Data Preprocessing and Feature Engineering - AI-ML 2 with python hands-on labs

 2. Data Preprocessing and Feature Engineering

This chapter focuses on preparing raw data for machine learning models by cleaning it, analyzing it, and transforming it into useful features.

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2.1 Data Collection and Cleaning

What You Will Learn:

• Collect data from different sources.
• Clean the data to ensure it is usable for machine learning models.

Key Concepts

1. Collecting Data:

   • From CSV Files:
     Data stored in files like .csv can be loaded using Python libraries like pandas.
     Example: Download the Titanic dataset from Kaggle.

     import pandas as pd
     data = pd.read_csv('titanic.csv')
     print(data.head())
     

   • From APIs:
     APIs provide dynamic data. Use Python libraries like requests to access data from APIs.
     Example: Fetch weather data from an API.

     import requests
     response = requests.get("API_URL")
     print(response.json())
     

   • From Web Scraping:
     Scrape websites using libraries like BeautifulSoup.
     Example: Scrape job listings from a website.

     from bs4 import BeautifulSoup
     import requests
     response = requests.get("URL")
     soup = BeautifulSoup(response.text, 'html.parser')
     print(soup.title.string)
     

2. Handling Missing Values:

   • Missing data can lead to inaccurate predictions.
   • Common strategies:
     • Remove rows/columns with missing values:

       data = data.dropna()
       

     • Fill missing values with mean/median/mode:

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

3. Handling Outliers:

   • Outliers can skew your results. Detect and handle them:
     • Boxplot Method: Use visualization to identify outliers.

       import matplotlib.pyplot as plt
       data['Age'].plot.box()
       plt.show()
       

     • Capping Values: Replace extreme values with a threshold.

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2.2 Exploratory Data Analysis (EDA)

What You Will Learn:

• Analyze data to understand its structure and distribution.
• Visualize trends, correlations, and patterns.

Key Concepts

1. Data Visualization with Matplotlib and Seaborn:

   • Matplotlib: A low-level library for creating basic visualizations.
     Example: Create a bar chart.

   import matplotlib.pyplot as plt
   data['Survived'].value_counts().plot(kind='bar')
   plt.show()
   

   • Seaborn: A high-level library for more complex visualizations.
     Example: Visualize the distribution of ages.

   import seaborn as sns
   sns.histplot(data['Age'], kde=True)
   

2. Analyzing Correlation, Distribution, and Trends:

   • Correlation:
     Check how features are related.

     print(data.corr())
     sns.heatmap(data.corr(), annot=True)
     

   • Distribution Analysis:
     Understand how data is spread across a range.
   • Trend Analysis:
     Identify patterns over time or groups.

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2.3 Feature Engineering

What You Will Learn:

• Transform raw data into features that improve model performance.

Key Concepts

1. Feature Scaling and Normalization:

   • Feature Scaling: Ensures all features have the same scale (important for ML algorithms like SVM).

     from sklearn.preprocessing import StandardScaler
     scaler = StandardScaler()
     data[['Age', 'Fare']] = scaler.fit_transform(data[['Age', 'Fare']])
     

   • Normalization: Scales data to a range (e.g., 0 to 1).

     from sklearn.preprocessing import MinMaxScaler
     scaler = MinMaxScaler()
     data[['Age', 'Fare']] = scaler.fit_transform(data[['Age', 'Fare']])
     

2. Encoding Categorical Variables:

   • Convert categorical data into numerical data for machine learning.
     Example: Encode the "Sex" column.

     data = pd.get_dummies(data, columns=['Sex'], drop_first=True)
     

3. Feature Selection Techniques:

   • Select the most important features for training your model.
     Example: Use correlation or statistical tests to identify key features.

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Hands-on Lab

Objective:

Preprocess and clean the Titanic dataset, perform EDA, and apply feature engineering.

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Steps:

Step 1: Load the Dataset

1. Download the Titanic dataset from Kaggle.
2. Load it into Python using pandas.

   import pandas as pd
   data = pd.read_csv('titanic.csv')
   

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Step 2: Clean the Data

1. Handle missing values in the "Age" and "Cabin" columns.

   data['Age'] = data['Age'].fillna(data['Age'].mean())
   data = data.drop(columns=['Cabin'])
   

2. Check for duplicates and drop them if necessary.

   data = data.drop_duplicates()
   

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Step 3: Perform EDA

1. Analyze survival rates based on gender.

   import seaborn as sns
   sns.countplot(x='Survived', hue='Sex', data=data)
   

2. Visualize the age distribution.

   sns.histplot(data['Age'], kde=True)
   

3. Check correlations.

   sns.heatmap(data.corr(), annot=True)
   

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Step 4: Apply Feature Engineering

1. Scale the "Age" and "Fare" columns.

   from sklearn.preprocessing import StandardScaler
   scaler = StandardScaler()
   data[['Age', 'Fare']] = scaler.fit_transform(data[['Age', 'Fare']])
   

2. Encode the "Sex" column.

   data = pd.get_dummies(data, columns=['Sex'], drop_first=True)
   

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Step 5: Save the Preprocessed Data

1. Save the cleaned dataset for future use.

   data.to_csv('titanic_cleaned.csv', index=False)
   

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Outcome:

By the end of this lab, you will have a cleaned and preprocessed Titanic dataset with visualizations and feature engineering applied, ready for machine learning models.

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Data Preprocessing and Feature Engineering in AI and Machine Learning

Data Preprocessing

Definition:
Data preprocessing refers to the initial cleaning and transformation of raw data to prepare it for model training.

Key Steps:

1. Cleaning:

   • Remove missing values, outliers, and inconsistencies.

2. Normalization:

   • Scale data to a common range (e.g., 0 to 1) to ensure all features are treated equally.

3. Handling Categorical Data:

   • Convert categorical variables into numerical representations using techniques like one-hot encoding.

4. Data Splitting:

   • Divide the dataset into training, validation, and testing sets to evaluate model performance.

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Feature Engineering

Definition:
Feature engineering involves creating new features or selecting relevant existing features to improve the model's predictive power.

Key Techniques:

1. Feature Creation:

   • Combine existing features to generate new, more informative features.

2. Feature Selection:

   • Identify and choose the most relevant features for the model to reduce noise and improve performance.

3. Feature Extraction:

   • Apply dimensionality reduction techniques (e.g., PCA) to extract meaningful features from complex data.

4. Feature Transformation:

   • Apply mathematical functions to features to improve their distribution or relationship with the target variable.

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Why Are Data Preprocessing and Feature Engineering Important?

1. Improve Model Performance:

   • Clean and relevant data enhances the accuracy and efficiency of machine learning models.

2. Reduce Computational Cost:

   • Removing unnecessary features significantly speeds up training time.

3. Gain Insights Into Data:

   • Feature engineering helps uncover hidden patterns and relationships in the dataset.

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Takeaway:
Both data preprocessing and feature engineering are critical steps for achieving optimal performance from machine learning algorithms, ensuring your models are accurate, efficient, and insightful.

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