Projects

You can find detailed descriptions of completed projects and the technologies used here.

Project 1:Olympics Athlete Events Analysis using Databricks

The dataset consists of 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa.

Goal:It is our duty to predict the sales price for each house by using spark machine learning regression. (SalePrice)

Link:https://github.com/elifkeskin/Prediction-of-House-Prices

Project 3: Monthly Target Report

Project 4: Sales Dashboard

The target achievement rate percentages of each employee in the company, based on the last 3-month period, all periods and the general table, are shown based on selections using an interactive dashboard.

Link:https://app.powerbi.com/links/WaJnTv5Esy?ctid=123a4777-a6f2-41d9-8861-07fde9bb2e16&pbi_source=linkShare

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Project 2 : Prediction of House Prices Using Spark Machine Learning

Olympics, the most prestigious event in the life of athletes and aspiring athletes is celebrated and respected around the world. This dataset consists of Olympics data of over a century, from the year 1896 to 2016.This project was implemented on Databricks using pyspark and sql.

Link:https://github.com/elifkeskin/Olympic-Games/tree/main

An interactive dashboard was prepared showing the distribution of the company's sales by products, regions, years and employees.

Link:https://app.powerbi.com/links/tECxRBUIz2?ctid=123a4777-a6f2-41d9-8861-07fde9bb2e16&pbi_source=linkShare

Project 5: Churn Modelling By Spark Machine Learning

This data set contains details of a bank's customers and the target variable is a binary variable reflecting the fact whether the customer left the bank (closed his account) or he continues to be a customer.

Goal:It is our duty to predict the customer left the bank (closed his account) or he continues to be a customer by using spark machine learning classification.

Link:https://github.com/elifkeskin/Churn-Modelling

Project 6: Sentiment Analysis & Modelling

Kozmos, which produces home textiles and daily wear and sells through Amazon, its sales by analyzing the comments on its products and improving its features according to the complaints it aims to increase.

Goal:Comments will be tagged by sentiment analysis and a classification model will be created with the labeled data.

Link:https://github.com/elifkeskin/Amazon

Project 7: Web Scraping with Beautiful Soup & Selenium

A company that sells books online, It was seen that it had low sales in the “Non-Fiction” and "Travel" categories.For this reason, it is allowed the rival company for the web scraping.

"https://books.toscrape.com/" in the "Travel" and "Nonfiction" category from the website taking information about books and analyzing competitors and prices needs to do.

Goal:It is our duty to identify each book in these categories, go to the detail pages and view some of the contents there.

Link:https://github.com/elifkeskin/Web-Scraping/tree/main