AYŞE DUMAN's repositories
Ensemble-Learning-with-Credit-Card-Application
In this study, the credit card operation dataset was used where the original dataset in the UCI Machine Learning Repository is available. The aim of the analyze is predict whether or not a given customer should be approved for their credit application. I am dealing with six different ensemble learning techniques such as Averaging, Weighted Averaging, Max Voting, Bagging, Boosting, Stacking. Explanations of the applied techniques have been made. While using these methods, Packt course was used for the necessary definitions.
K-Nearest-Neighbors
K Nearest Neighbors algorithm in Python and some applications
SEABORN-TUTORIAL
Relational Plot, Two Dimensional Plot, Scatter Plot, Pair Plot, Faceted Plot, Box Plot, Bar Graph, and Density Plot
Ensemble-learning-and-comparing-different-models
Data Set Description Dataset Overview Visualizing the Data Set Lost Value Analysis Data Pre-Processing Community Learning Simple Community Techniques Max Voting Averaging Weighted Average Advanced Community Techniques Stacking Blending Bagging Boosting Algorithms Using Bagging and Boosting Bagging meta-estimator Random Forest Gradient Boosting XGBoost LightGBM CatBoost The topics are covered in detail. Model performance results and analysis are included.
Matplotlib-Tutorial
Matplotlib Tutorial
Python-Pandas-Library-Exercises
Full Pandas Library Exercises
Sentiment-Analysis-On-Stocks-Data-Using-Natural-Language-Processing
This study is about creating a sensitivity classifier model using messages from customers. We have a binary classification problem that categorizes stock sensitivity data as positive or negative. 1 indicates positive sentiment and 0 indicates negative sentiment. The main resource I used in the study is the Python & Machine Learning for Financial Analysis course on Udemy. The main steps are as follows: Importing required libraries(pandas,numpy,seaborn,matplotlib,nltk,gensim,tensorflow) Explanatory Data Analysis Data cleaning (removing punctuations and stopwords from text) Visualization of cleaned dataset and plotting wordcloud Prepare the data by tokenizing and padding Building a custom-based deep neural network for sentiment analysis (embedding layer, LSTM network) Making prediction and assessing the model performance (confusion matrix)
-Preprocessing-and-data-visualization-of-the-titanic-dataset
Kodluyoruz istatistik ve veri ön işleme çalışma grubunda Eğitmenimiz tarafından önerilen Titanic veri seti üzerindeki çalışmam yer almaktadır. Bu çalışmada veri setinin betimsel istatistikleri, veri görselleştirmesi, eksik (kayıp) veri analizi yöntemleri (missing value analysis methods) , aykırı değer analizi (outlier detection) yöntemleri ilgili veri setine uygulanmıştır.
Clustering_by_Business_Income_and_Expenses
load and visualize data and clusters with scatter plots; prepare data for cluster analysis; perform centroid clustering with k-means; interpret clustering results and determine the optimal number of clusters for a given dataset.
Regression-and-Gradient-Descent-Algorithm
single and multivariate regression analysis and application of gradient descent algorithm
inzva-applied-ai-practice
In this repository, there are various exercises given during the 4-week training period.
linked-list
A school assignment was conducted on the linked list using the C programming language.
python-algorithm-exercises
In this repo, questions on hackerrank, edabit, arthead platforms have been solved with the python programming language.
Applied-AI-Study-Group
This is the repository for the content of inzva Applied AI Study Group.
datacamp-python-data-science-track
All the slides, accompanying code and exercises all stored in this repo. 🎈
Euler-RungeKutta4-Heuns-Method
Graphical representation of Euler-Runge Kutta 4 and Heun Method with numerical approach.
Kodluyoruz-Uygulamal-Veri-Bilimi-102-Bootcamp-odev-ve-Uygulamalar
Kodluyoruz Veri Bilimi 102 Bootcamp eğitiminde verilen ödev ve temel uygulamalar yer almaktadır.