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data-cleaning-preprocessing

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Abdullah321Umar / Internee.pk-DataAnalytics_Internship-Assignment1

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🌟 Internship Program Analysis 🌟 This project explores key trends in internship opportunities across various companies and roles. Using Python (Pandas, Matplotlib, Seaborn), the dataset was cleaned, analyzed, and visualized for insights. It highlights top internship titles, locations, durations, and stipend patterns.

documentation numpy heatmap pandas data-visualization seaborn python-programming matplotlib data-exploration bar-charts pie-charts comparing importing analyzing data-interpretation report-writing insight-generation data-cleaning-preprocessing visualization-design trend-pattern-identification
  • Updated Oct 28, 2025
  • Jupyter Notebook

nicolepcollins / nashville-housing-dataset-cleaning

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This project demonstrates data cleaning on the Nashville Housing dataset (2013–2016) using R and packages like tidyverse, lubridate, and janitor. Key steps included standardizing column names, handling missing values, formatting dates, cleaning text fields, and identifying outliers. The cleaned dataset is now ready for analysis and modeling to unco

machine-learning r tidyverse r-markdown outlier-detection missing-data data-preprocessing predictive-modeling data-cleaning real-world-project data-cleaning-preprocessing
  • Updated Jun 6, 2025
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