Electric Vehicle Market Share Analysis & Segmentation (Power BI)
Overview:
Designed a visual analytics report in Power BI to analyze Electric Vehicle (EV) sales distribution across different vehicle categories (2-Wheelers, 3-Wheelers, 4-Wheelers, Buses, and Others). This project translates raw sales metrics into categorical market share insights.Key Analysis & Process:Categorical Data Segmentation: Aggregated and grouped raw EV sales quantities by vehicle type to evaluate relative market composition.Proportional Data Visualization: Implemented a custom pie chart visual to display market share percentages and unit distributions clearly at a glance.Data (http://glance.Data) Preparation: Cleaned underlying dataset tables (Vehicle_Category, Vehicle_Class, EV_Sales_Quantity) for accurate aggregation across categories.Outcome:Delivered an intuitive visual summary that highlights category dominance—showing that 2-Wheelers represent the largest market segment ($56.2%$)—allowing stakeholders to quickly identify top revenue drivers and growth opportunities.
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6
Data Cleaning & Outlier Removal (SPSS / Statistical Analysis)
Overview:
Performed thorough data cleaning and preprocessing on a 300-observation dataset to handle extreme outliers and normalize the distribution. By identifying and removing skewed data points, I restored statistical validity to ensure accurate population-level modeling and reliable linear regression metrics.
Key Process & Deliverables:
Outlier Detection & Removal: Screened baseline metrics to detect extreme variance and eliminate influential outliers that skewed overall population estimates.
Residual & Distribution Analysis: Evaluated standardized regression residuals using histograms and normal probability curves to confirm a bell-shaped, normal distribution ($N = 300$, $\mu \approx 0$, $\sigma \approx 1$).
Data Integrity & Reporting: Produced clean, unskewed variables ready for downstream predictive modeling, regression analysis, and executive reporting.
Outcome:
Transformed raw, heavily skewed survey/financial data into a balanced, statistically sound dataset, improving model accuracy and preventing misleading conclusions.
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7
Sales Quantity Forecasting & Visual Analytics (Power BI)
Overview:
A predictive data analysis project focused on modeling and forecasting sales quantities using Power BI. Designed to help businesses anticipate demand, monitor key trends, and make data-backed inventory decisions.
Key Analysis & Process:
Data Preparation & Modeling: Cleaned historical sales data and established data relationships within Power BI.
Predictive Visualization: Applied Power BI’s built-in forecasting features and time-series visual trends to project future sales quantities.
Interactive Dashboard Design: Built dynamic charts, filters, and KPIs (e.g., total sales, forecasted growth, volume changes) for seamless data exploration.
Outcome:
Delivered an interactive dashboard that translates raw historical and projected sales metrics into clear visual insights, helping stakeholders identify future demand trends at a glance.
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26
Exploratory Data Analysis using Descriptive Statistics
An end-to-end data analysis project focused on summarizing complex datasets through descriptive statistical methods. Covered key metrics like central tendency, variability, and visual distribution modeling to extract clear, high-level business trends and baseline performance metrics.
Deliverables: Cleaned dataset, statistical summary report, and key insight visualizations.
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