CareerFoundry Project: Python

The Instacart stakeholders are very interested in the variety of customers in their database along with their purchasing behaviors. They assume they can't target everyone using the same methods, and they’re considering a targeted marketing strategy. They want to target different customers with applicable marketing campaigns to see whether they influence the sales of their products.

Objective:

Instacart's marketing team would like to uncover more information about their sales patterns. We were tasked to perform an initial data and exploratory analysis of some of their data to derive insights and suggest strategies for better segmentation based on the provided criteria. The goal being to provide the following:

Provide the marketing team with key data and patterns. This includes what the busiest times of the day are, which day of the week sees the most activity, how customer loyalty measures up, etc.

Analyze the data to discover user's spending habits and identify if they can be attributed to certain characteristics, including but not limited to their income, marital status, family size, region, etc.).

Identify spending habits and develop marketing strategies based on the behaviors of certain characteristics. This includes identifying and grouping products from various price points and their popularity among individuals based on their characteristics.

Data:

The following Instacart user data was provided for this project:

• Customer Order Information (orders.csv)
• Customer Re-Order Information (orders_products_prior.csv)
• Instacart Product Details (products.csv)
• Department Information for Products (department.csv)
• User Details (customers.csv)

Folders:

These are the contents of the folders utilized for this project:

• Project Management: The Project Brief PDF is located here.
• Data: This contained two directories, Original and Prepared Data to distinguish between the initial dataset and the altered versions.
• Scripts: A collection of the Jupyter notebooks created during this project. Contains the Python code utilized for the analysis process.
• Analysis: Contains the Visualizations directory which stores the graphs and charts created for this project.
• Sent to Client: The final report provided to the client is stored here.

*Note: The Data folder is not included, as the contents were about 80GB.

Tools:

Language: Python

Libraries: Pandas, NumPy, Seaborn, Matplotlib, SciPy

Software: Jupyter Notebooks, Excel, Anaconda Prompt

Skills Demonstrated:

Data Cleaning: Removal of duplicate entries and redundant or unnecessary columns/categories. Addressed missing values and imputed necessary data.

Merging Data: Prepared and combined data to centralize information in addition to creating opportunities to derive insight. Verified data was successfully merged and contents reflected expected results.

Exploratory Data Analysis: Identified key descriptive statistics and created various graphs and charts to visualize relationships between variables. Derived new variables to further categorize users based on their behaviors or life circumstances. Developed hypothesis and tested them to derive insight into user behaviors.

Visualizing Data: Created bar charts, scatterplots, histograms and other visual tools to help explain findings from analysis.