As a developer at pokémonunleashed.org, your responsibility is to develop an interactive Python script for your board members. These members, just like you, have a strong passion for the Pokémon world. To accomplish this task, you will be following the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology.
For this, you have done your research and broken down the process for this task as follows:
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Business understanding: The business requires a Python script that can interact with the Pokémon dataset and PokeAPI to enable users to ask various questions.
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Data understanding: The Pokémon dataset is a CSV file with 13 fields/attributes.
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Data preparation: The dataset is to be loaded into a dataframe, and irrelevant columns are to be removed.
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Modeling: The data must be analyzed for correlations, and a regression fit is performed between the correlated traits.
Files:
Pokemon_Analysis.ipynb: This is a Jupyter Notebook which contains the main script and is executed for analysis.Pokemon.csv: This is the dataset file which is loaded inPokemon_Analysis.ipynb.Pokemon_names.xlsx: This file lists all the names of the Pokémon, which are accessed through PokeAPI.
Execution:
When pokemon_Analysis.ipynb is executed, the following actions take place:
- The dataset is loaded and converted into a dataframe.
- The dataframe is curated to include only the necessary entries for analysis.
- Various plots and visualizations are generated to provide insights into the dataset.
- Based on user queries, the script can answer questions related to the dataset.
- Additionally, the script has the capability to retrieve photographs of Pokémon from the internet using a RESTful API.
For further reading, refer to the post on Medium