How to Solve Programming Assignments on Linear Regression and Classification

This blog walks you through a step-by-step process of how to tackle such assignments. We won’t directly solve any specific assignment but will instead build a structured approach. You’ll learn how to:

• Load and explore datasets,
• Preprocess data for modeling,
• Implement linear regression and logistic regression from scratch,
• Evaluate models with the right metrics,
• And reflect critically on model performance.

By the end, you’ll be ready to take on similar assignments with confidence.

Step 1: Understanding the Assignment Requirements

The first mistake students make is jumping directly into coding. Before you touch Python, carefully read the assignment instructions. These assignments are not just about writing correct code—they test your ability to understand concepts, apply them, and explain results.

Key things to look out for:

1. Implementation requirements – Are you allowed to use scikit-learn? Usually, you can use it for data handling and metrics but not for the core algorithm. That means you must implement formulas like the hypothesis, gradient descent, and sigmoid yourself using NumPy.
2. Submission format – Most instructors expect a Jupyter Notebook or Colab notebook with code, explanations, and plots. Submitting just a .py file won’t cut it.
3. Evaluation criteria – Note the marks distribution. Typically, a portion is for code, some for explanations, and some for discussions (e.g., effect of learning rate). Don’t ignore the non-coding parts.

Pro Tip: Treat your notebook as a story. Each section should have a heading, explanation, code, and results.

Step 2: Data Loading and Exploratory Data Analysis (EDA)

Almost every machine learning assignment begins with data exploration. This is your chance to show that you understand the dataset before jumping into modeling.

How to Do It:

1. Load the dataset with Pandas:

import pandas as pd

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

print(data.head())

2. Check summary statistics:

print(data.describe())

print(data.info())

3. Visualize distributions:

For regression: plot histograms of the target variable.

For classification: plot bar charts of class counts.

4. Feature relationships:

Use scatter plots (feature vs. target) for regression.

Use boxplots or swarm plots for classification.

5. Spot issues:

   Missing values, outliers, or skewed distributions.

Why this matters: Many students skip EDA and jump to modeling. But in most assignments, marks are allocated for demonstrating EDA. Plus, it informs your feature selection later.

Step 3: Data Preprocessing

Machine learning models are picky about data. Linear models, in particular, rely heavily on feature scaling and proper encoding.

Key Tasks:

• Feature selection – Don’t use all features blindly. Pick at least two features that are logically related to the target. Justify your choice in text.
• Data splitting – Always split into training and testing sets.

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

• Feature scaling – Use standardization or normalization.

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test)

Why scaling matters: Gradient descent is sensitive to feature scales. Without scaling, one feature may dominate the updates, making convergence painfully slow.

Step 4: Implementing Linear Regression from Scratch

Here’s where the real fun begins—implementing the math.

Hypothesis Function

For regression, the hypothesis is:

hθ(x)=θ0+θ1x1+θ2x2+⋯+θnxnh_\theta(x) = \theta_0 + \theta_1x_1 + \theta_2x_2 + \dots + \theta_nx_n

In code:

import numpy as np def hypothesis(X, theta): return np.dot(X, theta)

Loss Function (MSE)

J(θ)=1m∑i=1m(hθ(x(i))−y(i))2J(\theta) = \frac{1}{m} \sum_{i=1}^{m} (h_\theta(x^{(i)}) - y^{(i)})^2

def compute_loss(X, y, theta): m = len(y) predictions = hypothesis(X, theta) return np.sum((predictions - y) ** 2) / m

Gradient Descent

θj:=θj−α∂J∂θj\theta_j := \theta_j - \alpha \frac{\partial J}{\partial \theta_j}

def gradient_descent(X, y, theta, alpha, iterations): m = len(y) loss_history = [] for _ in range(iterations): predictions = hypothesis(X, theta) error = predictions - y gradient = (1/m) * np.dot(X.T, error) theta -= alpha * gradient loss_history.append(compute_loss(X, y, theta)) return theta, loss_history

Training and Plotting Loss

Students often forget to plot the loss curve. This visual proof shows whether your model is converging.

import matplotlib.pyplot as plt theta = np.zeros(X_train.shape[1]) theta, loss_history = gradient_descent(X_train, y_train, theta, alpha=0.01, iterations=1000) plt.plot(loss_history) plt.xlabel("Iterations") plt.ylabel("Loss") plt.title("Loss Curve") plt.show()

Step 5: Evaluating Regression Models

Evaluation is where you justify whether your model works.

Metrics to include:

• MSE – Lower is better.
• RMSE – Square root of MSE (same scale as target).
• R² Score – How much variance your model explains.

Example:

from sklearn.metrics import mean_squared_error, r2_score y_pred = hypothesis(X_test, theta) mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) r2 = r2_score(y_test, y_pred) print(f"MSE: {mse}, RMSE: {rmse}, R²: {r2}")

Discussion point: Experiment with different learning rates and iterations, then compare results. This section often carries marks.

Step 6: Implementing Logistic Regression (Linear Classification)

Classification is similar but introduces the sigmoid function.

Hypothesis (Sigmoid)

hθ(x)=11+e−θTxh_\theta(x) = \frac{1}{1 + e^{-\theta^Tx}}

def sigmoid(z): return 1 / (1 + np.exp(-z))

Loss Function (Log Loss)

J(θ)=−1m∑[ylog⁡(hθ(x))+(1−y)log⁡(1−hθ(x))]J(\theta) = -\frac{1}{m} \sum \left[ y \log(h_\theta(x)) + (1-y)\log(1-h_\theta(x)) \right]

Gradient Descent for Logistic Regression

Implementation is similar to regression but uses the sigmoid hypothesis.

Evaluation Metrics

Unlike regression, classification uses:

• Accuracy
• Precision, Recall, F1-Score
• Confusion Matrix

Discussion point: Explain the trade-off between precision and recall when adjusting the classification threshold.

Step 7: Writing the Discussion and Conceptual Answers

Assignments typically include short-answer conceptual questions. Don’t underestimate them—they’re often easy marks.

Examples:

• Learning Rate Impact: Small learning rate → slow convergence. Large → divergence.
• Decision Boundary: The line (or hyperplane) that separates classes.
• Difference Between Regression & Classification: Regression predicts continuous values; classification predicts discrete categories.

Pro Tip: Use simple language and include small plots wherever possible. Visuals impress graders.

Common Pitfalls to Avoid

1. Skipping explanations – Code alone won’t get full marks. Explain why you’re doing something.
2. Using scikit-learn shortcuts – If the instructions say “implement from scratch,” avoid LinearRegression() or LogisticRegression().
3. Messy notebook – Poor formatting, no comments, and no headings make it look rushed.
4. Ignoring metrics – Always evaluate your model. Plots and metrics are proof of understanding.

Final Thoughts

Assignments on linear regression and classification are designed to teach you fundamentals. They’re not about getting the most accurate model—they’re about learning how models work under the hood. Once you’ve built these from scratch, using advanced libraries later will make much more sense.

Approach each section systematically:

1. Explore the data,
2. Preprocess carefully,
3. Implement the math,
4. Evaluate with metrics,
5. Reflect critically.

If you follow this structured workflow, you won’t just complete the assignment—you’ll understand machine learning at its core. And that’s far more valuable in the long run.