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    Customer Segmentation workflow

    KNIME Workflow for KMeans Clustering and Visualization Overview This workflow demonstrates the process of performing KMeans clustering on a dataset, assigning descriptive labels to the clusters, and visualizing the results using scatter plots. The workflow includes data preprocessing steps, clustering, and post-processing to label and visualize the clusters. ## Nodes and Configuration ### 1. File Reader (Node 1) - Function: Reads the input dataset from a CSV file. - Configuration: Load your data from a CSV file. Make sure the file path is correctly specified. 2. K-Means - Function: Performs KMeans clustering on the dataset. - Configuration: - Number of clusters: Specify the desired number of clusters. - Features to use for clustering: Select the features that will be used for clustering. 3. Rule Engine (Node 12) - Function: Applies specific rules to the clustered data to assign descriptive labels to each cluster. - 4. Color Manager - Function: Manages the colors of the data points for better visualization. - Configuration: Assign different colors to each cluster based on the descriptive labels. 5. Shape Manager - Function: Manages the shapes of the data points for better visualization. - Configuration: Assign different shapes to each cluster based on the descriptive labels. 6. Scatter Plot (legacy) - Function: Visualizes the clustered data using a scatter plot. - Configuration: Select the appropriate axes and options for visualizing the clusters. 7. Scatter Plot (JavaScript) - Function: Visualizes the clustered data using an interactive scatter plot. - Configuration: Select the appropriate axes and options for visualizing the clusters interactively. Workflow Steps 1. Data Loading: The workflow starts by reading the input dataset using the File Reader node. 2. KMeans Clustering: The K-Means node performs clustering on the dataset based on the selected features. 3. Assign Descriptive Labels: The Rule Engine node assigns descriptive labels to each cluster based on specific rules. 4. Color and Shape Management: The Color Manager and Shape Manager nodes manage the colors and shapes of the data points for better visualization. 5. Visualization: The clustered data is visualized using the Scatter Plot (legacy) and Scatter Plot (JavaScript) nodes. Conclusion This workflow provides a comprehensive approach to clustering and visualizing data using KNIME. By following the steps outlined above, you can effectively perform KMeans clustering, assign descriptive labels to the clusters, and visualize the results. For any customizations accoerding to your niche you can contact me at guharaysree@gmail.com.

    Last edited  Jan 6, 2025 10:14 AM

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    Price prediction using Random Forest model

    This dataset is useful for analyzing Airbnb listings in terms of pricing, location, host activity, and availability. The dataset contains information about Airbnb listings with the following attributes: 1. Row ID: Unique identifier for each row. 2. ID: Unique identifier for each listing. 3. Name: Name of the listing. 4. Host ID: Unique identifier for the host. 5. Host Name: Name of the host. 6. Neighbourhood Group: Broad area or borough (e.g., Manhattan, Brooklyn). 7. Neighbourhood: Specific neighborhood within the borough. 8. Latitude: Latitude coordinate of the listing. 9. Longitude: Longitude coordinate of the listing. 10. Room Type: Type of room (e.g., Private room, Entire home/apt). 11. Price: Price per night.12. Minimum Nights: Minimum number of nights required for booking.13. Number of Reviews: Total number of reviews received.14. Last Review: Date of the most recent review.15. Reviews per Month: Average number of reviews per month.16. Calculated Host Listings Count: Total number of listings the host has.17. Availability 365: Number of days the listing is available in a year. 1. CSV Reader - Description: This node reads data from a CSV file. - Purpose: To import the dataset into the workflow for further processing. 2. Partitioning - Description: This node splits the dataset into training and test sets. - Purpose: To create separate datasets for training the model and evaluating its performance. 3. Random Forest Learner (Regression) - Description: This node trains a Random Forest regression model using the training dataset. - Purpose: To create a predictive model based on the training data. 4. Random Forest Predictor (Regression) - Description: This node applies the trained Random Forest model to the test dataset to make predictions. - Purpose: To generate predictions on the test data using the trained model. 5. Numeric Scorer - Description: This node evaluates the performance of the regression model by comparing the predicted values to the actual values. - Purpose: To assess the accuracy and performance of the model. 6. ROC Curve (legacy) - Description: This node generates a Receiver Operating Characteristic (ROC) curve to visualize the performance of the model. - Purpose: To provide a graphical representation of the model's performance, particularly in terms of true positive rate and false positive rate. This workflow is designed for performing regression analysis using a Random Forest model, evaluating its performance, and visualizing the results. Connect with me at guharaysree@gmail.com if there's anything else you'd like me to add or modify to match with your niche and requirements!

    Last edited  Jan 6, 2025 11:45 AM

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