ABOUT ME
ABOUT ME
ABOUT ME
Hi, I’m Kavya Sharma 👋 — a Data Analyst diving into the fascinating world of Machine Learning. I enjoy uncovering patterns, making predictions, and transforming data into stories that spark real action. Whether I’m experimenting with algorithms or fine-tuning models for machine learning projects, I love seeing how data can guide smarter decisions 📊.
My journey is curiosity-driven, and I’m steadily growing into an AI/ML Engineer who builds not just clever tools, but meaningful solutions 🚀.
Hi, I’m Kavya Sharma 👋 — a Data Analyst diving into the fascinating world of Machine Learning. I enjoy uncovering patterns, making predictions, and transforming data into stories that spark real action. Whether I’m experimenting with algorithms or fine-tuning models for machine learning projects, I love seeing how data can guide smarter decisions 📊.
My journey is curiosity-driven, and I’m steadily growing into an AI/ML Engineer who builds not just clever tools, but meaningful solutions 🚀.
Hi, I’m Kavya Sharma 👋 — a Data Analyst diving into the fascinating world of Machine Learning. I enjoy uncovering patterns, making predictions, and transforming data into stories that spark real action. Whether I’m experimenting with algorithms or fine-tuning models for machine learning projects, I love seeing how data can guide smarter decisions 📊.
My journey is curiosity-driven, and I’m steadily growing into an AI/ML Engineer who builds not just clever tools, but meaningful solutions 🚀.
EDUCATION & CERTIFICATES
EDUCATION & CERTIFICATES
EDUCATION & CERTIFICATES
Master of Computer Applications (MCA)
Master of Computer Applications (MCA)
Master of Computer Applications (MCA)
Sunderdeep Global University, Ghaziabad
Sunderdeep Global University, Ghaziabad
Sunderdeep Global University, Ghaziabad
2024-present
2024-present
Bachelor of Computer Applications (BCA)
Bachelor of Computer Applications (BCA)
Bachelor of Computer Applications (BCA)
SC Guria Institute of Management and Technology, Kashipur
SC Guria Institute of Management and Technology, Kashipur
SC Guria Institute of Management and Technology, Kashipur
2021-2024
2021-2024
Data Analytics and Visualization Job Simulation
Data Analytics and Visualization Job Simulation
Data Analytics and Visualization Job Simulation
Accenture
Accenture
Accenture
2024
2024
Data Science Foundations
Data Science Foundations
Data Science Foundations
Great Learning
Great Learning
Great Learning
2024
2024
Key Skills
Data & Analysis Toolkit
Data & Analysis Toolkit
Data & Analysis Toolkit
Technologies I use regularly for analysis and visualization.
Technologies I use regularly for analysis and visualization.
Technologies I use regularly for analysis and visualization.
MySQL
MySQL
MySQL
Matplotlib
Matplotlib
Matplotlib
Seaborn
Seaborn
Seaborn
NumPy
NumPy
NumPy
PostgreSQL
PostgreSQL
PostgreSQL
PowerBi
PowerBi
PowerBi
Excel
Excel
Excel
Python
Python
Python
SciPy
SciPy
SciPy
Pandas
Pandas
Pandas
Scikit-Learn
Scikit-Learn
Scikit-Learn
PROJECTS
PROJECTS
PROJECTS
Housing Price Prediction
Housing Price Prediction
Housing Price Prediction
Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Description: Built a regression model to predict California housing prices using demographic and geographic features.
Techniques:
Performed EDA with visualizations & correlation heatmaps.
Selected key features based on correlation analysis.
Trained and evaluated Linear Regression & Random Forest models.
Compared results using MAE, RMSE, and R² Score.
Visualized Actual vs Predicted prices & analyzed feature importance.
Outcome: Random Forest achieved R² ~0.81, outperforming Linear Regression and providing actionable insights into key price drivers.
Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Description: Built a regression model to predict California housing prices using demographic and geographic features.
Techniques:
Performed EDA with visualizations & correlation heatmaps.
Selected key features based on correlation analysis.
Trained and evaluated Linear Regression & Random Forest models.
