Analyzing and Predicting Food Prices in Nigeria Using Machine Learning and Python

Local Chapter Kano, Nigeria Chapter

Coordinated by Nigeria ,

Project background.

Food prices play a crucial role in the lives of Nigerians, directly impacting affordability, food security, and economic stability. This project aims to utilize Machine Learning (ML) techniques and Python programming to analyze historical food price data in Nigeria, predict future price trends, and provide valuable insights for consumers, policymakers, and stakeholders.

The problem.

The recent surge in food inflation has impacted livelihoods of Nigerians, particularly in crisis-affected areas. This additional shock has significantly affected households that were already living in fragile situations.

Governments, as well as humanitarian and development organizations, regularly monitor inflation rates to identify alarming trends and guide their actions to provide support. For example, high inflation can lead to a sharp increase in household spending needed to meet basic needs, requiring a policy response. In more extreme cases, a surge in food prices may indicate local food shortages, which signal the start or worsening of a food and nutrition crisis.

However, in many crisis situations, where conflict may make food markets inaccessible, detailed price data is not regularly collected. These disruptions often coincide with periods and locations of high price instability. The lack of data makes it difficult to assess price movements accurately – information critical for understanding the severity of conditions in these areas and informing potential responses. 

Project goals.

The primary objectives of this project are as follows: - Analyse historical food price data to identify trends, seasonality, and correlations. - Develop ML models to predict future food price trends for essential commodities. - Create an interactive web application using Python to visualize insights and predictions.

Project plan.

  • Week 1

    Project Preparation, Clarification and Brainstorming.

  • Week 2

    Data Collection and Preprocessing

  • Week 3

    Exploratory Data Analysis (EDA)

  • Week 4

    Feature Engineering

  • Week 5

    Model Development

  • Week 6

    Model Evaluation

  • Week 7

    Interactive Web Application

  • Week 8

    Result Presentation

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