rohitmacherla3 Goto Github PK
Name: Rohit Macherla
Type: User
Company: Rutgers University, New Brunswick
Bio: Data Science Graduate Student | Deloitte
Location: New Brunswick, NJ
Name: Rohit Macherla
Type: User
Company: Rutgers University, New Brunswick
Bio: Data Science Graduate Student | Deloitte
Location: New Brunswick, NJ
In this project, I have analyzed some 911 call data of Pensylvania, USA from Kaggle to find out the most common reasons, peak hours, and other such details.
In this project, I have worked on an advertising data set from Kaggle, indicating whether or not a particular internet user clicked on an Advertisement. I have created a logistic regression model that will predict whether or not they will click on an ad based off the features of that user.
This project aims to amplify the importance of effective modeling using data impuation techniques. MICE imputations were used and bootstrapping was performed to estimate the bias and variability and finally, Elastic-Net regression was applied to obtain the results.
This project aims to build a comprehensive wellness recommendation system across 4 different health pillar using different machine learning and deep learning techniques
This project aims to aims to predict the customer churn (likelihood of a customer leaving the company) for a telecom company using a variety of ML classification algorithms.
The project aims to detect various human emotions like "angry", "happy", and "sad" based on image data. It utilizes custom convolutional neural networks as a baseline model and incorporates the modern CNNs like ResNet and EfficientNet to get the optimal results
This repository contains multiple projects using LangChain and OpenAI chat model like Q&A bots, chatbots, MCQ generators, text summarizers
Rohit's Portfolio Website
This Project aims to utilize the object detection technique to identify the number of objects passing through a pre-determined path in real-time using the webcam. It employs a pre-trained YOLO model for object detection.
In this project, I have extracted the Stocks data of Apple, Netflix and Tesla and created the visualization of stock performance and revenue.
This is a final project for Data Management for Advanced Data Science Applications. The team members are:
In this project, I have used Linear Regression and Random Forest Regression models to predict housing prices for regions in the USA, compared the accuracies of the models to find out the best suitable model.
The main objective of this project was to analyze the sales data of the Walmart stores across USA and use time series models to predict the sales for the next two quaters
The project aims to utilize pre-trained Large Language Models (LLMs) for text summarization through diverse fine-tuning techniques. Comparative analysis with baseline RNN/LSTM language models is undertaken, utilizing established metrics such as Rouge score and BLEU.
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