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DDAVINDER
Last edited May 31, 2019
Created on May 31, 2019

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.

DDAVINDER
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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.

DDAVINDER
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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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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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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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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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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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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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