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Contains code and documentation for conducting Exploratory Data Analysis (EDA) on various datasets. The EDA process involves examining and visualizing datasets to better understand their characteristics, relationships, and insights that can be derived from them.
Construction d’un pipeline PySpark pour l’analyse des performances footballistiques saison par saison, avec calcul de KPI, ranking des équipes et stockage optimisé en Parquet partitionné.
"Exploratory Data Analysis (EDA) scripts and resources for uncovering insights from datasets. Includes data cleaning, visualization, statistical summaries, and correlation analysis using Python libraries like pandas, matplotlib, and seaborn."
Welcome to SafeVault Analytics! This project isn't just about code; it's about solving a real-world dilemma: How do we use data to save lives without spying on people? In 2026, data privacy is a human right. But for researchers studying conditions like PCOD, privacy laws often mean they can't get the data they need to build helpful AI.
A Python Tkinter-based data visualization tool that converts CSV data into interactive charts using Pandas, Seaborn, and Matplotlib enabling no-code data analysis.
Analyzed traffic accident data to identify patterns related to road conditions, weather, and time of day. Visualized accident hotspots using heatmaps and bar plots.