Discover naikshubham/Building-Webapps-with-R-Shiny Open Source project

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GNU General Publi...
Created over 4 years ago
Updated over 4 years ago

naikshubham

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Building interactive web applications with R shiny.

Predictive-Analytics-in-Python

Build ML model with meaningful variables. Use model for predictions

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Bank-Cheque-OCR

Used computer vision with OCR to extract feilds from bank cheques, thereby automating the process of cheque processing in Banks.

Python

Forecasting-using-Python

Used ARIMA class models to forecast the future.

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Object-Oriented-Python

Object Oriented Python codes

Python

Image-Clustering-Using-Convnets-Transfer-Learning-and-K-Means-

Image Clustering using Convnets Transfer Learning and KMeans. Builds clusters of similar Images.

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NLP-Python

Feature Engineering for NLP in Python

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Biomedical-Image-Analysis

Fundamentals of image analysis using NumPy, SciPy, and Matplotlib. We'll navigate through a whole-body CT scan, segment a cardiac MRI time series, and determine whether Alzheimer’s disease changes brain structure.

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Machine-Learning-Interview-Questions-in-Python

Machine Learning Interview Questions in Python

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Grand-Travel-Booking-Portal-using-Flask

Travel booking Webapp using Flask and python.

Python

ML-for-Time-Series-Data

ML on time series data like audio files. Visualizing, cleaning, feature enigineering and modeling time series data.

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Spoken-Language-Processing-in-Python

Load transform and transcribe audio files.

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Amazon-Reviews-Classifier

Objective is to create a ML model that will classify the Review as positive or negative using k-NN.

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PySpark-Data-Engineering-Pipelines

Spark is a tool for doing parallel computation with large datasets and it integrates well with Python.

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Automated-Incident-Metrics

This script will automatically create the Incident Metrics and trigger automatic mail.

Python

Web-APIs-with-Python-and-Flask

Creating Web APIs with Python and Flask

Python

Credit-Card-Fraud-Detection

Objective is to find the top 10 similar transactions for any given transaction in the dataset using Linear Algebra.Calculate similarity(i,j) = cosine^-1(dot product (vi, vj) / (length(vi) * length(vj)) ) Find out top 10 transactions in the dataset which have the lowest similarity(i,j).

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Deep-Learning-Using-Python

This repository contains keras/TensorFlow/Pytorch code for building Deep Learning models on datasets.

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Parallel-Programming-in-Python

Python is now well established as a major platform for data analysis and data science. For many data scientists, the largest limitation of Python is that all data must fit into the resident memory of the available workstation. Further, traditionally, Python has only been able to utilize one CPU. Data scientists constantly ask, "How can I read and process large amounts of data?" and "How can I make use of more computational processing resources?"

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Designing-Machine-Learning-Workflows

Deploying machine learning models in production seems easy with modern tools, but often ends in disappointment as the model performs worse in production than in development. How to exhaustively tune every aspect of our model in development; how to make the best possible use of available domain expertise; how to monitor our model in performance and deal with any performance deterioration; and finally how to deal with poorly or scarcely labelled data. Digging deep into the cutting edge of sklearn, and dealing with real-life datasets from hot areas like personalized healthcare and cybersecurity.

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Gender_Predictor-ML

This is a simple data analysis project using sci-kit learn. In this project we have taken a sample data of Male and Female containing height,weight and shoe size. Later using DecisionTree classifier we are predicting whether the given pair of height,weight and shoesize is of Male or Female.

Python

Website-Blocker-Using-Python

Python Code to block the desired website during specific time frame.

Python

Categorical-data-ML

Deal with Categorical data to solve data problems

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Predicting-Customer-Churn-

Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries, from telecommunications to cable TV to SaaS, and a company that can predict churn can take proactive action to retain valuable customers and get ahead of the competition.

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LSTM-on-Amazon-Food-Reviews-Dataset

Applied LSTM Recurrent Neural Network on Amazon Food Reviews Dataset to classify the sentiment of the reviews. Whether the review is poisitive or negative

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Statistics-for-Data-Science

Statistics for Data Science using spreadsheets, python.

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search-engines

Build search engine using ElasticSearch more different purposes video search, imagesearch, text search

videoanalytics

Python3-

Face-Identification

Detect face in a picture and recognize the identity of the face.

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Network-Analytics-Using-Python

Analyze, visualize, and make sense of networks using the powerful NetworkX library.

