Basic Idea Introduction: Python is an interpreted, high-level, and general-purpose programming language. Python’s design philosophy emphasizes code readability with it’s notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large scale projects. (wikipedia) Python is the most used…

The basic concept with a simple explanation. Data Science is one of the most burning topics in the world right now. Data is dominating the world, this is the best I could say to emphasize the importance of data science. But so often it is possible to make a wrong…

Using mlogit function to build up the choice model and predict product share — Introduction: ​Choice Modelling is a scientific methodology used by academics, economists, and policy-makers to measure consumer preferences. It is regarded as the most scientifically robust method to investigate and understand how choices are made. (surveyengine) Multinomial Logistic Regression is used to predict a choice from a set of alternatives based on…

Using GLM, Decision Tree and Random Forest to predict Churn and compare the models with their accuracy and AUC values — What is Churn ? Churn rate, when applied to a customer base, refers to the proportion of contractual customers or subscribers who leave a supplier during a given time period.(wikipedia) Why Churn Prediction is important? Churn is directly related to the profitability of a company. The more some can learn…

A complete comparison between two widely used machine learning models to forecast the price of commodities shown in this article. — D. S. A. Aashiqur Reza and Tanmoy Debnath Mathematics Discipline, Khula University Introduction: A reliable forecasting model to predict future scenario of the price of two most commonly used commodity in our day to day life: wheat (retail) and rice (coarse). In this study, Seasonal Auto-Regressive Moving Average (ARIMA) and…

Time series forecasting with ARIMA model in R with code Follow all the steps carefully on the data you have to forecast the price of commodities in R. Step 1: Load all the required libraries library(tidyverse) library(lubridate) library(forecast) library(tseries) Step 2: Take input of the data and use glimpse() to…