AI Model for Analyzing the Impact of Climate Change on Agriculture This project builds an AI model to assess the effects of climate change on agriculture using machine learning techniques. The dataset used is sourced from Kaggle, and the project is implemented in Python using Google Colab. The goal is to predict agricultural trends based on climate variables.
Project Overview This project explores the relationship between climate change and its impact on agricultural productivity. By applying machine learning algorithms, the model attempts to provide insights into how different climate factors affect crop yields and other agricultural indicators.
Features Data cleaning and preprocessing with Pandas Model building using Scikit-learn Analyzed climate and agriculture-related data Accuracy testing and performance metrics Dataset The dataset used for this project can be found on Kaggle. It contains data related to climate variables (temperature, precipitation, etc.) and agricultural outputs (crop yields, production, etc.).
Tools and Libraries Google Colab: Used for coding and cloud-based execution. Pandas: For data cleaning and preprocessing. Scikit-learn: This is used to build the machine learning model and evaluate its performance.