This project analyzes hotel booking cancellations and revenue generation factors for City Hotel and Resort Hotel. The dataset contains booking data from July 2015 to August 2017, covering bookings, cancellations, and arrivals. Insights gained aim to improve revenue efficiency and reduce cancellations.

The Food Price Estimation project focuses on providing estimates of food prices to capture local price fluctuations in regions where people are vulnerable to localized price surges. The project utilizes a machine-learning algorithm designed to predict ongoing subnational price surveys, demonstrating accuracy comparable to direct price measurements.

Understanding Historical data to interpret customers that are likely to churn by performing predictive modelling

The primary goal of this exploratory data analysis is to create a reliable and effective Python program. Based on the training data, the program should select the four best-fit ideal functions from a set of 50 functions and then map the test data to these chosen functions while taking a deviation criterion into account.

This is tool which can be used by students and teachers in order to predict a students grade based on 19 different attributes that needs to be filled by the user. It uses a simple Linear Regression model to predict continuous values

This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.

To clean and analyze data to find trends in global population, fertility, and life expectancy from 1960 to 2016. This idea was inspired by hans rosling . To analyze the data, I used a scatter bubble chart, which clearly shows how's the population increased and the fertility rate decreased from 1960 to 2016.

From Concept to Code: Breathing Life into Ideas.