tinstel79

tinstel79's Stars

• leriomaggio/deep-learning-keras-tensorflow

  Introduction to Deep Neural Networks with Keras and Tensorflow

  Language:Jupyter Notebook2.9k199171.3k

• naiveHobo/InvoiceNet

  Deep neural network to extract intelligent information from invoice documents.

  Language:Python2.5k74104389

• yashk2810/Image-Captioning

  Image Captioning using InceptionV3 and beam search

  Language:Jupyter Notebook3251211122

• Wirg/stqdm

  stqdm is the simplest way to handle a progress bar in streamlit app.

  Language:Python2412125

• yooongchun/Invoice2Excel

  提取PDF电子发票内容内容保存到Excel

  Language:Python228121394

• krivi95/option-pricing-models

  Simple python/streamlit web app for European option pricing using Black-Scholes model, Monte Carlo simulation and Binomial model. Spot prices for the underlying are fetched from Yahoo Finance API.

  Language:Python1613346

• ideonate/streamlit-launchpad

  Browse a folder containing multiple streamlit apps and launch them immediately

  Language:Python1374925

• venky14/Machine-Learning-with-Iris-Dataset

  Data Visualization and Machine Learning with Iris Dataset.

  Language:Jupyter Notebook8231101

• ahmedbesbes/playground

  A Streamlit application to play with machine learning models directly from the browser

  Language:Python743126

• abhiwalia15/AI-for-Finance-Stocks-real-time-analysis-

  1. First we fetch data of stocks in realtime from nse India website, perform basis data visualizations using python to analyze the stock. 2. Then we use machine learning LSTM technique to predict the future stock price and at last create an interactive web-app using Streamlit in python.

  Language:Jupyter Notebook553132

• udaylunawat/Automatic-License-Plate-Recognition

  Source code of ALPR (Automatic License Plate Recognition System) 🔥

  Language:Jupyter Notebook452212

• advikmaniar/ML-Healthcare-Web-App

  This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.

  Language:Python381113

• AUT-Twitter-Analytics/Streamlit-Dashboard

  This application is a dashboard used to analyze tweets. 🐦

  Language:Jupyter Notebook22102

• sd2001/Image2Caption

  🎥Image2Caption🔤: Upload an image and let the model generate a caption for you🤖.

  Language:Jupyter Notebook14300

• nimitsolanki/IPL-2019

  Indian Premier League data analysis

  Language:Jupyter Notebook124119

• sakshamj74/IPL-Analysis

  The aim of the project is to analyze the previous year's IPL data to get some interesting insights.

  Language:Jupyter Notebook725822

• soheil647/Parkinson_Detection

  Detect Parkinson Disease based on Audio data using simple Machine Learning Algorithms

  Language:Jupyter Notebook7200

• srinathkr07/IPL-Data-Analysis

  Data Analytics with IPL dataset

  Language:Jupyter Notebook732

• amrrs/iq18_workshop

  Data Science in Python - Workshop Data and Notebook

  Language:Jupyter Notebook54013

• byrontang/project-parkinsons-disease-classification

  Reviewing the algorithms of popular classifiers in details and demonstrating the application with scikit-learn package with standardization and PCA to improve prediction performance.

  Language:Jupyter Notebook51

• mahsa-teimourikia/AdvertisingClickPrediction

  Predicting if internet users will click on the advertisement or not using a Logistic Regression Model

  Language:Jupyter Notebook5303

• ANJALIAGARWAL-IT/IPL

  Language:Jupyter Notebook41

• Donutson/Parkinson-dataset-analysis

  Simple analysis of parkinson disease for prediction (classification)

  Language:Jupyter Notebook4

• Premm98/Machine-Learning-Advertisement-click-prediction

  Language:Jupyter Notebook4100

• hernanrazo/UCI-iris-classification

  A python script that classifies iris flower species based on their various dimensions.

  Language:Python3100

• ZhangZexi-hugo/NYC-Jobs

  Project Preview: the datasets taken are provided by City of New York’s official jobs site from 2011 to 2020. It mainly contains the agency, career level, job title, salary, posting date, etc. Details like career level, no matter where you are in your career, job offerings are divided into five levels, such as entry level, experienced, manager, tec. In addition, the data types are mainly categorical,For this dataset, before we do any data exploration, we create one new column “the average salary”, the focus will be what is the prediction of average salary for different levels of career. This can be broken down into two small parts: distribution of jobs posted at each career level, and the average salary status at each career level, from which, we will provide five different job search strategies for each career level job, which takes into account two perspective of the number of job position and salary. All the questions will be answered starting from the visualization and descriptive analysis. Furthermore, we will forecast the total average salary by modeling.

  Language:Jupyter Notebook2

• aquaregion/UCI-Iris

  Very basic manipulation and visualizations on UCI Iris data

  Language:Jupyter Notebook1

• dawoodbasha7/Parkinson

  Parkinson predictive model

  Language:Jupyter Notebook1

• mrinmaisharma/Iris-Dataset

  UCI - IRIS dataset implementing machine learning classification models

  Language:Python1

• tanishkaa31/Parkinson

  Detecting Parkinson's disease

  Language:Jupyter Notebook11