maninderpreetpuri Goto Github PK

analysis-of-repeated-measures-using-r-studio

This project uses R studio to explore the three sample datasets to determine the correlation of various parameters in the datasets with statistical tests using r-packages (nlme, multcomp, lmerTest, foreign, tidyverse, plyr).

apj-workshops-2024

artificial-intelligence-in-bioinformatics-p3

artificial-intelligence-in-bioinformatics-project-2

artificial-intelligence-in-bioinfromatics-project1

Computer Science and Information Technology Professionals are being employed in many diverse areas of Science. In this project are focusing on Bioinformatics and the knowledge and skills to understand and participate in this field of research or as an advance step to greater employment opportunities. This project will clarify the fundamentals of Bioinformatics from the end user perspective and will allow us to participate in information gathering.

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

berkeley-stat-157

Homepage for STAT 157 at UC Berkeley

big-data-manipulation-on-cloud

I used big data tools (Hive, SparkRDDs, and Spark SQL). I solved challenging big data processing tasks by finding highly efficient solutions. Experienced processing four different types of real data: Standard multi-attribute data (video game sales data), Time series data (Twitter feed), Bag of words data, A News aggregation corpus.

bioinformatics-neucleotide-translation-transcription

This project is to read a fasta file and read the nucleotide bases. Then translate it to mRNA and then to a protein sequence. Python code is used for the translation and transcription of sequences.

business-resilliance-program-data-analysis

I utilized Power BI and MS excel to bring insights into MIC's Business Resilience Program(BRP) which aims to support small businesses during COVID pandemic in states: Victoria, South Australia and Tasmania. BRP is an initiative by MIC in collaboration with the Australian Small Business Advisory Services (ASBAS) digital solutions.

convolutional-neural-networks_mnist_dataset_image_classification

The project is to predict the value of a handwritten digit, using the very popular MNIST dataset - a collection of 70,000 grayscale handwritten numbers between 0 and 9.

customer-churn-analysis

Customer retention is a critical stage for customer relationship management (CRM), especially for established businesses after their initial exponential growth. Churn management or attrition management is important as when customers leave, there arenegative impacts on revenues. Churn analytics has been widely applied to proactive customer retention where descriptive and predictive analytics are utilised to identify and predict customer propensity to churn.

customer-segmentation-and-profiling

Customer segmentation is a pivotal task for business analytics. Customer segmentation is the process of splitting customers into different groups with similar characteristics for potential business value proposition. Many companies find that segmenting their customers enable them to communicate, engage with their customers more effectively. Future Bank is conducting an analysis on the existing customer profiles and the marketing campaign data to identify the target customers who are mostly likely to subscribe long-term deposits. As a member of the data analytics team, I am tasked to analyse historical data and develop predictive models for marketing purposes. I have used SAS Enterprise Miner and Rstudio to perform the analysis.

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 200 universities.

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.

d2l-zh-colab

Automatically Generated d2l-zh Notebooks for Colab

data-analysis-using-sas-viya

data-visualisation-using-r

data_exploration_and_analysis

data_mining

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

examples_ai

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

gfpgan

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

image-augmentation-techniques

Data augmentation is the name for the collection of techniques used to increase the amount of usable data. In computer vision this usually means applying various spatial and colour transformations to images. In this project I explored some image augmentation techniques and how they can boost training performance.

interview-revision

lesion-dataset---deep-learning-project

Uses deep learning in Python with Keras, Pandas, Numpy, Tensorflow, ScIkit-Learn libraries.

machine-learning-models

Built neural networks (NNs) and Regression models for supervised learning. The NN task is formulated as multi-class classification problem for hand-written images, and the goal is to model the relationship between an image’s content and label. Also uses knowledge on Regression models to predict housing prices in Boston to develop Machine Learning skills.

predicting-forecast-cover-in-region-using-cnn--debugging-neural-networks

In this project I used cartographic variables of small regions of forest (30m x 30m) to predict the type of forecast cover in each region. Example cover types include: spruce/fir (type 1), lodgepole pine (type 2), etc.