DeepSparkChaker

Anomaly-Behavior-Detection

Detecting a behavior anomaly The dataset below contains a location timeserie of a person living alone in their appartment. The data indicates in which location/room he was at which point in time. This data has been collected by sensors installed in the home. This person had a health incident in the night from 2019-09-18 to 2019-09-19, probably at 3:00 or 10:00 which shows in a drastic change in location behavior and resulted in the person going to hospital. By the sensor setup in the home, the location entrance and livingroom are really one single bigger room. Merge them. Explore the data and give an overview of key metrics (graphically and quantitatively) Can you say something about the living routines of the person? Propose one or more methods to detect the incident in "real time" by analyzing the location data. Real-time means, that while time passes more and more of the data gets "known" to your detection method. It can trigger as soon as the incident is detected, an action can be triggered. We are interested in understanding how you proceed in analyzing this case. Show your thought process What methods did you try and why What are their strength and weaknesses of the approaches. Are they robust and generalizable to other users? How do you test your code for correctness? We are looking forward to your propositions! PS: You are free to use other Python libraries as desired. Please return your Notebook as an answer.

CRISPDM_ULTIME

DataVisualization

Data Science Guide

Fastapi_NLP_Docker

Deploy sentiment analyis with Fastapi

FraudDetection_Fastapi

this api will detect fraud

FraudDetection_Fastapi_VF

GiveMeCredit_Top5_Solution_Kaggle

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal of this competition is to build a model that borrowers can use to help make the best financial decisions.

kaggle-solutions

🏅 Collection of Kaggle Solutions and Ideas 🏅

machine-learning-imbalanced-data

Code repository for the online course Machine Learning with Imbalanced Data

PythonCheatSheetforDataScience

DeepSparkChaker/GiveMeCredit_Top5_Solution_Kaggle

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal of this competition is to build a model that borrowers can use to help make the best financial decisions.

DeepSparkChaker/FraudDetection_Fastapi

this api will detect fraud

DeepSparkChaker/FraudDetection_Fastapi_VF

DeepSparkChaker/CRISPDM_ULTIME

DeepSparkChaker/Fastapi_NLP_Docker

Deploy sentiment analyis with Fastapi

DeepSparkChaker/kaggle-solutions

🏅 Collection of Kaggle Solutions and Ideas 🏅

DeepSparkChaker/machine-learning-imbalanced-data

Code repository for the online course Machine Learning with Imbalanced Data

DeepSparkChaker/30-Days-ML-

DeepSparkChaker/anonymization-api

How to build and deploy an anonymization API with FastAPI

DeepSparkChaker/awesome-mlops

A curated list of references for MLOps

DeepSparkChaker/booking_extra_baggage

At eDreams ODIGEO we are always looking for ways to improve customer satisfaction. With this objective in mind, we would like to predict whether a new customer

DeepSparkChaker/COURS

DeepSparkChaker/Credit_Risk_Small_Data_SyntheticSamples_EnsembleMethods

DeepSparkChaker/deploy-keras-model-in-production

Using Flask to deploy saved keras model in production. Flask based REST API to expose the prediction endpoints.

DeepSparkChaker/deploying-machine-learning-models

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

DeepSparkChaker/fastapi-model-deployment

An example for deploying Tensorflow 2 models with Docker and Fast API

DeepSparkChaker/fastapi-nlp

To showcase the features of building REST API's by FastAPI for Machine learning and Deep learning models

DeepSparkChaker/FasterDataScienceEducationMiniCourses

DeepSparkChaker/Income_Prediction

DeepSparkChaker/Jenkins

DeepSparkChaker/Kmeans_scartch

DeepSparkChaker/MLOps

Learn how to responsible deliver value using MLOps.

DeepSparkChaker/ner_spacy_app

Flask app of named entity recognition with spacy

DeepSparkChaker/NLP-UsesCases

DeepSparkChaker/Spam_Detection_Fastapi

This api will let us detect spam messages

DeepSparkChaker/Spam_Detection_Fastapi_Streamlit_Docker_Compose

This repo contains code for a small webapp to predict if we have a spam messages . The application consists of a frontend and a backend part and is used to demonstrate how to serve ML models as microservices through an API. The frontend is built with Streamlit and used to acquire messages.

DeepSparkChaker/spam_detection_streamlit

DeepSparkChaker/TextClassification-Keras

Text classification models implemented in Keras, including: FastText, TextCNN, TextRNN, TextBiRNN, TextAttBiRNN, HAN, RCNN, RCNNVariant, etc.

DeepSparkChaker/TimeSeriesAnalysis

DeepSparkChaker/Titanic_Deep_Spark