Last weekend, our long-awaited picnic was ruined by a sudden downpour. I couldn’t help but complain, “How great it would be to know the weather in advance!” Wait a minute, as a Python enthusiast, I can actually do something about it! Let’s see how we can use Python and machine learning to predict the weather and prepare for our weekend trip!
Data, Data, and More Data!
As the saying goes, a clever woman can’t cook without rice. To predict the weather, we first need weather data! Don’t worry, we don’t need to become meteorology experts; there are plenty of public weather data available online for us to “take advantage of.”
import pandas as pd
import requests
# Assume we found an API that provides historical weather data
url = "https://api.weatherdata.com/historical"
response = requests.get(url)
data = response.json()
# Convert JSON data to DataFrame
df = pd.DataFrame(data)
print(df.head())By running this code, we can see a beautiful data table containing information such as date, temperature, humidity, and wind speed. Looking at these numbers, I can almost see the sunny beach for the weekend!
Tip: When using this in practice, you may need to register for an account to get an API key. Don’t forget to protect your key, just like it’s important to guard your snack stash!
Cleaning Data, Not Doing Laundry
We have the data, but it might be a bit “dirty,” just like our picnic last weekend. Data cleaning may not be as laborious as doing laundry, but it still requires our careful attention.
# Handle missing values
df = df.dropna()
# Convert date to datetime type
df['date'] = pd.to_datetime(df['date'])
# Extract useful features
features = ['temp', 'humidity', 'wind_speed', 'pressure']
X = df[features]
y = df['weather_condition']
print("Cleaned data:")
print(X.head())Look, the data has become so refreshing! It’s like washing a dirty picnic blanket, ready to use again.
Training the Model, Not Training a Pet
Next comes the exciting part: training the model. Don’t be intimidated by the term “training the model”; it’s much easier than training a disobedient dog.
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train the random forest model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Evaluate the model
accuracy = model.score(X_test, y_test)
print(f"Model accuracy: {accuracy:.2f}")Wow! Did you see that accuracy? Our model has learned to predict the weather, much better than my guessing skills!
Future Predictions, Not Fortune-Telling
Now, let’s use this model to predict next week’s weather. Although we are not fortune tellers, with data and model support, our prediction accuracy is definitely more reliable than just watching the weather forecast.
# Assume we have next week's weather feature data
next_week_features = pd.DataFrame({
'temp': [25, 26, 24, 23, 27],
'humidity': [60, 58, 65, 70, 55],
'wind_speed': [10, 12, 8, 9, 11],
'pressure': [1010, 1008, 1012, 1015, 1009]
})
predictions = model.predict(next_week_features)
print("Next week's weather prediction:", predictions)Look at this prediction result; the weather looks good for the weekend! It seems our picnic plan can be back on the table.
In fact, the usefulness of this code is far broader than one might think. You can use it to predict stock trends (though I might end up losing all my money), predict if you’ll be late tomorrow (spoiler alert: you will), or even predict if your crush at work will smile at you tomorrow (let’s skip that one and keep some surprises in life).
Remember, Python and machine learning are just tools; what matters is how you apply them. Maybe the next time you see a weather forecast, you’ll think, “Hey, I can do that too!” Now, I need to prepare the picnic food; happy coding and have a great weekend!