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Performed data engineering and analysis on a customer churn dataset to facilitate better insights and decision-making for a financial institution.

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mehdi-touil/Churn-Data-Engineering-and-Analysis

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pip install -r requirements.txt

Steps

write_csv_to_postgres.py-> gets a csv file from a URL. Saves it into the local working directory as churn_modelling.csv. After reading the csv file, it writes it to a local PostgreSQL table

create_df_and_modify.py -> reads the same Postgres table and creates a pandas dataframe out of it, modifies it. Then, creates 3 separate dataframes.

write_df_to_postgres.py -> writes these 3 dataframes to 3 separate tables located in Postgres server.

PostgreSQL

I am using a local Postgres Server and installed PgAdmin to control the tables instead of using psql.

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Performed data engineering and analysis on a customer churn dataset to facilitate better insights and decision-making for a financial institution.

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