Deovam Singh - Fab Futures - Data Science

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I started the Machine Learning assignment with first understanding the code shown by Prof Neil. Was very difficult so I tried learning about DecisionTreeClassifier through chatgpt.

I gave chatgpt the prompt "help me write a simple python code to help understand machine learning with an example". This gave me an example of Decision Tree model. After that I asked chatgpt to explain the code to me line by line and chatgpt gave me the following

Here is a clear, line-by-line explanation of the full code. I will break it into sections so you understand exactly what each line does.

---

Step 1: Import libraries

import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
import numpy as np

Explanation:

• matplotlib.pyplot as plt Used for plotting graphs. plt is a shorthand.

• DecisionTreeClassifier This is the machine-learning model we will train.

• numpy as np Numpy is used for numerical operations and to create arrays.

---

Step 2: Prepare the dataset

# Features: [age, training_hours_per_week]
X = np.array([
    [10, 2],
    [15, 3],
    [20, 5],
    [25, 1],
    [30, 4],
    [35, 0],
    [40, 3],
    [45, 2]
])

Explanation:

• X contains input features.
• Each row is one person.
• [age, training_hours] is a feature pair.

Example:

• [10, 2] → Age 10, trains 2 hours per week.

---

y = np.array([0, 0, 1, 0, 1, 0, 1, 0])  # 1 -> Likes sport, 0 -> Does not

Explanation:

• y contains the labels (correct answers).
• 1 → Likes sport
• 0 → Does not like sport

So:

• First person (age 10, trains 2 hrs) → label = 0
• Third person (age 20, trains 5 hrs) → label = 1

---

Step 3: Train a Decision Tree model

model = DecisionTreeClassifier()

Explanation:

Creates an empty decision tree model with default settings.

---

model.fit(X, y)

Explanation:

• .fit() trains the model.
• The model learns patterns between features X and labels y.

---

Step 4: Visualize the decision boundary

First, create the plotting range:

x_min, x_max = X[:,0].min() - 5, X[:,0].max() + 5
y_min, y_max = X[:,1].min() - 1, X[:,1].max() + 1

Explanation:

• X[:,0] → all values from column 0 (ages)
• X[:,1] → all values from column 1 (training hours)

We find:

• min() → smallest age or training hour
• max() → largest

Then we expand the range a little by subtracting / adding:

• to make the graph nicer
• so points aren't on the edges

Example: if min age = 10, we use 10 - 5 = 5.

---

Generate a grid of points

xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))

Explanation:

• meshgrid creates a grid covering the entire feature space.
• arange(..., 0.1) generates values every 0.1 step.
• xx and yy are huge matrices representing all possible (age, hours) points.

This grid lets us "ask the model": What would you predict at each point on this plane?

---

Predict the class for each point in the grid

Z = model.predict(np.c_[xx.ravel(), yy.ravel()])

Explanation:

• .ravel() → flattens the grid into a long list.
• np.c_[] → combines the flattened xx and yy into (x,y) pairs.

Example: [ [age1, hour1], [age2, hour2], ... ]

The model predicts a class (0 or 1) for each point.

---

Z = Z.reshape(xx.shape)

Explanation:

Reshape the predictions back into the grid shape so we can plot it as a filled contour.

---

Step 5: Plot the results

Plot the decision boundary:

plt.contourf(xx, yy, Z, alpha=0.3, cmap=plt.cm.coolwarm)

Explanation:

• contourf draws a filled region plot.
• This shows which area the model predicts as class 0 and class 1.
• alpha=0.3 makes it semi-transparent.
• cmap=coolwarm gives red/blue colors.

---

Plot the original data points:

plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.coolwarm, edgecolors='k', s=100)

Explanation:

• Plot each person as a point.
• c=y → color based on class (0 or 1)
• edgecolors='k' → black outline
• s=100 → size of the points.

---

Add labels and title:

plt.xlabel("Age")
plt.ylabel("Training hours per week")
plt.title("Decision Tree Classification")

Self-explanatory.

---

Show the plot:

plt.show()

Shows the final graph with:

• data points
• decision boundary
• colors showing class regions

# Step 1: Import libraries
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
import numpy as np

# Step 2: Prepare the dataset
# Features: [age, training_hours_per_week]
X = np.array([
    [10, 2],
    [15, 3],
    [20, 5],
    [25, 1],
    [30, 4],
    [35, 0],
    [40, 3],
    [45, 2]
])

y = np.array([0, 0, 1, 0, 1, 0, 1, 0])  # 1 -> Likes sport, 0 -> Does not

# Step 3: Train a Decision Tree model
model = DecisionTreeClassifier()
model.fit(X, y)

# Step 4: Visualize the data and decision boundary
x_min, x_max = X[:,0].min() - 5, X[:,0].max() + 5
y_min, y_max = X[:,1].min() - 1, X[:,1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))

# Predict for each point in the grid
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plot
plt.contourf(xx, yy, Z, alpha=0.3, cmap=plt.cm.coolwarm)
plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.coolwarm, edgecolors='k', s=100)
plt.xlabel("Age")
plt.ylabel("Training hours per week")
plt.title("Decision Tree Classification")
plt.show()

# Step 1: Import libraries
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Step 2: Prepare the dataset
# Features: [age, training_hours_per_week]
# Labels: 1 -> Likes the sport, 0 -> Does not like
X = [
    [10, 2],
    [15, 3],
    [20, 5],
    [25, 1],
    [30, 4],
    [35, 0],
    [40, 3],
    [45, 2]
]

y = [0, 0, 1, 0, 1, 0, 1, 0]  # 1 means likes sport, 0 means does not

# Step 3: Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# Step 4: Create and train a model
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Step 5: Make predictions
predictions = model.predict(X_test)

# Step 6: Evaluate the model
accuracy = accuracy_score(y_test, predictions)
print("Predictions:", predictions)
print("Actual labels:", y_test)
print("Accuracy:", accuracy)

# Step 7: Test with a new example
new_person = [[30, 5]]  # Age 28, 3 hours training per week
predicted = model.predict(new_person)
print("Predicted like/dislike for new person:", predicted[0])

Predictions: [1 0]
Actual labels: [0, 0]
Accuracy: 0.5
Predicted like/dislike for new person: 1