Ayan Chattaraj's repositories
ayan-chattaraj
Config files for my GitHub profile.
Bike_Sharing_Assignment
This assignment was for a US bike-sharing provider, BoomBikes, wherein a multiple linear regression model was used to predict demand for shared bikes to help the company come up with a mindful business plan to accelerate its revenue.
Credit_EDA_Case_Study
Developing a basic understanding of risk analytics in BFSI sector
detecting_melanoma_from_images
In this project, I created a multiclass classification CNN-based model which can detect melanoma with a validation accuracy of 80%. The dataset consists of 2357 images of malignant and benign oncological diseases.
eye_for_blind
In this capstone project, I created a deep learning model to explain the contents of an image in the form of speech through caption generation with an attention mechanism on the Flickr 8K dataset. The caption generated through the CNN-RNN model has been converted to speech using the Google translate python library.
gesture_recognition
My team partner and I did this project where we developed a feature in a company’s smart TV that can recognise five different predetermined gestures performed by the user, which will help users control the TV without using a remote.
house_price_prediction
I executed this assignment for a US-based housing company named Surprise Housing, wherein a regression model with regularisation was used to predict the actual value of the prospective properties and decide whether to invest in them or not
lead_scoring_case_study
My team partner and I did this analysis for X Education to find ways to get more industry professionals (Hot Leads) to join their courses.
RSVP_movies_case_study
My team partner and I did this analysis for a production company to release a new movie for a global audience in 2022. Here we analyzed the past three years' IMDB data using SQL.
telecom_churn_prediction
My team partner and I did this project where we analysed customer-level data of a leading telecom firm, built predictive models to identify customers at high risk of churn and identified the top indicators.