https://github.com/arthurdsant/dataanalysis-agricultural_raw_material

This Python project performs analysis and visualization of agricultural raw material price data using a Kaggle dataset. Based on Jupiter Notebook and Python.
https://github.com/arthurdsant/dataanalysis-agricultural_raw_material

jupyter-notebook matplotlib numpy pandas python seaborn

Last synced: 5 months ago
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This Python project performs analysis and visualization of agricultural raw material price data using a Kaggle dataset. Based on Jupiter Notebook and Python.

• Host: GitHub
• URL: https://github.com/arthurdsant/dataanalysis-agricultural_raw_material
• Owner: ArthurDSant
• Created: 2024-10-20T01:43:46.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-21T17:09:03.000Z (over 1 year ago)
• Last Synced: 2024-12-19T14:49:51.395Z (over 1 year ago)
• Topics: jupyter-notebook, matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 3.78 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
![Thumb](https://github.com/user-attachments/assets/d1f83eca-ec6c-4b22-ba0b-72cd34040ba5)

# Data Analysis - Agricultural Raw Material

This Python project performs the analysis and visualization of agricultural raw material price data using a dataset from Kaggle.

## Stack used

![Jupyter](https://img.shields.io/badge/Jupyter-%23F37626.svg?style=for-the-badge&logo=Jupyter&logoColor=white) ![Python](https://img.shields.io/badge/Python-%2314354C.svg?style=for-the-badge&logo=python&logoColor=white) ![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white) ![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white) ![Matplotlib](https://img.shields.io/badge/Matplotlib-%2311557C.svg?style=for-the-badge&logo=Matplotlib&logoColor=white) ![Seaborn](https://img.shields.io/badge/seaborn-%234C7EAB.svg?style=for-the-badge&logo=plotly&logoColor=white) ![Kaggle](https://img.shields.io/badge/Kaggle-%2320BEFF.svg?style=for-the-badge&logo=Kaggle&logoColor=white) ![Google Colab](https://img.shields.io/badge/Google%20Colab-F9AB00.svg?style=for-the-badge&logo=google-colab&logoColor=white)

## Features

1 - **Data Loading and Cleaning:** The dataset is loaded from a CSV file. Percent values, commas, and other symbols are removed to ensure data consistency. Missing values (NaN) are handled by removing the rows with incomplete data.

2 - **Type Conversion:** The columns related to prices and percentage changes are converted to the float type to allow for precise numerical analysis.

3 - **Date Indexing:** The dates are formatted to the yyyy-mm-dd standard and set as the index, facilitating analysis over time.

4 - **Correlation Analysis:** Correlation matrices are generated to analyze the relationship between different raw material prices, with visualization through heatmaps.

5 - **Visualizations:** The project includes various graphs to explore the data:

- **Heatmap** of raw material price correlations.

- **Percentage Change Graphs** of prices over time.

- **Histograms** of the distribution of percentage changes.

- **Evolutionary Graphs** of prices over time.

6 - **Libraries Used:**

- **NumPy** for array manipulation and data handling..

- **Pandas** for dataframe manipulation.

- **Seaborn** and **Matplotlib** for graphical visualization.

## Installation

```bash

    pip install numpy

    pip install pandas

    pip install seaborn

    pip install matplotlib

    or

    pip install numpy pandas seaborn matplotlib

```

## Screenshots

![DA - Agricultural Raw Material](https://github.com/user-attachments/assets/6cf223fc-ee0f-419c-812f-1865c27a2bb6)