01 Introduction

Last updated on 2025-07-10 | Edit this page

Introduction to Classification

What is Classification?

Classification is a type of supervised learning where the goal is to predict categorical class labels. Given input data, a classification model attempts to assign it to one of several predefined classes.

Some examples include: - Email spam detection (spam vs. not spam) - Disease diagnosis (positive vs. negative) - Image recognition (cat, dog, or other)

Workshop Goals

By the end of this workshop, you will be able to: - Understand common classification algorithms - Apply them using Scikit-Learn, NumPy, Pandas, and Matplotlib - Evaluate and optimise models

Topics Covered

  1. Logistic Regression
  2. Support Vector Machines (SVM)
  3. Model Evaluation: Accuracy, Precision, Recall, F1-Score, ROC-AUC
  4. Neural Networks (MLPClassifier)
  5. Random Forest Classifier
  6. Optimisation and Tuning

Required Libraries

We will use the following Python libraries throughout the workshop: - NumPy – numerical operations - Pandas – data manipulation - Scikit-Learn – machine learning models and tools - Matplotlib – data visualisation - Seaborn - data visualisation

Let’s get started! 🚀

Installing Libraries

Uncomment and run the commands below only if packages are not installed.

PYTHON 

# !pip install numpy
# !pip install pandas
# !pip install scikit-learn
# !pip install matplotlib
# !pip install seaborn

Check your environment has the necessary libraries installed

PYTHON 

import numpy
print("NumPy version:", numpy.__version__)

import pandas
print("Pandas version:", pandas.__version__)

import sklearn
print("sklearn version:", sklearn.__version__)

import matplotlib
print("matplotlib version:", matplotlib.__version__)

import seaborn
print("sklearn version:", seaborn.__version__)

SH 

NumPy version: 2.2.6
Pandas version: 2.2.3
sklearn version: 1.7.0
matplotlib version: 3.10.3
sklearn version: 0.13.2

PYTHON 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets

Preview Example Dataset

We use the load_breast_cancer() dataset from Scikit-Learn. It includes 30 numeric features extracted from breast mass images.

PYTHON 

from sklearn.datasets import load_breast_cancer
import pandas as pd

data = load_breast_cancer()
X = data.data
y = data.target

df = pd.DataFrame(X, columns=data.feature_names)
df['target'] = y
df.head()
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension … worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 … 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890 0
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 … 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902 0
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 … 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758 0
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 … 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300 0
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 … 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678 0

5 rows × 31 columns

PYTHON 

df.describe()
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension … worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
count 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 … 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000
mean 14.127292 19.289649 91.969033 654.889104 0.096360 0.104341 0.088799 0.048919 0.181162 0.062798 … 25.677223 107.261213 880.583128 0.132369 0.254265 0.272188 0.114606 0.290076 0.083946 0.627417
std 3.524049 4.301036 24.298981 351.914129 0.014064 0.052813 0.079720 0.038803 0.027414 0.007060 … 6.146258 33.602542 569.356993 0.022832 0.157336 0.208624 0.065732 0.061867 0.018061 0.483918
min 6.981000 9.710000 43.790000 143.500000 0.052630 0.019380 0.000000 0.000000 0.106000 0.049960 … 12.020000 50.410000 185.200000 0.071170 0.027290 0.000000 0.000000 0.156500 0.055040 0.000000
25% 11.700000 16.170000 75.170000 420.300000 0.086370 0.064920 0.029560 0.020310 0.161900 0.057700 … 21.080000 84.110000 515.300000 0.116600 0.147200 0.114500 0.064930 0.250400 0.071460 0.000000
50% 13.370000 18.840000 86.240000 551.100000 0.095870 0.092630 0.061540 0.033500 0.179200 0.061540 … 25.410000 97.660000 686.500000 0.131300 0.211900 0.226700 0.099930 0.282200 0.080040 1.000000
75% 15.780000 21.800000 104.100000 782.700000 0.105300 0.130400 0.130700 0.074000 0.195700 0.066120 … 29.720000 125.400000 1084.000000 0.146000 0.339100 0.382900 0.161400 0.317900 0.092080 1.000000
max 28.110000 39.280000 188.500000 2501.000000 0.163400 0.345400 0.426800 0.201200 0.304000 0.097440 … 49.540000 251.200000 4254.000000 0.222600 1.058000 1.252000 0.291000 0.663800 0.207500 1.000000

8 rows × 31 columns

PYTHON 

df.hist(bins=20, figsize=(15, 10))
plt.tight_layout()
png
png

PYTHON 

from sklearn.preprocessing import StandardScaler

# Apply StandardScaler
scaler = StandardScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns, index=df.index)
df_scaled.describe()
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension … worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
count 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 … 5.690000e+02 5.690000e+02 569.000000 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02 5.690000e+02
mean -1.373633e-16 6.868164e-17 -1.248757e-16 -2.185325e-16 -8.366672e-16 1.873136e-16 4.995028e-17 -4.995028e-17 1.748260e-16 4.745277e-16 … 1.248757e-17 -3.746271e-16 0.000000 -2.372638e-16 -3.371644e-16 7.492542e-17 2.247763e-16 2.622390e-16 -5.744282e-16 -4.995028e-17
std 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 … 1.000880e+00 1.000880e+00 1.000880 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00 1.000880e+00
min -2.029648e+00 -2.229249e+00 -1.984504e+00 -1.454443e+00 -3.112085e+00 -1.610136e+00 -1.114873e+00 -1.261820e+00 -2.744117e+00 -1.819865e+00 … -2.223994e+00 -1.693361e+00 -1.222423 -2.682695e+00 -1.443878e+00 -1.305831e+00 -1.745063e+00 -2.160960e+00 -1.601839e+00 -1.297676e+00
25% -6.893853e-01 -7.259631e-01 -6.919555e-01 -6.671955e-01 -7.109628e-01 -7.470860e-01 -7.437479e-01 -7.379438e-01 -7.032397e-01 -7.226392e-01 … -7.486293e-01 -6.895783e-01 -0.642136 -6.912304e-01 -6.810833e-01 -7.565142e-01 -7.563999e-01 -6.418637e-01 -6.919118e-01 -1.297676e+00
50% -2.150816e-01 -1.046362e-01 -2.359800e-01 -2.951869e-01 -3.489108e-02 -2.219405e-01 -3.422399e-01 -3.977212e-01 -7.162650e-02 -1.782793e-01 … -4.351564e-02 -2.859802e-01 -0.341181 -4.684277e-02 -2.695009e-01 -2.182321e-01 -2.234689e-01 -1.274095e-01 -2.164441e-01 7.706085e-01
75% 4.693926e-01 5.841756e-01 4.996769e-01 3.635073e-01 6.361990e-01 4.938569e-01 5.260619e-01 6.469351e-01 5.307792e-01 4.709834e-01 … 6.583411e-01 5.402790e-01 0.357589 5.975448e-01 5.396688e-01 5.311411e-01 7.125100e-01 4.501382e-01 4.507624e-01 7.706085e-01
max 3.971288e+00 4.651889e+00 3.976130e+00 5.250529e+00 4.770911e+00 4.568425e+00 4.243589e+00 3.927930e+00 4.484751e+00 4.910919e+00 … 3.885905e+00 4.287337e+00 5.930172 3.955374e+00 5.112877e+00 4.700669e+00 2.685877e+00 6.046041e+00 6.846856e+00 7.706085e-01

8 rows × 31 columns