jianninapinto

Awesome-Profile-README-templates

A collection of awesome readme templates to display on your profile

Bandersnatch

This project implements a machine learning model using Random Forest, XGBoost, and Support Vector Machines algorithms with oversampling and undersampling techniques to handle imbalanced classes for classification tasks in the context of predicting the rarity of monsters.

Coffee-Shops-Review-Analysis-using-NLP

Performed feature engineering and data cleaning on text data using lemmatization techniques and stop word removals.

COVID19_Crisis

Used SQL queries to perform data analysis of the COVID-19 pandemic, exploring key trends, statistics, and insights sourced from Our World in Data.

Loan-Default-Risk-Prediction

Trained a classifier by using labeled data and oversampling and undersampling techniques to predict if a borrower will default on a loan. The model is intended to be used as a reference tool to help investors make informed decisions about lending to potential borrowers based on their ability to repay. The purpose is to lower risk & maximize profit.

Sketch-Classification-Quickdraw

Used tensorflow, keras and a sample of the Quickdraw dataset to build a sketch classification model.

SQL_Chinook_Database_Explorer

Used SQLite to explore the Chinook database with SQL queries and gain insights into this fictional music store's data, from customers to tracks and albums.

tanzanian-water-pumps

Used supervised machine learning classification algorithms such as Decision Tree and Random Forest to predict whether or not water pumps in Tanzania require repair.

Vector-Representation-of-Indeed-Job-Listings-NLP

Used spaCy tokenizer to process the text and BeautifulSoap to remove HTML tags from the job descriptions. Built tokenizer and used CountVectorizer to get the word counts for each listing. Created dtm and tf-idf feature matrix. Built search engine to query the job listings and find documents that are similar to the desired job listings.

Who-Tweeted-What

Web application that uses a NLP machine learning model to analyze the textual content of tweets and predict which of two Twitter users most likely authored a user-generated text.

jianninapinto/Coffee-Shops-Review-Analysis-using-NLP

Performed feature engineering and data cleaning on text data using lemmatization techniques and stop word removals.

jianninapinto/Sketch-Classification-Quickdraw

Used tensorflow, keras and a sample of the Quickdraw dataset to build a sketch classification model.

jianninapinto/SQL_Chinook_Database_Explorer

Used SQLite to explore the Chinook database with SQL queries and gain insights into this fictional music store's data, from customers to tracks and albums.

jianninapinto/Vector-Representation-of-Indeed-Job-Listings-NLP

Used spaCy tokenizer to process the text and BeautifulSoap to remove HTML tags from the job descriptions. Built tokenizer and used CountVectorizer to get the word counts for each listing. Created dtm and tf-idf feature matrix. Built search engine to query the job listings and find documents that are similar to the desired job listings.

jianninapinto/Awesome-Profile-README-templates

A collection of awesome readme templates to display on your profile

jianninapinto/Bandersnatch

This project implements a machine learning model using Random Forest, XGBoost, and Support Vector Machines algorithms with oversampling and undersampling techniques to handle imbalanced classes for classification tasks in the context of predicting the rarity of monsters.

jianninapinto/BFT-in-Binary-Trees-and-Graphs

This repository contains Python code demonstrating the Breadth-First Traversal (BFT) algorithm for binary trees and graphs.

jianninapinto/Binary_Trees

This repository contains Python code for exercises related to binary trees, implementing BFS and DFS traversal of a binary tree recursively and iteratively.

jianninapinto/bloomdata_package

jianninapinto/COVID19_Crisis

Used SQL queries to perform data analysis of the COVID-19 pandemic, exploring key trends, statistics, and insights sourced from Our World in Data.

jianninapinto/Financial-Statement-Analysis

Used Python to calculate financial metrics for an organization such as profit after taxes for each month, profit margin, good months, bad months, best and worst months.

jianninapinto/Loan-Default-Risk-Prediction

Trained a classifier by using labeled data and oversampling and undersampling techniques to predict if a borrower will default on a loan. The model is intended to be used as a reference tool to help investors make informed decisions about lending to potential borrowers based on their ability to repay. The purpose is to lower risk & maximize profit.

jianninapinto/tanzanian-water-pumps

Used supervised machine learning classification algorithms such as Decision Tree and Random Forest to predict whether or not water pumps in Tanzania require repair.

jianninapinto/Who-Tweeted-What

Web application that uses a NLP machine learning model to analyze the textual content of tweets and predict which of two Twitter users most likely authored a user-generated text.

jianninapinto/CodeSignal-Solutions

This is a collection of solutions for the Codesignal Arcade challenges, showcasing my code implementations.

jianninapinto/Convolutional-Neural-Network-to-Classify-Images-of-Forest-and-Mountains

Utilized CNN models to classify images of mountains and forests, treating mountains as the positive class and forests as the negative class. We compare the performance of a pre-trained model, a custom CNN model, and a CNN model with data augmentation.

jianninapinto/COVID-19-Detection-using-Multilayer-Perceptron-Neural-Network

Used a Multilayer Perceptron (MLP) neural network to detect COVID-19 in lung scans.

jianninapinto/Data-Structures-Recursion

Implemented basic data structures such as stacks, queues, and linked lists, along with recursive solutions to some problems.

jianninapinto/DS_Unit1_Sprint3

jianninapinto/Fibonacci-with-memoization-and-hash-maps

Used dynamic programming to speed up calculations by storing the results of previously calculated results in a cache. This significantly reduces the time complexity of the algorithm from exponential O(2^n) to linear O(n).

jianninapinto/Instacart-market-basket-analysis

jianninapinto/Intro_to_sql

jianninapinto/jianninapinto

My personal repository readme

jianninapinto/Merge_Sort_Algorithm

This repository contains a Python implementation of the Merge Sort algorithm, which is a divide-and-conquer sorting algorithm for efficiently sorting lists of elements.

jianninapinto/No_SQL_and_document_oriented_dbs

Set up and insert data from a sqlite database into a MongoDB instance.

jianninapinto/Shakespearean-ish-Text-Generation

jianninapinto/Simple_Linear_Regression_Project

Used Python to predict for how long a body has been buried.

jianninapinto/Spotify-App

jianninapinto/SQL-hackerrank-problems

jianninapinto/Unit2-Data-Science-BloomTech