Irrigation Needs Analysis

This project aims to analyze the Irrigation Needs Dataset to better understand the key factors affecting crop water requirements, such as temperature, humidity, soil type, and rainfall. The goal is to uncover patterns and relationships within the data to improve water usage efficiency.

The work includes data cleaning, handling missing values, and performing exploratory data analysis (EDA) using tools like Python along with libraries such as Pandas and Matplotlib.

Additionally, a predictive model can be developed using machine learning techniques to estimate irrigation needs based on input features. This helps support data-driven decision-making for farmers and contributes to optimizing water consumption.

The final deliverables will include clear analytical reports, data visualizations, and (optionally) a predictive model that can be used for future irrigation planning.