bolaram/Creditcard-Fraud-Analysis

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bolaram/Creditcard-Fraud-Analysis

bolaram

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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

Go-Language-in-Bengali

Go Language in Bengali

bolaram

SEC Mozilla Study Group

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Jupyter Notebook

grpc-gateway-GSOC

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Unzip.php

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videolan-gsoc

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Logistic-Regression-based-OCR

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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