1. Project Planning & Problem Definition DONE

• ~~Define objectives: What business question are you trying to answer?~~
• ~~Identify stakeholders: Who needs the results and what decisions will they make?~~
• ~~Set success metrics: How will you measure project success?~~
• ~~Timeline and resource planning: Deadlines, team members, tools needed~~

2. Data Acquisition & Collection DONE

• ~~Identify data sources: Internal databases, APIs, web scraping, surveys, external datasets~~
• ~~Data access setup: Permissions, API keys, database connections~~
• ~~Data extraction: Pull data using SQL queries, API calls, file downloads~~
• ~~Initial data cataloging: Document what data you have and its source~~

3. Data Exploration & Understanding DONE

• ~~Initial data inspection: Shape, columns, data types, missing values~~
• ~~Exploratory Data Analysis (EDA): Statistical summaries, distributions, correlations~~
• ~~Data quality assessment: Completeness, accuracy, consistency, validity~~
• ~~Domain knowledge integration: Understanding business context and data meaning~~

4. Data Cleaning & Preprocessing DONE

• ~~Handle missing values: Imputation, removal, or flagging strategies~~
• ~~Data type corrections: Convert strings to dates, fix categorical variables~~
• ~~Outlier detection and treatment: Statistical methods or business rules~~
• ~~Data standardization: Consistent formats, units, naming conventions~~