First ML prediction (linear regression) in ML Python - ML Experiment: Train & Evaluate

Experiment - First ML prediction (linear regression)

Problem:We want to predict house prices based on their size using a simple linear regression model.

Current Metrics:Training loss: 2500000, Training R2 score: 0.65

Issue:The model is underfitting, meaning it does not capture the relationship well enough and has low accuracy.

Your Task

Improve the model so that the training R2 score is above 0.85 and the loss decreases below 1000000.

Use only linear regression without adding complex models.

Do not add more features or data.

Keep the code simple and easy to understand.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler

# Sample data: house sizes (in square feet) and prices (in $)
X = np.array([500, 750, 1000, 1250, 1500, 1750, 2000]).reshape(-1, 1)
y = np.array([150000, 200000, 250000, 300000, 350000, 400000, 450000])

# Scale the input feature
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Create and train the linear regression model
model = LinearRegression()
model.fit(X_scaled, y)

# Make predictions
predictions = model.predict(X_scaled)

# Calculate metrics
loss = mean_squared_error(y, predictions)
r2 = r2_score(y, predictions)

print(f"Training loss: {loss:.0f}")
print(f"Training R2 score: {r2:.2f}")

Scaled the input feature using StandardScaler to help the model learn better.

Ensured the input data is reshaped correctly for the model.

Kept the model simple with LinearRegression but improved data preprocessing.

Results Interpretation

Before scaling:
Loss = 2,500,000
R2 score = 0.65

After scaling:
Loss = 0
R2 score = 1.00

Scaling input features can help linear regression models learn better and improve prediction accuracy, reducing underfitting.

Bonus Experiment

Try adding a new feature like the number of bedrooms to see if the model improves further.

💡 Hint

Create a new input array with two features and retrain the model to check if R2 score increases.

Practice

1. What is the main goal of linear regression in machine learning?

easy

A. To classify data into categories

B. To find a line that best fits the data points

C. To cluster data points into groups

D. To reduce the number of features

Solution

1. Step 1: Understand linear regression purpose

   Linear regression aims to find a straight line that best fits the relationship between input and output data.

2. Step 2: Compare with other options

   Classification, clustering, and feature reduction are different tasks, not the goal of linear regression.

3. Final Answer:

   To find a line that best fits the data points -> Option B

4. Quick Check:

   Linear regression = line fitting [OK]

Hint: Linear regression fits a line, not groups or categories [OK]

Common Mistakes:

• Confusing regression with classification
• Thinking linear regression clusters data
• Believing it reduces features

2. Which of the following is the correct Python syntax to create a simple linear regression model using scikit-learn?

easy

A. model = LinearRegression()

B. model = LinearRegression.fit()

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

D. model = LinearRegression(X, y)

Solution

1. Step 1: Recall model creation syntax

   In scikit-learn, you first create the model object with LinearRegression(), without arguments.

2. Step 2: Understand fitting method

   The fit method is called on the model object with data, e.g., model.fit(X, y), not during creation.

3. Final Answer:

   model = LinearRegression() -> Option A

4. Quick Check:

   Model creation uses class name with parentheses [OK]

Hint: Create model with class(), fit separately [OK]

Common Mistakes:

• Calling fit during model creation
• Passing data to constructor
• Using fit as a class method

3. Given the code below, what will be the predicted value for X_new = [[5]]?

from sklearn.linear_model import LinearRegression
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
model = LinearRegression()
model.fit(X, y)
prediction = model.predict([[5]])
print(prediction)

medium

A. [9.]

B. [12.]

C. [8.]

D. [10.]

Solution

1. Step 1: Understand the training data pattern

   The output y doubles the input X: y = 2 * X.

2. Step 2: Predict for X_new = 5

   Using the pattern, prediction = 2 * 5 = 10.

3. Final Answer:

   [10.] -> Option D

4. Quick Check:

   2 x 5 = 10 [OK]

Hint: Check pattern in training data, apply to new input [OK]

Common Mistakes:

• Ignoring the pattern and guessing
• Using last y value as prediction
• Confusing input and output

4. What is wrong with the following code snippet for training a linear regression model?

from sklearn.linear_model import LinearRegression
X = [1, 2, 3, 4]
y = [2, 4, 6, 8]
model = LinearRegression()
model.fit(X, y)

medium

A. X should be a 2D array, not a 1D list

B. y should be a 2D array, not a list

C. LinearRegression() requires parameters in constructor

D. fit() method should be called with no arguments

Solution

1. Step 1: Check input shape requirements

   scikit-learn expects X as a 2D array (list of lists), but X is a 1D list here.

2. Step 2: Confirm y shape and method usage

   y can be 1D list, constructor needs no parameters, fit requires X and y arguments.

3. Final Answer:

   X should be a 2D array, not a 1D list -> Option A

4. Quick Check:

   X must be 2D for fit() [OK]

Hint: X must be 2D array for sklearn fit() [OK]

Common Mistakes:

• Passing 1D list for X
• Thinking y must be 2D
• Adding parameters to LinearRegression()

5. You trained a linear regression model on data where y = 3x + 2. After training, you want to predict the output for x = 10. Which code snippet correctly predicts and prints the result?

hard

A. model.predict([10])\nprint(prediction)

B. prediction = model.predict([10, 2])\nprint(prediction)

C. prediction = model.predict([[10]])\nprint(prediction[0])

D. prediction = model.predict(10)\nprint(prediction)

Solution

1. Step 1: Understand input shape for prediction

   scikit-learn expects a 2D array for prediction input, so [[10]] is correct.

2. Step 2: Access prediction output correctly

   predict returns an array; to print the single value, use prediction[0].

3. Final Answer:

   prediction = model.predict([[10]]) print(prediction[0]) -> Option C

4. Quick Check:

   Input 2D array, print first prediction [OK]

Hint: Use 2D input for predict, print first element [OK]

Common Mistakes:

• Passing 1D list or scalar to predict
• Printing prediction array directly without indexing
• Passing wrong shaped input with extra values