Skip to content
View Aviator10's full-sized avatar
💭
Lead Data Scientist @TransOrg Analytics
💭
Lead Data Scientist @TransOrg Analytics

Block or report Aviator10

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse

Pinned Loading

  1. Analysis-and-prediction-of-online-shoppers-purchasing-intention-using-various-algorithms-CAPSTONE Analysis-and-prediction-of-online-shoppers-purchasing-intention-using-various-algorithms-CAPSTONE Public

    Build a predictive machine learning model that could categorize users as either, revenue generating, and non-revenue generating based on their behavior while navigating a website. In order to predi…

    Jupyter Notebook 6 1

  2. Brand-perception-analysis-Social-media-analytics-Text-mining Brand-perception-analysis-Social-media-analytics-Text-mining Public

    Do a brand perception of a Twitter trend by extracting Twitter mentions for the brand and conduct text mining (Correlation, Frequency, Topic Modelling, Sentiment Analysis) on it.

    HTML 1

  3. Forecast-5-year-souvenir-data-sales Forecast-5-year-souvenir-data-sales Public

    Forecast 5 years sales of souvenir data using Holts-winters and ARIMA methods.

    R 1

  4. India-credit-risk-default-model India-credit-risk-default-model Public

    Creating an India credit risk(default) Logistic Regression model

    R 1

  5. Loan-Purchase-Modelling-TheraBank Loan-Purchase-Modelling-TheraBank Public

    Build a model that will help them identify the potential customers who have a higher probability of purchasing the loan.

    R 1

  6. Telecom-Customer-Churn-Prediction-Assessment Telecom-Customer-Churn-Prediction-Assessment Public

    Simulate one case of customer churn where we work on a data of postpaid customers with a contract. Predict whether a customer will cancel their service in the future or not.

    R 1