HTML

Optimization-ML

This repository contains solutions using analytics, machine learning and optimizations.

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Machine-Translation

Behind the language translation services are the machine translation models.

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Interactive-Dictionary-Using-Python

I have build a code which does the function of Dictionary, it will ask user to enter a word and show the meaning of that word. Apart from that ,additional features included are auto similar words recognition.

Python

Importing-Managing-Financial-Data-in-Python

Data Science Skills for financial data

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t-sne

T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization developed by Laurens van der Maaten and Geoffrey Hinton.It is a nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.

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Distance-Calculator-Webapp-using-Flask-and-Google-Map-API

In this project I have created a distance calculator Webapp and hosted it on cloud using Python's Flask Framework. User needs to enter the source and destination the Python code will then call Google Maps API and display the distance and time to reach the destination from source. You can access the webapp using https://naikshubham.pythonanywhere.com

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Iris_Data_Analysis_Model

Toy Dataset: Iris Dataset: [https://en.wikipedia.org/wiki/Iris_flower_data_set] A simple dataset to learn the basics. 3 flowers of Iris species. [see images on wikipedia link above] 1936 by Ronald Fisher. Petal and Sepal: http://terpconnect.umd.edu/~petersd/666/html/iris_with_labels.jpg Objective: Classify a new flower as belonging to one of the 3 classes given the 4 features. Importance of domain knowledge.

Python

CNN-Hyperparameter-tuning-using-Hyperas

Convolutional Neural Network Hyperparameter tuning using Hyperas and Hyperopt. The advantage of hyperas over sklearn GridSearchCV and RandomSearchCV is parallel execution using GPUs.

Jupyter Notebook

Credit-Risk-Modeling

Prepare credit application data. After that, apply machine learning and business rules to reduce risk and ensure profitability.Ever applied for a credit card or loan, we know that financial firms process our information before making a decision. This is because giving us a loan can have a serious financial impact on their business. But how do they make a decision?

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Market-Basket-Analysis

What do Amazon product recommendations and Netflix movie suggestions have in common? They both rely on Market Basket Analysis, which is a powerful tool for translating vast amounts of customer transaction and viewing data into simple rules for product promotion and recommendation.Market Basket Analysis using the Apriori algorithm, standard and custom metrics, association rules, aggregation and pruning, and visualization.

Jupyter Notebook

Introudction-to-MongoDB-in-Python

MongoDB is a tool to explore data structured as you see fit. As a NoSQL database, it doesn't follow the strict relational format imposed by SQL. By providing capabilities that typically require adding layers to SQL, it collapses complexity. With dynamic schema, you can handle vastly different data together and consolidate analytics. The flexibility of MongoDB empowers you to keep improving and fix issues as your requirements evolve.

Python

Unit-Testing-in-Python-for-Data-Science

Every data science project needs unit testing. It comes with huge benefits - saving a lot of development and maintenance time, improving documentation, increasing end-user trust and reducing downtime of productive systems. As a result, unit testing has become a must-have skill in the industry, used by almost every company.

Python

Text-Summarizer

Summarize long text sentences.

Tableau-Introduction

Introduction to Tableau

Customer-Segmentation-in-Python

Customer Segmentation in Python

Data-Scientist-Track

All necessary stuff to be a Data Scientist

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Miscellaneous-ML-and-Python

The solution to the problems which I encounter while solving AI/ML/Python usecases.

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Rasa-Chatbot

Chatbot using Rasa framework.

Python

Tensorboard-Visualization

Visualize data using tensorboard in 3 dimensions.

Python

Rasa-Introduction

Chatbot powered by Rasa. Demonstrating strong NLP and NLU capabilities.

Python

Feature-Engineering-ML

Feature Engineering techniques for ML

Jupyter Notebook

Data-Visualization-in-Python

Various data vizualizations

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Recommendation-Engines

collaborative filtering and content-based filtering, measure similarities like the Jaccard distance and cosine similarity, and how to evaluate the quality of recommendations on test data using the root mean square error (RMSE).

Generalized-Linear-Models-GLM

Extend regression toolbox with the logistic and Poisson models, by learning how to fit, understand, assess model performance and finally use the model to make predictions on new data.

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Gradient-Boosting-with-XGBoost

Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries.

Jupyter Notebook

naikshubham/Building-Webapps-with-R-Shiny

naikshubham

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