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Crop Yield Forecasting

Use satellite and weather data to predict agricultural yields in sub-Saharan Africa.

Learn First

  1. 1

    Introduction & Ethics

    Introduction

    Understand the data sources, formats, and the ethics of working with African data.

    • Where the data comes from
    • What questions it can answer
    • Responsible use
  2. 2

    Data Cleaning Fundamentals

    Data Cleaning

    Handle missing values, outliers, and inconsistencies.

    import pandas as pd
df = pd.read_csv("data.csv")
df = df.dropna()
  3. 3

    Exploratory Data Analysis

    Exploratory Data Analysis

    Visualize patterns across regions and time.

    • Distributions
    • Correlations
    • Trends by district
  4. 4

    Modeling & Evaluation

    Modeling

    Build and evaluate a simple model, then write your recommendations.

    • Train/test split
    • Baseline model
    • Metrics that matter

Dataset

Title
West Africa Crop Yields
Country
Nigeria, Ghana
Source
FAO (sample)
License
CC BY 4.0
Ethics note
Use responsibly. Aggregated / anonymised data — cite the source and avoid re-identification.
Open dataset →Data dictionaryDataset details

Instructions

Goal

Forecast seasonal crop yields from weather and farm-survey features.

The data

Use the linked Agricultural Survey of African Farm Households dataset.

Steps

  1. Open the starter notebook in Colab.
  2. Join household, climate, and yield fields; handle missing values.
  3. Engineer features and train a baseline regression model.
  4. Evaluate (MAE / RMSE) and interpret the drivers of yield.

Deliverables

  • Your Colab notebook link
  • Key findings (3–5 bullets)

How to submit

Sign in and paste your work links in Submit your work for mentor review.

Rubric

  • Data Cleaning20 pts
  • Exploratory Analysis20 pts
  • Visualizations20 pts
  • Model / Evaluation20 pts
  • Report / Recommendations20 pts
  • Total100 pts