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@DAVINDERYADAVRJIT·31 public vizzes

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This visualization presents a hierarchical tree of analytical customer data, organized into categories like CAR, B2B, customer profile, contract, and service tickets. The tree shows the structure of raw and derived data fields, with leaf nodes sized by a numeric value. The code uses D3.js v4 to render the tree as SVG nodes and links. Each node is drawn as a circle with a text label, while the connecting links use a stroke-based path, and the visualization is animated.

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CAR_PAR_JAR_Collapse_Dendo

A hierarchical dendrogram displays the nested structure of analytical data records, organized under top-level categories like Customer Profile, Contract, and Campaign. The tree branches split into raw and derived attribute groups, with leaf nodes labeled by their field names. Rendered as an SVG using D3.js v4, the visualization uses the tree layout to position nodes and links, with white circles for nodes and gray bezier curves for connections. The static diagram presents the full data taxonomy without interaction or animation.

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Cars Report

This example demonstrates a JavaScript tutorial that generates a text-based report of cars priced under $2,000 from a hardcoded JSON array. The interface initially shows “Loading…”, then displays the filtered list as formatted strings like “2012 Nissan Leaf: $1800” after a two-second delay. The code uses `Promise`-based asynchronous data retrieval via `getCars`, array filtering and mapping with `generateReport`, and `formatCar` to construct each line. It relies on standard JavaScript modules and DOM manipulation via `getElementById` to update the page content.

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This example shows a hierarchical tree of analytical data categories, such as CAR, B2B, and CUSTOMER PROFILE, with branches like RAW and DERIVED leading to detailed attribute names. It renders the structure using SVG circles and links, with d3.v4's tree layout and animated transitions. The data is embedded directly in the source code as a JavaScript object, and node labels use a 10px sans-serif font.

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Created on Cognitive Class Labs

This example shows the process of segmenting and clustering neighborhoods in New York City. It maps each neighborhood to its latitude and longitude coordinates, then uses the Foursquare API to fetch common venue categories per neighborhood. The code applies k-means clustering to group neighborhoods into clusters and visualizes the results on an interactive Folium map, with each cluster color-coded. The underlying neighborhood dataset is downloaded from a public web source, and the map rendering relies on Folium’s mapping library.

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Created on Cognitive Class Labs

This example demonstrates k-means clustering on two datasets, first illustrating the algorithm on an engineered set of 30 data points in two distinct clusters, then applying it to customer segmentation. The visualization uses Matplotlib’s inline plotting to render the resulting cluster assignments and centroids, computed via scikit-learn's `KMeans` API. The initial dataset is defined manually as x1 and x2 coordinate lists, while the segmentation relies on customer data, both processed within a Jupyter notebook environment using NumPy for computation.

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Created on Cognitive Class Labs

This notebook demonstrates a K-Nearest Neighbors classification workflow on a telecommunications customer dataset, segmented into four service-usage groups. It uses pandas for data loading and preprocessing, and scikit-learn’s `preprocessing` module for feature scaling. The core visualization combines `matplotlib.pyplot` scatter plots to display demographic features (such as region and age) colored by customer category, alongside a manually inserted diagram of the K-NN algorithm. The analysis shows how varying the value of K affects class predictions for unknown data points.

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Created on Cognitive Class Labs

This example demonstrates polynomial regression by modeling the relationship between vehicle engine size and CO2 emissions using a fuel consumption dataset from Canada. It plots the data points and overlays a polynomial trend line to visualize the non-linear correlation. Using scikit-learn's `PolynomialFeatures` and `LinearRegression`, the notebook fits a model, then evaluates its performance on a test set. The visualization employs Matplotlib and pylab to render the scatter plot and regression curve, showing how increasing engine size correlates with higher emissions.

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Created on Cognitive Class Labs

This example demonstrates multiple linear regression using scikit-learn to model the relationship between vehicle features, such as engine size and fuel consumption, and CO2 emissions from the FuelConsumption.csv dataset. It splits the data into training and test sets, fits a regression model, and evaluates its performance. The notebook then uses the trained model to predict CO2 emissions for new input values, visualizing the results through Matplotlib plots.

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Created on Cognitive Class Labs

This example demonstrates simple linear regression on a dataset of fuel consumption and CO2 emissions, plotting the relationship between engine size and emissions. The code uses scikit-learn to split the data into training and test sets, then fits a regression line to predict emission values. Using matplotlib and pylab, it visualizes the training data, the regression line, and the test data points to evaluate the model's accuracy. The dataset originates from a CSV file downloaded via wget from IBM Object Storage.

