feature Selection, Feature Extraction, Feature Engineering and Feature Learning Process of Feature Engineering  And examples of Feature Engineering Both Feature engineering and feature extraction are similar: both refer to creating new features from the existing features. Feature …

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Feature Selection using sklearn

By Dr. Shripad Bhat, Data Scientist September 4, 2019 In this post, we will understand how to perform Feature Selection using sklearn. 1) Dropping features which have low variance If any features have low variance, they may not contribute in the model. For example, in the following dataset, features “Offer” and “Online payment” have zero …

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Building a Deep Learning Model

By Dr. Shripad Bhat, Data Scientist September 4, 2019 Building a Deep Learning Model using Keras In this post, let us see how to build a deep learning model using Keras. If you haven’t installed Tensorflow and Keras, I will show the simple way to install these two modules. 1. Installing Tensorflow and Keras Open …

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Deep Learning Basics

By Dr. Shripad Bhat, Data Scientist September 4, 2019 Deep learning is a powerful machine learning technique. These are widely used in Natural Language Processing (NLP) image/speech recognition robotics and many other artificial intelligence projects What is Deep Learning? A model which consists of more than three layers in a neural network model is a …

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ARIMA/SARIMA with Python

By Dr. Shripad Bhat, Data Scientist September 4, 2019 Autoregressive Integrated Moving Average (ARIMA) is a popular time series forecasting model. It is used in forecasting time series variable such as price, sales, production, demand etc. 1. Basics of ARIMA model As the name suggests, this model involves three parts: Autoregressive part, Integrated and Moving …

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Time Series Components

By Dr. Shripad Bhat, Data Scientist September 4, 2019 Components of Time Series In this post, let us explore the four components of time series data. Trend (T) Cyclicality (C) Seasonality (S) Irregular component (I) Let us look at these components one by one. Trend (Secular Trend) Trend is long term movement of the time …

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Basic concepts

By Dr. Shripad Bhat, Data Scientist September 4, 2019 Time Series – Basic concepts Resampling: A) Downsampling In simple terms, it is like aggregating. For example: converting daily data to monthly data, or quarterly data to yearly data etc. In the following example, I have converted daily data to weekly data. B) Upsampling Here we …

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Importing Time Series data

By Dr. Shripad Bhat, Data Scientist September 4, 2019 Importing data into Python In this post, we will learn:  How to import data into python How to import time series data How to handle different time series formats while importing A) Importing Normal Data Suppose you have a data file saved in csv format on …

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