This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.

🌌 The R easystats-project

Dashboard Builder connected to Km4City smart city platform, developed per REPLICATE H2020

This repository contains a comprehensive Power BI dashboard project focused on analyzing credit card transactions, customer demographics, and overall customer analytics. The dashboard consists of three main reports: Credit Card Transaction Report , Credit Card Customer Report & Customer Analytics

It serves as a central repository for tracking my analysis on Spotify Popular Song dataset.

This project is based on the real time dataset provided by telangana government open data website.

A/B Testing means analyzing two marketing strategies to choose the best marketing strategy that can convert more traffic into sales (or more traffic into your desired goal) effectively and efficiently.

This repository holds an ever increasing and updated list of AI and data learning resources grouped into skills.

Explore the H1B Visa data from 2014 through a comprehensive multivariate analysis.

By Daryan Dana , Our project collects key customer details for a Café, such as Customer ID, Age, Gender, Beverage Ordered, Size, Total Amount Spent, Payment Method, Transaction ID, Staff Name, and Staff ID. These insights are crucial for analyzing Coffee Shop operations and customer preferences.

Inside this repository, you'll discover a comprehensive notebook dedicated to showcasing various NumPy array methods and operations. From basic array manipulation to advanced techniques, I've compiled a collection of examples and explanations to help both beginners and seasoned Python developers deepen their understanding of NumPy.

Detailed exploration of random forest regressors, including data cleaning, model building, and performance evaluation on various datasets.

Comprehensive exploration of decision tree regressors, including data cleaning, model building, and performance evaluation on various datasets.

In-depth exploration of linear regression models, including data cleaning, model building, and performance evaluation on various datasets.

Detailed exploration of random forest classifiers, including data cleaning, model building, and performance evaluation on various datasets.

👏Comprehensive exploration of decision tree classifiers, including data cleaning, model building🏩, and performance evaluation on various datasets.

An in-depth exploration of logistic regression models, including data cleaning, model building, and performance evaluation on various datasets.

During my internship as a Data Analyst, I had the opportunity to work on various projects that involved analyzing and interpreting data to help make informed business decisions. I worked on projects such as HR Data Analysis, Diabetes Prediction, Pharma Data Analysis, and Employee Data Analysis.

This include all my data analysis study works and assignments.

Analyzed supply chain data to identify trends and key factors. Visualized sales, defect rates, lead times, and costs. Used Decision Tree Regressor to find top features impacting product costs and lead times.