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Vannessa622/AgriPriceForecast-Kenya

Domain:

agriculture

Record type:

project
Creator:
Van
Host:
# AgriPriceForecast Kenya AgriPriceForecast Kenya is a comprehensive data science project that leverages advanced forecasting techniques and sentiment analysis to provide actionable insights into Kenya’s agricultural market. The project aims to empower farmers, traders, policymakers, and other stakeholders by predicting commodity price trends and analyzing market sentiment through social media data. * Table of Contents * Overview * Business Understanding * Project Objectives * Data Sources * Data Cleaning and Processing * Modeling and Forecasting * Sentiment Analysis * Usage * Tableau Dashboard * Presentation Link AgriPriceForecast Kenya is designed to analyze historical agricultural market data and forecast future commodity prices using advanced machine learning models. The project also incorporates sentiment analysis from social media to capture public perceptions that influence market behavior. Through these combined approaches, the system delivers insights that help in market planning, price stabilization, and informed decision-making for all stakeholders in Kenya's agricultural sector. ## Business Understanding The agricultural sector in Kenya is a critical component of the economy, with significant contributions to livelihoods. However, the market is subject to: * Price Volatility: Seasonal fluctuations and supply chain inefficiencies lead to unpredictable commodity prices. Market Inefficiencies: Differences in regional market conditions and infrastructure challenges affect pricing. Public Sentiment: Consumer and stakeholder perceptions, often expressed on social media, can influence market dynamics. By addressing these challenges, AgriPriceForecast Kenya aims to: * Enable farmers and traders to time their sales for maximum profit. * Inform government policies for market stabilization. * Provide financial institutions with data-driven insights for risk assessment. ## Project Objectives * Assess Commodity Price Fluctuations: * Analyze historical data to …