Use IRIS dataset from Scikit and apply data preprocessing methods

Using the Iris dataset from Scikit-learn as a case study for applying data preprocessing methods directly relates to several key topics in Unit 4 of Predictive Data Analytics with Python. Here’s how this case study ties in with the unit topics:

Relating the Case Study to Unit Topics

Essential Python Libraries

NumPy and Pandas: These libraries are fundamental for manipulating and analyzing data. In the case study, you'll use Pandas to load and manipulate the Iris dataset, and NumPy for efficient numerical operations.
```
import pandas as pd
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()
data = pd.DataFrame(iris.data, columns=iris.feature_names)
data['target'] = iris.target
```

Basic Examples

The case study provides hands-on examples of loading and exploring data, which is essential for understanding basic data operations. This includes displaying the first few rows of the dataset and getting summary statistics.
```
# Display first few rows
print(data.head())

# Summary statistics
print(data.describe())
```

Data Preprocessing

Removing Duplicates: Although the Iris dataset does not contain duplicates, this step demonstrates the process of checking and removing duplicates in a dataset.
```
data = data.drop_duplicates()
```

Transformation Using Functions or Mapping: You might need to transform the feature values or target labels for better modeling.

# Example: Converting target labels to a categorical type
data['target'] = data['target'].map({0: 'setosa', 1: 'versicolor', 2: 'virginica'})

Replacing Values: Demonstrates how to replace specific values within the dataset.

# Example: Replacing a specific value (if necessary)
data['sepal length (cm)'] = data['sepal length (cm)'].replace({5.1: 5.2})

Handling Missing Values: Although the Iris dataset does not have missing values, this step involves simulating and then handling missing data.

# Simulate missing values
import numpy as np
data.iloc[0:10, 0] = np.nan  # Introduce NaN values

# Handling missing values
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
data.iloc[:, 0] = imputer.fit_transform(data[['sepal length (cm)']])

Types of Data Analytics

This case study primarily focuses on Predictive Analytics. By preprocessing the data and then applying machine learning models, you can predict the species of iris flowers based on their features.

Key Algorithms

Regression and Classification Algorithms: The Iris dataset is commonly used for classification tasks. You can apply various algorithms like Logistic Regression, Decision Trees, and Naive Bayes for classification.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Split data into training and testing sets
X = data.drop('target', axis=1)
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Apply Logistic Regression
model = LogisticRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions)}")

Introduction to Scikit-learn

Installation and Dataset: Using the built-in Iris dataset demonstrates how to load and use datasets from Scikit-learn.
Filling Missing Values: This method showcases using Scikit-learn’s SimpleImputer to handle missing data, as shown in the data preprocessing steps.
Regression and Classification: Implementing classification algorithms using Scikit-learn's API provides a practical example of how to use these tools in real-world scenarios.

Conclusion

The case study on the Iris dataset provides a practical application of the theoretical concepts covered in Unit 4. By working through the data preprocessing steps, implementing various algorithms, and using essential Python libraries, students gain hands-on experience that reinforces their understanding of predictive data analytics. This case study not only helps in mastering the technical aspects but also prepares students to apply these techniques to real-world datasets and problems.