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Projects Repository

Welcome to the Projects Repository! This repository houses a diverse range of projects, each designed to solve unique challenges through data analysis, web scraping, computer vision, and more. The projects are built using Python and leverage various modern libraries to ensure efficiency and scalability.

Projects Overview

1. Thesis Analysis

Description:
This undergraduate thesis project provides a statistical description and summary graphs illustrating the evolution of the real estate market. It uses Python for data analysis and visualization.

Key Features:

  • Statistical analysis of real estate market trends.
  • Data visualization through summary graphs.
  • Implementation using Pandas, Matplotlib, and NumPy.

2. Web Scrapping V2

Description:
This project focuses on extracting contact details of companies within a specific economic sector. It is designed to efficiently scrape websites without overloading them by using Python libraries tailored for web scraping.

Key Features:

  • Efficient extraction of company contact details.
  • Respectful scraping to avoid overloading target websites..
  • Utilizes BeautifulSoup, Requests, and Pandas.

3. US Air Traffic Analysis

Description:
This project involves an exploratory data analysis of US Air Traffic data from 2022. It also features a web-based dashboard that provides dynamic visualizations to facilitate data insights.

Key Features:

  • In-depth exploratory data analysis of air traffic data.
  • Interactive web-based dashboard with dynamic graphics.
  • Developed using Dash and Plotly.

3. Pose Estimation

Description:
This project implements pose estimation techniques to analyze human body positions in images and videos. It employs state-of-the-art computer vision models for real-time detection and tracking of poses.

Key Features:

  • Real-time pose estimation using advanced computer vision models.
  • Detection and tracking of human poses in images and videos.
  • Built with OpenCV and MediaPipe.

4. EEG Eye State Classifier

Description:
This project focuses on classifying EEG signals to determine eye state (open or closed). By leveraging signal processing techniques and machine learning algorithms, the model aims to accurately classify eye state from EEG data, providing a robust pipeline for analysis and evaluation.

Key Features:

  • Classification of EEG signals to identify eye state.
  • Data preprocessing and feature extraction tailored for EEG analysis.
  • Implementation of machine learning models for effective classification.
  • A reproducible workflow for training, validation, and testing.

5. Metacritic Game Data Visualization

Description:
This project aims to analyze and visualize video game data sourced from Metacritic. By integrating game ratings, sales figures, and other relevant metrics, it provides insights into the gaming industry's trends and patterns.

Key Features

Key Features:

  • Data collection from Metacritic, including ratings, genres, publishers, and platforms.
  • Exploratory Data Analysis (EDA) to identify patterns and correlations within gaming data.
  • Interactive visualizations using Python libraries such as Matplotlib and Seaborn.
  • Comprehensive insights into factors influencing game success across various platforms and genres.

6. Forecasting the Electric Sector in Ecuador: Monthly Evolution by Source Type (2013-2023) [Master's Thesis]

Description:
This project aims to develop predictive models for Ecuador's electric sector by analyzing monthly electricity generation data from 2013 to 2023, categorized by source type. Utilizing SARIMA and Prophet forecasting models, the study provides high-precision forecasts based on data from the Electricity Regulation and Control Agency. The findings offer valuable insights into sector behavior, informing improvements in energy planning and policy-making.

Key Features

  • Advanced Forecasting Techniques: Utilizes SARIMA and Prophet models to predict monthly electricity generation by source type, enhancing forecasting accuracy.
  • Historical Data Analysis: Analyzed monthly electricity generation data (2013-2023) by source type.​
  • Performance Evaluation: Assessed model accuracy using RMSE and MAPE metrics.

External Repository

Licence

This repository is distributed under the MIT License.