expolatory-data-analysis

Data Analysis on COVID19 dataset, published by John Hopkins University

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.

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

Basic pipeline for exploratory data analysis(EDA) which can be applied to various datasets with one main function.

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.

Predictive analysis of Japanese media IP to optimise global market expansion.

An end-to-end data analysis of retail superstore sales, customer behavior, and profitability dynamics. Covers data cleaning, feature engineering, exploratory data analysis across 5 core business questions, and RFM customer segmentation

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.

From Concept to Code: Breathing Life into Ideas.

Modern Data Warehouse with SQL Server using Medallion Architecture (Bronze, Silver, Gold). Includes robust ETL pipelines, dimensional data modeling, and advanced analytics to deliver scalable, high‑quality insights with best practices in governance and performance.

Aims to uncover trends, identify influencing factors, and provide actionable insights

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.

Complete data analytics workflow on hotel booking demand: data cleaning, EDA, feature engineering, visualization, and machine learning for cancellation prediction.

My AIML Journey

LSTM-based pipeline with dynamic thresholding for detecting anomalies in time series (subset of full FYP)

This project applies Exploratory Data Analysis (EDA) using visualizations like box plots, scatter plots, and correlation matrices. It helps identify patterns, select ideal functions via least squares error, and map test data while evaluating deviations.

analisis data berskala besar untuk mengeksplorasi perbedaan perilaku antara pengguna Member (berlangganan) dan Casual (menit) pada sistem peminjaman sepeda Divvy Bikes di Chicago.

ML-based time series forecasting for 7 merchants using LightGBM. Includes date, lag, rolling mean, and EWM feature engineering with strict forecast horizon enforcement to prevent data leakage. Custom MAPE metric evaluates per-merchant performance independently.

House price prediction user can multiple input and predict price

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