What's included

• Data Cleaning & Preparation

  Processing and structuring raw data for accuracy and consistency. Removing duplicates, fixing errors, and formatting datasets for analysis.

• Exploratory Data Analysis (EDA)

  Using Python or R to uncover key trends, patterns, and correlations through descriptive statistics and visual summaries.

• Statistical Analysis & Reporting

  Applying statistical tests or regression methods to draw insights and actionable conclusions from your data.

• Insight Summary & Recommendations

  Clear written report or presentation summarizing findings, business implications, and suggested next steps based on the analysis.

• Documentation & Handoff

  Organized project files, Jupyter notebooks, and a README explaining code, data sources, and methods for easy reuse or follow-up work.