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Project Summary
This project revolves around a 2019 online catalog dataset showcasing Belarus’ used car market. Extracted from car ads, this dataset serves both buyers and sellers. Sourced from Kaggle and using Python, it underwent thorough data cleaning to address missing values, outliers, and unit standardization.
Data visualization employed Seaborn, revealing insights through boxplots, swarmplots, jointplots, regplots, and heatmaps. Descriptive statistics illuminated frequency distributions, skewness, mean, median, and standard deviation.
Quantitative exploratory analysis featured ANOVA, Z-test, and Chi-Squared Test. This project uncovers market dynamics, benefiting stakeholders with comprehensive insights.
Features
- Importing & exploring data using Numpy & Panda data frames
- Cleaning data by handling missing values, outliers, and standardizing units
- Data visualization with Seaborn using boxplots, swarmplots, jointplots, regplots & heatmaps
- Descriptive statistics looking at frequency distributions, skewness, & central tendency measures like mean, median, and standard deviation
- Quantitative exploratory analysis with ANOVA, Z-test, Chi-Squared Test