KennedyWolfie

STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA. Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

FORECASTING-1.0. Predictive algorithm for forecasting the mexican stock exchange. Machine Learning approach to forecast price and Indicator behaviours of MACD, Bollinger and SuperTrend strategy

URI-URL-CLASSIFICATION-USING-RECURRENT-NEURAL-NETWORK-SVM-AND-RANDOMFOREST. URI-URL Classification using Recurrent Neural Network, Support Vector and RandomForest. The Implementation results follows with classification report, confusion matrix and precision_recall_fscore_support for each validation result of a 10-fold crossval

ALGORITHM-TRADING-AND-STOCK-PREDICTION-USING-MACHINE-LEARNING. ALGORITHM TRADING AND STOCK PREDICTION USING MACHINE LEARNING

NLP-PROJECT-BOOK-INSIGHTS-WITH-PLOTLY. Plotly-Dash NLP project. Document similarity measure using Latent Dirichlet Allocation, principal component analysis and finally follow with KMeans clustering. Project is completed with dynamic visual interaction.

TRANSFER-LEARNING-AND-OPTIMAL-TRANSPORT. Transfer Learning and Optimal Transport. A demonstration of Subspace Alignment algorithm and Entropy Regularized Optimal Transport (Sinkhorn's Algorithm) on Office/Caltech dataset.

ADVANCE-MACHINE-LEARNING-KERNEL-METHOD. Advance machine Learning: Kernel methods implemented for PCA, KMeans, Logistic Regression, Support Vector Machine (SVM) and Support Vector Data Description (SVDD)

Active-learning-and-online-learning-machine-learning-algorithms.. Passive-Aggressive Algorithm and Active Passive-Aggressive Online Algorithm. Kernel Passive-Aggressive Algorithm.

ADVANCE-ALGORITHMS-TRAVELLING-SALESMAN-PROBLEM. An implementation of the travelling salesman problem using Brute-force, Branch-and-bound, removing-edges, MST-approximationn, Nearest_neighbour(greedy), Dynamic Programming, Randomized approach, Genetic programming

HIGH-DIMENSIONAL-DATA-CLUSTERING. Implementation of hierarchical clustering on small n-sample dataset with very high dimension. Together with the visualization results implemented in R and python

blockchain_with_python. Step 1: Building a Blockchain Open up your favourite text editor or IDE. We’ll only use a single file, but if you get lost, you can always refer to the source code. Representing a Blockchain We’ll create a Blockchain class whose constructor creates an initial empty list (to store our blockchain), and another to store transactions. Here’s the blueprint for our class:

EditDistanceAdvanceAlgoProject. Minimum Edit Distance (Advance Algorithm Project)- Implementing Dynamic, Greedy, Branch and Bound, K-strip Algo

POSTGRE-DATABASE-MANAGEMENT-USING-DJANGO-REST-API. Django application for automatically populating a postgre database and manipulating the frontend of the application. Users can easily search keywords from the frondend and save query result in different formats.

EIGEN-FREQUENCY-CLUSTERING-USING-KMEANS-DBSCAN-PCA-HDBSCAN. EIGEN FREQUENCY CLUSTERING USING [KMEANS] [KMEANS & PCA ] [DBSCAN] [HDBSCAN]

SEMI-SUPERVISED-NAIVE-BAYES-FOR-TEXT-CLASSIFICATION. Semi-supervised machine learning for text classification. Increased accuracy of 99% on unlabelled data.

n-th_monsien_number. find the n-th Monisen number. A number M is a Monisen number if M=2**P-1 and both M and P are prime numbers. For example, if P=5, M=2**P-1=31, 5 and 31 are both prime numbers, so 31 is a Monisen number. Put the 6-th Monisen number into a single text file and submit online.

GCVAE. Generalized-Controllable Variational Autoencoder (GCVAE)

Social-Mining-Recommendation-System. Datamining for Big Data