An end-to-end data analytics project using Python for statistical validation and Tableau for market segmentation modelling.
PROJECT OVERVIEW This project executes a dual-stage data pipeline to analyze the global smartphone market. It focuses on identifying how pricing tiers, brand positioning, and consumer demographics (age, income, gender) directly impact product performance and user satisfaction.
TECHNICAL STACK Languages: Python (Pandas, NumPy, SciPy, Matplotlib, Seaborn) Statistical Frameworks: ANOVA, Chi-Square Testing, Hypothesis Validation Business Intelligence: Tableau Desktop
KEYPROJECT PHASES Phase 1: Data Architecture & Statistical Modeling (Python)
- Developed a robust data cleaning pipeline to resolve missing values, duplicate records, and data type inconsistencies.
- Conducted Exploratory Data Analysis (EDA) using boxplots, heatmaps, and pair plots to isolate key data distributions.
- Applied ANOVA and Chi-Square tests to mathematically evaluate the relationships between brand pricing and consumer ratings. Phase 2: Interactive Visualization & Storytelling (Tableau)
- Engineered dynamic dashboards featuring Level of Detail (LOD) expressions, calculated fields, and advanced parameters.
- Built interactive market segmentation models categorizing devices into Budget, Mid-Range, and Premium tiers.
- Formulated a structured Tableau Story to translate technical metrics into strategic business insights.
REPOSITORY STRUCTURE
notebook/: Contains the complete Python code, data cleaning documentation, and statistical outputs.data/: Technical overview of the dataset structure.- Tableau Public Dashboard