DS-U5-CN

Certainly! Here is a condensed summary of Unit 5: Data Analytics and Model Evaluation in a concise, bullet-point format.

Unit 5: Data Analytics and Model Evaluation

Clustering Algorithms

• K-Means Clustering:
  • Partition data into K clusters based on feature similarity.
  • Steps: Initialize centroids, assign data points to nearest centroid, update centroids, iterate until convergence.
  • Application: Customer segmentation, pattern recognition.
• Hierarchical Clustering:
  • Create a hierarchy of clusters using agglomerative or divisive strategies.
  • Agglomerative: Start with individual points, merge closest pairs.
  • Divisive: Start with all points, split iteratively.
  • Dendrogram: Visual representation of the hierarchy.
• Time-Series Clustering:
  • Apply clustering techniques to time-series data.
  • Identify patterns or trends over time.
  • Techniques: Dynamic Time Warping (DTW), k-means.

Introduction to Text Analysis

• Text-Preprocessing:

  • Tokenization, stemming, lemmatization, removing stop words.
  • Prepare text data for analysis.

• Bag of Words (BoW):

  • Represent text as a collection of word frequencies.
  • Simple and effective for basic text representation.

• TF-IDF (Term Frequency-Inverse Document Frequency):

  • Weight words based on their importance in a document.
  • TF: Frequency of a term in a document.
  • IDF: Inverse frequency of the term across documents.
  • Formula: TF-IDF = TF * IDF.

• Topic Modeling:

  • Discover abstract topics within a collection of documents.
  • Techniques: Latent Dirichlet Allocation (LDA).

Social Network Analysis

• Understanding Social Networks:

  • Analyze relationships and influence patterns within a network.
  • Metrics: Centrality (degree, closeness, betweenness), clustering coefficient.

• Applications:

  • Marketing, sociology, information dissemination, influence maximization.

Business Analysis

• Analytical Techniques:

  • Importance of data analytics in business decision-making.
  • Techniques: Descriptive analytics, predictive analytics, prescriptive analytics.

• Use Cases:

  • Customer segmentation, sales forecasting, risk analysis.

Model Evaluation and Selection

• Metrics for Evaluating Classifier Performance:

  • Accuracy, precision, recall, F1-score.
  • Confusion Matrix: True Positives (TP), True Negatives (TN), False Positives (FP), False Negatives (FN).

• Holdout Method and Random Subsampling:

  • Splitting data into training and testing sets.
  • Random subsampling to create multiple train-test splits.

• Parameter Tuning and Optimization:

  • Techniques: Grid search, random search, Bayesian optimization.

• Result Interpretation:

  • Interpreting model performance metrics to make informed decisions.

Practical Implementation

• Using Scikit-learn for Clustering and Time-Series Analysis:

  • Implement clustering algorithms and analyze time-series data using Scikit-learn.

• Evaluation Tools in Sklearn.metrics:

  • Confusion Matrix: Visualize model performance.
  • AUC-ROC Curves: Evaluate trade-offs between true positive rate and false positive rate.
  • Elbow Plot: Determine the optimal number of clusters in K-Means clustering.

By summarizing the unit in this way, you can focus on the key concepts, techniques, and applications related to data analytics and model evaluation, ensuring a comprehensive yet concise understanding of the material.