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• Thorough Analysis: I conducted a comprehensive examination of net sales performance, analyzing data across multiple dimensions, including year, customer, market, product, growth, and division.

• Target Alignment: Ensured alignment with strategic objectives by closely scrutinizing market performance against predefined targets.

• In-Depth Insights: Generated comprehensive insights illuminating critical aspects of sales operations.

• Tools Used: Employed MS Excel, utilizing tools like Pivot Tables, Power Pivot, and Conditional Formatting.

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• Metric Development: I designed and implemented essential financial metrics such as Net Sales, COGS, Gross Margin and Gross Margin % for the purpose of creating a P&L statement.

• Decision-Making: These metrics are responsible for generating valuable insights to perform data-driven decision-making .

• Tools Used: Employed MS Excel, utilizing tools like Pivot Tables, Power Pivot, and Conditional Formatting.

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•Customized Dashboard: Developed a multi-functional Power BI dashboard to specifically meet AtliQ Hardware's needs, allowing them to gain insights into their various departments.

•Data Integration: Data was sourced from disparate locations, including Excel/CSV files and a MySQL database, and seamlessly integrated into Power BI for comprehensive analysis.

•Data Model and Visualizations: A robust data model was developed within Power BI, enabling the creation of visually appealing and interactive visualizations to represent key performance indicators (KPIs).

•Performance Optimization: The dashboard's performance was significantly improved using DAX Studio, resulting in a 5% increase in report efficiency.

•Data-Driven Decisions: AtliQ Hardware can now analyze trends across different departments, empowering them to make data-driven decisions.

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Key Insights:

• Top Power Hitters/Openers : Identified the most explosive batsmen in the tournament, crucial for setting high run targets.

• Top Anchors/Middle Order: Recognized players who provided stability and maintained the innings during crucial phases.

• Top Finishers/Lower Order : Highlighted batsmen with a knack for finishing games effectively under pressure.

• Top All-rounders: Identified versatile players who excelled both with the bat and ball, adding a dynamic dimension to the team.

• Top Fast Bowlers: Recognized the leading wicket-takers and most economical bowlers, pivotal for restricting the opposition's scoring.

Playing 11 Selection:

• This project forms a Playing 11 team, using top performers from these categories for a balanced and competitive squad.

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• Tool Usage: Employed Python libraries (NumPy, Pandas, Seaborn, scikit-learn) for system development, enabling data handling, visualization, and model creation.

• Model Design: Developed a logistic regression-based diabetes prediction model, using eight key variables: pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, BMI, diabetes pedigree, and age.

• Dependent Variable: Employed the 'outcome' variable to indicate diabetes presence or absence, serving as the model's dependent factor.

• Model Accuracy: Achieved an 81% accuracy rate with the logistic regression model, demonstrating its proficiency in predicting diabetes based on the specified variables.

• Web Integration: Integrated the model into a user-friendly Django-based web application, simplifying data input and offering clear result visualization, enhancing accessibility for diabetes prediction.

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