Pinned Repositories
AB-Testing-Ad-campaign-performance-R
An advertising company is running an online ad for a client with the intention of increasing brand awareness. The advertiser company earns money by charging the client based on user engagements with the ad it designed and serves via different platforms. To increase its market competitiveness, the advertising company provides a further service that quantifies the increase in brand awareness as a result of the ads it shows to online users. The main objective of this project is to test if the ads that the advertising company runs resulted in a significant lift in brand awareness.
absenteeism_to_work_prediction
Employees can be absent to work due to many reasons. In this data science project, I used machine learning algorithm on an available dataset to predict if the employee will be absent to to some illness categories and other features.
Caption-project-Unsupervised-anomalies-detection-in-IoT-IIoT-devices-FL
This repository contains the code for the the caption project submitted to be able to graduate in masters of computing.
causal_inference
Work on clients’ data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.
Data-engineer-end-to-end-project-airflow-dbt-soda-bigquery
An end to end data engineering project for loading data into bigquery with airflow, perform transformations using dbt and do data quality check with soday
Data-warehouse--Postgres-Dbt-Airflow
A city traffic department wants to collect traffic data using swarm UAVs (drones) from a number of locations in the city and use the data collected for improving traffic flow in the city and for a number of other undisclosed projects. Your startup is responsible for creating a scalable data warehouse that will host the vehicle trajectory data extracted by analysing footage taken by swarm drones and static roadside cameras. The data warehouse should take into account future needs, organise data such that a number of downstream projects query the data efficiently. You should use the Extract Load Transform (ELT) framework using DBT. Unlike the Extract, Transform, Load (ETL), the ELT framework helps analytic engineers in the city traffic department setup transformation workflows on a need basis.
in-context-learning-LLMs
The world is going through a revolution in art (DALL-E, MidJourney, Imagine, etc.), science (AlphaFold), medicine, and other key areas, and this approach is playing a role in this revolution. .
Machine-Learning-Process-A-Z
The end-to-end process of building a Machine Learning Model.
Refund-by-Location-Smart-Contract
produce an Ethereum based dApp that has both the smart contract tested and deployed in a testnet and a front end that will allow monitoring of the status.
Trading-Engineering_Scalable-Backtesting
Cryptocurrency trading engineering: A scalable back testing infrastructure and a reliable, large-scale trading data pipeline
niyotham's Repositories
niyotham/C-_datastructure
Data structure in C++ repos
niyotham/audio_purchase_prediction_using_Deep_learning
The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertizing to him/her. If we can focus our efforts ONLY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities. You have a .csv summarizing the data. There are several variables: Customer ID, Book length in mins_avg (average of all purchases), Book length in minutes_sum (sum of all purchases), Price Paid_avg (average of all purchases), Price paid_sum (sum of all purchases), Review (a Boolean variable), Review (out of 10), Total minutes listened, Completion (from 0 to 1), Support requests (number), and Last visited minus purchase date (in days). So these are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number). The targets are a Boolean variable (so 0, or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again.
niyotham/AWS-Slides
Contains screenshots of all the slides of Andrew Brown's AWS Course
niyotham/connecting_python_and_sql
niyotham/Detecting_COVID-19_with_Chest-X-Ray
Detecting COVID-19 with Chest X Ray using PyTorch
niyotham/EDA_Telecom_Industry
Telecommunication Industry exploratory ana,ysis
niyotham/ELT_Pipeline_Project_2
Postgress based warehouse and superset based visualizer
niyotham/IBM-Data-Engineering
niyotham/MNIST-Federated
Experiments on MNIST dataset and federated training using Flower framework
niyotham/PySyft
A library for answering questions using data you cannot see
niyotham/relation_extraction_transformer
niyotham/Support_Vector_mushroom_classification-
This repos is about using the Support vector classifier for distinguishing poisonous mushroons from edible ones
niyotham/Tensorflow-MNIST-
Classification of Image of MNIST dataset using DL Tensorflow.
niyotham/Twitter-Data-Analysis-Template
Twitter-Data-Analysis