Abhishek Ghosh's repositories
Smoke-Detection-using-Tensorflow-2.2
An experimental repository to build ML models and perform efficient wildfire smoke detection.
Python-Data-Structures
A repository to learn data structures and algorithms in Python.
Disaster-Response-ML-Project
This project involves ETL and a Machine Learning Pipeline implementation that classifies tweets to assist people with a disaster emergency response using a Flask Web App.
Collaboration-covid19-analysis
Analysis of COVID-19 data from Kaggle to perform exploratory data analysis and create visualizations.
ArvatoAnalytics-Identify-Customer-Segments
Unsupervised Learning Project
BI-Consolidated-Report-for-Dominick-Fine-Food
Dominick’s Fine Food (DFF) is a grocery store chain and subsidiary of Safeway Inc. with locations mainly in the Chicago area. We are planning to design and develop a data warehouse for this retail store chain. The data is collected from the DFF database from Chicago Booth which consists of 25 product categories in over 100 retail chain stores. Given these important data files, such as customer, location, and sale information, we would generate a bunch of tables with attributes that help managers to make decisions regarding product sales.
Customer-Creditworthiness-Binary_Classification_Modelling
Comparison of logistic regression, decision tree, random forest and boosted model to predict customer loan creditworthiness
Data-Scientists-vs-Software-Developers
A survey analysis of Stack Overflow Developer Survey Results 2019.
Dog-Breed-Classifier-using-CNN
The project focuses on implementing a classifier to identify dog breeds using CNN from scratch and CNN using transfer learning. At the end of the project, we have an algorithm that predicts the closest dog breed a human face resembles.
Forecasting-Fresh_Produce_Sales
Build an ETS/ARIMA model to forecast fresh produce sales of existing and new stores to ensure optimal stock availability.
IBM-Recommendation-Engine
Build a recommendation system using IBM Watson Studio platform.
Predict-Catalog-Demand
Build a linear regression model to predict the expected profit of a new catalog from 250 new customers. Based on the prediction, recommend the company to make a decision on potential rollout of the catalog.