mirror of
https://github.com/The-Art-of-Hacking/h4cker.git
synced 2024-10-01 01:25:43 -04:00
124 lines
4.4 KiB
Markdown
124 lines
4.4 KiB
Markdown
# Machine Learning Basics with Scikit-learn
|
|
|
|
#### **Objective**
|
|
|
|
To introduce students to the fundamental concepts and techniques of machine learning using the Scikit-learn library.
|
|
|
|
#### **Prerequisites**
|
|
For convenience you can use the terminal window at the OReilly interactive lab: https://learning.oreilly.com/scenarios/ethical-hacking-advanced/9780137673469X002/
|
|
|
|
1. Basic understanding of Python programming.
|
|
2. Familiarity with data manipulation libraries like Pandas and NumPy.
|
|
3. Python and necessary libraries installed: Scikit-learn, Pandas, and NumPy.
|
|
|
|
#### **Lab Outline**
|
|
|
|
1. **Introduction to Machine Learning**:
|
|
- Brief explanation of machine learning and its types (Supervised, Unsupervised).
|
|
- Introduction to Scikit-learn library.
|
|
|
|
2. **Setting Up the Environment**:
|
|
- Installing Scikit-learn, Pandas, and NumPy:
|
|
```bash
|
|
pip3 install scikit-learn pandas numpy
|
|
```
|
|
|
|
3. **Data Preprocessing**:
|
|
|
|
- **Step 1**: Importing Necessary Libraries:
|
|
```python
|
|
import numpy as np
|
|
import pandas as pd
|
|
from sklearn import datasets
|
|
```
|
|
|
|
- **Step 2**: Loading a Dataset:
|
|
```python
|
|
iris = datasets.load_iris()
|
|
X, y = iris.data, iris.target
|
|
```
|
|
|
|
- **Step 3**: Handling Missing Values (if any):
|
|
```python
|
|
# Using SimpleImputer to fill missing values
|
|
from sklearn.impute import SimpleImputer
|
|
imputer = SimpleImputer(strategy="mean")
|
|
X_imputed = imputer.fit_transform(X)
|
|
```
|
|
|
|
- **Step 4**: Splitting the Dataset into Training and Testing Sets:
|
|
```python
|
|
from sklearn.model_selection import train_test_split
|
|
X_train, X_test, y_train, y_test = train_test_split(X_imputed, y, test_size=0.2, random_state=42)
|
|
```
|
|
|
|
4. **Building Machine Learning Models**:
|
|
|
|
- **Step 5**: Training a Decision Tree Model:
|
|
```python
|
|
from sklearn.tree import DecisionTreeClassifier
|
|
dt_classifier = DecisionTreeClassifier(random_state=42)
|
|
dt_classifier.fit(X_train, y_train)
|
|
```
|
|
|
|
- **Step 6**: Training a Logistic Regression Model:
|
|
```python
|
|
from sklearn.linear_model import LogisticRegression
|
|
lr_classifier = LogisticRegression(random_state=42)
|
|
lr_classifier.fit(X_train, y_train)
|
|
```
|
|
|
|
5. **Evaluating Models**:
|
|
|
|
- **Step 7**: Making Predictions and Evaluating Models:
|
|
```python
|
|
from sklearn.metrics import accuracy_score
|
|
|
|
# For Decision Tree
|
|
y_pred_dt = dt_classifier.predict(X_test)
|
|
dt_accuracy = accuracy_score(y_test, y_pred_dt)
|
|
|
|
# For Logistic Regression
|
|
y_pred_lr = lr_classifier.predict(X_test)
|
|
lr_accuracy = accuracy_score(y_test, y_pred_lr)
|
|
|
|
print(f"Decision Tree Accuracy: {dt_accuracy}")
|
|
print(f"Logistic Regression Accuracy: {lr_accuracy}")
|
|
```
|
|
|
|
6. **Hyperparameter Tuning and Cross-Validation**:
|
|
|
|
- **Step 8**: Implementing Grid Search Cross-Validation:
|
|
```python
|
|
from sklearn.model_selection import GridSearchCV
|
|
|
|
# For Decision Tree
|
|
param_grid_dt = {'max_depth': [3, 5, 7], 'min_samples_split': [2, 5, 10]}
|
|
grid_search_dt = GridSearchCV(dt_classifier, param_grid_dt, cv=3)
|
|
grid_search_dt.fit(X_train, y_train)
|
|
|
|
# Best parameters and score for Decision Tree
|
|
print(grid_search_dt.best_params_)
|
|
print(grid_search_dt.best_score_)
|
|
```
|
|
|
|
7. **Conclusion and Further Exploration**:
|
|
- Discuss the results and explore how to further improve the models.
|
|
- Introduce more advanced machine learning techniques and algorithms.
|
|
|
|
8. **Assignment/Project**:
|
|
- Assign a project where students have to apply the techniques learned in the lab to a real-world dataset and build a predictive model.
|
|
|
|
#### **Assessment**
|
|
|
|
- **Lab Participation**: Active participation in lab exercises.
|
|
- **Quiz**: Conduct a short quiz to assess the understanding of students regarding the concepts taught in the lab.
|
|
- **Project Evaluation**: Evaluate the project based on the application of concepts, the accuracy of the model, and the presentation of results.
|
|
|
|
#### **Resources**
|
|
|
|
1. Scikit-learn [documentation](https://scikit-learn.org/stable/documentation.html) for detailed guidance on using the library.
|
|
2. Online courses and tutorials to further explore machine learning concepts.
|
|
|
|
By the end of this lab, students should be able to understand and implement basic machine learning concepts using the Scikit-learn library. They should also be capable of building and evaluating simple machine learning models.
|