Lecture 10 – Data Mining Implementation Using Python

Lecture 10 provides a complete hands-on guide to Data Mining implementation using Python with Pandas, NumPy, Matplotlib, Scikit-learn, and MLxtend. Includes preprocessing, classification, clustering, association rule mining, examples, diagrams, code, and evaluation techniques.

Python has become the most widely used language in Data Mining and Machine Learning. Its simplicity, flexibility, and huge library ecosystem make it ideal for students, researchers, data scientists, and AI engineers.

This lecture transforms the theoretical knowledge from previous lectures into practical, real-world Python implementation.

Python will be used to:

• Load datasets
• Clean and preprocess data
• Create training/testing sets
• Build models (classification & clustering)
• Run association rule mining
• Visualize results
• Evaluate performance

By the end of this lecture, you will be able to build full data mining pipelines using Python.

Introduction to Python for Data Mining

Why Python Is Used in Data Mining

Python dominates the data industry due to:

• Easy syntax
• Huge community support
• Powerful libraries
• Industry adoption (Google, Meta, Netflix, OpenAI)
• Fast model development

Key Libraries Overview

Here are the main Python libraries used in Data Mining:

Library	Purpose
Pandas	Data manipulation
NumPy	Numerical computing
Matplotlib / Seaborn	Visualization
Scikit-learn	ML algorithms
MLxtend	Association rules
SciPy	Statistics
NetworkX	Graph mining
NLTK / SpaCy	Text mining

Installing the Required Tools

Python, Pip & Anaconda

Beginners can download Anaconda, which includes:

• Python
• Jupyter Notebook
• Data science libraries

Installing Libraries

Run:

pip install pandas numpy matplotlib seaborn scikit-learn mlxtend

Python for Data Science

Loading and Understanding Data

Using Pandas DataFrames

Dataset example: students.csv

import pandas as pd

df = pd.read_csv("students.csv")
print(df.head())

Reading CSV, Excel, SQL

df = pd.read_excel("file.xlsx")
df = pd.read_sql("SELECT * FROM students", connection)

Data Preprocessing in Python

Preprocessing prepares raw data for analysis.

Handling Missing Values

Check missing:

df.isnull().sum()

Fill missing values:

df['Age'] = df['Age'].fillna(df['Age'].mean())

Encoding Categorical Data

Convert categories → numbers:

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
df['Gender'] = le.fit_transform(df['Gender'])

Scaling & Normalization

Used for KNN, clustering, SVM.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(df[['Age', 'Marks']])

Feature Selection

corr = df.corr()
print(corr['Target'])

Python.org

Exploratory Data Analysis (EDA)

Statistical Summary

df.describe()

Data Visualization

Histogram:

df['Age'].hist()

Correlation heatmap:

import seaborn as sns
sns.heatmap(df.corr())

Implementing Classification Algorithms

We train models using Scikit-learn.

Split dataset:

from sklearn.model_selection import train_test_split

X = df.drop('Target', axis=1)
y = df['Target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

Decision Tree Classifier

from sklearn.tree import DecisionTreeClassifier

dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)

pred = dt.predict(X_test)

Naive Bayes Classifier

from sklearn.naive_bayes import GaussianNB

nb = GaussianNB()
nb.fit(X_train, y_train)
pred = nb.predict(X_test)

Logistic Regression

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression()
lr.fit(X_train, y_train)

KNN Classifier

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

Lecture 9 – Data Mining Trends and Research Frontiers

Implementing Clustering Algorithms

K-Means Clustering

from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3)
kmeans.fit(X_scaled)
labels = kmeans.labels_

Hierarchical Clustering

from sklearn.cluster import AgglomerativeClustering

hc = AgglomerativeClustering(n_clusters=3)
labels = hc.fit_predict(X_scaled)

Association Rule Mining

We use MLxtend.

Convert data into basket format (one-hot encoded).

from mlxtend.frequent_patterns import apriori, association_rules

frequent_items = apriori(df, min_support=0.2, use_colnames=True)
rules = association_rules(frequent_items, metric="lift", min_threshold=1)
print(rules)

Model Evaluation

Confusion Matrix

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test, pred)

Accuracy, Precision, Recall, F1

from sklearn.metrics import accuracy_score, classification_report

print(accuracy_score(y_test, pred))
print(classification_report(y_test, pred))

Silhouette Score for Clustering

from sklearn.metrics import silhouette_score

silhouette_score(X_scaled, labels)

Final Python Project: Complete Data Mining Pipeline

A complete pipeline includes:

1. Loading data
2. Cleaning & preprocessing
3. Encoding & scaling
4. Splitting
5. Model training
6. Evaluation
7. Saving model

Complete Pipeline Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load data
df = pd.read_csv("data.csv")

# Preprocessing
df.fillna(df.mean(), inplace=True)

# Split
X = df.drop('Target', axis=1)
y = df['Target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Model
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Evaluation
pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, pred))

Common Mistakes & Solutions

Using incomplete data
Not scaling before clustering
Using wrong algorithms for categorical data
Forgetting train/test split
Misinterpreting evaluation metrics

Next. Lecture 11 – Ethical Legal and Privacy Issues in Data Mining

Summary

Lecture 10 provided a complete introduction to implementing Data Mining using Python. You learned how to perform preprocessing, EDA, classification, clustering, association rules, feature engineering, and model evaluation. You also explored complete Python pipelines used in real-world industry applications.

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