Creation of the Dataset for Analysis and Data Visualization to generate insights and information about the World Cup event.
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Data cleaning through Webscraping and Feature Engineering to enhance performance and analyze data for generating statistics and general information about the World Cup.
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Exploratory Data Analysis (EDA) via SweetViz and Pandas.
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Data clustering using K-Means and DBSCAN to create player profiles and to recognize patterns based on the dataset.
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Creation of Machine Learning models for predicting World Cup matches.
Automatic recognition of car plates using OCR and YOLO algorithms.
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Implementation of OCR algorithms, comparison, and performance analysis using CER and WER metrics.
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Utilization of YOLOv5 for automatic plate cropping based on created labels.
Creation of Machine Learning models to study predictions and classifications in a region of Brazil.
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Creation of the property dataset using Webscraping.
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Implementation and comparison of various Machine Learning models for regression and classification.
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Metric improvements using diverse resources such as GridSearchCV, Feature Engineering, Normalizations, etc.