## EDA on Nepali News Data

EDA on News Data Hello everyone, in this blog, we are going to perform EDA on Nepali News data that I scraped back in 2021. EDA is the basic step done in any Data Science project and here, we are going to perform simple analysis like counts of title, words and news in category. Import […]

## Introduction to Probability for Data Science: Getting Started

Introduction Hello there welcome to the new blog series about Probability in the Data Science field. Here in this blog, we will start from basic concepts needed in using Probability in some datasets. This blog is going to be very short and basic yet informative. Probability is all about measurement of some event’s occurrence. We […]

## Linear Regression Using Different Gradient Descent

Gradient Descent Gradient Descent is the most popular optimizer to update parameters and it uses the gradient of the error with respect to the parameter. But the parameter update rule is different and thus there are different variants of Gradient Descent. Mini- Batch Gradient Decent It is the simplest algorithm, where we update parameters in […]

## Logistic Regression from Scratch in Python: Exploring MSE and Log Loss

Logistic Regression From Scratch Hello everyone, here in this blog we will explore how we could train a logistic regression from scratch. We will start from mathematics and gradually implement small chunks into our code. Import Necessary Module pandas : Working for DataFrame numpy : For array operation matplotlib : For visualization time : function […]

## R Exercise: Validation & Cross-validation for Predictive Modeling

Validation & Cross-validation for Predictive Modeling including Linear Model as well as Multi Linear Model Before starting topic, let’s be familier on some term. Validation : An act of confirming something as true or correct. Also, Validation is the process of establishing documentary evidence that a procedure, process, or activity was carried out in testing […]

## Python for Stock Market Analysis: Growth Rates

Introduction Interactive plot version of blog is available at here. This is the part 3 of our Python for Stock Market Analysis series and here, we will explore some of popular growth rates that can be used to see how well is our value is changing over the period of time. Lets take some of […]

## Data Analysis Using Pipe Operator in R

Data Analysis Using Pipe Operator in R What is pipe operator? Pipe operators are strong tools for expressing a series of numerous operations in a clear and concise manner. The pipe is a part of the magrittr package. Pipe allows us to write code in a more readable and understandable manner. Lets see how. Why […]

## Central Tendency vs Dispersion

Hello everyone, welcome back! In this blog we will again focus into the some of widely used central tendency techniques and then measure of spread in the Statistical Analysis of the EDA part. If you are looking for a brief walk-through of a Statistical Data Analysis in Data Science please refer to this blog of […]

## R Exercise: Association Rule Mining in R

Association Rule Mining Association rule mining (also known as Association Rule Learning) is a typical technique for determining relationships (co-occurrence) between many variables. It is mostly used in grocery stores, e-commerce websites, and other similar establishments, in addition to massive transactional databases. Amazon knows what else you want to buy when you order something on […]

## Python for Stock Market Analysis: Exploring Technical Trend Indicators

Introduction Hello and welcome back everyone to our second part of the new blog series [Python for Stock Market Analysis](). In the last part, we explored different [types of moving averages]() like Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA) and explored other moving metrics like Moving Median and Moving Variance. […]

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