monicambabazi Goto Github PK
Name: Monica Mbabazi
Type: User
Company: Makerere University
Bio: A Bioinformatics PhD Fellow
Twitter: Monica25036838
Location: Kampala, Uganda
Name: Monica Mbabazi
Type: User
Company: Makerere University
Bio: A Bioinformatics PhD Fellow
Twitter: Monica25036838
Location: Kampala, Uganda
Seminar materials for BCBB ACE Workshops
A place to put some of the lecture notes I use to teach
Short course at IDI Dec 10th-13th 2019
This project is geared towards predicting house prices using advanced regressor models and this was carried out through extensive data exploration with the use of advanced data visualization libraries such as Seaborn and Plotly, and the use of regularized regression models such as Ridge, Lasso, Elastic Net Regressor and Ensemble models such as RandomForest, XGBoost, Gradient Boost to predict house prices in Ames, Iowa and further engineer features that will help optimize the models performance.
Run your code in the cloud, with technology so advanced, it feels like magic!
Classification of Breast Cancer diagnosis Using Support Vector Machines
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
This folder contains my first exploitations with Neural Nets
Materials for the International Data Science Lecture Series for Spring 2023
A collaborative project on RNA sequencing_Gene expression analysis
"Buying a house is a stressful thing." We built a model to predict the prices of residential homes in Ames, Iowa, using advanced regression techniques. This model will provide buyers with a rough estimate of what the houses are actually worth. We first analyzed the data to find trends. Then dimensionality reduction was performed on the dataset using PCA algorithm and feature selection module in sklearn package for python 3.5. The final house prices are predicted using linear regression models like Ridge and Lasso. We also utilised advanced regression techniques like gradient boosting using XGBoost library in python 3.5.
Introductions to high throughput/performance and cloud computing
Source Code for 'Learn Keras for Deep Neural Networks' by Jojo John Moolayil
Linear Regression model used for predicting house sale prices
This is a repository for big data analysis
In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
A suite of computational tools used for molecular inversion probe design, data processing, and analysis.
A collection of video resources for machine learning
Exercise to practise lesson from Module 1 (data organisation, shell, python)
Training module of the Bioinformatics Community of Practice on R and biostatistics
The fourth module of the 2018 Bioinformatics Community of Practice at BecA-ILRI Hub
Module 5 of the 2018 Bioinformatics CoP at Beca, on quantitative genetics, genotyping-by-sequencing, genome-wide association studies and genomic selection
MOI and Allele Frequency Recovery from Noisy Polyallelic Genetics Data
Practice your pandas skills!
Repository to store sample python programs for python learning
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.