Organized HacktoberFest 2019

Last year I participated on the Hacktoberfest 2018 and learned about open source culture and community. Hacktoberfest is a worldwide celebration of open source software and is open for everyone! It is all about encouraging meaningful contributions to the open source ecosystem, for beginners and veterans alike. Hacktoberfest is yearly open event which is oraganized by DigitalOcean on month of October. I first participated in Hacktoberfest 2018 and did more than 10 pull request out of which some got merged.

Statistics with R

In this post we will discuss about the statistics with R. Introduction Statistics is a branch of mathematics working with data collection, organization, analysis, interpretation and presentation.Statistics is very important in Data Analysis ,Data Science and AI. In this post we will learn about the descriptive statistics with R. Descriptive Statistics Descriptive Statistics is used to summarize the data and it focuses on Distribution , the central tendancy and dispersion of the data .

Data Science with R Workshop

RNepal and Kathfest collaborated to organize the 3 days “Data Science with R” workshop. This workshop was held in Kathford Engineering College on occasion of the Kathfest 2019. Introduction There were 15 participants from the kathford college studying bachelor in computer engineering. I talked about data Science, steps and skills needed to do data science. Day 1 In day 1 I talked about the What is Data Science, How to do Data Science.

Local Hack Day 2018

I organized MLH Local Hackday DataHackthon in ACHS College on Dec 1st,2018. MLH Local Hackday Hackathon is 12 hours global event which is held on December 1 every year around the world. RNepal was also one of the organizers of the MLH Local Hackday with the moto of Learning, Building, Sharing. Me Presenting" Me Presenting The DataHackthon was a learning platform where people learned about the data analysis and Data Science by working on some datasets which were provided by the organizer.

Classification of Survival in Titanic

In this blog, I will create a machine learning model which will predict the survival of the people in the Titanic accident. I will use titanic survival dataset and use the knn algorithm to find the survival of the people in the dataset. RMS Titanic was a British passenger liner that sank in the North Atlantic Ocean in the early hours of 15 April 1912, after colliding with an iceberg during its maiden voyage from Southampton to New York City.

Data Visualization with ggplot2

We have seen lots of visuals in our life like pictures, animations, and some graphical plots. Graphics help or make easy to get an idea or understand anything. Visualization is an important part of the Data science or Data Analysis. In this blog, we will learn about visualization in R by creating different type of plots. Lets Visualize We will use ggplot2 package which follows the grammar of graphics. Its very powerful tool for visualization and very famous in R community.

EDA on Titanic

In this post, I am going to do Exploratory Data Analysis(EDA) on Titanic disaster datasets from kaggle Titanic: Machine Learning Disaster Competition. Before building machine learning model, I want to do EDA on this dataset find some idea about the features and structure of the dataset. RMS Titanic was a British passenger liner that sank in the North Atlantic Ocean in the early hours of 15 April 1912, after colliding with an iceberg during its maiden voyage from Southampton to New York City.

Machine Learning Iris

In this blog, we will use some machine learning concept with help of ScikitLearn a Machine Learning Package and Iris dataset which can be loaded from sci-kit learn. we will use numpy to work on the Iris dataset and Matplotlib for Visualization. Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper. The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.

Population Analysis

Today I am writing about Population though my last two posts were also about population.But, today in this post I will analyse the data and make some visuals using the data and find some answers. In this blog, I am going to use the dataset from the UN databank (Link: This dataset is about the world population from 1950 to 2015. It has the population data of all the Continents, Country.

US Immigration

In this blog, I am going to read data about the no of migrants in the US from all around the globe from 1980 to 2013 taken from and loaded in python. I am filtering data from given data and only using data of SAARC countries(Afganistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Srilanka). In this program, I am using matplotlib python package to visualize the given data. The data I will use is No of people moved to the US in given Years from their homeland.