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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 StocksFORECASTING-1.0
Predictive algorithm for forecasting the mexican stock exchange. Machine Learning approach to forecast price and Indicator behaviours of MACD, Bollinger and SuperTrend strategyURI-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 crossvalALGORITHM-TRADING-AND-STOCK-PREDICTION-USING-MACHINE-LEARNING
ALGORITHM TRADING AND STOCK PREDICTION USING MACHINE LEARNINGNLP-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.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 pythonADVANCE-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 programmingblockchain_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 AlgoPOSTGRE-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.SEMI-SUPERVISED-NAIVE-BAYES-FOR-TEXT-CLASSIFICATION
Semi-supervised machine learning for text classification. Increased accuracy of 99% on unlabelled data.EIGEN-FREQUENCY-CLUSTERING-USING-KMEANS-DBSCAN-PCA-HDBSCAN
EIGEN FREQUENCY CLUSTERING USING [KMEANS] [KMEANS & PCA ] [DBSCAN] [HDBSCAN]Love Open Source and this site? Check out how you can help us