Pinned Repositories
batch-performance-prediction
Data is being recorded from various condition monitoring sensors in a manufacturing plant. There are hundreds of such sensors which may be impacting the quality of final product which is getting manufactured, hence we can model this problem as a multivariate regression problem.
Coronavirus-cases-prediction
Coronavirus cases prediction for India which is Updated till september 2020 & the web deployment link can be found in the description
Deployed-models
Repository of web-deployed models.
Flight-Price-Prediction
Flight ticket prices can be something hard to guess, today we might see a price, check out the price of the same flight tomorrow, it will be a different story. This is the reason why flight prices are quiet unpredictable. Data consisting of several details and prices of flight tickets for various airlines between the months of March and June of 2019 and between various cities is used in this project.
Job-Reviews-Analysis-Prediction
Data from a website that provides job reviews. The website wants to analyze texts and the corresponding rating that is provided by the user about startups. Based on the texts, try to verify if it corresponds to the score provided by the reviewer. the task helps the website to rank user's reviews or ratings
Predict-Ad-Response
Product-size-recommendation-and-fit-prediction
Product size recommendation and fit prediction are critical in order to improve customers’ shopping experiences and to reduce product return rates. However, modeling customers’ fit feedback is challenging due to its subtle semantics, arising from the subjective evaluation of products and imbalanced label distribution.
Rental-price-prediction
Stayze is an online market for providing lodging or primary homestays. The company does not own any real estate or properties, it acts as a broker receiving commission from each booking. The hosts rent out their property, its availability, area, type of room, price etc. and the travellers can book accordingly. The travellers put in their reviews, which is visible to others. People have used this service extensively and the company is recognized throughout the globe. All the online activities of the hosts as well as the travellers are being captured and have resulted in a rich database. This data can be used to gain business insights, make decisions, improve security, understand the customers' and providers' (hosts) behaviour and performance on the platform, guiding marketing initiatives, implementation of innovative additional services and much more. The stakeholders with the help of the available data want to know the ideal prices at which the properties can be rented, as it will help them decide upon the ideal investment to be done. Can we build a machine learning model to predict the ideal price of the rental ? Datasets : The data folder data.zip that is provided to you contains the following files: Train.csv - It is the training data containing the features, along with the price of the rentals. Data_Dictionary.xlsx - It contains a brief description of every variable provided in the training and test set. Test.csv: - It contains details of the customers for which the participants need to predict the price of the rentals. sample_submission.csv - This is a sample file of the format in which you have to submit your predictions on GLabs. Evaluation: A solution with low root-mean-squared error (RMSE) was desired.
Retail-Price-Analysis-and-Prediction
We have a data of retail transactions over two year. Apart from data analysis and visualization, a regression model is developed to predict the price of retail items belonging to different categories. Foretelling the Retail price can be a daunting task due to the huge datasets with a variety of attributes ranging from Text, Numbers(floats, integers), and DateTime. Also, outliers can be a big problem when dealing with unit prices.
Telecom-churn-prediction
Customer churn, also known as customer attrition, customer turnover, or customer defection, is the loss of clients or customers. Telephone service companies, Internet service providers, pay-TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics because the cost of retaining an existing customer is far less than acquiring a new one. Predictive analytics use churn prediction models that predict customer churn by assessing their propensity of risk to churn. Since these models generate a small prioritized list of potential defectors, they are effective at focusing customer retention marketing programs on the subset of the customer base who are most vulnerable to churn. For this project, we will be exploring the dataset of a telecom company and try to predict the customer churn Problem Statement Using the method of Boosting, classify whether or not the customer will churn
kumaranurag7's Repositories
kumaranurag7/batch-performance-prediction
Data is being recorded from various condition monitoring sensors in a manufacturing plant. There are hundreds of such sensors which may be impacting the quality of final product which is getting manufactured, hence we can model this problem as a multivariate regression problem.
kumaranurag7/Rental-price-prediction
Stayze is an online market for providing lodging or primary homestays. The company does not own any real estate or properties, it acts as a broker receiving commission from each booking. The hosts rent out their property, its availability, area, type of room, price etc. and the travellers can book accordingly. The travellers put in their reviews, which is visible to others. People have used this service extensively and the company is recognized throughout the globe. All the online activities of the hosts as well as the travellers are being captured and have resulted in a rich database. This data can be used to gain business insights, make decisions, improve security, understand the customers' and providers' (hosts) behaviour and performance on the platform, guiding marketing initiatives, implementation of innovative additional services and much more. The stakeholders with the help of the available data want to know the ideal prices at which the properties can be rented, as it will help them decide upon the ideal investment to be done. Can we build a machine learning model to predict the ideal price of the rental ? Datasets : The data folder data.zip that is provided to you contains the following files: Train.csv - It is the training data containing the features, along with the price of the rentals. Data_Dictionary.xlsx - It contains a brief description of every variable provided in the training and test set. Test.csv: - It contains details of the customers for which the participants need to predict the price of the rentals. sample_submission.csv - This is a sample file of the format in which you have to submit your predictions on GLabs. Evaluation: A solution with low root-mean-squared error (RMSE) was desired.
kumaranurag7/Job-Reviews-Analysis-Prediction
Data from a website that provides job reviews. The website wants to analyze texts and the corresponding rating that is provided by the user about startups. Based on the texts, try to verify if it corresponds to the score provided by the reviewer. the task helps the website to rank user's reviews or ratings
kumaranurag7/Product-size-recommendation-and-fit-prediction
Product size recommendation and fit prediction are critical in order to improve customers’ shopping experiences and to reduce product return rates. However, modeling customers’ fit feedback is challenging due to its subtle semantics, arising from the subjective evaluation of products and imbalanced label distribution.
kumaranurag7/Coronavirus-cases-prediction
Coronavirus cases prediction for India which is Updated till september 2020 & the web deployment link can be found in the description
kumaranurag7/Deployed-models
Repository of web-deployed models.
kumaranurag7/Flight-Price-Prediction
Flight ticket prices can be something hard to guess, today we might see a price, check out the price of the same flight tomorrow, it will be a different story. This is the reason why flight prices are quiet unpredictable. Data consisting of several details and prices of flight tickets for various airlines between the months of March and June of 2019 and between various cities is used in this project.
kumaranurag7/Predict-Ad-Response
kumaranurag7/Retail-Price-Analysis-and-Prediction
We have a data of retail transactions over two year. Apart from data analysis and visualization, a regression model is developed to predict the price of retail items belonging to different categories. Foretelling the Retail price can be a daunting task due to the huge datasets with a variety of attributes ranging from Text, Numbers(floats, integers), and DateTime. Also, outliers can be a big problem when dealing with unit prices.
kumaranurag7/Telecom-churn-prediction
Customer churn, also known as customer attrition, customer turnover, or customer defection, is the loss of clients or customers. Telephone service companies, Internet service providers, pay-TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics because the cost of retaining an existing customer is far less than acquiring a new one. Predictive analytics use churn prediction models that predict customer churn by assessing their propensity of risk to churn. Since these models generate a small prioritized list of potential defectors, they are effective at focusing customer retention marketing programs on the subset of the customer base who are most vulnerable to churn. For this project, we will be exploring the dataset of a telecom company and try to predict the customer churn Problem Statement Using the method of Boosting, classify whether or not the customer will churn