Hey, l am Takudzwa

Welcome to my site!

Apart from designing this website you can check out my other projects below.
If you like anything on my site, just want to say hi, or want to offer some advice you are free to hit me up anytime

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Built using the Opencv python library, this app can detect faces and blur them

Try out!!

A nifty dictionary application you can use to look up english words

Try out!!

A drumkit application you can use play drums

Play!!

This Machine learning model can classify news articles in to Fake or real

Try out!!

A fun little game, built in Javascript, where the player is required to repeat a sequence of tones.

PS: l gave up on level 7, can you beat my high score?

Play!!

Face blur app

Built using the Opencv python library, this app can detect faces and blur them

Dictionary app

A nifty dictionary application you can use to look up english words

Drumkit

A drumkit application you can use play drums

Real/fake news classifier

This Machine learning model can classify news articles in to Fake or real

Machine learning and AI

Welcome to the Machine learning section. This is a collection of all my Jupyter notebook files

In this section l have compiled some of the projects that l have done in the past that have something to do with Machine learning, data science and AI.

You will find the links to each of the projects below

One of the first types of models l ever did was the Logistic regression. You can check out the Jupyter notebook files below.

Titanic dataset

This is a very famous data set and was one on my first first steps into machine learning! l'll be trying to classify members of the titanic crew into two classes, that is survival or deceased. This dataset is hosted directly on Kaggle.

Logistic Project

In this project l was working with advertising data and trying to create a model that will predict whether or not customers will click on an ad based off the features of that user Pretty cool, right??

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs

Random Forest and Decision trees Project

Creating a model that will help predict whether a person will pay back money borrowed or not. This information is very useful to investors as they would want to invest in people who showed a profile of having a high pro bability of paying you back.

SVM Project

This model is based on the famous Iris dataset. l will create a model that can classify given data into either one of 3 different iris flowers.

This is one of my favourite uses of machine learning. It can be used in a lot of ways and the examples below are just the tip of the iceberg

Spam/non-spam messege classifier

Here l try to create a model that can classify text messages messages into spam and "Ham". The reason why you dont receive too many spam messages in your inbox or email account these days is mostly because of nlp classifiers.

NLP Project- Yelp reveiw data

Here l create a model based on Yelp review data to try and determine whether a review is 1/5 star(s).

Have you ever wondered how Netflix is able to some degree of accuracy "Recommend" a good movie for you. We have all encountered one or more uses of the recomender systems in our lifes. Below you will find a simple implementaion of a recommender systems

Movie Recommender

A model than can recommend movies for you based on what movies you like

More to come soon...

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network

Tensorflow

Using the iris dataset again, this time on a deep learning model

More to come soon...

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information.

K-means clustering

Given just a set of features, a model is created to attempt to use KMeans Clustering to cluster Universities into two groups, Private and Public.

Principal component analysis

A demostration of how the PCA can be used to reduce the number of features to a given data set whilst keeping the infomation intacked

Profile

Self-motivated university student with a record of academic and extracurricular success studying Bcom Financial Accounting and critical experience in technology.

And when I am not working with numbers, I would be coding out a program or doing some graphics designing works.

Coding

In my spare time l decided to take in upon myself to learn how to code and l am very proud of my progress. l am :