Gems & Holiday Package Prediction

Last update: Jan 27, 2022

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Overview

Predictive_Modelling

Gems & Holiday Package Prediction

This project is based on 2 cases studies : Gems Price Prediction and Holiday Package prediction. In the first case study, concepts of linear regression are tested and it is expected from the learner to predict the price of gems based on multiple variables to help company maximize profits. In the second case, concepts of logistic regression and linear discriminant analysis are tested. One has to predict if the customer will purchase the holiday package to target the relevant customer base.

Skills and Tools

Linear Regression, Logistic Regression, Linear discriminant Analysis

Owner

Avnika Mehta

GitHub Repository

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Gems & Holiday Package Prediction

Related tags

Overview

Predictive_Modelling

Owner

Avnika Mehta

📝 Wrapper library for text generation / language models at char and word level with RNN in TensorFlow

Codes of the paper Deformable Butterfly: A Highly Structured and Sparse Linear Transform.

Extreme Lightwegith Portrait Segmentation

A toolset of Python programs for signal modeling and indentification via sparse semilinear autoregressors.

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Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

A simple implementation of Kalman filter in Multi Object Tracking

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An open-source Kazakh named entity recognition dataset (KazNERD), annotation guidelines, and baseline NER models.

M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

Predict bus arrival time using VertexAI and Nvidia's Jetson Nano

Angle data is a simple data type.

Official Implementation of "Third Time's the Charm? Image and Video Editing with StyleGAN3" https://arxiv.org/abs/2201.13433

A collection of resources on GAN Inversion.

nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.

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Transformer Tracking (CVPR2021)

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