Applied undersampling and oversampling using SMOTE.
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Updated
Feb 7, 2020 - Jupyter Notebook
Applied undersampling and oversampling using SMOTE.
The Mulan Framework with Multi-Label Resampling Algorithms
The project is based on Indian and Southeast Asian market where mostly prepaid payment model is prevelant In this project we will use the usage-based chrun definition i.e. customers who have not done any usage either incoming or outgoing in terms of calls, internet etc. over a period of time. We focus only the High Value customers, as typically …
The final project for the CE888: Data Science and Decision Making module (Spring Term) at the University of Essex
Deep Regression Tracking with Shrinkage Loss (ECCV 2018).
pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss
Submission for HR Analytics Hackathon - AnalysticsVidya.
Classification of Body postures using different ML algorithms and comparing their performances.
compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance
Customer Retention Analysis : Predict customer churn
Dice loss for data-imbalanced NLP tasks
A real world data analysis and sentiment analysis using NLP and supervised classification machine learning model #4
This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.
software vulnerability detection
Predicting whether a client will subscribe for a term deposit after a bank marketing campaign
Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.
Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.
ECG Arrhythmia Detection with ResNet and Transfer Learning
Detection of dermoscopic structures for melanoma diagonsis
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