I am an Environmental Data Scientist working at the intersection of artificial intelligence, environmental systems, and geospatial data. My focus is on developing intelligent solutions to better understand and manage soil, water, and ecological dynamics.
With a background in biology and software engineering, I bridge scientific knowledge with scalable computational tools to tackle real-world environmental challenges.
- 🌱 Soil Informatics & Soil Microbiome Analysis
- 💧 Hydrology & Water Systems Modeling
- 🧠 Machine Learning for Environmental Data
- 🛰️ Geospatial Analytics & Remote Sensing
- 📊 Data Visualization & Environmental Insights
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# Simulated environmental dataset
data = pd.DataFrame({
'rainfall_mm': [10, 20, 5, 0, 15, 30, 25],
'temperature_c': [25, 28, 30, 35, 27, 24, 26],
'humidity_%': [80, 70, 60, 50, 75, 85, 78],
'soil_moisture': [0.30, 0.45, 0.20, 0.10, 0.40, 0.55, 0.50]
})
X = data[['rainfall_mm', 'temperature_c', 'humidity_%']]
y = data['soil_moisture']
# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Model
model = RandomForestRegressor()
model.fit(X_train, y_train)
# Prediction
predictions = model.predict(X_test)
# Evaluation
mse = mean_squared_error(y_test, predictions)
print(f"Model MSE: {mse:.4f}")To build AI-driven environmental systems that enable sustainable decision-making, climate resilience, and equitable access to natural resources.
- GitHub
- Research collaborations welcome
"Turning environmental data into actionable intelligence."