Sai Charan (ichrnn)

ichrnn

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Sai Charan's repositories

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TechniquesLib-Hackerrank

Each problem here, leaves a technique or a better way to solve a similar kind of problem

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awesome-large-action-model

Awesome Large Action Model (LAM): Models that could help gets things done.

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Machine-Learning-Driven-Portfolio-Maximization

A python application, that demonstrates optimizing a portfolio using machine learning.

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AnomalyDetectionTimeSeriesData

Anamoly Detection in Time Series data of S&P 500 Stock Price index (of top 500 US companies) using Keras and Tensorflow

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QR-code-AI-art-generator

Generate artistic QR codes using control net and text to image models

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Fooocus

Focus on prompting and generating

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data-engineering-interview-questions

More than 2000+ Data engineer interview questions.

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Heart-stroke-predicition

Built a model using K-Nearest Neighbors algorithm to predict likelihood of heart attack based on parameters such as age, smoking history and BMI. Achieved accuracy of 95.4%.

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New-Grad-2024

👋 Hey there new grad🎉! We've put together a collection of full-time job openings for SWE, Quant, PM and tech roles in 2024! 🚀

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annoy

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

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churnometer

WT02 - Predictive Analytics - Churnometer

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Applied-Machine-Learning-for-Horizontal-Wells-Optimization-and-Risk-Assessment

This paper proposes a risk matrix to optimize lateral length in drilling horizontal wells. It uses a torque and drag model enhanced with machine learning, considers wellbore's step-out and geometrical torsion, and is quantitative. The risk assessment is summarized in a matrix for easy comparison of designs.

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Automated-lawn-mower

Developed a machine learning program for an automated lawn mower with obstacle detection and waste collection. Mapped the area and optimized mowing path for shortest time possible, saving 18% in cost.

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Fraud-Detection-in-Online-Transactions

Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting

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