shubhampachori12110095 Goto Github PK
Name: Shubham Pachori
Type: User
Bio: Learning to extract signal from noise.
Location: Somewhere in India
Name: Shubham Pachori
Type: User
Bio: Learning to extract signal from noise.
Location: Somewhere in India
Python library for serializing any arbitrary object graph into JSON. It can take almost any Python object and turn the object into JSON. Additionally, it can reconstitute the object back into Python.
🎨 Classic MS Paint, REVIVED + ✨Extras
Lightweight Scala kernel for Jupyter / IPython 3
Custom Jupyter Notebook Themes
A collection of various notebook extensions for Jupyter
Start Tensorboard in Jupyter Notebook
JupyterLab computational environment.
Random xkcd comic in a JupyterLab panel
A tutorial on the most important features and idioms of Scala that you need to use Spark's Scala APIs.
Code for "Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization"
k-poselets CVPR2014 code
Python client for Apache Kafka
This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, DeepLearning4 and other technologies.
Contains all Kaggle meetup documents: tutorials, examples etc.
Additional files for the Otto Group Challenge hosted by Kaggle
Kaggle 项目实战(教程) = 文档 + 代码 + 视频
2nd Place Solution in Kaggle Airbnb New User Bookings competition
Official Kaggle API
Code for the 3rd place finish for Avazu Click-Through Rate Prediction
2nd place solution for Avazu click-through rate prediction competition
Python kernels for exploratory data analysis, feature engineering, modeling and evaluation, using two different approaches: gradient boosting machines with LightGBM, and logistic regression.
Top 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market
Kaggle Criteo https://www.kaggle.com/c/criteo-display-ad-challenge
6th place solution in "Denoising Dirty Documents"
10th place solution in "State Farm Distracted Driver Detection"
Keras tutorial for Kaggle 2nd Annual Data Science Bowl
Predict sales prices and practice feature engineering, RFs, and gradient boosting
Chose Kaggle competition of Music Recommendation System which involves application of various machine learning methodologies. By using the dataset from KKBox which includes 10 million rows, we will be predicting the chances that a user will listen to the song again within a time window. It involves preprocessing of datasets along with the cross validation so as to get better results. The best classifier was LGBM giving an accuracy of 66.65%. The various classifiers used are: -ANN -Deep Learning -Gradient Boosting -Naive Bayes -Random Forest -Support Vector Machines -Extreme Gradient Boosting -Decision Trees -LBGM -Logistic Regression -Perceptron Languages used: Python(Jupyter Notebook) IDE- Anaconda Navigator The code file and the output screenshots are attached within every folder. The accuracy, precision, recall and ROC courve and area under ROC is used as an evaluation metrics.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.