Part 2: Guide to build accurate Time Series Forecasting models - Auto Regressive Models
Learn about different types of AR models for time series analysis, including AR, MA, ARMA, ARIMA, SARIMA, and SARIMAX. Discover how to use ACF and PACF plots to diagnose and build accurate time series forecasting models. Enhance your understanding with examples and practical tips to help you build robust models for various applications. ... Read More
Part 1: Guide to build accurate Time Series Forecasting models
Time series forecasting is a statistical technique that predicts future values over time based on past observations. Unlike other forms of data analysis, time series forecasting involves analyzing data ordered in time. This means that each observation in the dataset is associated with a specific point in time, such as hourly, daily, weekly, monthly, or yearly. ... Read More
Everything you need to know about Ethereum blockchain network
Ethereum is a decentralized open source blockchain with smart contract functionality. The network works on POS consensus. To know more about ether, gas, and how the transaction happens on the network, refer to this valuable post. ... Read More
Decoding the Magic of Probabilistic Graphical Models: A Comprehensive Guide to Understanding and Applying PGMs in Data Science
Learn about the powerful framework of Probabilistic Graphical Models, their concepts, algorithms, and applications in data science. Explore how PGMs work and how they can be used to model complex dependencies between variables in a wide range of fields. Our comprehensive guide provides an in-depth understanding of PGMs, making it the perfect resource for beginners and experts alike. ... Read More
Principal Component Analysis Detailed Explanation
Principal Component Analysis (PCA) is a powerful statistical technique for dimensionality reduction and data visualization. PCA allows us to transform high-dimensional data into a lower-dimensional space while retaining most of the original variance in the data. This makes it easier to visualize, analyze and model complex datasets. ... Read More
Naïve Bayes Algorithm Detailed Explanation
The naïve Bayes algorithm is a popular machine learning algorithm used for text-based tasks. Naïve Bayes is a probabilistic classifier that returns the probability of a test point belonging to a class rather than the label of the test point. To learn in detail about the algorithm refer to the post. ... Read More
Understanding Popular Statistical Tests To Perform Hypothesis Testing Is Not Difficult At All!
With an exhaustive list of hypothesis tests available, we generally struggle to figure out the best test that we can perform based on the data availability. In this post, I have provided an follow diagram which will help you to choose the right hypothesis test that you can use based on your data. ... Read More
Explaining ML model results using Cumulative gains and lift instead of ROC curve are much more intuitive
As Data Scientists, our day-to-day challenge is to break down technical jargon so that businesses can understand the results of the machine learning model. One simple way to explain the results of the classification prediction model is using cumulative and lift curves. In this post, we have covered the magical calculation that can help you to explain the business results of the ML model. ... Read More
Angular CLI Cheat Sheet
T he Angular team has brought in a really handy tool to bootstrap the Angular application and also to add a new feature to the app. The tool is known as angular-cli. To install angular-cli to the system we need to install nodeJS. Once we have installed it we need to install npm dependency angular-cli globally. Once installed, open the terminal/ command prompt and run a command: ... Read More
