Python_InstaCart_Analysis

Analyzing Instacart data with Python to optimize profitability for individual customer profiles. Exploring data cleaning, feature manipulation, and data visualization techniques.

Python_InstaCart_Analysis

Overview

Welcome to the "Python_InstaCart_Analysis" repository. This project aims to optimize profitability for individual customer profiles by analyzing Instacart data using Python. The analysis involves data cleaning, feature manipulation, and data visualization techniques to gain insights into customer behavior and make data-driven decisions.

Repository Contents

01 Project Management

• Brief.pdf: A PDF file containing a project brief.

02 Data (Not in the Repository)

• The data for this project is not included in the repository. It may be available online.

03 Scripts

• 4.02 IC python intro: Introduction to Python for Instacart data analysis.
• 4.03 IC Data_import_descriptive_analysis.ipynb: Data import and initial descriptive analysis.
• 4.04 IC Data_wrangling.ipynb: Data wrangling to prepare the dataset for analysis.
• 4.05 IC Data_consistency.ipynb: Ensuring data consistency and quality.
• 4.06 IC combining_df.ipynb: Combining dataframes for comprehensive analysis.
• 4.06.2 IC pickle_combining_df.ipynb: Using pickle for optimized dataframe combination.
• 4.07 IC Deriving_new_variables.ipynb: Creating new variables from the data.
• 4.08 IC Grouping_data.ipynb: Grouping and summarizing data.
• 4.09 IC Customer_wrangle.ipynb: Customer data manipulation.
• 4.09.2 IC Data_visualization__python.ipynb: Visualizing data for insights.
• 4.09.3 IC Merging_customers.ipynb: Merging customer profiles.
• 4.09.4 IC Visualizations.ipynb: In-depth data visualizations.
• 4.10 IC Feature_engineering_visualizations.ipynb: Feature engineering and visualizing analysis.
• 4.10.2 IC Feature_engineering_visualizations.ipynb: Further feature engineering and visualizations.

04 Analysis

• Visualizations: This folder contains 36 charts and graphs, primarily created in Python. It also includes a few visualizations made in Tableau. Tableau provides the bulk of the customer profile and department preference analysis.
• Tableau Visualizations: Explore the Tableau visualizations for a detailed analysis of regional department preferences.

05 Client

• Analysis_Report.xlsx: An Excel file containing analysis results and relevant metrics for presentation to the client. This serves as a project deliverable.

Usage

Feel free to explore the scripts and folders in this repository for your own data analysis projects. Each script provides valuable insights and techniques that can be applied to similar datasets.

Getting Started

To get started with the scripts in this repository, you'll need Python and Jupyter Notebook installed. You can clone this repository to your local machine and run the scripts as needed.

Contributing

If you would like to contribute to this project, please fork the repository and create a pull request. Your contributions and improvements are welcome!

Acknowledgments

Thanks to Instacart for providing the dataset. This project is inspired by a passion for data analysis and the desire to optimize profitability for businesses.