Jupyter notebooks published as blogs

Game of Thrones ⚔️ 🐉: Visualising networks using Networkx, Pyvis and Community detection

In this article, we delve into the application of network analysis theory, a branch of mathematics and computer science, and apply this to the world of “Game of Thrones.” Using Python libraries such as NetworkX, Pandas, and Pyvis, we will examine the intricate character relationships within the popular series. This analysis will provide insights into the dynamics of the narrative, identify key characters, and reveal the underlying connections that shape the fascinating world of Westeros.

Four ways to embed images in your Jupyter notebook-powered blog

Learn how to embed images in your posts created from Jupyter notebooks# Title

Streaming data animation with Bokeh

Learn to create animated bokeh charts that show data streaming in over time.

NLP for classification

Using NLP (TF/IDF , LSTM recurrent neural network) to classify Udemy courses

DATA CLEANING AND DATA ANALYSIS OF MOVIES DATASET USING MYSQL

Analysis of the movie dataset to identify trends and gain insights into the data. It has 1000 rows and 15 columns.

Image denoising using Convolutional Neural Networks

In this project three noisy images were denoised using Convolutional Neural Networks

Fuzzy C Means Clustering

In this project I used Fuzzy C Means to determine the clusters in several datasets which are in image form.

Predicting Students' Dropout and Academic Success via Classification

I used KNN, SVM, SGDClassifier and Random Forest to classify student data in order to identify who will graduate, drop out or stay enrolled. The report of this project can be found at https://github.com/betulmesci/classification-of-students/blob/main/Classification_final.pdf

Image Processing Tutorial Using scikit-image - Thresholding

In this section of the tutorial, I explored various thresholding techniques. I measured and compared the bacterial growth in several petri dishes using masking and thresholding techniques as an exercise.

Cement_Manufacturing_Analysis

This is an analysis of the Compressive Strength of cement. Building a Predictive Model on the variables that affect the Strength of cement.

Sampling and Quantization of Images

Sampling and Quantization of Images are discussed in detail.

Image Segmentation using Several Methods

In this project image segmentation using K-Means Clustering, Contour Detection, Thresholding and Color Masking was discussed.

Image denoising using TOMOGANS

In this project we used GANs (specifically TOMOGAN) to denoise three noisy images. We had much better results than CNNs.

Edge Detection

In this project, Roberts, Sobel and Prewitt filters were implemented from scratch and applied to images to detect edges of the objects.

Predicting Stock Prices of Home Depot Based on Trends and the Sentiment of News and Tweets

In this project, I applied Linear Regression and CNN+LSTM on Home Depot's historical stock price data. I gathered tweets and Google news results related to Home Depot and ran sentiment analysis on them. I also incorporated the number of Google searches of Home Depot (trends). I applied Linear Regression and CNN+LSTM on the historical + sentiment + trends data. Finally, I applied K-Means clustering and incorporated that information in the data as well and ran the same analysis. The models, especially CNN+LSTM, could predict the target variable almost perfectly.

Image Processing Tutorial Using scikit-image - Noise

In this part, Gaussian (random) noise and adding and removing different kinds of noise to the images are discussed.

Image Processing Tutorial Using scikit-image - Image Segmentation

In this section, I talked about two kinds of image segmentation techniques: Supervised and Unsupervised. For Supervised method, I explored Active Contour and Random Walker methods to detect the face of a person. For Unsupervised method, Simple Linear Iterative Clustering (SLIC) and Felzenszwalb Clustering techniques were discussed.

DeepWalk Algorithm

DeepWalk is a type of graph neural network that uses language model to learn latent representations of vertices in a network. DeepWalk includes two main components: Firstly, it uses random walk to infer main local components in the graph. Secondly, it uses SkipGram algorithm to train parameters for embeddings.

Let's build! Hartree-Fock code from first principles part 1

Introduction to the diagonalization of a quantum mechanical Hamiltonian