Pinned Repositories
book-recommendation-engine-using-KNN
In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors. You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
crypto-price-app-heroku
This app retrieves cryptocurrency prices for the top 100 cryptocurrency from the CoinMarketCap!
data-science-apps
Data Science Applications I have Built.
decision-trees-and-model-architecture
The goal of this exercise is to use a decision tree classifier to predict whether an individual crime will be resolved, based on simple information such as where it took place and what kind of crime it was.
demographic-data-analyzer
Analyzing demographic data using Pandas.
Intruder_Alert_System
An alarm is activated when an intruder is detected and a keypad is present to deactivate the system using a secret password
medical-data-visualizer
A program to visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas.
MiteyD.github.io
Data Science Portfolio
Project-Phase-of-the-Google-Africa-Developer-Scholarship-Program
Projects are designed to help you practice your new skills before applying them in a professional setting. You’ll complete hands-on labs via Qwiklabs, which provides you with a real-world environment, not a simulation or demo.
ReinforcementLearning-for-matching-values
I would like to test if reinforcement learning is a good candidate to calibrate an engineering model. I have a 3D model that has various properties. By varying these properties the model can be properly calibrated. I would like to first setup a test case using a very simple model, varying only 1 parameter, and an reinforcement learning loop and check if it will work. A successful outcome is the algorithm can identify the correct value(or close) of the parameter that correctly calibrates the engineering model.
MiteyD's Repositories
MiteyD/medical-data-visualizer
A program to visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas.
MiteyD/book-recommendation-engine-using-KNN
In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors. You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
MiteyD/crypto-price-app-heroku
This app retrieves cryptocurrency prices for the top 100 cryptocurrency from the CoinMarketCap!
MiteyD/data-science-apps
Data Science Applications I have Built.
MiteyD/decision-trees-and-model-architecture
The goal of this exercise is to use a decision tree classifier to predict whether an individual crime will be resolved, based on simple information such as where it took place and what kind of crime it was.
MiteyD/demographic-data-analyzer
Analyzing demographic data using Pandas.
MiteyD/Intruder_Alert_System
An alarm is activated when an intruder is detected and a keypad is present to deactivate the system using a secret password
MiteyD/MiteyD.github.io
Data Science Portfolio
MiteyD/Project-Phase-of-the-Google-Africa-Developer-Scholarship-Program
Projects are designed to help you practice your new skills before applying them in a professional setting. You’ll complete hands-on labs via Qwiklabs, which provides you with a real-world environment, not a simulation or demo.
MiteyD/ReinforcementLearning-for-matching-values
I would like to test if reinforcement learning is a good candidate to calibrate an engineering model. I have a 3D model that has various properties. By varying these properties the model can be properly calibrated. I would like to first setup a test case using a very simple model, varying only 1 parameter, and an reinforcement learning loop and check if it will work. A successful outcome is the algorithm can identify the correct value(or close) of the parameter that correctly calibrates the engineering model.
MiteyD/dog-cat-image-classifier
For this challenge, you will use TensorFlow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy.
MiteyD/git_practice
MiteyD/hello-world
Trial
MiteyD/hyperparameter-tuning-with-random-forests
The goal of this unit is to explore how hyperparameters change training, and thus model performance. The line between model architecture and hyperparameters is a bit blurry for random forests because training itself actually changes the architecture of the model by adding or removing branches. We will again pursue our goal of predicting which crimes in San Francisco will be resolved.
MiteyD/linear-regression-health-costs-calculator
In this challenge, you will predict healthcare costs using a regression algorithm. You are given a dataset that contains information about different people including their healthcare costs. Use the data to predict healthcare costs based on new data.
MiteyD/mean-variance-standard-deviation-calculator
Creates a function that uses Numpy to output the mean, variance, and standard deviation of the rows, columns, and elements in a 3 x 3 matrix.
MiteyD/MiteyD
MiteyD/neural-network-sms-text-classifier
A machine learning model that will classify SMS messages as either "ham" or "spam". A "ham" message is a normal message sent by a friend. A "spam" message is an advertisement or a message sent by a company.
MiteyD/page-view-time-series-visualizer
Project to visualize time series data using a line chart, bar chart, and box plots.
MiteyD/penguin-prediction-app-heroku
MiteyD/personal-website-with-widgets
My website
MiteyD/rock-paper-scissors
A program to play Rock, Paper, Scissors. A program that picks at random will usually win 50% of the time. This program plays matches against four different bots, winning at least 60% of the games in each match.
MiteyD/sea-level-predictor
A project analyzing a dataset of the global average sea level change since 1880 and using the data to predict the sea level change through year 2050.
MiteyD/spark-installation-ubuntu
pySpark 3 Ubuntu 20.04 Installation
MiteyD/train-and-evaluate-advanced-clustering-models
The goal is to perform clustering automatically.