Project Description:

In this project, I focused on analyzing and optimizing pricing data through various data engineering tasks including craeting a pricing engine for insurance prediction.. Below are the specific tasks and contributions I made during this project, as demonstrated in the provided Python file:

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Project Description:
In this project, I focused on analyzing and optimizing pricing data through various data engineering tasks. Below are the specific tasks and contributions I made during this project, as demonstrated in the provided Python file:
- Data Extraction and Processing:
  - Loaded pricing data from CSV files using Pandas.
  - Cleaned and preprocessed the data, handling missing values and ensuring data consistency.
- Data Analysis:
  - Conducted exploratory data analysis to understand the distribution and patterns in the pricing data.
  - Calculated key statistical measures such as mean, median, and standard deviation to summarize the data.
- Visualization:
  - Created visualizations using Matplotlib and Seaborn to display trends and distributions in the pricing data.
  - Generated bar plots, histograms, and scatter plots to illustrate findings from the data analysis.
- Modeling:
  - Built and evaluated a linear regression model to predict pricing based on key features.
  - Assessed model performance using metrics such as R-squared and Mean Squared Error.
- Interpolation:
  - Developed interpolation techniques to estimate missing pricing data, ensuring a more complete and accurate dataset.
  - Applied linear interpolation methods to fill in gaps in the data, improving the overall quality and usability of the pricing data.
- Pricing Strategy Development:
  - Analyzed historical pricing trends to develop a robust pricing strategy.
  - Utilized data insights to recommend optimal pricing adjustments, aiming to maximize profitability while maintaining competitive market positioning.

Optimizing Pricing Strategies through Advanced Data Engineering and Analytics