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Basic concept

In this post, let us explore the basic concepts about time series. We will also learn about resampling techniques, how to check for stationarity and ways to convert non stationary series into stationary series. What is time series In time series, data are recorded over time. Time interval may be daily, monthly, yearly etc. How …

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Time Series Components

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 series. Trend can be increasing or decreasing or absent (that means series is …

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ARIMA/SARIMA with Python

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 Average part. Let us explore these parts one by …

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Deep Learning Basics

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 deep learning model. More than three layers means one …

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Building a Deep Learning Model

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 Anaconda Navigator. Under Environments, create new environment in Python 3.6. Create a new environment in Anaconda- …

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Feature Selection using sklearn

In this post, we will understand how to perform Feature Selection using sklearn. Dropping features which have low variance Dropping features with zero variance Dropping features with variance below the threshold variance Univariate feature selection Model based feature selection Feature Selection using pipeline 1) Dropping features which have low variance If any features have low …

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Feature Engineering for machine learning

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 engineering refers to creating a new feature when we could …

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