Data Analysis Projects in Pune
Data Analysis Projects in Pune
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Sarbjot Singh
Data Analytics Project | BlinkIT Grocery Sales Analysis Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights š š What I worked on: Analyzed sales performance across product categories, outlet types, sizes, and locations Identified top-performing item categories and customer preferences Studied the impact of outlet size, establishment year, and visibility on sales Converted raw data into actionable insights and business recommendations š” Key Insights: Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales FOR FULL PROJECT VIST GIT HUB ā( https://lnkd.in/d34sdAPz )
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Rohit Poddar
Data Visualisation and Storytelling using Power BI
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VARSHA ZOKE
Cricket WorldCup Dashboard
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Daniyal Shaikh
E-Commerce Reverse Logistics Analysis and Prediction
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3
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Ateeb Mahedvi
HR Analytics Dashboard
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Smita S
MRR & Churn Analysis Dashboard for SaaS Retention and Growth
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Aishwarya Mahajan
US Honey Production Case Study
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Kaustubh Bhiwsankar
Geospatial Analysis and Feature Extraction šš
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saikat nandi
Sales Analysis E-Commerce Power BI
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3
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Jash Joshi
Enhancing Operational Efficiency through ETL Solutions
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4
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Shiwangi Ukey
Project Control Process in Project Management
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sai mahurkar
Data Analysis
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6
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Aavej Patel
House Price Prediction
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Krishna Jha
Real-Time Sentiment Analysis for Financial News
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Saniya Shah
Drugs Effects and medical conditions
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Sarbjot Singh
Iām excited to share my latest Data Analytics project where I analyzed Customer Shopping Behavior to uncover trends in sales, demographics, and purchasing habits.This project was a great exercise in building a complete data pipeline. The Tech Stack: Python (Pandas & NumPy): Used in Jupyter Notebooks for initial data exploration and statistical analysis. PostgreSQL: Utilized for rigorous data cleaning, querying, and structuring the dataset for analysis. Power BI: Built an interactive dashboard to visualize key metrics like seasonal trends, subscription impacts, and category performance. Project Highlights: ā Data Cleaning: Leveraged PostgreSQL and Pandas to handle missing values and standardize categorical data.
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