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120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

adbc

ar-cutpaste

Cut and paste your surroundings using AR

artificial-neural-network-

By using deep learning artificial neural network, predict the Customer churn. the bank data-set contain some information about the customer by using these information create the ANN model that will predict the customer churn.

autopilot

A self driving car model for humans.

basics-of-python-programming-for-data-science

This repository contains all the fundamentals of Python programming. which is mostly used in Data science and machine learning algorithm

books

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

classification-on-indian-liver-patient

The given data set is related to Indian patients who have been tested for a liver disease. Based on chemical compounds (bilrubin,albumin,protiens,alkaline phosphatase) present in human body and tests like SGOT, SGPT the outcome mentioned is whether person is a patient i.e, whether he needs to be diagnosed further or not. Perform data cleansing, and required transformations and build a predictive model which will be able to predict most of the cases accurately. Following are the feature names for the given data: Age,Gender,Total_Bilirubin,Direct_Bilirubin,Alkaline_Phosphotase, Alamine_Aminotransferase,Aspartate_Aminotransferase,Total_Protiens,Albumin, Albumin_and_Globulin_Ratio,Class.

co-learning-lounge

Welcome to the one point community-driven encyclopedia for anything in technology.

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

credit-card-fraud-detection

In this project I use various Machine Learning models and Deep learning models such as ANN to see how accurate they are in detecting whether a transaction is a normal payment or a fraud.

customer-segmentation-using-rfm-in-r-

For database marketing or direct marketing people, they are always concerned about two questions before they send out mails or make calls to their customers:- How can they segment the customers in the database to find out who are more likely to response to their mails or buy their products? Which type of customers they should send the mails to so that they can reach breakeven and make profit? The RFM method is a very simple but effective customer analysis way to address the above questions. Where, R, F, and M stand for1 Recency – How recently did the customer purchase? Frequency – How often do they purchase? Monetary Value – How much do they spend (each time on average)?

data-analysis-with-r

Using gglot2, tidyr, dplyr, ggmap, choroplethr, shiny, logistic regression, clustering models and more

data-science--all-cheat-sheet

datasets

Datasets used in Plotly examples and documentation

day9

decision-tree-and-ensemble-learning

create model to evaluate car conditions based on Decision tree and Ensemble learning

decision-tree-for-classificatioin-in-r

A small Decision tree model will be developed in an attempt to predict whether a patient is likely (represented by 1) or not likely (represented by 0) to develop diabetes in the next 5 years.

default-prediction

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default-prediction-using-svm

THIS PROJECT IS USED TO TO CHECK DEFULT

defualt-prediction-with-api-using-ml

This project is used to predict default using ML algorithm, and created API for same with UI

earthquake-prediction

face-pga17

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faq-search-enginee

This project is used to search similar FAQ in database

fashion-class-classification-using-cnn

The global fashion industry is valued at three trillion dollars and accounts for 2 percent of the world's. GDP the fashion industry is undergoing a dramatic transformation by adopting new computer vision and Machine learning and deep learning techniques. In this case study we'll look at a hypothetical situation. We assume that if a retailer hired you to build a virtual stylist assistant that looks at customer Instagram and Facebook images and classifies what fashion category they are wearing either bags dresses and pants. The virtual assistant can help the retailer detect and forecast fashion trends and launch targeted marketing campaigns. In this story we're going to use the fashionmnist data. It's a data set that contains images of bags shoes and dresses. And we're asking the deep network to classify the images into 10 classes. So we wanted to build kind of an app per se or a model. They can look at images and can tell us exactly what category in this image. Is it like a short. Is it a bag. Is it like a hat. And so on. That's the whole objective. The data again they are divided into 28 by 28 greyscale images and the target class is actually No. 1 out of 10 which is kind of a target label which can be categorized as you can see into either like maybe a shoe maybe like like pants. Basically these are the target classes. We have the t shirts trousers pullovers ankle boots sneakers and so on so forth.

herokutest

hierarchical-clustering-in-r

We need to club or group the state which are similar to each other in terms of crime

image-scraper-with-end-to-end-deployment-

income-category-prediction