Use the Chart Viewer template to display bar charts, line charts, pie charts, histograms, and scatterplots to complement a map. Include multiple charts to view with a map or side by side with other charts for comparison. Up to three charts can be viewed side by side or stacked, but you can access and view all the charts that are authored in the map. Examples: Present a bar chart representing average property value by county for a given area. Compare charts based on multiple population statistics in your dataset. Display an interactive scatterplot based on two values in your dataset along with an essential set of map exploration tools. Data requirements The Chart Viewer template requires a map with at least one chart configured. Key app capabilities Multiple layout options - Choose Stack to display charts stacked with the map, or choose Side by side to display charts side by side with the map. Manage chart - Reorder, rename, or turn charts on and off in the app. Multiselect chart - Compare two charts in the panel at the same time. Bookmarks - Allow users to zoom and pan to a collection of preset extents that are saved in the map. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Perfect! Here's the fully updated Kaggle dataset description with your model results, comparison graphs, and confusion matrices added:

📝 Dataset Description: Loan Approval Prediction

This dataset is designed to help beginners understand and practice classification problems using machine learning. It includes real-world loan application data with features relevant to determining whether a loan should be approved or not.

📂 File Information

• Filename: loan.csv
• Size: 38.01 KB
• Total Records: 614
• Columns: 13

🔍 Features

🧪 ML Model Performance

The dataset was tested using various classification models. Below are the results:

📌 Observations:

• Random Forest and Decision Tree show overfitting due to perfect training accuracy.
• SVM performed well with better generalization.
• The data is ideal for evaluating models and understanding overfitting/underfitting.

📊 Visualization & Analysis

✅ Model Comparison:

• Bar graphs were used to compare training and testing accuracies of all models side by side.

✅ Confusion Matrix:

• Individual confusion matrices were generated for each model to evaluate prediction performance, class-wise accuracy, false positives, and false negatives.

These visualizations help in interpreting model strengths, weaknesses, and real-world applicability.

🎯 Use Cases

• Classification modeling (predicting loan approval)
• Data cleaning & preprocessing practice
• Handling categorical and missing data
• Exploratory Data Analysis (EDA)
• Model evaluation techniques (accuracy, confusion matrix, visualization)

✅ Perfect For

• Beginners in ML & Data Science
• ML model comparison and overfitting/underfitting analysis
• Kaggle Notebooks, Portfolio Projects, ML practice tasks

Network of 43 papers and 109 citation links related to "Thermal Integration of a Distillation Column Through Side-Exchangers".

The files here are data files from the OREGANO project, which consists of building a holistic knowledge graph on drugs, including natural compounds. Here is the list of files:- OREGANO_V2.tsv : The triplet file used for link prediction. 3 columns : Subjet ; Predicate ; Object- oreganov2.1_metadata_complet.ttl : The OREGANO knowledge graph in turtle format with the names and cross-references of the various integrated entities.The following files contain the cross-references of OREGANO entities according to their type. They are all organised as follows: the external sources are the titles of the columns and each line begins with the identifier of the entity in OREGANO :- TARGET.tsv: Cross-reference table of the 22,096 targets.- PHENOTYPES.tsv: Cross-reference table of the 11,605 phenotypes.- DISEASES.tsv: Cross-reference table of the 18,333 diseases.- PATHWAYS.tsv: Cross-reference table of the 2,129 pathways.- GENES.tsv: Cross-reference table of the 35,794 genes.- COMPOUND.tsv: Cross-reference table of the 90,868 compounds.- INDICATIONS.tsv: Cross-reference table of the 2,714 indications.- SIDE_EFFECT.tsv: Cross-reference table of the 6,060 side-effects.- ACTIVITY.tsv: Names of the 78 activities.- EFFECT.tsv: Names of the 171 effects.The OREGANO knowledge graph is composed of 11 types of nodes and 19 types of links. The current version of the graph contains 88,937 nodes and 824,231 links.A SPARQL endpoint has been provided to enable users to retrieve and explore the knowledge graph at OREGANO SPARQL endpoint .The integration files and the knowledge graph are available on the GitHub of the OREGANO project in the Integration folder: Gitub repository .