Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

judeonuh/Food_Price_Recommendation

Domain:

agriculture

Record type:

project
Creator:
jud
Host:
This repository contains an end-to-end data analysis project on food price trends conducted for an Agritech startup in Lagos, Nigeria. # Food Price Recommendation Strategy ## Background Agricultural markets are inherently volatile — prices of food commodities fluctuate due to factors such as seasonality, transportation costs, supply chain inefficiencies, and regional demand differences. For FarmGrow Naija LTD, an agritech startup, understanding these variations is crucial for: - Setting competitive yet profitable prices for their products. - Anticipating market changes and managing risk. - Supporting farmers and vendors with fair and transparent pricing recommendations. Currently, FarmGrow Naija LTD lacks a structured approach to analyzing historical and regional food price data. This project addresses that gap by developing a data-driven framework to monitor and interpret price movements and suggest pricing strategies to the stakeholders. --- ## Dashboard An interactive dashboard for the food price analysis can be accessed here --- ## 📁 Table of Contents - Background - Aim - Objectives - About the Dataset - Project Workflow - Tools and Technologies - Insights from Analysis - Pricing Recommendations - Conclusion ## Aim To understand food price trends and derive actionable insights that can inform pricing strategies. ## Objectives - Data Exploration & Cleaning – Assess data quality, handle missing values, and ensure consistency. - Trend Analysis – Examine historical price movements across products, regions, and time periods. - Seasonality & Volatility – Identify cyclical patterns and price fluctuations. - Price Correlations – Analyze relationships between different food items and market factors. - Predictive Insights – (Optional) Build forecasting models to estimate future price trends. - Business Recommendations – Translate findings into clear, actionable insights for strategic decision-making. ## About the Dataset - Source: Food Prices from the National Bureau of Statistics, Nigeria. - Format: CSV - Key Fields: Date (Recording date of food price, 2024 - 2025), Food Item (Food Item Name), …

Visit

github.com