ML for Climate Data

Before we write code, let's practice being data detectives. Your mission: explore the interactive chart below to find the answers to the treasure hunt questions. You can click and drag to zoom in, and hover your mouse to see the exact numbers!

• What was the temperature on Jan 1, 1981?
• What was the temperature on July 1, 1985?
• Can you find the hottest day on the whole chart? What temperature was it?

Look at the bumpy line in the chart above. It goes up and down every single day! But is there a hidden, long-term trend? Before we use code, use your eyes. Can you guess where the "average" line would go? Try tracing the general direction with your finger on the screen! This is exactly what we're about to ask the computer to do.

Ready to use the same tools as real data scientists? We're going to use a tool called Google Colab. It's like a Google Doc, but for writing and running Python code right in your browser. No installation needed! Let's go!

Here's the secret plan:

• STEP 1 🧰: We import our "toolboxes" (special code libraries).
• STEP 2 ☁️: We load a real weather dataset from the internet.
• STEP 3 🤔: We ask how many clues (days of data) we have.
• STEP 4 👀: We take a quick peek at the first few rows of data.
• STEP 5 📊: We tell the computer to turn all that data into a cool-looking chart!

Now you're ready! Open a new Colab notebook and copy-paste this code into a cell. Press the play button (▶️) to run it!

# 1. Get our super-toolboxes (called libraries)
import pandas as pd # Pandas helps us work with data tables

# 2. Load a real dataset from the internet
# This is a history of minimum daily temperatures in Melbourne, Australia
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv'
df = pd.read_csv(url)

# 3. How many clues do we have?
print(f"This dataset has {len(df)} days of weather clues!")

# 4. Be a detective: let's peek at the first 5 clues!
print("\nHere's a peek at the data:")
print(df.head())

# 5. The magic moment: turn the data into a picture!
print("\nCreating your temperature plot... check it out!")
df.plot(title='Daily Minimum Temperatures in Melbourne')

When you run this, you'll see how many clues our AI detective has to work with (that's almost 10 years of data!), a table of data, AND a chart that visualizes it all. You just used Python to analyze real-world climate data.

Okay, you've seen the data. But a true data scientist looks for the hidden story. Is the temperature *generally* going up or down? It's hard to tell with all those daily spikes! Let's use that "rolling average" trick to find the trend line. Add this new code to a new cell in your Colab notebook and run it!

# This calculates the average temperature over a full year (365 days)
# and plots that trend! It smooths out the daily bumps.
# You need to run this in a NEW cell after the first one.
# It also needs the 'Temp' column to be a number, let's fix that first!
df['Temp'] = pd.to_numeric(df['Temp'], errors='coerce')

print("Calculating the 365-day average trend...")
df['Temp'].rolling(window=365).mean().plot(title='Smoothed Temperature Trend in Melbourne')

WHOA! See that new line? It's much smoother. It shows the real, long-term pattern hidden in the data. You just used your first data analysis model to find a hidden signal.

Finding a trend is cool, but what about predicting the future? This is where true Machine Learning comes in. We'll use a famous toolbox called `scikit-learn` to build a simple "model" that learns from all the past data and makes a guess about the next day. This is it—the big moment! Add this to a new code cell.

# This is real Machine Learning!
# We'll use a simple model to predict the next value.
from sklearn.linear_model import LinearRegression
import numpy as np

# We need to reshape our data for the model
X = np.array(range(len(df))).reshape(-1, 1)
y = df['Temp'].dropna() # Drop any missing values
X = X[:len(y)] # Make sure X and y are the same size

model = LinearRegression()
model.fit(X, y)

# Let's predict the temperature for the very next day!
next_day_index = len(df)
prediction = model.predict([[next_day_index]])

print(f"\n🤖 AI Prediction for the next day's temp: {prediction[0]:.2f} degrees")

You did it! That number is your AI's best guess for the future based on all the data from the past. You just built and ran a real predictive model!