tanya9691
Passionate Data Science and Machine Learning Enthusiast having work history with Python MySQL , Web scraping,Flask,API and Tableau. Skilled in Data Science & ML
Pinned Repositories
Arts-Culture
awesome-computer-vision
A curated list of awesome computer vision resources
Bank-Loan-Application-Status-Prediction
The loan default dataset has 8 variables and 850 records, each record being loan default status for each customer. Each Applicant was rated as “Defaulted” or “Not-Defaulted”. New applicants for loan application can also be evaluated on these 8 predictor variables and classified as a default or non-default based on predictor variables.
Bank-Loan-Defaulter-Prediction
Loans default will cause huge loss for the banks, so they pay much attention on this issue and apply various method to detect and predict default behaviours of their customers. The loan default dataset has 8 variables and 850 records, each record being loan default status for each customer. Each Applicant was rated as “Defaulted” or “Not-Defaulted”. New applicants for loan application can also be evaluated on these 8 predictor variables and classified as a default or non-default based on predictor variables.
Bike-rental-count-prediction
The objective of this Case is to Prediction of bike rental count on daily basis on the environmental and seasonal settings. The details of data attributes in the dataset are as follows - instant: Record index dteday: Date season: Season (1:springer, 2:summer, 3:fall, 4:winter) yr: Year (0: 2011, 1:2012) mnth: Month (1 to 12) hr: Hour (0 to 23) holiday: weather day is holiday or not (extracted fromHoliday Schedule) weekday: Day of the week workingday: If day is neither weekend nor holiday is 1, otherwise is 0. weathersit: (extracted fromFreemeteo) 1: Clear, Few clouds, Partly cloudy, Partly cloudy 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog temp: Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale) atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_maxt_min), t_min=-16, t_max=+50 (only in hourly scale) hum: Normalized humidity. The values are divided to 100 (max) windspeed: Normalized wind speed. The values are divided to 67 (max) casual: count of casual users registered: count of registered users cnt: count of total rental bikes including both casual and registered
ComputerVision-Projects
Some simple computer vision implementations using OpenCV
Deep_Learning_with_Keras
Deployment-Heroku
Deployment-Heroku
online-learning-for-data-scientists
This repo lists important pointers for data engineers, machine learning engineers, and data scientists
reviewScraper
Built and deployed a Review Scrapper which scrapes reviews from E-commerce Platforms Flipkart
tanya9691's Repositories
tanya9691/online-learning-for-data-scientists
This repo lists important pointers for data engineers, machine learning engineers, and data scientists
tanya9691/Arts-Culture
tanya9691/awesome-computer-vision
A curated list of awesome computer vision resources
tanya9691/Bank-Loan-Application-Status-Prediction
The loan default dataset has 8 variables and 850 records, each record being loan default status for each customer. Each Applicant was rated as “Defaulted” or “Not-Defaulted”. New applicants for loan application can also be evaluated on these 8 predictor variables and classified as a default or non-default based on predictor variables.
tanya9691/Bank-Loan-Defaulter-Prediction
Loans default will cause huge loss for the banks, so they pay much attention on this issue and apply various method to detect and predict default behaviours of their customers. The loan default dataset has 8 variables and 850 records, each record being loan default status for each customer. Each Applicant was rated as “Defaulted” or “Not-Defaulted”. New applicants for loan application can also be evaluated on these 8 predictor variables and classified as a default or non-default based on predictor variables.
tanya9691/Bike-rental-count-prediction
The objective of this Case is to Prediction of bike rental count on daily basis on the environmental and seasonal settings. The details of data attributes in the dataset are as follows - instant: Record index dteday: Date season: Season (1:springer, 2:summer, 3:fall, 4:winter) yr: Year (0: 2011, 1:2012) mnth: Month (1 to 12) hr: Hour (0 to 23) holiday: weather day is holiday or not (extracted fromHoliday Schedule) weekday: Day of the week workingday: If day is neither weekend nor holiday is 1, otherwise is 0. weathersit: (extracted fromFreemeteo) 1: Clear, Few clouds, Partly cloudy, Partly cloudy 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog temp: Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale) atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_maxt_min), t_min=-16, t_max=+50 (only in hourly scale) hum: Normalized humidity. The values are divided to 100 (max) windspeed: Normalized wind speed. The values are divided to 67 (max) casual: count of casual users registered: count of registered users cnt: count of total rental bikes including both casual and registered
tanya9691/ComputerVision-Projects
Some simple computer vision implementations using OpenCV
tanya9691/Deep_Learning_with_Keras
tanya9691/Deployment-Heroku
Deployment-Heroku
tanya9691/reviewScraper
Built and deployed a Review Scrapper which scrapes reviews from E-commerce Platforms Flipkart
tanya9691/ArtGrih
E-Commerce platform for 'Tribal arts and crafts of Chattisgarh' - A project for C.G State Government.
tanya9691/Pytorch
tanya9691/RawData
Consists of Raw Data for utilization
tanya9691/tanya9691