A Recommender system that predicts your next order based on your previous purchases. Also, it discuss the association between product purchases.

Basics of ML libraries Explained through Jupyter Notebooks

Divar's 2021 Data Analyst summer camp entrance task.

This repository contains my learning path of python for data-science essential training(part-2). Here, I have included chapter-wise topics and my practice problems. Also, feel free to checkout for better understanding.

This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course

What attributes influence the selection of a romantic partner?

🏘 Ames house dataset modelled and explained

Crawl data, process data, visualize, and create ML model for laptop price prediction

Predict house price using linear regression model

University of California Davis Specialization Certificate in Data Visualization with Tableau

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

The objective of this work is to investigate factors affecting borrower rate and loan amount.

# PISA 2012 Data ## by Nadine Amin ## Dataset > PISA is a survey of students' skills and knowledge as they approach the end of compulsory education. It focuses on examining how well prepared the students are for life beyond school. > Around 510,000 students in 65 economies took part in the PISA 2012 assessment of reading, mathematics and science…

This is the 6th project in my data analysis nanodegree and it focuses on prforming exploratory data analysis ( or EDA for short ) in R

This repository was just for my practice. Here, I have performed explanatory data analysis on the famous titanic dataset from kaggle.

Stock Price Prediction of APPLE Using Python

The analysis and prediction of TMDB dataset

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

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

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.