VISHAL BHURIYA

Eager and results-driven Data Analyst with hands-on experience at Cognifyz Technologies, mastering Python programming and analytical thinking to deliver actionable insights. Excelled in big data analytics and data cleaning, leveraging tools like MS Excel and Power BI to enhance data quality and support decision-making processes.

1. Zomato Data Analysis:

Tools Used: Python (Pandas, Matplotlib, Seaborn)

· Conducted a comprehensive analysis of Zomato’s growth and performance metrics.

· Clarity: Rewrote vague phrases for precision (e.g., "data completed" → details omitted for brevity).

· Professional Tone: Removed informal expressions and ensured a polished tone.

· Specificity: Highlight measurable impacts where possible to emphasize your contributions.

2. Car Sales  :

Tools Used:  Excel, Power BI

• Designed and managed interactive dashboards to support data-driven car sales strategies.
• Utilized advanced analytics and visualization tools to identify trends and optimize revenue.

Utilized advanced analytics and visualization tools to identify trends and optimize revenue.

· Analyzed sales data to identify key performance indicators (KPIs) and provide actionable recommendations.

· Collaborated with cross-functional teams to define dashboard requirements and streamline workflows.

Delivered insights through effective communication and presentations to stakeholders.

3.Heart Disease Predictor :

Tools Used: Python, Machine learning model, Jupyter Notebook, Visualization 

• Developed a machine learning model to predict heart disease risk using clinical data (e.g., age, cholesterol, blood pressure).
• Employed algorithms like Logistic Regression and Random Forest with metrics such as ROC-AUC for performance evaluation.
• Preprocessed data (handling missing values, feature selection) and ensured interpretability using SHAP values.
• Built a user-friendly interface for healthcare professionals to input patient details and receive risk predictions.
• Focused on early detection and preventive care, reducing diagnostic delays.