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Go-Language-in-Bengali
Go Language in Bengalibolaram
SEC Mozilla Study GroupMachine-Learning---DiabetesTest
It's a machine learning project based on data set from kaggle dataset website named pima-data.csv.grpc-gateway-GSOC
Sample project for updateing existing documentation of grpc-gatewayUnzip.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 websitegRPC-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 PluginCreditcard-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 problemLogistic-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.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.csvCross-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.Love Open Source and this site? Check out how you can help us