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Created on Cognitive Class Labs

This example demonstrates three distinct data-visualization techniques applied to Canadian immigration data from 1980 to 2013: waffle charts, word clouds, and regression plots. It uses Matplotlib, seaborn, and WordCloud within a Jupyter notebook environment. Waffle charts display categorical proportions using colored grid squares, word clouds render text frequency by font size, and seaborn’s `regplot` generates scatter plots with fitted regression lines, showing relationships between variables like year and immigration numbers.

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Created on Cognitive Class Labs

This example demonstrates model evaluation and refinement for predicting vehicle prices using Python. It loads an automobile dataset from an IBM Cloud object storage URL, saves it locally, and prepares the numeric data for analysis. The notebook uses pandas to read the CSV file and numpy for numerical operations, displaying a sample of the structured data frame with features like engine size, horsepower, and price. The workflow focuses on building and assessing predictive models, though the visible code only covers data import, preprocessing, and initial inspection rather than the full modeling steps.

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Created on Cognitive Class Labs

This example shows how to build predictive models for car pricing using the Python data science stack. The notebook loads an automobile dataset from IBM Cloud Object Storage into a pandas DataFrame, then imports pandas, NumPy, and Matplotlib for analysis. It demonstrates model development techniques for estimating vehicle prices based on features like horsepower, engine size, and fuel efficiency. The code prepares data by normalizing columns and creating binary indicators, showcasing a practical workflow for regression modeling in an educational notebook environment.

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Created on Cognitive Class Labs

This exploratory data analysis notebook examines automobile features to predict car prices. It uses pandas and numpy to load and manipulate a dataset hosted on IBM Cloud, then applies statistical methods including descriptive statistics, grouping, correlation analysis, and ANOVA. Visualizations reveal patterns between individual characteristics and price, highlighting which attributes most strongly influence the target variable. The analysis culminates in identifying key predictors for car pricing.

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Created on Cognitive Class Labs

This example demonstrates the process of data wrangling on an automobile dataset using Python. It shows how to import data from a URL into a Pandas DataFrame, assign descriptive column headers, and prepare the data for analysis. The notebook uses Pandas for data manipulation and matplotlib for potential visualization. The dataset is sourced from an IBM-hosted CSV file, and the code handles tasks such as reading the data and setting up the structure for further cleaning and transformation.

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Created on Cognitive Class Labs

This example demonstrates the process of data wrangling on an automobile dataset using Python. It shows how to import data from a URL into a Pandas DataFrame, assign descriptive column headers, and prepare the data for analysis. The notebook uses Pandas for data manipulation and matplotlib for potential visualization. The dataset is sourced from an IBM-hosted CSV file, and the code handles tasks such as reading the data and setting up the structure for further cleaning and transformation.

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Created on Cognitive Class Labs

This example demonstrates how to load three Chicago public datasets—socioeconomic indicators, public school performance, and crime reports—from CSV files into a Db2 database using the `ibm_db` API, then query them with SQL. It uses the `pandas` library to read the CSV data and the `ibm_db` connection functions to create and populate tables. The notebook guides through understanding the datasets, downloading them, and storing them in database tables, with the SQL queries highlighting relationships between community areas, schools, and crime incidents.

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Created on Cognitive Class Labs

This notebook demonstrates how to access and query a Db2 database from JupyterLab using the SQL magic extension (`ipython-sql`). It shows the process of loading the SQL extension, connecting to a database via a SQLAlchemy connection string (using the `ibm_db_sa` dialect), and executing SQL commands. The example includes a connection attempt with provided credentials and illustrates error handling when the connection string is malformed. The code relies on `%load_ext sql`, `%sql` for single-line queries, and `%%sql` for cell-level SQL operations.

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Created on Cognitive Class Labs

This example demonstrates how to connect to an IBM Db2 on Cloud database using Python and the `ibm_db` API. It shows the step-by-step process of importing the library, establishing a connection with credentials, creating a table, inserting data, and querying the result set into a pandas DataFrame. The notebook is written as an instructional lab from Cognitive Class, using a gist source, and runs entirely within Jupyter cells on IBM's cloud platform.

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Created on Cognitive Class Labs

A Jupyter notebook demonstrates how to connect to an IBM Db2 database on the cloud using Python. It shows the sequential steps of importing the `ibm_db` library, defining connection credentials for a dashDB instance, and constructing a DSN connection string with the driver, hostname, port, protocol, and user credentials. The rendered DSN output, including the hardcoded host and password, is displayed. The notebook relies on the `ibm_db` API, which uses the IBM Data Server Driver for ODBC and CLI, to establish the database connection.

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Created on Cognitive Class Labs

This notebook demonstrates how to write and append text to files using Python’s built-in `open()` function with the `write()` method, as part of a Cognitive Class Labs lesson. It opens files in write (`'w'`) and append (`'a'`) modes, writing lines like "This is line A" to `/resources/data/Example2.txt`, then reads them back with the `read()` method to verify results. The code highlights the difference between overwriting and adding content, using the `with` statement for proper file handling. The example is sourced from a Gist by DAVINDERYADAVRJIT.