Compared results using MAE, RMSE, and R² Score.
Visualized Actual vs Predicted prices & analyzed feature importance.
Outcome: Random Forest achieved R² ~0.81, outperforming Linear Regression and providing actionable insights into key price drivers.
Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Description: Built a regression model to predict California housing prices using demographic and geographic features.
Techniques:
Performed EDA with visualizations & correlation heatmaps.
Selected key features based on correlation analysis.
Trained and evaluated Linear Regression & Random Forest models.
Compared results using MAE, RMSE, and R² Score.
Visualized Actual vs Predicted prices & analyzed feature importance.
Outcome: Random Forest achieved R² ~0.81, outperforming Linear Regression and providing actionable insights into key price drivers.
Tool: Power BI
Type: Live Interactive Dashboard
Description: Designed and developed a dynamic HR Analytics Dashboard to uncover key workforce trends and support strategic decision-making in HR. The dashboard focuses on employee attrition, salary distribution, tenure, and demographic breakdowns, enabling data-driven insights for retention and hiring strategies.
Key Highlights:
Analyzed attrition patterns across age, gender, education, job roles, and salary bands.
Queried and transformed HR data to create meaningful visualizations and KPIs.
Visualized metrics like attrition rate (20.6%), average salary, tenure, and job role distribution using Power BI.
Empowered HR teams with actionable insights to improve employee engagement and reduce turnover.
Impact: Helped stakeholders understand attrition drivers, identify high-risk segments, and shape more effective talent retention strategies.
Tool: Power BI
Type: Live Interactive Dashboard
Description: Designed and developed a dynamic HR Analytics Dashboard to uncover key workforce trends and support strategic decision-making in HR. The dashboard focuses on employee attrition, salary distribution, tenure, and demographic breakdowns, enabling data-driven insights for retention and hiring strategies.
Key Highlights:
Analyzed attrition patterns across age, gender, education, job roles, and salary bands.
Queried and transformed HR data to create meaningful visualizations and KPIs.
Visualized metrics like attrition rate (20.6%), average salary, tenure, and job role distribution using Power BI.
Empowered HR teams with actionable insights to improve employee engagement and reduce turnover.
Impact: Helped stakeholders understand attrition drivers, identify high-risk segments, and shape more effective talent retention strategies.
Tool: Power BI
Type: Live Interactive Dashboard
Description: Designed and developed a dynamic HR Analytics Dashboard to uncover key workforce trends and support strategic decision-making in HR. The dashboard focuses on employee attrition, salary distribution, tenure, and demographic breakdowns, enabling data-driven insights for retention and hiring strategies.
Key Highlights:
Analyzed attrition patterns across age, gender, education, job roles, and salary bands.
Queried and transformed HR data to create meaningful visualizations and KPIs.
Visualized metrics like attrition rate (20.6%), average salary, tenure, and job role distribution using Power BI.
Empowered HR teams with actionable insights to improve employee engagement and reduce turnover.
Impact: Helped stakeholders understand attrition drivers, identify high-risk segments, and shape more effective talent retention strategies.
Customer Churn Prediction
Customer Churn Prediction
Description: This project focuses on predicting customer churn — identifying whether a customer is likely to leave the company or continue using its services. By analyzing customer demographics, account details, and service usage patterns, the project delivers actionable insights that can help businesses improve retention strategies.
Tools & Techniques:
Programming & Analysis: Python, Pandas, NumPy, Matplotlib, Seaborn.
Machine Learning Models Tested: Logistic Regression, Random Forest, XGBoost.
Model Evaluation: Train-Test Split, Accuracy Score, Confusion Matrix, Classification Report.
Prediction Outcome: The best-performing model, Random Forest, provided the highest accuracy of ~82% in predicting churn.
1 (Yes): Customer likely to churn
0 (No): Customer likely to stay
This precise prediction outcome makes the project valuable for businesses seeking to identify at-risk customers and take proactive measures.
Description: This project focuses on predicting customer churn — identifying whether a customer is likely to leave the company or continue using its services. By analyzing customer demographics, account details, and service usage patterns, the project delivers actionable insights that can help businesses improve retention strategies.
