Discover @bolaram Open Source projects

Bolaram Paul (@bolaram)

bolaram

Stars
33
Global Rank 458,219 (Top 16 %)
Followers 59
Following 265
Registered over 10 years ago
Most used languages

Jupyter Notebook
50.0 %

HTML
20.0 %

PHP
10.0 %

JavaScript
10.0 %

CSS
10.0 %
Location 🇧🇩 Bangladesh
Country Total Rank 1,548
Country Ranking

Jupyter Notebook
305

CSS
1,983

PHP
2,150

HTML
2,737

JavaScript
5,472

Go-Language-in-Bengali

Go Language in Bengali

bolaram

SEC Mozilla Study Group

Machine-Learning---DiabetesTest

It's a machine learning project based on data set from kaggle dataset website named pima-data.csv.

Jupyter Notebook

grpc-gateway-GSOC

Sample project for updateing existing documentation of grpc-gateway

Unzip.php

This script lists all of the .zip files in a directory and allows you to unzip. Unlike CPanel's file manager, it _will_ overwrite existing files.

videolan-gsoc

website

My Personal website

gRPC-Gateway

The gRPC-Gateway brings the power and safety of designing APIs with Protobuf and gRPC to the JSON/HTTP API world.

Creative-Commons-WordPress-Plugin-Documentation

Documentation for Creative Commons WordPress Plugin

Creditcard-Fraud-Analysis

I've used data from kaggle. There are 284,807 data in that csv file. First, we review the data, make histogram for each column and also display the correlation of each column with each other using heatmap. Then we import two unsupervised anomaly detection algorithms and finally, compare them with each other by classification report. This is example of unsupervised anomaly detection problem

Jupyter Notebook

Logistic-Regression-based-OCR

It's a machine learning classification problem. I've used data from sklearn datasets. First, I've load data , split it, checked if the splitting working or not. Then, train and predict the data. The accuracy of this model is 95%. This project is on Logistic Regression based Optical Character Recognition.

Jupyter Notebook

Uber-Data-Checkpoint-Analysis

I've used free dataset mlink is given below . here are almost 800,000 data here. Firtly we'll review the data and then add some efficient column for analyzation. Then, analyze the Date of Month (DoM) and sorting them gradually. Then plot the weekday and hour and then plot the data in the New York,USA state map. Data set: https://github.com/fivethirtyeight/uber-tlc-foil-response/blob/master/uber-trip-data/uber-raw-data-apr14.csv

Jupyter Notebook

COVID-19-Tracking

This COVID-19 graph plots the number of new confirmed cases in the past week, versus the total number of confirmed cases. When plotted in this way, exponential growth is displayed as a straight line. Notice that most countries follow a similar straight line path, indicating that the growth rate is similar across countries. Web:

Cross-Sell-Prediction

Here, we are predicting sell growth of a product by spending money in advertising by TV, Radio, Newspaper. Here in the dataset we have 200 rows and 4 columns. We split the data into 70-30%, fit the data, predict the data. And finally calculate the error. In this linear regression model our target is minimize the error as much as possible.

Jupyter Notebook