Analyze Data with statsmodels

For data science and machine learning, having a solid understanding of statistics is essential for effectively applying these techniques. Fortunately, there’s a Python library called statsmodels that offers a wide range of statistical tools for data analysis, such as descriptive statistics, hypothesis testing, and regression analysis. With statsmodels, we can gain the knowledge and skills necessary to excel in this field.

Learning statsmodels can help people to:

• Gain a deeper understanding of statistical concepts.

• Perform more sophisticated data analysis.

• Build more accurate and reliable models.

• Communicate their findings more effectively.

In this project, we will learn to perform exploratory data analysis, clean a dataset with the pandas library, and analyze housing prices in California with the statsmodels. We have a California real estate dataset with 13 variables that affect the median value of a home. Our aim is to use Ordinary Least Squares (OLS) regression to forecast future prices. Along with cleaning the data and implementing the model, we shall also analyze trends and provide a statistical argument that demonstrates the rise or fall in the median home value.