Investigate Ford GoBike Project

This is a part of the exercise project provided by Dicoding in "Learn Data Analytics with Python" course.

Cleaned FordGoBike data for 2019 was analyzed using different pots (univariate and multivariate) to draw conclusion over the distribution relation between different categorical and numerical variables

Divar's 2021 Data Analyst summer camp entrance task.

A Comprehensive exploratory data analysis (EDA) on a loan dataset to uncover key trends, patterns, and relationships among various loan attributes. By visualizing and analyzing the data, we aim to gain insights into loan performance, borrower characteristics, and market dynamics. 🪙🏦

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.

This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others. The analysis explore the factors and patterns in the creditworthiness of borrowers and the borrowing trend of Prosper Loan Business.

This repository demonstrates the use of Pandas Profiling library for Exploratory Data Analysis (EDA) within a Jupyter Notebook. By automating much of the EDA process, the library generates comprehensive and interactive reports, complete with insightful visualizations to facilitate data understanding.

This is an implementation for the famous Kaggle competition "House Prices" using EDA tools, feature engineering, handling outliers and missing data and finally machine learning linear models and regularization.

Online Gaming Case Study

A project is to make a simple Exploratory Data Analysis to find if there is a direct relationship between income and the level of education in Canada.

Exploratory & Explanatory Analysis of Prosper Loan Data.

Analysing the data of uber using R

This project explores the relationship between features and diagnosis in cancer data. Using methods like boxplots, scatterplots, PCA, k-means clustering, and logistic regression, we analyze and visualize data to understand health indicators.

University of California Davis Specialization Certificate in Data Visualization with Tableau

🍷 Quality analysis of red and white variants of the Portuguese "Vinho Verde" wine

Predicting House Prices using Linear Regression Model

performed end-to-end reproducible data analyzes in the IE48A - Essentials of Data Analysis course taught at Boğaziçi University