Udacity - Data Analyst Nanodegree - Project 5 - Data Visualization

Udacity Data Analyst Nanodegree - Project V

A python package with standard data visualization functions with reasonable defaults for use in Exploratory Data Analysis and Model Diagnostics.

Auto-Forecasting is a web application that takes in an excel file with univariate time series data and provides forecasts. Auto-Forecasting works on SARIMA modeling.

Performed an exploratory data analysis using python and presented explanatory plots that convey insights of data.

Descriptive Statistics 2023/2024

HHA507 / Data Science / Assignment 3 / Exploratory Data Analysis

Simple data analysis project to analysis performance team and player of VCT (Valorant Champions Tour) APAC 2023

A presentation of my exploratory data visualization results with univariate, bivariate, and multivariate features.

A food aggregator company has stored data of different orders made by registered customers in their online portal. They want to analyze data to draw some actionable insights for business. Perform data analysis to find answers that will help the company to improve business.

As a Data Scientist for the food aggregator company, I'll analyze order data to derive actionable insights. I'll address key questions like popular cuisines, peak order times, customer preferences, and delivery efficiency. Insights will guide improvements to boost customer satisfaction and streamline operations.

A complete Data Visualization tutorial using Seaborn and Matplotlib

Exploratory Data Analysis of Sleep Efficiency Classifier

Udacity Data Analyst Nanodegree Project 5 (Data Visualization)

Explanatory/Exploratory Data Analysis on Ford-bike data-set

Explanatory/Exploratory Data Analysis on Telecom-Customer-churn.

This project is conducted as a part of Udacity Data Analyst Nanodegree. The purpose of this project is to perform exploratory data analysis, then create a presentation with explanatory charts that conveys findings and insights from the data set provided.

Engage in the critical phase of Exploratory Data Analysis (EDA) using the tools and techniques from Python to uncover patterns, spot anomalies, test hypotheses, and identify the main structures of your dataset.