Pinned Repositories
astroquery
Functions and classes to access online data resources. Maintainers: @keflavich and @bsipocz
Bootstrap-Confidence-Intervals
I used the bootstrap method to compute confidence intervals in order to explore the relationship between the physiology of hockey players and their overall performance scores.
Code-Astro-Group-23-Project
Our project work for Code/Astro 2021 workshop
codeastro
Course Material for the Code/Astro Workshop
Consumer-Activity-Clustering
In this exercise I use clustering in order to segment customers by Recency, Frequency, and Monetary Value in order to improve quantitative marketing strategies and to develop commercial strategies.
Cross-Validation-Model-Selection-Exercise
Exploring cross-validation and model selection using data collected regarding features describing a set of soccer players.
Cs9860A-Final-Project
Final Project for CS 9860A- Advanced Machine Learning; using regression techniques to predict housing prices using the House Price- Advanced Regression Techniques Kaggle dataset
Energy-Use-Extrapolation
I use trees and Random Forest in order to extrapolate on a data set for energy usage in household appliances. Testing of the extrapolation model will be performed 'out-of-sample' on a data set containing appliances with the top 10% highest energy consumption rates.
eng-intern-assessment-data
My submission for the Summer 2024 Data Eng Internship
Housing-Market-CNN
I explore multimodal learning by using a Convolutional Neural Network (CNN) in order to predict housing prices based on the basic information on the house (such as the number of bedrooms, bathroom, square footage,zipcode, etc) and images of the house.
sofiapasquini's Repositories
sofiapasquini/Cs9860A-Final-Project
Final Project for CS 9860A- Advanced Machine Learning; using regression techniques to predict housing prices using the House Price- Advanced Regression Techniques Kaggle dataset
sofiapasquini/Housing-Market-CNN
I explore multimodal learning by using a Convolutional Neural Network (CNN) in order to predict housing prices based on the basic information on the house (such as the number of bedrooms, bathroom, square footage,zipcode, etc) and images of the house.
sofiapasquini/astroquery
Functions and classes to access online data resources. Maintainers: @keflavich and @bsipocz
sofiapasquini/Bootstrap-Confidence-Intervals
I used the bootstrap method to compute confidence intervals in order to explore the relationship between the physiology of hockey players and their overall performance scores.
sofiapasquini/Code-Astro-Group-23-Project
Our project work for Code/Astro 2021 workshop
sofiapasquini/codeastro
Course Material for the Code/Astro Workshop
sofiapasquini/Consumer-Activity-Clustering
In this exercise I use clustering in order to segment customers by Recency, Frequency, and Monetary Value in order to improve quantitative marketing strategies and to develop commercial strategies.
sofiapasquini/Cross-Validation-Model-Selection-Exercise
Exploring cross-validation and model selection using data collected regarding features describing a set of soccer players.
sofiapasquini/Energy-Use-Extrapolation
I use trees and Random Forest in order to extrapolate on a data set for energy usage in household appliances. Testing of the extrapolation model will be performed 'out-of-sample' on a data set containing appliances with the top 10% highest energy consumption rates.
sofiapasquini/eng-intern-assessment-data
My submission for the Summer 2024 Data Eng Internship
sofiapasquini/Feature-Selection-Regularization-Exercise
sofiapasquini/fetch_analytics_engineer_assessment
My completed 2024 Fetch Analytics Engineer assessment.
sofiapasquini/Fraudulent-Transaction-Predictor
sofiapasquini/IMBD-Sentiment-Analysis
I use dimensionality reduction techniques in order to analyse and predict sentiment in a set of texts.
sofiapasquini/Keras-DL-Classification
Exploring using a deep learning model (built with Keras) in order to classify on the Pima Indians Diabetes Database Challenge.
sofiapasquini/Linear-Regression
Using my own functions in order to fit a linear model to data regarding possum physiology. Functions employ both Ordinary Least Squares and Least Absolute Deviation loss functions
sofiapasquini/LSTM-AAPL-Prediction
I explore training machine learning architecture on time series data to extrapolate values into the future. I use a LSTM network trained on the opening prices of the Apple stock from 2013-2017 to predict the prices in the month of January 2018.
sofiapasquini/Maximum-Likelihood-Poisson
Maximizing the negative log likelihood function for a Poisson random variable in order to make predictions using a toy data set. Predictions are compared to those made using Ordinary Least Squares regression.
sofiapasquini/NLP-spam-or-ham
Exploration of NLP in the context of a text-message classification problem
sofiapasquini/RK-Solve-Lane-Emden
sofiapasquini/Seq-2-Seq
I explore sequence-to-sequence NLP with a Keras Encoder-Decoder model.
sofiapasquini/Shopify-Summer-2022-Data-Science-Challenge
sofiapasquini/sofiapasquini.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes