mahtabek Goto Github PK

a-data-scientist-for-a-professional-football-club

In this project, you work as a Data Scientist for a professional football club. The owner of the team is very interested in seeing how the use of data can help improve the team's performance, and perhaps win them a championship! The draft is coming up soon (that's when you get to pick new players for your team), and the owner wants you to create a model to help score potential draftees. The model will look at attributes about the player and predict what their "rating" will be once they start playing professionally. The football club's data team has provided the data for 17,993 footballers from the league. Our job is to build a model or model, perform model selection, and make predictions on players you have not yet seen.

a-data-scientist-for-a-professional-football-club-part2

This project is part 2 of the project "A Data Scientist for a Professional Football Club". In this project, managers want to test some hypotheses relating a player's overall rating and some of their characteristics in order to make better decisions on what players to trade/sign. They would like to create some statistical models for inference instead of prediction. And for that reason, in this project, I took off my "data" hat and put on my "science" hat :D

airplane

written in c++

aut-editor

developed in C++

black-box-testing

In this project, I have applied three of the foundational black box techniques on the ATM system.

cache

classification-with-logistic-regression

The Dataset: This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. You can read more about the data and the variables here.

classification-with-support-vector-machines

The Dataset: This dataset consists of 3921 e-mails to a single account, some of which are spam. These data represent incoming emails for the first three months of 2012 for an email account. The table has 3921 (1252) observations on 21 variables. The data are from this R package: https://cran.r-project.org/web/packages/openintro/openintro.pdf

clothster_final-project

The final project of internet engineering course

clothster_part1

online Clothing Store

clothster_part2

clustering

In this project, I have worked with some age (measured in years) and height (measured in fractional feet. So, for instance, 5 feet 6 inches would be 5.5 since there are 12 inches in a foot). In the data, there is a feature called true_cluster. Usually, this column would never be available (after all, clustering is a form of unsupervised learning). I have not used this column in my clustering either. This column has been included for the sole purpose of comparing clustering methods to ground truth.

confidence-intervals-the-bootstrap

Exploring the confidence-Interval concept and bootstrapping.

datamining2-prediction

datamining3-naive-bayes

elastic-search

The goal of this project is to become familiar with the process of installing and starting Elasticsearch. Elasticsearch is an information retrieval system that can index documents as we saw in class and can respond to queries. This assignment will help you begin to understand how modern search engines work, and how documents and queries can be represented for information retrieval tasks.

engineering-mathematics--matlab

github-slideshow

A robot powered training repository :robot:

graphics

developed in java

image-classification-experiments

In this notebook, we're going to explore the use of a few different ways of setting up an image classification model. The images and more details are available here: https://tiny-imagenet.herokuapp.com/. The training set contains 500 images for each of 200 different classes. The validation set contains 50 images for each of the 200 classes. First, we're going to load images from the tiny-imagenet-200 folder into a flattened format that is suitable for training any of the scikit-learn classifier models, such as a support vector machine (SVM) or logistic regression. Later, we'll take advantage of data loading functions included in PyTorch that will preserve the 2D-shape of images and load batches instead of the entire training or validation set all at once.

intro-to-datascience

In this project, you can find the use of data analytics techniques such as loss functions, predicting outcomes using provided dataset, visualization, simple linear model, model selection, confidence interval, bootstrap, and etc.

jnote

developed in java

jrobokill-java-version-of-robokill-

jtank

written in java

kibana

The goal of this project is to gain familiarity with Kibana and ElasticSearch.

mahtabek.github.io

maximum-likelihood

The Poisson distribution https://en.wikipedia.org/wiki/Poisson_distribution is a discrete probability distribution often used to describe count-based data, like how many snowflakes fall in a day. If we have count data 𝑦 that are influenced by a covariate or feature 𝑥, we can use the maximum likelihood principle to develop a regression model relating 𝑥 to 𝑦.

mongodb

The goal of this project is to get experience with MongoDB, one of the most widely-used tools for the management and querying of big, unstructured data.

nested-spheres

Simulation is an incredibly useful tool in data science. We can use simulation to evaluate how algorithms perform against ground truth, and how algorithms compare to one another. In this project, I will be implementing and extending the nested spheres simulation study found in Elements of Statistical Learning page 339.

os---xv6

developing efficient scheduling policies