Welcome to my portfolio

Recent Projects

DataProcessingSQL

Link to github repo Know our data The design of the schema that contains the data from the FakeNews Corpus data set In our design of the schema, we have tried to reduce various anomalies as much as possible. We started by cramming all the data into a single relation, but we quickly found out that this created a lot of anomalies. Therefore, we iteratively decomposed this relations until we ended up with the design that can be viewed on figure \ref{fig:er-diagram-fakenews-corpus}.
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image from Linear regression - House Prices

Linear regression - House Prices

Link to github repo Boston Housing Data - House Price TV adds - Sales Olympic race data - Time Medical Insurance Patients - Cost of Product This Project aims to visualize some relations between features and their respective values. In the case of the Boston Housing Data, with different features such as: age of the house, infrastructure in area, ecology, as well as other data about the residents.
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image from PCA and classifications

PCA and classifications

Principal component analysis: Some few tool to analyze the specific impact of each feature, and their impact on the label. The tool is applied to a dataset of flowers, where we try to classify which features of the plant correspond to the specific flower. We have four features sepal width, sepal length, petal width, and petal length. All are in cm. The labels or the flowers we are trying to classify are Iris Setosa (label = 0), Iris Versicolour (label = 1), Iris Virginica (label = 2)
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Support vector machine - Linear

Link to github repo The Support Vector Machine Support vector machine is a classification algorithm. The idea behind the algorithm is that it tries to seperate 2 different labeled datasets. Compared to kNN and RandomForest, this is a supervised algorithm. Hence meaning that we use our labeled data to divide/classify our datapoints. We divide the dataset utilizing a hyperplane. A hyperplane is a function of degree (Features-1), 2 features will entail a function of degree 1 (a line).
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Urban Sound Processing and Neural Network Model

Link to github repo Link to kaggle repo Link To Dataset Data exploration The dataset we want to analyze consists of different soundbites each of length 1 second containing the highest intensity in a larger soundbite. The dataset has sounds such as a dog barking, children playing, car horn, and streetmusic. Lets have a look and see how the different kinds of sounds look like in comparison to one another.
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