rajeshmore1

Capstone-Project-2

Corona Virus Sentiment Analysis.This challenge asks you to build a classification model to predict the sentiment of COVID-19 tweets.The tweets have been pulled from Twitter and manual tagging has been done then.

DataScience_Mentorship

Course Material - Data Science Program

GCP-Certification-Professional-Machine-Learning-Engineer

Notes For Reference

icc-data-analysis

Analyzing T20 match data

Loan-Default-Prediction

Numerous companies from financial indutry often invest considerable resources to improve their predictive models with the aim of having better insights into their customers. Such an interest in model improvement has intensified in recent years mostly because of fast development of machine learning and artificial intelligence. For standard lending institution default predictive model with high performance helps to considerably minimize Credit Loss, resulting in higher revenue and profits. Usually the better predictive model the more efficient is the underwriting policy and collection process. A well-functioning model should distinguish creditworthy customers from those that are credit risks. Often, more-predictive credit-decisioning model can identify a greater number of customers within an institution’s specified risk tolerance, which should expand revenues as well. In this project the goal is to increase detection of defaulted loans before the loan is issued/offered by P2P lending company - Lending Club. Peer-to-peer lending differs from traditional financial institutions like banks or commercial lending companies. So, Lending Club is a mediator between investors and borrowers, earning money by charging both. The main Lending Club interest is to attract more clients and maintain protfolio size. The motivation of borrowers is clear, they want to find as cheap capital as possible, so they're seeking for the best offer at the market, which is available for them. In case of investors the motivation is obvious as well. Investors look for high ROI (return of investments), but remembering that returns are proportional to risks, we may formalize saying, that investors look for appropriate returns/risks ratio. If investors experience losses it may cause churn rate growth. The underwriting process for Lending Club looks like this. Borrower applies for the loan, then if he/she meets all the basic requirements - Lending Club using their scoring model assigns client to respective grade. There are 7 grades and 35 sub-grades. Interest rate is dependent on sub-grade. After that, Lending Club gives access to the loan for investors with information about the loan and the borrower (incl. grade and sub-grade) and investors decide whether or not to invest money in this loan. The lower the grade the higher the interest rate, which means, that investors may take higher risks to gain potentially higher returns. Seeking for default rate reduction we can end up with too restrictive underwriting policy which does not neccessary correlate with higher ROI for investors, because we'll not let investors choose risky loans, which means lower interests. For Lending Club it probably means the loss of investors with high risk appetite and borrowers with weak credit history, or in case of Lending Club those who need higher loan amount.

Natural-Language-Processing-Course-

Author: Rajesh More

Predict-whether-a-startup-will-get-funded-in-the-next-three-months.

There has been a staggering growth in investments in young age startups in the last 5 years. A lot of big VC firms are increasingly getting interested in the startup funding space. You are given a task to predict whether a startup will get a funding in the next three months using app traction data and startup details. This funding can be either seed funding, Series A, Series B, so on and so forth.

Semiconductor-manufacturing-process

CONTEXT: A complex modern semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/ variables collected from sensors and or process measurement points. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be applied to identify the most relevant signals. The Process Engineers may then use these signals to determine key factors contributing to yield excursions downstream in the process. This will enable an increase in process throughput, decreased time to learning and reduce the per unit production costs. These signals can be used as features to predict the yield type. And by analysing and trying out different combinations of features, essential signals that are impacting the yield type can be identified. • DATA DESCRIPTION: sensor-data.csv : (1567, 592) The data consists of 1567 examples each with 591 features. The dataset presented in this case represents a selection of such features where each example represents a single production entity with associated measured features and the labels represent a simple pass/fail yield for in house line testing. Target column “ –1” corresponds to a pass and “1” corresponds to a fail and the data time stamp is for that specific test point. • PROJECT OBJECTIVE: We will build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not

Statmike-Vertex-AI-Repo

https://github.com/statmike/vertex-ai-mlops.git

unsupervised-topic-modelling-of-unlabeled-text-descriptions

rajeshmore1/DataScience_Mentorship

Course Material - Data Science Program

rajeshmore1/Statmike-Vertex-AI-Repo

https://github.com/statmike/vertex-ai-mlops.git

rajeshmore1/Load-Performance-Testing-For-ML-Applications

Performance Testing is a type of software testing which ensures that the application is performing well under the workload. The goal of performance testing is not to find bugs but to eliminate performance bottlenecks. It measures the quality attributes of the system.

rajeshmore1/MLOPs-Specialization

rajeshmore1/Git-and-Github

rajeshmore1/test

rajeshmore1/A-B-Testing-Detailed-Analysis

rajeshmore1/AWS-MLOps-Masteclass

AWS Sagemker

rajeshmore1/Certified-DeveOps-Associate-

Cloud Train Course

rajeshmore1/codespaces-jupyter

rajeshmore1/crop-classification

rajeshmore1/Deep-Learning-Mentorship--Rajesh-More

Deep Learning Material collected by Rajesh More

rajeshmore1/Deep-Learning-Projects-Bundle

rajeshmore1/deveopsdemo

rajeshmore1/Entity_Resolution

rajeshmore1/gitdemo

rajeshmore1/myproject

rajeshmore1/Prompt-Engineering-With-Llama3

Prompt Engineering With Llama3. Use case Health / Fitness Industry

rajeshmore1/rajeshmore1

rajeshmore1/RNN-Tutorial

rajeshmore1/Tasks---Python

Python Assignments

rajeshmore1/VertexAI_Model_Monitoring

rajeshmore1/Walter-Pitts-Squadron

Covered basic Python Topics like List, Tuple

rajeshmore1/walter_pitts

rajeshmore1/World-Tourism-Data-Analysis

EDA Project

rajeshmore1/CNN-Tutorial

CNN Tutorial

rajeshmore1/Image_Captioning

Image_Captioning

rajeshmore1/Jeremy_Howard_Squadron

This is the test repo

rajeshmore1/Large-Language-Models-and-Their-Applications

rajeshmore1/SemanticSearch-With-Approximate-Nearest-Neighbour-Search

SemanticSearch/Text Similarity With Approximate Nearest Neighbour Search On GPU