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 increase the frequency in

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

In this post, let us explore: Ensemble Models Bagging Boosting Stacking Ensemble Models/Methods/Learning Ensembling is clubbing predictions from different models to get better performance. How to club different predictions from different models to get a single prediction? There are different ways of doing it. Bagging Boosting Stacking These are the

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 your computer. How to

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 variance, that means all

Logistic regression is a supervised learning technique applied to classification problems. In this post, let us explore: Logistic Regression model Advantages Disadvantages Example Hyperparemeters and Tuning Logistic Regression model Logistic functions capture the exponential growth when resources are limited (read more here and here). Sigmoid function is a special case of Logistic function

Suppose there are two independent variables (features): x1 and x2. And there are two classes Class A and Class B. The following graphic shows the scatter diagram. If want to partition these two classes using a line (or hyperplane), the green hyperplane will seperate the two classes with maximum margin

Understanding Naive Bayes using simple examples Thomas Bayes was an English statistician. As Stigler states, Thomas Bayes was born in 1701, with a probability value of 0.8! (link). Bayes’ theorem has a useful application in machine learning. His papers were published by his friend, after his death. It is also

Decision Tree models are simple and easy to interpret. In this post, let us explore What are decision trees When to use decision trees Advantages Disadvantages Examples with code (Python) 1. What are decision trees? Decision trees are a tree like non-parametric supervised learning method. Components of decision tree: Root Node: It

Heatmap depicts the two-dimensional data (matrix form) in the form of graph. Data requirement: Data can be in the form of: Matrix such as correlation matrix Or a pandas cross tabulated dataframe Example: Importing the data Cross-tabulate the data using pd.crosstab Plot the heatmap using seaborn library  Add linewidths (width