From 7553031d37ff5e47522ca4ca3b3eb5b80dc11864 Mon Sep 17 00:00:00 2001 From: mgfree Date: Sat, 8 Aug 2020 15:07:58 +1000 Subject: [PATCH 1/3] Group 4 added the list of contributors. --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 355f56b..5e1e33d 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,7 @@ Group 3 Group 4 === +Rohan Pereira, Joseph Tristram, Mo Hussain, and Rato Li Group 5 === From dcff47c8a1cb7a0cae33ec07b8b0fda7ad66d346 Mon Sep 17 00:00:00 2001 From: mgfree Date: Sat, 8 Aug 2020 15:10:53 +1000 Subject: [PATCH 2/3] Changed name of Group 4 to Fantastic 4! --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5e1e33d..2c5540c 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Group 2 Group 3 === -Group 4 +Fantastic 4 === Rohan Pereira, Joseph Tristram, Mo Hussain, and Rato Li From 506dc3ba9bce1ca5c140e7b0fd2b007f42a0867d Mon Sep 17 00:00:00 2001 From: Rato Li <60586908+rato-li@users.noreply.github.com> Date: Tue, 1 Jul 2025 23:30:59 +1000 Subject: [PATCH 3/3] Create Analytics and ML Ideas.txt Ideas on Analytics using Machine Learning --- Analytics and ML Ideas.txt | 252 +++++++++++++++++++++++++++++++++++++ 1 file changed, 252 insertions(+) create mode 100644 Analytics and ML Ideas.txt diff --git a/Analytics and ML Ideas.txt b/Analytics and ML Ideas.txt new file mode 100644 index 0000000..dd78e38 --- /dev/null +++ b/Analytics and ML Ideas.txt @@ -0,0 +1,252 @@ +To analyze an underlying dataset with multiple attributes and two numerical measures using advanced techniques in +Tableau, you can leverage its powerful visualization and analytics features. Below are some suggestions for +advanced analysis and visualization approaches: + +--- + +### 1. **Distribution Analysis** + - Use **histograms** or **box plots** to visualize the distribution of one numerical measure across different +categories. + - Example: Analyze how a sales figure is distributed across regions, products, or time periods. + +--- + +### 2. **Trend Analysis Over Time** + - Create **time series visualizations** (line charts, area charts, bar charts) to show trends over time. + - Example: Track monthly website traffic or quarterly revenue growth. + +--- + +### 3. **Correlation Analysis** + - Use a **scatter plot** to visualize the correlation between two numerical measures. + - Add a **trend line** (linear regression) to highlight relationships. + - Example: Examine the relationship between advertising spend and sales revenue. + +--- + +### 4. **Segmentation** + - Segment data based on categorical attributes using **color coding**, **treemaps**, or **heatmaps**. + - Example: Group customers into segments (e.g., high-value, low-value) and compare their behavior. + +--- + +### 5. **Advanced Calculations** + - Create calculated fields to perform complex analyses like: + - **Moving averages**: Smooth out fluctuations in time-series data. + - **Running totals**: Track cumulative metrics over time. + - **Percentiles or quartiles**: Analyze distribution percentiles of a numerical measure. + +--- + +### 6. **Filter and Parameter Driven Analysis** + - Use **parameters** to allow users to filter data based on specific criteria (e.g., year, region). + - Apply **conditional formatting** to highlight key trends or anomalies in the visualization. + - Example: Filter data by product category or time period to see how it affects numerical measures. + +--- + +### 7. **Advanced Visualization Techniques** + - Use **parallel coordinates plots**, **boxplots**, or **violin plots** for multivariate analysis. + - Example: Compare multiple attributes (e.g., customer satisfaction, price, and rating) across different +products or services. + +--- + +### 8. **Geospatial Analysis** + - If the dataset includes geographical data, use maps to visualize spatial distributions or correlations. + - Example: Map sales performance by region or country. + +--- + +### 9. **Forecasting** + - Use Tableau's forecasting tools (e.g., exponential smoothing) to predict future values of numerical measures. + - Example: Predict future sales based on historical data. + +--- + +### 10. **Correlation Matrix** + - Create a **correlation matrix** using heatmaps or color-coded cells to visualize correlations between +multiple attributes. + - Example: Identify which product features are most strongly correlated with customer satisfaction. + +--- + +### 11. **Advanced Charts and Maps** + - Use **treemaps** or **mosaic plots** to show