Time Series Exploratory Data Analysis of Intel Data with Python

This project performs an exploratory data analysis (EDA) using Python on a time series dataset from Intel. Through detailed notebooks, it explores trends, patterns, and relationships over time to derive meaningful insights.

Language / Idioma

This project and all analysis are written in Spanish. If you prefer, you can contact me for any help or translation.

Objectives

• Analyze data quality and temporal structure.
• Visualize distributions and correlations in the time series.
• Extract actionable insights relevant to Intel’s time-dependent data.

Estructura del Repositorio

├── data/ # Datasets used in the analysis

├── notebooks/ # Notebooks with the complete analysis

├── scripts/ # Complementary and helper scripts

├── results/ # Graphs and exported tables

├── requirements.txt # Needed Python dependencies

└── README.md # Project description and guide

Technologies and Libraries Used

• Python (pandas, numpy, matplotlib, seaborn, scikit-learn, etc.)
• Jupyter Notebook

How to Run This Project

1. Clone the repository.
2. Install dependencies listed in requirements.md:
3. Open the notebook notebooks/Analisis_con_Python_de_Intel.ipynb in Jupyter or Google Colab.
4. Run the cells to reproduce the analysis and see the results.

Credits and Data Source

• Data intel
• Analysis and development by Ricardo Tovar