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Aman

Data Scientist
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Location Unnao
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Member since30+ Days ago
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Contact Details
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Candidate Information
  • User Experience
    Experience 1 Year
  • Cost
    Hourly Rate$1
  • availability
    AvailabilityImmediate
  • work from
    Work FromAny
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    CategoryInformation Technology & Services
  • back in time
    Last Active OnJune 11, 2024
Key Skills
PythonSQLPower BIRegression Testing
Education
2014-2017

Bsc mathematics honours (Mathematics )
Delhi University

Summary

PERSONAL PROJECTS Shopper Intention Project Internship 07/2022 - Present, Developed and implemented a machine learning model to predict shopper intention based on customer behavior and browsing patterns. Utilized advanced data analysis techniques to identify key factors influencing shopper intention and provided actionable insights for marketing and sales strategies Conducted thorough data preprocessing, feature engineering, and model evaluation to optimize the accuracy and performance of the prediction model. Collaborated with cross-functional teams to integrate the shopper intention model into the company's customer relationship management (CRM) system, resulting in improved targeting and personalized marketing campaigns. T20 world cup cricket data analytics Kaggle 2023 - Present, Created a Power BI report to identify top 11 players for a T20 cricket team by cleaning and transforming the data with pandas, and evaluating various player performance metrics. Used the resulting Power BI dashboard to select players for various categories (openers, middle order/anchors, finishers, all-rounders, specialist fast bowlers) and ultimately choose the top 11 players to play in the match. Selected teams using the Power BI dashboard have 90% of chances to win the game. Movie Recommendation Kaggle 2023 - Present, Developed a movie recommendation system using collaborative filtering algorithms and Python, incorporating movie data from multiple sources and utilizing natural language processing techniques for more personalized recommendations. Fine-tuned model parameters to optimize recommendation accuracy and implemented a user-friendly web interface for easy access to recommendations. Collaborated with cross-functional teams to deploy the system to production, improving user engagement and satisfaction. 

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