Agricultural Production Analysis

This repository contains a Jupyter notebook that performs data analysis on agricultural production metrics. The goal of the analysis is to explore and understand various factors affecting crop yields, such as soil nutrients, climate, and regional data. The insights from this analysis could assist in making data-driven decisions in agricultural planning and management.

Project Overview

This project leverages a dataset containing:

• Crop-specific production areas and yields.
• Environmental and soil characteristics (nitrogen, phosphorous, potassium levels, temperature, humidity, etc.).
• Regional and yearly agricultural statistics.

Using this data, we can analyze the impact of different environmental and soil factors on crop productivity.

Files

• agricultural-production.ipynb: The main Jupyter notebook containing the data analysis and visualization.

Dataset Columns

Key columns in the dataset include:

• Location & Year: Dist Code, Year, State Code, State Name, Dist Name.
• Crop Areas and Yields: RICE AREA (1000 ha), RICE PRODUCTION (1000 tons), RICE YIELD (Kg per ha), WHEAT AREA (1000 ha), etc.
• Soil & Environmental Data: Nitrogen, Phosphorous, Potassium, avg_temp, humidity, soil pH, rainfall.

Requirements

To run the notebook, you will need the following libraries:

pip install pandas numpy matplotlib seaborn

Usage

git clone https://github.com/2022BCD0052/agriculture-crop-predection-using-ann-machineLearning.git cd agriculture-crop-predection-using-ann-machineLearning ```

3. Run the cells to execute the analysis.

Analysis Objectives

1. Data Cleaning: Handle missing values and convert data types as needed.
2. Descriptive Statistics: Generate summary statistics for crops and environmental factors.
3. Correlation Analysis: Assess relationships between soil characteristics, climate, and crop yields.
4. Visualization: Use graphs and charts to highlight trends and correlations.

Contributing

Contributions are welcome! If you'd like to improve the analysis or add more visualizations, please fork the repository and create a pull request.

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Let me know if you'd like to adjust this template based on specific content or analysis within the notebook.