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Predicting the Price of Bitcoin: Using Machine Learning Time Series Methods
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Ulutaş, Sezer; Utku Koç
Cryptocurrencies have greatly increased their Bitcoin-led popularity in recent years due to increased trading volumes and massive capitalization in the market. These cryptographic forms of money are not just utilized for exchanging nowadays, they are additionally acknowledged for fiscal exchanges. It appears to be evident that financial specialists, dealers and people, in general, are progressively intrigued by bitcoin and altcoins as costs rise and the arrival on ventures made increments. This examination centres around applying estimate models that will make precise value forecasts forcryptographic forms of money. The data were taken from two different exchanges and evaluated as combined dataset. As a result of the evaluation, it was determined that the prices were close to each other in terms of value and the data were combined. We obtained the daily time series data by determining the Bitcoin weighted price as a dependent variable and Open, Close, High, Low and Volume as independent variable. We predicted the next 6 months with ARIMA, LSTM and XGBoost methods. We compared these estimates using MSE, MAE, MAPE and R squared performance metrics. LSTM is the model with the best R squared value of 29.7%. In the process performed by taking the average of LSTM, XGBoost and ARIMA performed with the name of Average ML method, the R square value was found to be 41.6% as a much better result than LSTM.
Product Recommendation for C2c Marketplace With Collaborative Filtering Als Algorithm
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kıran Çelebi, Bilgehan; Utku Koç
In this project, a machine learning recommendation model is created for an e-commerce company which runs a customer to customer business. The raw data consisted order reviews, order details, product like event information and product details. The explicit and implicit feedbacks are used together and a rating generation logic per user-product couple is applied to create the source data of the model by using Google Cloud BigQuery tool. The ALS algorithm which uses matrix factorization is applied for predicting the top items which have highest ratings for each user. PySpark which is Apache Spark’s python API is used for implementing the ALS model. The best hyperparameters are determined comparing the root mean square error results by using grid search and cross validation and 0.78 of RMSE is reached. The predictions for the empty ratings are sorted then top rated 10 products are taken as recommendations. The evaluation of the model is done by comparing those recommendations with the user preferences. The user preferences are specified by using averagely top rated product categories and most interacted product categories in count. The recommendations are observed to be consistent with the user preferences.
Big Data Analytics on Used Car Information
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Demir, Efe; Utku Koç
In this research, a decision support system is implemented on a used car dataset. The main purpose is to predict the price information and reveal the related features. The price prediction problem is classified as a regression problem. The key point is to find the best-fitting model and obtain the best accurate prediction outcomes. Should we buy this car, or at what price may I sell my car? This work is about to answer these questions. Various regression models are compared, and detailed results are explained correspondingly.The constructed models will help customers to know about their car price and salability. And they can identify the buying opportunities. The percentage error approach which is detailed in the results section will be a guideline for customers/firms to make a market analysis or detect fraudulent listing information.

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