Analyzing of local and global temperature data and comparing the temperature trends with a residence to overall global temperature trends.
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Updated
Dec 28, 2019 - Jupyter Notebook
Analyzing of local and global temperature data and comparing the temperature trends with a residence to overall global temperature trends.
machine learning à partir du dataset titanic
In this project, we created an 'expected goals' metric to help us assess a team's performance rather than the actual number of goals scored. We merged this metric with the calculation of a team's offensive and defensive ratings, which are updated after every game, to create a classification model that predicts the outcome of future matches, as w…
📈 Data Science Using Python
Exploratory Data Analysis
EDA for more than 30K game ratings collected from [IGDB API](https://api-docs.igdb.com/#about) using [igdb-api-v4 for python](https://github.com/twitchtv/igdb-api-python). This notebook explores any common trends for games that have ratings from igdb and external critics.
Exploratory and explanatory analysis of a small, generated dataset. pandas + missingno + seaborn.
Exploratory Data Analysis and Data Cleaning on a Amazon E-Commerce Dataset
A classification approach to the machine learning Titanic survival challenge on Kaggle.Data visualisation, data preprocessing and different algorithms are tested and explained in form of Jupyter Notebooks
OpenClassrooms Data Analyst 2022-2023 - Projet 5
OpenClassrooms Data Analyst 2022-2023 - Projet 9
Exploring 118 wells of 1 MM+ rows and 29 columns of wireline petrophysical data using the Pandas library. Analysed & Visualised wireline logs petrophysical dataset using - Pandas, Numpy, Matplotlib, Plotly & seaborn libraries Discovered insights of wireline logs quality & interpretation (missing data and imbalance class
Este espaço é dedicado para treinar minhas habilidades em ciência de dados, concentrando-se principalmente no aproveitamento da biblioteca Pandas para manipulação e análise de dados.
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