sahar-farhat
Passionate Data Analyst pursuing a Master's in Big Data Analytics, translating complex data into actionable insights. Experienced in sales and marketing. 📊📈🔍
Bahcesehir universityTurkey
Pinned Repositories
Data-scrapping
This is a code to scrup data from a PMS system on a daily bases.
Femicide-In-Turkey-2008-2020
The project aims to analyse the Femicide cases in Turkey and forecast the deaths until 2030 using ARIMA.
Marketing-Campaign-Acceptance-Prediction-with-Machine-Learning-And-Deep-Learning
This project analyses a large dateset from an e-commerce website, to predict the acceptance of their marketing campaign using a deep learning algorithm.
Online-shoppers-intention
For this study, by using a data collected from the usage history over a year, we tried to understand first the consumer’s profile. Second, we applied three different classification algorithms. We concluded that the random forest classifier is the best to predict consumption pattern for online shopping.
Sales-forecasting-for-e-commerce-Application-of-Hybrid-ARIMA-and-Auto-ARIMA
. In this work, we test a hybrid model that is suitable for modeling linear and non-linear sales trends by combining an ARIMA with the most predictive clustering model and then testing the Auto-Arima model to forecast sells for near future.
sahar-farhat's Repositories
sahar-farhat/Data-scrapping
This is a code to scrup data from a PMS system on a daily bases.
sahar-farhat/Femicide-In-Turkey-2008-2020
The project aims to analyse the Femicide cases in Turkey and forecast the deaths until 2030 using ARIMA.
sahar-farhat/Marketing-Campaign-Acceptance-Prediction-with-Machine-Learning-And-Deep-Learning
This project analyses a large dateset from an e-commerce website, to predict the acceptance of their marketing campaign using a deep learning algorithm.
sahar-farhat/Online-shoppers-intention
For this study, by using a data collected from the usage history over a year, we tried to understand first the consumer’s profile. Second, we applied three different classification algorithms. We concluded that the random forest classifier is the best to predict consumption pattern for online shopping.
sahar-farhat/Sales-forecasting-for-e-commerce-Application-of-Hybrid-ARIMA-and-Auto-ARIMA
. In this work, we test a hybrid model that is suitable for modeling linear and non-linear sales trends by combining an ARIMA with the most predictive clustering model and then testing the Auto-Arima model to forecast sells for near future.