Predictive Modelling for Agriculture

Data Science

Machine Learning

ML pipeline predicting agricultural outcomes from environmental features.

Overview

This project builds a supervised ML pipeline to predict [CROP YIELD / SOIL TYPE / HARVEST QUALITY] from environmental and agricultural input features across [N] observations.

Methodology

Pipeline:

Data cleaning and feature engineering
EDA — distributions, correlations with target
Baseline — OLS regression
Model comparison — Random Forest vs XGBoost vs [OTHER]
Hyperparameter tuning — [GridSearchCV / RandomizedSearchCV]
Evaluation — [METRIC] on held-out test set (80/20 split)

Key Findings

[BEST MODEL] achieved [METRIC VALUE] on the test set, outperforming the baseline by [X]%. Most important features: [FEATURE 1], [FEATURE 2], [FEATURE 3].

Model	[Metric]
Baseline OLS	[VALUE]
Random Forest	[VALUE]
XGBoost	[VALUE]

Code

Python Notebook

---
title: "Predictive Modelling for Agriculture"
description: "ML pipeline predicting agricultural outcomes from environmental features."
categories: [Data Science, Machine Learning]
---

## Overview

This project builds a supervised ML pipeline to predict
[CROP YIELD / SOIL TYPE / HARVEST QUALITY] from environmental
and agricultural input features across [N] observations.

## Methodology

**Pipeline:**

1. Data cleaning and feature engineering
2. EDA — distributions, correlations with target
3. Baseline — OLS regression
4. Model comparison — Random Forest vs XGBoost vs [OTHER]
5. Hyperparameter tuning — [GridSearchCV / RandomizedSearchCV]
6. Evaluation — [METRIC] on held-out test set (80/20 split)

## Key Findings

::: {.result-box}
[BEST MODEL] achieved [METRIC VALUE] on the test set, outperforming
the baseline by [X]%. Most important features: [FEATURE 1],
[FEATURE 2], [FEATURE 3].
:::

| Model | [Metric] |
|---|---|
| Baseline OLS | [VALUE] |
| Random Forest | [VALUE] |
| XGBoost | [VALUE] |

## Code

[Python Notebook](https://github.com/tkay305/predictive-modeling-agriculture)