In one of my previous blog posts, I discussed how machine learning can be used to solve the problem of climate change. Machine learning models can be trained on historical climate data. Future climate change predictions can be made using such machine learning models. Python is commonly used by machine learning practitioners and data scientists in climate change research. In this blog post, I will give an introduction to how we can use various Python libraries like scikit-learn on a climate change dataset.

The first stage in any data science project is importing the important Python libraries.

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

The second stage is to import the dataset in the form of a csv file.

data = pd.read_csv("climate_data.csv")

The third stage after importing the climate change dataset is to split the dataset into testing and training datasets.

X_train, X_test, y_train, y_test = train_test_split(data, data["temperature"], test_size=0.25)

Since this is a simple scenario of a regression problem, we can use a machine learning algorithm like linear regression.

model = LinearRegression()

The next stage is to train the machine learning model on a dataset.

model.fit(X_train, y_train)

After training, let's predict the temperature on the test dataset

# Predict the temperature on the test set predictions = 
model.predict(X_test)

Once testing has been done, we can evaluate the linear regression model.

# Evaluate the model
print("Accuracy:", model.score(X_test, y_test))