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+# 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.