Chirag Hire

Dynamic Project Engineer at Wipro with extensive experience in developing automated test scripts using Selenium and Java. Demonstrates strong SQL skills for data validation and excels in optimizing test strategies through effective collaboration. Committed to leveraging advanced analytics and data manipulation to enhance project outcomes.

• SQL: Data Analytics & Visualizations
• Python Libraries: Data Analytics & Visualizations
• EDA Fundamentals

Netflix Dataset Analysis

https://github.com/Chirag4git/Netflix_Dataset_Analysis

• Analyzed a comprehensive dataset of 8,807 movies and TV shows from Netflix, generating actionable insights that enhanced strategic decision-making.
• Cleaned and transformed data by addressing null values in 5 columns and unnesting 4 multi-valued columns, refining the dataset to 201,708 records.
• Utilized Python libraries such as Pandas, Matplotlib, and Seaborn for exploratory data analysis, uncovering critical content trends that informed strategy.
• Identified top genres, including 2,752 International Movies and 2,426 Dramas, to optimize content strategy and improve viewer engagement.
• Highlighted leading content producers, revealing that the United States contributed 2,748 movies and 932 TV shows, guiding regional content focus.

Target Business Case Analysis

https://github.com/Chirag4git/Target_Business_Case_SQL

• Conducted comprehensive SQL analysis on 100,000+ e-commerce orders for Target Brazil, generating insights that shaped strategic business decisions.
• Identified a 25% growth trend in orders between 2017 and 2018, with November 2017 achieving peak volume, driving strategic planning initiatives.
• Mapped customer distribution across 27 states and 4,119 cities, highlighting Rio de Janeiro as a key market for targeted marketing campaigns.
• Evaluated payment preferences, revealing credit cards as the top choice, and recommended strategies projected to increase sales by 15%.

IPL Dynamic Dashboard | Tableau

• Developed an interactive Tableau dashboard to analyze IPL match data (2008-2020) with dynamic season filtering to extract key performance insights.
• Utilized IPL Matches 2008-2020 and IPL Ball-by-Ball 2008-2020 datasets to derive actionable insights.
• Displayed seven key insights using advanced Tableau visualizations, including:

     - Top run-scorer and top wicket-taker per season

     - Total sixes and fours hit in the tournament

     - Tournament winner and match wins based on toss decisions

     - Toss decision-based winning percentage for strategic analysis

Tools & Technologies Used: Tableau, SQL, Data Wrangling, Data Visualization, Exploratory Data Analysis (EDA), KPI Dashboards