hierarchical data distributions. + - Create **geographical heatmaps** to display the density of a numerical measure across regions. + - Example: Show the concentration of a particular metric (e.g., sales) in different geographic locations. + +--- + +### 12. **Parameter-Driven Analysis** + - Use parameters to dynamically filter or segment data and update visualizations in real-time. + - Example: Allow users to select a specific time range or attribute category to filter the dataset. + +--- + +### 13. **Drill-Down Analysis** + - Enable drill-down functionality to explore detailed information about selected data points or segments. + - Example: Click on a segment of customers to see more granular metrics like individual customer spending +patterns. + +--- + +### 14. **Custom Calculations and Aggregations** + - Perform advanced aggregations (e.g., MIN, MAX, AVG) and use these in calculated fields to derive new +insights. + - Example: Calculate the average sales per customer for each segment. + +--- + +### 15. **Dashboard Best Practices** + - Use ** tooltips** and **hover effects** to provide additional context or details when users interact with +visualizations. + - Maintain clear and meaningful titles, descriptions, and legends for each visualization. + - Ensure the dashboard is visually appealing by using consistent colors, labels, and formatting. + +--- + +By combining these advanced techniques in Tableau, you can create an interactive and insightful dashboard that +provides deep insights into your dataset. + +Machine learning (ML) techniques can significantly enhance advanced analytics by enabling predictive modeling, uncovering +hidden patterns, and providing actionable insights from complex datasets. When combined with visualization tools like Tableau, +ML can unlock deeper levels of analysis that are not possible with basic techniques alone. Here's how machine learning can +enable advanced analytics and the types of insights it can provide: + +--- + +### **How Machine Learning Can Enable Advanced Analytics** +1. **Predictive Modeling:** + - ML algorithms (e.g., regression, decision trees, random forests) can predict future outcomes based on historical data. + - Example: Predicting customer churn or forecasting sales. + +2. **Anomaly Detection:** + - ML can identify unusual patterns or outliers in data that do not conform to expected behavior. + - Example: Detecting fraudulent transactions or identifying abnormal customer spending patterns. + +3. **Customer Segmentation:** + - Unsupervised learning techniques (e.g., clustering) can group customers based on their attributes, behaviors, or +preferences. + - Example: Segmenting customers into high-value, at-risk, and churn-prone groups to tailor marketing strategies. + +4. **Feature Importance and Driver Analysis:** + - ML can identify which features or variables are most influential in predicting an outcome. + - Example: Determining which product features drive customer satisfaction or purchasing decisions. + +5. **Text Analytics:** + - When combined with NLP (natural language processing), ML can analyze unstructured text data (e.g., reviews, feedback) to +extract meaningful insights. + - Example: Analyzing customer reviews to identify common pain points or sentiment. + +6. **Time-Series Forecasting:** + - ML models (e.g., ARIMA, LSTM) can forecast future trends based on historical time-series data. + - Example: Predicting quarterly revenue or website traffic. + +7. **Sentiment Analysis:** + - ML can analyze the sentiment of large datasets to gauge public opinion about products, services, or brands. + - Example: Monitoring social media or review sites in real-time to track customer satisfaction. + +8. **Optimization:** + - ML can optimize business processes by identifying the best configurations or strategies for maximizing efficiency or +performance. + - Example: Optimizing supply chain logistics or resource allocation. + +9. **Complex Pattern Recognition:** + - ML algorithms can detect non-linear relationships and complex patterns in data that are difficult to identify with +traditional methods. + - Example: Identifying customer purchase patterns influenced by multiple variables simultaneously. + +10. **Ensemble Models:** + - Combining multiple ML models (ensembles) can improve prediction accuracy and robustness. + - Example: Using ensemble models for stock market predictions or risk assessment. + +--- + +### **Types