Saeed Kasmani's repositories
NLP-Consumer-Complain-Classification
Consumer Complain Classification
churn-prediction
Deploying a Churn prediction Model as an API on Red Hat OpenShift Container Platform
Connect-your-Machine-Learning-models-to-chatbot-and-WhatsApp
Modernize your Machine Learning Workflow with Low Code Tools & Quickly Integrate a Machine Learning Model in your Chatbot & Deploy to Whatsapp
Credit-Fraud-Detection
Context It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.
CtCI-6th-Edition-Python
Cracking the Coding Interview 6th Ed. Python Solutions
darts
A python library for easy manipulation and forecasting of time series.
docker-python-text-classifier
From Dev to Ops - building a text classifier in Docker and Python
examples
TensorFlow examples
flask-iris-classification
This is a simple iris flower classification model deployment project as flask app on Docker or Kubernetes.
image-segmentation
This repo will develop and deploy an image segmentation model using Openshift platform.
imdb_deploy
Deploy an IMDB sentiment analysis model using kubernetes
licence-plate-workshop
Licence plate recognition workshop
lodestar-deployment
LodeStar - Deployment Manifest
Machine-Learning-on-Kubernetes
Machine Learning on Kubernetes
MLOPS
This repo is for showcasing the capability of Openshift for data science task
odh-manifests
A repository for Open Data Hub Kustomize manifests extending upstream Kubeflow manifests
rhods-ai-demo
This page provide the practical example for development and deploying machine learning and deep learning models for various AI applications.
rhods-fraud-detection
A lab/workshop for Red Hat OpenShift Data Science using simple fraud detection as an example workload
Semantic-Image-Segmentation-Workshop
This project will do Semantic Image Segmentation with efficentnetV2 model
streamlit-watson-studio-blog
This repo contains code used for a blog post series covering Streamlit + Cloud Pak for Data integration.
TensorFlowASR
:zap: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords
TensorFlowTTS
:stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
watson-assistant-slots-intro
A Chatbot for ordering a pizza that demonstrates how using the IBM Watson Assistant Slots feature, one can fill out an order, form, or profile.