Oh no, it’s that time of the semester again. I wonder if you are like me, always thinking before an exam: “How many points will I score this time?” If only we could know the score in advance! Wait, don’t we have Python? Come on, let’s use Python and machine learning to predict our scores and maybe find the key to improving them!
Data, Data, and More Data!
We need data to get started. I dug up my study records from the past few years, including daily study time, sleep time, whether I had breakfast on time, and so on. I didn’t expect to be so organized! This data will be our “ingredients”.
Let’s first organize the data:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# Assume we already have a CSV file named 'study_data.csv'
data = pd.read_csv('study_data.csv')
# Let's see what the data looks like
print(data.head())Wow, seeing this data makes me feel like a little data scientist! But then again, why do I feel like there’s no obvious relationship between my study time and my scores? Could it be my study methods are the problem?
Splitting Data: Training Set vs Test Set
Next, we need to split the data into two parts: one part for “training” our model and the other part for testing the model’s accuracy. Just like our usual study routine, learn first, then test!
# Select features and target variable
features = ['study_time', 'sleep_time', 'breakfast', 'exercise']
X = data[features]
y = data['score']
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)Tip: The <span>random_state=42</span> is not some magic number; it’s just to make the results reproducible. You can change it to any number, just like picking a lucky number!
Training the Model: Let the Machine Learn to Ace!
Now, we need to teach the machine how to “learn”. We’ll use the simplest linear regression model, just like we learned y=kx+b when we were kids:
# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Check how well the model learned
train_score = model.score(X_train, y_train)
print(f"Model performance on the training set: {train_score:.2f}")Seeing this score, I can’t help but feel a little proud. Hey, I didn’t know I could “teach” so well! But don’t celebrate too early; we still need to see how it performs on the “exam” (test set).
Predicting Scores: Let’s Check the Results
Alright, now it’s the exciting moment — predicting scores!
# Make predictions on the test set
y_pred = model.predict(X_test)
# Evaluate the model
from sklearn.metrics import mean_squared_error, r2_score
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"Mean Squared Error: {mse:.2f}")
print(f"R² Score: {r2:.2f}")Wow, looking at these results, my mood is like a roller coaster. The R² score is pretty good, but the mean squared error… hmm, it seems our “learning model” still has room for improvement!
Finding the Key to Improve Scores: So That’s It!
Let’s see which factors have the most impact on scores:
for feature, coef in zip(features, model.coef_):
print(f"{feature}: {coef:.2f}")Seeing this result, I can’t help but laugh. It turns out that sleep time has such a big impact on scores! No wonder my late-night study sessions never work well. Also, having breakfast is really important; I guess I need to make sure to eat breakfast from now on.
You see, with Python and machine learning, we not only predicted scores but also found the key to improvement! Although this model may not be perfect yet, it has already given us a lot of insights. Maybe next time we can collect more data or try other machine learning algorithms; who knows, we might get more accurate predictions!
Remember, whether it’s studying or programming, the most important thing is to maintain curiosity and a spirit of exploration. Who knows, maybe your next Python project will help you achieve your dream of being a top student!