Ritik Grover

Dynamic Data Analyst and Flutter Developer at Webmonde SoftTech Solutions LLP, adept at leveraging statistical analysis and machine learning to drive business insights. Enhanced decision-making through effective data visualization and optimized models, achieving significant improvements in accuracy. Proficient in SQL and Python, with a strong focus on delivering impactful results and fostering collaboration.

Movie Recommendation System using Machine Learning

• Built a machine learning–based movie recommendation system to suggest personalized movies based on user preferences and content similarity.
• Performed data preprocessing and feature extraction on movie metadata including genres, ratings, and user interactions.
• Achieved an overall recommendation accuracy of 83% by evaluating model performance using similarity and relevance metrics.
• Evaluated model performance using train-test split and feature importance analysis.

Tools: Python, Pandas, NumPy, Scikit-learn

Titanic Survival Prediction | Machine Learning

• Built a binary classification model to predict passenger survival using the Kaggle Titanic dataset.
• Performed data preprocessing, feature engineering. (Age groups, Fare groups, family-based features, gender–class interaction)
• Trained and optimized ensemble models including Random Forest , achieving 77%+ accuracy.
• Evaluated model performance using train-test split and feature importance analysis.

Tools: Python, Pandas, NumPy, Scikit-learn

Sales Data Analysis & Business Insights

• Performed EDA on retail sales data to identify revenue trends, high-performing products, and seasonal demand patterns.
• Delivered business insights through data cleaning, analysis, and visualization.

Employee Attrition Analysis Dashboard | Power BI

• Designed an interactive Power BI dashboard to analyze employee attrition trends and identify key factors influencing employee turnover.
• Analyzed attrition across departments, job roles, age groups, and experience levels to uncover high-risk employee segments.
• Identified major attrition drivers such as overtime, job satisfaction, salary levels, and years at company.
• Provided data-driven insights to support employee retention and HR decision-making.