Jan Nordin's repositories
Twitter-moods-as-stock-price-predictors-on-Nasdaq
An attempt to predict next day's stock price movements using sentiments in tweets with cashtags. Six different ML algorithms were deployed (LogReg, KNN, SVM etc.). Main libraries used: Pandas & Numpy
Analysing-IMDB-reviews-using-GloVe-and-LSTM
Using the IMDB data found in Keras here a few algorithms built with Keras. The source code is from Francois Chollet's book Deep learning with Python. The aim is to predict whether a review is positive or negative just by analyzing the text. Both self-created as well as pre-trained (GloVe) word embeddings are used. Finally there's a LSTM model and the accuracies of the different algorithms are compared. For the LSTM model I had to cut the data sets of 25.000 sequences by 80% to 5.000, since my laptop's CPU was not able to run the data crunching, making the model's not fully comparable.
Predicting-the-Popularity-of-Online-News
Building a model which can predict the number of online 'shares' an article will get based on a set of variables attached to it. (Python)
Star-Wars-opening-text-crawl
Mimic of Star Wars' opening title text crawl
K-means-clustering-on-US-crime-data
Unsupervised machine learning using U.S. crime data and k-means clustering. Crime categories: murder, assault & rape in all 50 states in 1973.
Multivariate-Regression---King-County-House-Prices
Supervised Machine Learning Using Regression Analysis
Predicting-Nordea-stock-price-using-an-LSTM-neural-network-
Using an 80/20 split in the historical data daily closing prices where predicted using a LSTM network based on data observed in the past 30 days for each prediction
House-Prices-Advanced-Regression-Techniques
This is my contribution to a competition on kaggle.com, where you have a dataset with 79 explanatory variables describing (almost) every aspect of c. 1500 residential homes in Ames, Iowa. The aim is to predict the final price of each home.
Predicting-terrorism-in-Europe-through-Decision-Trees-and-Random-Forests
Given enough data, could we make predictions on whether a terrorist attack will be successful, or not? This analysis aims to do just that using Decision Trees and Random Forests created with scikit-learn. (Python)
Sentiment-Analysis-on-Donald-Trump-s-tweets
Trump has been tweeting since December 2009, altogether more than 23000(!) tweets. Here I analyzed the last two years only, between May 2016 and April 2018, because that era covers the most active part of his presidential campaign, as well as his presidency so far.Full article available on my linkedin-page.
awesome-oneliner-bugbounty
A collection of awesome one-liner scripts especially for bug bounty tips.
deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Digit-recognizer-Neural-Network-with-Keras
A deep neural network model for identifying images of handwritten digits for a competition on Kaggle.com (Python)
h4cker
This repository is primarily maintained by Omar Santos and includes thousands of resources related to ethical hacking / penetration testing, digital forensics and incident response (DFIR), vulnerability research, exploit development, reverse engineering, and more.
hacks
A collection of hacks and one-off scripts
handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
Image-recognition-with-Convolutional-Neural-Networks
A few notebooks on building binomial as well as multilabel image classifiers from scratch. Also, how to use a pretrained ConvNet for image classification.
MAC-changer
How to change your computer's MAC address using a Python script
Mammogram-Mass-prediction-through-Machine-Learning
The aim is to apply several different supervised machine learning techniques, and see which one yields the highest accuracy. (Python)
Markdown-XSS
List of XSS Payloads With Filter Bypass
Paper_Rock_Scissors_game
The classic, simple game written in java where you play against the computer.
Predicting-Fraud-in-Financial-Payment-Services
Trying to recogize and predict fraud in financial transactions is a good example of binary classification analysis. A transaction either is fraudulent, or it is genuine. What makes fraud detection especially challenging is the is the highly imbalanced distribution between positive (genuine) and negative (fraud) classes.
Spam-Classifier-with-Naive-Bayes
Supervised machine learning using a data set of 2500 ham and 500 spam emails. Data is also split into train and test sets of various sizes to test the classifier's efficiency. (Python)
TicTacToe
A very simple version where two players can play against each other. No AI involved...
Web-Attack-Cheat-Sheet
Web Attack Cheat Sheet
WebHackersWeapons
⚔️ Web Hacker's Weapons / A collection of cool tools used by Web hackers. Happy hacking , Happy bug-hunting
xss-payload-list
🎯 Cross Site Scripting ( XSS ) Vulnerability Payload List