of Insights Machine Learning Can Provide** +1. **Predictive Insights:** + - Understand what will happen in the future based on historical data. + - Example: Predicting which customers are likely to churn, which products will sell out, or which markets to invest in. + +2. **Descriptive Insights:** + - Uncover hidden patterns and relationships in the data that explain "why" certain outcomes occur. + - Example: Identifying why sales dropped during a specific season or understanding what drives customer retention. + +3. **Behavioral Insights:** + - Analyze how customers behave, make decisions, or interact with products or services. + - Example: Detecting over-pricing behavior in pricing datasets or identifying common purchase sequences. + +4. **Operational Insights:** + - Optimize business processes and operations by analyzing operational data. + - Example: Identifying bottlenecks in supply chain workflows or improving customer service response times. + +5. **Competitive Insights:** + - Gain insights into competitors' market positions, strategies, and performance to inform your own competitive strategy. + - Example: Analyzing competitor pricing trends or market share fluctuations. + +6. **Network Analysis:** + - Understand relationships and dependencies between entities (e.g., customers, products, suppliers) using graph-based ML +models. + - Example: Mapping customer loyalty networks or supply chain vulnerabilities. + +7. **Fraud Detection:** + - Identify unusual or suspicious activities that may indicate fraudulent behavior. + - Example: Detecting credit card fraud or identifying suspicious insurance claims. + +8. **Resource Allocation:** + - Optimize the allocation of limited resources (e.g., budget, personnel, inventory) to maximize efficiency and impact. + - Example: Allocating marketing budgets to campaigns based on their predicted ROI. + +--- + +### **How to Leverage Machine Learning in Tableau** +1. **Preprocess Data:** + - Use Tableau to clean, transform, and prepare your data for ML analysis (e.g., handling missing values, encoding +categorical variables). + +2. **Integrate with External Models:** + - Connect Tableau with external ML tools or platforms (e.g., Python, R) using APIs or custom calculations. + - Example: Running an ML model in Python to predict customer churn and importing the results into Tableau for +visualization. + +3. **Use Prebuilt ML Models:** + - Leverage prebuilt ML models available through Tableau’s partners (e.g., Amazon Machine Learning, Microsoft Azure AI, +etc.). + +4. **Leverage Advanced Visualizations:** + - Use advanced charts, heatmaps, and interactive dashboards to visualize complex ML outputs. + - Example: Creating a heatmap to show customer segmentation clusters or a dashboard that updates in real-time as models +retrain. + +5. **Deploy Models on Tableau Server:** + - Deploy ML models directly onto Tableau Server for scalable, on-premise analytics. + +--- + +### **Limitations of Machine Learning for Advanced Analytics** +1. **Data Quality:** + - ML requires high-quality data to deliver accurate insights. Poor or missing data can lead to unreliable results. + +2. **Computational Resources:** + - Some ML techniques require significant computational power, which may not be feasible in all environments. + +3. **Interpretability:** + - Complex models (e.g., deep learning) can be difficult to interpret, making it harder to explain insights to non-technical +stakeholders. + +4. **Overfitting:** + - Models may perform well on historical data but fail to generalize to new data. + +5. **Ethical Considerations:** + - ML models can perpetuate biases present in the training data, leading to unfair or discriminatory outcomes. + +--- + +### **Conclusion** +Machine learning techniques can significantly enhance advanced analytics by enabling predictive modeling, anomaly detection, +customer segmentation, and more. When combined with tools like Tableau, businesses can create powerful dashboards that provide +actionable insights across various domains (e.g., marketing, finance, operations). However, it's important to consider the +limitations of ML and ensure that the data, models, and visualizations are aligned with business goals. + +By integrating machine learning with advanced visualization techniques in Tableau, organizations can unlock deeper levels of +insight into their data, enabling more informed decision-making and strategic planning.