amithasen Goto Github PK

5th-project-predicting-device-failure-imbalanced-data

5th Project-Predicting Device Failure-Imbalanced Data

aipm

AIPM project files

alzheimer-prediction-model

A simple prediction model using kmeans Algorithm and a Flask application which takes real time data and predicts the Alzheimer type of the patient.

amazing-feature-engineering

amazon_electronic_items_recommendationsystem

arima-sarimax-forecasting

Forecasting monthly champagne sales

capstone_credit_card_classification

Explore training a computer model to simulate approving or denying someone a credit card and learn more about possible factors that impact credit card application algorithms.

chatterbot-streamlit

A chatbot made using the Chatterbot library in Python and locally hosted using Streamlit. Dataset used were collected during ConvAI2 competition.

chexnet-

Pneumonia detection using Dense Convolution Networks

chexnet-implementation-in-keras

This is a reimplementation of CheXNet using Python3 and Keras. CheXNet, is a 121-layer convolutional neural network that inputs a chest X-ray image and outputs the probability of pneumonia along with a heatmap localizing the areas of the image most indicative of pneumonia.

chexnet_localization

Repository for Biomedical Imaging Skoltech course project: pneumonia detection on Chest X-Ray scans using CheXNet and class activation maps for localization

churn_classification_finetune

Example of using Azure OpenAI to fine-tune a model for churn risk classification

classification-predict_failure

Machine Learning to predict device failure

computer-vision

Applying Convolutional Neural Networks on Images, Transfer Learning , Autoencoders

credit-card-approval-model

RF_credit_limit

credit-card-approval-prediction-using-python

Attempt to predict if a credit card should be approved for a person

credit-card-fraud-detection

The bank has provided the data of customers with certain attributes. You need to predict if the customer would be approved a credit card or not. If the credit card is approved, it is denoted with ‘+’ and if it is not approved then it is denoted with '-'.

credit_card_approval

The problem at hand is to build a model to classify, the applicants for credit card into approved or not-approved status, based on the applicants data.

data-challenge-detecting-failure

A timed data science challenge in predicting failed states of fleets of devices using aggregated telemetry attributes.

data_science_blogs

A repository to keep track of all the code that I end up writing for my blog posts.

datascience-project-governance-framework

Data Science Project Governance Framework is a framework that can be followed by any new Data Science business or team. It will help in formulating strategies around how to leverage Data Science as a business, how to architect Data Science based solutions and team formation strategy, ROI calculation approaches, typical Data Science project lifecycle components, commonly available Deep Learning toolsets and frameworks and best practices used by Data Scientists. A lot of research is happening all around the world in various domains to leverage Deep Learning, Machine Learning and Data Science based solutions to solve problems that would otherwise be impossible to solve using simple rule based systems. All the major players in the market and businesses are also getting started and setting up new Data Science teams to take advantages of modern State-of-the-Art ML/DL techniques. Even though most of the Data Scientists are great at knowledge of mathematical modeling techniques, they lack the business acumen and management knowledge to drive Data Science based solutions in a corporate/MNC setup. On the other hand, management executives in most of the corporates/MNCs do not have first hand knowledge of setting up new Data Science team and approach to solving business problems using Data Science. This framework intends to help bridge the above mentioned gap and provide Executives and Data Scientists with a common ground around which they can easily build any Data Science business/team from ground zero.

deep-learning-based-projects

Projects such as Image classification using CNN, Object detection using YOLO algorithm, face recognition and verification, neural style transfer using RNN algorithm are included.

demand-forecasting

Store Items Demand Forecasting

device-failure-prediction

Company has a fleet of devices transmitting daily aggregated telemetry attributes.Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.

device-failure-prediction-1

device_failure

Predict the failure of devices in Dataset

devicefailure_prediction

Predict the probability of device failure based on the historical data

ecom_scrapy

scrap eCommerce site data using scrapy

face-recognition-through-transfer-learning

Dataset on which I worked on 5 Celebrity Faces Dataset (https://www.kaggle.com/dansbecker/5-celebrity-faces-dataset) then added some of my photos to expand the dataset. Then I used 'haar cascade classifier' for face detection and finally trained the data by adding 2 custom layers on top of MobileNetV2 CNN

facedetector_tf

A faster rcnn implementation of face detector. Use WIDER FACE dataset, and tensorflow.