This project conducted a comprehensive data analysis on a huge dataset, boasting over a million rows. The dataset underwent multiple stages of modification and scrutiny to produce insights of interest.
Data cleaning was a critical step of our pipeline since the quality of data directly influenced the outcomes of our analysis. This process involved handling missing values, correcting inconsistent entries, and validating the correctness of the data. The cleansing process improved the reliability and accuracy of our data.
We performed a detailed data analysis using diverse analytical techniques such as descriptive statistics, inferential statistics, and data visualization. Each method offered its unique vantage point into the dataset, enabling a deeper understanding of the patterns and relationships within the data.
In any large dataset, outliers can distort the results and conclusions of data analysis. Therefore, we used various outlier detection methods to identify and manage these data points. This step significantly improved the robustness of our analysis by reducing the influence of extreme values.
This project serves as a comprehensive guide to dealing with massive datasets. It employs various statistical techniques and visualization tools to reveal patterns and correlations within the data.
This project explores a large dataset containing over a million rows, covering various cross-sections of data. The exact nature of the data has been anonymized to maintain privacy and confidentiality. Languages / Libraries Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
Please refer to the Jupyter Notebook for detailed code, visualizations and comments explaining our exploratory data analysis process.