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Created on Cognitive Class Labs

This example demonstrates basic Python programming concepts taught in a Cognitive Class Labs notebook, including printing output, writing comments, and introducing data types like integers and floats. The notebook uses markdown cells for instructional text and code cells that execute `print()` statements to display results, such as "Hello, Python!". The material is sourced from a gist and covers foundational syntax, expressions, and variables, with exercises for learners. No data-visualization libraries are used; the focus is on interactive coding practice within the Jupyter notebook environment.

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Created on Cognitive Class Labs

This notebook from a Cognitive Class Labs course demonstrates Python string operations, including indexing, negative indexing, slicing, stride, concatenation, escape sequences, and string methods. The examples show how to define strings with single or double quotes, combine spaces, digits, and special characters, and apply methods like `upper()`, `replace()`, and `find()`. The code is executed in Jupyter cells, with outputs displayed inline as executed results, and the notebook is sourced from a gist authored by DAVINDERYADAVRJIT.

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Created on Cognitive Class Labs

This example demonstrates the basics of tuples in Python, including indexing, slicing, and sorting operations, using a dataset of album recommendations from friends. The notebook presents a table containing album attributes like artist, release year, genre, and sales figures, then applies tuple methods to access and manipulate the data. The visualization relies on the cognitiveclass.ai platform, with the code written in a Jupyter notebook and rendered through WebGL for interactive display. A quiz section reinforces the concepts covered, and the tutorial is designed to be completed in about 15 minutes.

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Created on Cognitive Class Labs

This example demonstrates how to manipulate and analyze dataset information using Python lists, focusing on album metadata such as artist, release year, genre, and sales figures. The notebook guides through indexing, list operations, and copying techniques, with the dataset structured as a table of friend recommendations. The code uses standard Python list methods and conditional logic to query and modify the data, while markdown cells provide documentation. The visualization aspect is minimal, relying on inline tables and text formatting rather than graphical libraries. The dataset originates from album recommendations compiled by the user, with sales data referenced from the SONG://DATABASE website.

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Created on Cognitive Class Labs

This notebook demonstrates Python dictionary operations through a series of executed cells. It shows how to create dictionaries with various key types—strings, tuples, and mixed values—and access values by key. The code uses standard Python dictionary syntax, including colon-separated key-value pairs and curly-brace notation, with examples from an album release-year dataset. The rendered markdown includes instructional text and images, while the executable code cells run in a Jupyter environment on Cognitive Class Labs.

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Created on Cognitive Class Labs

This notebook demonstrates how to manipulate sets in Python using built-in set operations. It shows how to create sets from lists, remove duplicates, and perform operations such as adding elements with the `add()` method. The code uses native Python structures like curly braces and the `set()` constructor, along with methods like `add()`. The example is based on music genres and album data, illustrating core set concepts through code cells and accompanying markdown explanations.

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Created on Cognitive Class Labs

This notebook demonstrates how to use condition statements in Python, including comparison operators, branching, and logical operators. It shows example code snippets that compare variables using operators like `==`, `>`, and `<`, producing Boolean outputs. The notebook is rendered on Cognitive Class Labs and includes markdown cells that explain each concept, with code cells that run inline to display True or False results. The material is sourced from a gist and authored by DAVINDERYADAVRJIT, presented as an interactive educational lab for learning Python fundamentals.

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Created on Cognitive Class Labs

This notebook introduces object-oriented programming in Python through the creation of Circle and Rectangle classes. It illustrates how classes define attributes and methods, and how objects are instantiated from these blueprints, using markdown cells with embedded images to visualize class hierarchies and object instances. The content is presented as an interactive tutorial within a Jupyter notebook on Cognitive Class Labs, sourced from a gist by DAVINDERYADAVRJIT, with no external data visualization libraries employed.

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Created on Cognitive Class Labs

This notebook demonstrates how to read text files in Python. It shows the process of downloading a sample file using a `wget` command, then opening and reading the file's contents with the built-in `open` function. The example explains file modes, specifically read mode (`r`) and write mode (`w`), and uses the file object's methods to access data. The code relies on standard Python file I/O APIs, with no external visualization libraries, to illustrate basic text file handling in a Jupyter notebook environment.

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A collapsible hierarchical tree diagram displays the structure of analytical customer data records, organized into nested categories such as raw and derived attributes. The visualization uses the D3.js v3 library with its tree layout and diagonal path generator to render SVG nodes and links. Clicking a node toggles its children, animating the expansion and collapse with transitions. The data is loaded from the dendogram.json file.