This is an exciting project that aims to predict cryptocurrency prices using artificial intelligence (AI) and machine learning (ML) techniques. The project uses historical data of various cryptocurrencies and applies different algorithms to predict their prices in the future.

Successfully established a supervised machine learning model which can accurately predict the gross sales generated by an XYZ company based on its weekly spends on distinct marketing channels across a span of 4 years from 2015 to 2019.

Successfully established a supervised machine learning model which can accurately forecast the total weekly sales amount obtained at Walmart stores, based on a certain set of features provided as input.

Successfully established a machine learning regression model which can estimate the gross Black Friday sales for a particular customer, based on a distinct set of related and meaningful features, to a fair level of accuracy.

This repository contains several ML algorithms wriiten from scratch that are covered in ML lab.

NU Bootcamp Module 21

The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.

This project showcases a cats vs dogs image classification model using image augmentation and Keras. It employs deep learning and convolutional neural networks (CNNs) to accurately classify images of cats and dogs.

Successfully established a supervised machine learning model that can accurately predict whether the travel insurance claim of a particular customer should be approved or not by a travel insurance agency.

Successfully established a supervised machine learning model which can predict the quality of a wine to a high level of accuracy based on a certain set of features associated with the chemical properties and characteristics of that specific wine.

The nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures

🏡 Empower property market decisions with a machine learning model predicting house prices using the Boston Housing dataset. 💸🏠💹

Code that can be used for training a neural network model to detect faults (sticky notes, folded corners etc.) in input documents.

Code that can be used for training a neural network model to classify input documents into distinct classes.

Nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures. Using machine learning and neural networks, you’ll use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded.

Zomato delivery time prediction

Successfully developed a machine learning model which can accurately classify the weather based on various features pertaining to weather-related data and atmospheric conditions.

Successfully developed a machine learning model which can accurately classify the credit score of a customer based on his/her's basic bank details and a lot of other credit-related features.

Successfully established a machine learning model to accurately predict the price of a flight in India based on several features such as duration, days left, arrival time, departure time and so on.

Successfully established a machine learning model that can accurately classify an e-commerce product into one of four categories, namely "Books", "Clothing & Accessories", "Household" and "Electronics", based on the product's description.