serdargoler

Analyzing-impact-of-the-dump-site-on-house-prices

If we need to locate a new garbage dump site near our city, we must look for the optimal location to minimize its impact on house prices in the area. In the report, the dataset is analyzed, and a machine learning technique is constructed to determine how large the impact of a dumpsite on house prices in the area is. Dataset is analyzed and the model is developed in R.

Challenge

concrete

confusion_matrix

Contains cf_matrix.py file with a function to make a pretty visualization of a confusion matrix.

Correlation-between-cryptos

CTG

datacamp

debt-collection-agency

• Framework for calculating IRRs for a given set of cash flows and an initial investment • 5Y cashflow forecasts for each of the claims in the new portfolio • Model including cashflows, costs, IRRs The project was performed using MS Excel, R, and MicroStrategy. Some parts of the project on R can be seen on Kaggle.com using the link below.

lazypredict

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

web-scraping-ElsevierAPI

serdargoler/web-scraping-ElsevierAPI

serdargoler/Analyzing-impact-of-the-dump-site-on-house-prices

If we need to locate a new garbage dump site near our city, we must look for the optimal location to minimize its impact on house prices in the area. In the report, the dataset is analyzed, and a machine learning technique is constructed to determine how large the impact of a dumpsite on house prices in the area is. Dataset is analyzed and the model is developed in R.

serdargoler/Challenge

serdargoler/concrete

serdargoler/confusion_matrix

Contains cf_matrix.py file with a function to make a pretty visualization of a confusion matrix.

serdargoler/Correlation-between-cryptos

serdargoler/CTG

serdargoler/datacamp

serdargoler/debt-collection-agency

• Framework for calculating IRRs for a given set of cash flows and an initial investment • 5Y cashflow forecasts for each of the claims in the new portfolio • Model including cashflows, costs, IRRs The project was performed using MS Excel, R, and MicroStrategy. Some parts of the project on R can be seen on Kaggle.com using the link below.

serdargoler/lazypredict

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

serdargoler/demo-project

serdargoler/Pandas-Data-Science-Tasks

Set of real world data science tasks completed using the Python Pandas library

serdargoler/Performing-business-analytics

• Combining the datasets, so it is possible to monitor monthly sales trends for each of the flower types at each store location. • Taking note of any discrepancies or apparent faults in the data, so Parviflora can use that information to amend their systems. • Preparing a presentation in R Markdown, showing the most important observations from the data analysis. • Preparing the R code for data importing in such a way that will be able to import data for more than 3 months with no modifications to code. • Preparing the R Markdown presentation in such a way that it will automatically update when more than 3 months of data is used as an input.

serdargoler/R-bar-chart

Simply visualizing economic data in R

serdargoler/serdargoler

serdargoler/water-access-effect-on-life-expectancy

In this case, I am going to look at the strength of the correlation between ‘accessed to improved water sources’ and ‘life expectancy at birth’. Therefore, I have searched data sources and found ‘life expectancy 2014 dataset’, in https://www.kaggle.com/kacperk77/life-expectancy