Developed a machine learning model in Python to analyze historical data and generate predictions. Used libraries such as Pandas, Scikit-learn, and NumPy to preprocess datasets, train models, and evaluate performance. The model identifies patterns in structured datasets and generates predictive insights useful for business decision-making.
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Created a Python script to clean and process raw datasets automatically. The script removes duplicates, handles missing values, and formats datasets into structured tables ready for analysis. This reduces manual processing time and improves data quality for reporting.
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Created SQL queries to analyze structured datasets and extract insights from relational databases. Used joins, aggregations, and filtering techniques to generate reports and improve data accessibility.
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Developed a Python data analysis project using Pandas and Matplotlib to clean, process, and analyze structured datasets. The project generates visual insights through charts and graphs to identify trends and support data-driven decision-making.