Tools & Techniques:
Programming & Analysis: Python, Pandas, NumPy, Matplotlib, Seaborn.
Machine Learning Models Tested: Logistic Regression, Random Forest, XGBoost.
Model Evaluation: Train-Test Split, Accuracy Score, Confusion Matrix, Classification Report.
Prediction Outcome: The best-performing model, Random Forest, provided the highest accuracy of ~82% in predicting churn.
1 (Yes): Customer likely to churn
0 (No): Customer likely to stay
This precise prediction outcome makes the project valuable for businesses seeking to identify at-risk customers and take proactive measures.
Description: This project focuses on predicting customer churn — identifying whether a customer is likely to leave the company or continue using its services. By analyzing customer demographics, account details, and service usage patterns, the project delivers actionable insights that can help businesses improve retention strategies.
Tools & Techniques:
Programming & Analysis: Python, Pandas, NumPy, Matplotlib, Seaborn.
Machine Learning Models Tested: Logistic Regression, Random Forest, XGBoost.
Model Evaluation: Train-Test Split, Accuracy Score, Confusion Matrix, Classification Report.
Prediction Outcome: The best-performing model, Random Forest, provided the highest accuracy of ~82% in predicting churn.
1 (Yes): Customer likely to churn
0 (No): Customer likely to stay
This precise prediction outcome makes the project valuable for businesses seeking to identify at-risk customers and take proactive measures.
Tool: Power BI
Type: Live Interactive Dashboard
Description: Developed an end-to-end sales analytics and forecasting dashboard using Power BI to track, analyze, and forecast Superstore sales across multiple dimensions. This project combines real-time performance tracking with predictive insights to support strategic sales planning.
Key Features & Contributions:
Cleaned and transformed raw sales data for accurate reporting and analysis.
Visualized KPIs including total sales, profit, orders, shipping days, and payment modes across regions and segments.
Analyzed regional and state-wise performance to uncover high-revenue locations and untapped markets.
Identified seasonal sales trends, monthly growth, and key drivers by segment and sub-category.
Implemented time series forecasting using Power BI’s analytics tools to project 15-day future sales.
Impact: Enabled data-backed sales decisions by revealing demand patterns, optimizing regional performance, and forecasting future sales to improve planning accuracy.
Tool: Power BI
Type: Live Interactive Dashboard
Description: Developed an end-to-end sales analytics and forecasting dashboard using Power BI to track, analyze, and forecast Superstore sales across multiple dimensions. This project combines real-time performance tracking with predictive insights to support strategic sales planning.
Key Features & Contributions:
Cleaned and transformed raw sales data for accurate reporting and analysis.
Visualized KPIs including total sales, profit, orders, shipping days, and payment modes across regions and segments.
Analyzed regional and state-wise performance to uncover high-revenue locations and untapped markets.
Identified seasonal sales trends, monthly growth, and key drivers by segment and sub-category.
Implemented time series forecasting using Power BI’s analytics tools to project 15-day future sales.
Impact: Enabled data-backed sales decisions by revealing demand patterns, optimizing regional performance, and forecasting future sales to improve planning accuracy.
Tool: Power BI
Type: Live Interactive Dashboard
Description: Developed an end-to-end sales analytics and forecasting dashboard using Power BI to track, analyze, and forecast Superstore sales across multiple dimensions. This project combines real-time performance tracking with predictive insights to support strategic sales planning.
Key Features & Contributions:
Cleaned and transformed raw sales data for accurate reporting and analysis.
Visualized KPIs including total sales, profit, orders, shipping days, and payment modes across regions and segments.
Analyzed regional and state-wise performance to uncover high-revenue locations and untapped markets.
Identified seasonal sales trends, monthly growth, and key drivers by segment and sub-category.
Implemented time series forecasting using Power BI’s analytics tools to project 15-day future sales.
Impact: Enabled data-backed sales decisions by revealing demand patterns, optimizing regional performance, and forecasting future sales to improve planning accuracy.
Housing Price Prediction
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