Data Mining Process and Machine Learning Concepts

Data Mining Process

• Understanding the Problem: Identify the pain points or problems before determining the necessary data.

Phases of Data Mining

• Business Understanding: Define objectives and requirements.
• Data Understanding: Collection and description of data.
• Data Preparation: Cleaning and transforming data into the required format.
• Modeling: Applying machine learning algorithms.
• Evaluation: Assessment of the model’s performance.
• Deployment: Implementing the model in the business context.

Data Terminology

• Label: Variable to predict (e.g., type of animal, spam email indicator, housing price).
• Features: Input variables representing the data (used for prediction).
• Examples of Features: Balance, Age, Default status of customers.
• Representations: Set of values representing a dataset.
• Dataset: A set of examples.

Types of Features

• Numerical Features: Contain numerical values. Examples: age, income, revenue (can be continuous or discrete).
• Categorical Features: Contain non-numerical values grouped into categories. Examples: color, gender, occupation (can be nominal or ordinal).

Machine Learning Terminology

• Labeled data: Data tagged with correct outputs for algorithm learning.
• Unlabeled data: Data without output tags, used in unsupervised learning.
• Supervised Learning: Model learns from labeled data (e.g., classification, regression).
• Unsupervised Learning: Learns from unlabeled data to identify patterns (e.g., clustering).
• Reinforcement Learning: Learns through interaction with an environment (rewards/punishments).

Supervised Learning: Classification

• Definition: Predicts a categorical label based on input features.
• Input: Labeled data with categorical labels.
• Output: Probabilities of each label indicating the likelihood of each category.
• Examples: Spam detection, image classification.
• Models: Decision trees, logistic regression, neural networks.

Probability in Classification

• Definition: Measures uncertainty of an event (P(event)).
• Range: Probability values range from 0 to 1 (e.g., predicting 'Heads' in a coin toss).

Classification Types

• Binary Classification: Two classes (positive and negative).
• Multiclass Classification: More than two classes present.

Supervised Learning: Regression

• Definition: Predicts a continuous numerical value based on features.
• Input: Labeled data with numerical values.
• Output: Numerical predictions. E.g., housing price prediction, sales forecasting.

Data Preparation Process

• Analogy: Similar to cooking processes (collection, cleaning, integration, analysis).
• Sources of Data: Internal (company databases), external (government data, third-party providers).
• Data Collection Considerations: Size and quality are crucial for effective model training.

Importance of Data Annotation

• Vital for AI breakthroughs. E.g., Google Cloud’s Data Labeling Service.
• Examples: Netflix recommendations, ride-sharing apps

Data Preparation Methods

Structured Data Preparation Methods

• Feature-Level Preparation: Handle missing data, outliers, and transform features.
• Dataset-Level Preparation: Clean the dataset efficiently.

Loading Data from CSV Files

• CSV Format: Standard format for storing tabular data.
• Reading CSV in Pandas: Use the pandas library for data manipulation.

Handling Missing Values

• Definition: Missing value is unavailable data.
• Methods: Delete or impute missing values (e.g., mean imputation).

Outlier Management

• Definition: Outliers are values significantly different from bulk data.
• Handling: Assess the context; decide to remove or retain.

Transforming Categorical Features

• Conversion: Categorical values must be converted to numerical formats for analysis.
• One-hot Encoding: Converts categorical values into dummy columns.

Transforming Numerical Features

• Normalization: Applies to features with large ranges. Improves model performance.
• Techniques: Scaling, clipping, log scaling, Z-score normalization.

Feature Selection: Filter Methods

• Purpose: Remove irrelevant or redundant features before model training.
• Methods: Variance method, correlation method.

Class Distribution in Imbalanced Datasets

• Definition: Skewed distribution (majority vs. minority classes).
• Example Applications: Healthcare, fraud detection.

Balancing Imbalanced Datasets

• Downsampling: Remove samples from the majority class.
• Upsampling: Replicate samples from the minority class.

Training vs. Testing

• Purpose: Essential for evaluating model accuracy.
• Methods: Learning with a training set, evaluating with a separate test set.

Decision Trees

• Definition: Supervised learning method for classification and regression.
• Advantages: Easy to understand and implement, computationally inexpensive.
• Structure: Nodes represent decisions based on features.

Decision Tree Learning Process

Goals and Methodology

• To create a tree mapping features to labels.
• Loss Function: Minimize impurity at nodes for clear decision making.

Node Impurity

• Definition: Measures label diversity within nodes.
• Measures: Entropy, Gini Index, Variance.

Information Gain

• Purpose: Identify optimal features for data splitting.
• Calculation: Consider reduction of entropy from parent to child nodes.

Overfitting in Decision Trees

• Definition: Model memorizes training data but fails to generalize.
• Causes: Learning random noise as patterns.

Evaluating Overfitting

• Error Analysis: Evaluate performance discrepancies between training and test sets.

Avoiding Overfitting

• Techniques: Favor simpler structures, early stopping, hyperparameter tuning.

Hyper-Parameter Tuning

• Concept: Adjust parameters for optimized model performance.
• Methods: Grid search, random search, K-Fold cross-validation.

Model Evaluation Essentials

• Points for Evaluation: Final evaluation on test set; hyperparameter tuning on validation set.

Confusion Matrix

• Definition: Table for classification model performance.
• Key Information: True Positives, True Negatives, False Positives, False Negatives.

Limitations of Accuracy

• High accuracy may not reflect model performance in imbalanced datasets.

Importance of Precision and Recall

• Precision: True positives among predicted positives.
• Recall: True positives among actual positives.

Dynamics of Precision and Recall

• Inverse relationship; the adjustment of the threshold affects both metrics.

ROC Curve Analysis

• Graphical representation of True Positive Rate vs. False Positive Rate.

Evaluating with Cost/Benefit Analysis

• Cost-Sensitive Evaluation: Takes into account varying impacts of misclassifications.

F1 Score

• Definition: Harmonic mean of precision and recall, essential for classification evaluation.

Regression Overview

• Definition: Predict continuous values based on features (e.g., housing prices).

Linear Regression

• Definition: Predicts continuous values based on linear relationships of input features.
• Optimization Method: Gradient descent to minimize loss function.

Interpretability of Linear Regression

• Coefficient impacts on label predictions; helps to understand feature significance.

Strategies to Avoid Overfitting

• Increase data, feature selection, regularization (e.g., Lasso regression).

Summary of Linear Regression

• Pros: Simple and interpretable.
• Cons: Assumes linear relationships, sensitive to outliers.

Introduction to Logistic Regression

• Definition: Predicts probabilities of binary outcomes, foundational for advanced models.

Understanding Neural Networks

• Structure: Consists of interconnected layers that learn complex patterns.
• Components: Neurons, weights, activation functions.

Deep Neural Networks

• Definition: Multiple layer structures designed for complex task performance.
• Applications: NLP, image recognition, speech recognition.

Summary of Neural Networks

• Pros and Cons: High learning capability vs. computational burden and interpretability issues.