Pinned Repositories
applying-gradient-descent-data-science-intro-000
applying-gradient-descent-lab-data-science-intro-000
applying-nearest-neighbors-data-science-intro-000
Mod1_Project
Mod2_Project
Mod3_Project
Recipe_Recommender
Restaurant_Inspections_Map
In this project, geocoding (through GeoPy) and Folium were used to visualize the NYCC restaurants with noted critical violations in 2019 according to the NY Department of Mental Health and Hygiene.
Social_Media_Analysis
The basis for this project was the question: is it possible to infer if a user is at risk for depression from social media activity. With the emergence of the internet where individuals spend most of their time online, there is a wealth of data that can be tapped for analysis to better understand the user's overall mood. Due to the unavailability of medical data, we approached it indirectly from another perspective. The problem evaluated in this project was: can we predict the user's overall mood based on certain features from tweets? If a user is on the happier side of the spectrum, s/he is less at risk for depression than if a user is on the sadder side of the spectrum. This became a binary classification problem with the target variable as the user's overall mood (0 = happy, 1 = sad). 1-year of tweets, scraped from Twitter based on specific keywords and hashtags, were analyzed using Natural Language Processing, specifically sentiment analysis. Multiple classification algorithms were tested and the final model was a Support Vector Machine (SVM) with an accuracy and F1 score of 0.84. It was concluded that this topic is worth further exploration.
lasoya's Repositories
lasoya/Mod1_Project
lasoya/Social_Media_Analysis
The basis for this project was the question: is it possible to infer if a user is at risk for depression from social media activity. With the emergence of the internet where individuals spend most of their time online, there is a wealth of data that can be tapped for analysis to better understand the user's overall mood. Due to the unavailability of medical data, we approached it indirectly from another perspective. The problem evaluated in this project was: can we predict the user's overall mood based on certain features from tweets? If a user is on the happier side of the spectrum, s/he is less at risk for depression than if a user is on the sadder side of the spectrum. This became a binary classification problem with the target variable as the user's overall mood (0 = happy, 1 = sad). 1-year of tweets, scraped from Twitter based on specific keywords and hashtags, were analyzed using Natural Language Processing, specifically sentiment analysis. Multiple classification algorithms were tested and the final model was a Support Vector Machine (SVM) with an accuracy and F1 score of 0.84. It was concluded that this topic is worth further exploration.
lasoya/Mod2_Project
lasoya/Mod3_Project
lasoya/Recipe_Recommender
lasoya/Restaurant_Inspections_Map
In this project, geocoding (through GeoPy) and Folium were used to visualize the NYCC restaurants with noted critical violations in 2019 according to the NY Department of Mental Health and Hygiene.
lasoya/dsc-2-14-12-gradient-descent-step-sizes-lab-nyc-ds-career-031119
lasoya/dsc-2-14-13-gradient-descent-in-3d-nyc-ds-career-031119
lasoya/dsc-2-14-14-the-gradient-in-gradient-descent-nyc-ds-career-031119
lasoya/dsc-2-14-15-gradient-to-cost-function-nyc-ds-career-031119
lasoya/dsc-2-14-16-applying-gradient-descent-lab-nyc-ds-career-031119
lasoya/dsc-2-21-08-bayes-theorem-nyc-ds-career-031119
lasoya/dsc-2-31-01-introduction-nyc-ds-career-031119
lasoya/dsc-3-27-02-distance-metrics-nyc-ds-career-031119
lasoya/dsc-3-27-04-k-nearest-neighbors-nyc-ds-career-031119
lasoya/dsc-3-27-05-k-nearest-neighbors-lab-nyc-ds-career-031119
lasoya/dsc-3-27-06-confusion-matrices-nyc-ds-career-031119
lasoya/dsc-3-27-08-evaluation-metrics-lab-nyc-ds-career-031119
lasoya/dsc-3-27-09-finding-the-best-value-for-k-nyc-ds-career-031119
lasoya/dsc-3-27-12-knn-with-sklearn-lab-nyc-ds-career-031119
lasoya/dsc-3-29-04-logistic-regression-in-scikit-learn-nyc-ds-career-031119
lasoya/dsc-3-29-05-fitting-a-logistic-regression-model-lab2-nyc-ds-career-031119
lasoya/dsc-3-29-06-evaluating-logistic-regression-models-lab-nyc-ds-career-031119
lasoya/dsc-3-29-08-roc-curves-and-auc-nyc-ds-career-031119
lasoya/dsc-3-31-02-introduction-to-PAC-learning-theory-nyc-ds-career-031119
lasoya/dsc-3-31-03-introduction-to-decision-trees-nyc-ds-career-031119
lasoya/dsc-cross-validation-nyc-ds-career-031119
lasoya/flask-black-dashboard
Flask Black Dashboard - Open-source seed project | AppSeed
lasoya/Flask_template_auth_with_Dash
Flask_template_auth_with_Dash
lasoya/gitlfstest