Ali Unlu (ali-unlu)

ali-unlu

Geek Repo

Location:Helsinki

Home Page:https://aliunlu.com/en/

Twitter:@aunlu

Github PK Tool:Github PK Tool

Ali Unlu's repositories

Amazon-Kindle-recommendation-model

Create a sentiment model to identify the product features and recommendation trends

Language:HTMLStargazers:0Issues:1Issues:0

Amazon-product-review

Amazon Kindle reviews in Amazon Store

Language:HTMLStargazers:0Issues:1Issues:0

awesome-community-detection

A curated list of community detection research papers with implementations.

Language:PythonLicense:CC0-1.0Stargazers:0Issues:0Issues:0

awesome-computational-social-science

A list of awesome resources for Computational Social Science

License:CC0-1.0Stargazers:0Issues:0Issues:0

bank-customer-churn-analysis

The aim of this post is to predict the churn of bank customers.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

Language:PythonLicense:CC-BY-SA-4.0Stargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:1Issues:0

Comparative-Principal-Component-Analysis

In this analysis, I will demonstrate how PCA works in different tasks and how much time and resources we save in our daily analysis.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

Comparison-of-Finnish-Sentiment-dictionaries

Sentiment analysis for the Finnish Language

Language:HTMLStargazers:0Issues:1Issues:0

Data-science

Collection of useful data science topics along with code and articles

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

License:MITStargazers:0Issues:0Issues:0

EDA-Car-Dataset

In this analysis, I will demonstrate how to explore the cars dataset using Python. I will first load the dataset and then process the data. I will also visualize the dataset and then finally apply ttest to explore the relationship between variables.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:1Issues:0

group-projects

Learning materials

Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

K-Means-Cluster-Analysis

In this analysis, I will demonstrate how PCA and K-Means clustering can be applied to credit risk data. In this data set, we do not have a target variable, which leads us to build an unsupervised machine learning model.

Language:Jupyter NotebookStargazers:0Issues:1Issues:1

machine-learning-book

Code Repository for Machine Learning with PyTorch and Scikit-Learn

License:MITStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

network-analysis

Course web page

License:GPL-3.0Stargazers:0Issues:0Issues:0

Network-Analysis-Made-Simple

An introduction to network analysis and applied graph theory using Python and NetworkX

Language:Jupyter NotebookLicense:MITStargazers:0Issues:0Issues:0

predicting-drug-use

Machine learning for social science

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Predicting-game-sales-with-pyhton

The aim of this demonstration is to illustrate how regression models could be used to predict global game sales. I will use three regression models, including Lasso, Ridge and ElasticNet with cross validation tecnique.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Predicting-global-game-sales

Predicting global game sales with R

Language:HTMLStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

sentiment-analysis-with-tensorflow

In this project we will create and train a neural network model to classify movie reviews taken from IMDB as either a positive review or a negative review. Essentially, we want to create and train a neural network model which, given a text review, will be able to predict if the overall sentiment of the review is more negative or more positive.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Time-Series-Analysis-with-Facebook-Prophet

I previously analyzed the same data with SAMIRA to predict the future temperature for Istanbul. In this analysis, I will re-analyze the same topic with a different model. As known, fbprophet is provided by Facebook to predict the future trend. Prophet follows the sklearn model API.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Time-Series-Analysis-with-LSTM

I previously illustrated two forecasting implementations with the same data set. This is the last one in these series using deep learning model.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

Time-series-analysis-with-SARIMA

In this demonstration, we make a time series analysis based on a temperature data set.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

Travel-Customer-Churn-with-Oversampling

The aim of this post is to identify and visualize factors that contribute to customer churn of a travel company.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

Vehicle-gas-consumption-by-transmission-types

In this exercise, I will show the relationship between a car’s transmission and the number of miles per gallon of gasoline, along with a set of other variables that could affect this relationship.

Language:HTMLStargazers:0Issues:0Issues:0