House Price Prediction through Effective Data Preprocessing and Linear Regression Modeling

By $Salar$ $Mokhtri$ $Laleh$

Salar.M.Laleh

This project aims to predict the sale prices of houses based on various features such as the size of the house, the number of rooms, the location, and so on. The approach used is linear regression, which is a commonly used method for predicting continuous values.

Methodology

Data Preprocessing

Before training the linear regression model, we need to preprocess the data. This involves several steps:

• Removing outliers: Outliers are data points that are significantly different from other data points. They can have a large impact on the model's accuracy, so we remove them from the dataset.

• Handling missing values: Missing values are data points that are not available. We need to either remove them or fill them in with appropriate values.

• Encoding categorical variables: Categorical variables are variables that can take on a limited number of values, such as the type of a house (e.g., single-family, townhouse, etc.). We need to convert these variables into numerical values that the model can use.

• Aligning columns: The training and testing datasets may have different columns. We need to make sure that they have the same columns so that the model can use them.

## Linear Regression Linear regression is a method for modeling the relationship between a dependent variable (y) and one or more independent variables (x). The goal is to find the line of best fit that minimizes the sum of the squared errors between the predicted values and the actual values. The equation for a simple linear regression model is:

$y = mx + b$

where y is the dependent variable, x is the independent variable, m is the slope of the line, and b is the y-intercept.

For multiple linear regression, the equation becomes:

$y = b0 + b1x1 + b2x2 + ... + bnx_n$

where $y$ is the dependent variable $x1$, $x2$, ..., $xn$ are the independent variables, $b0$ is the y-intercept, and $b1$, $b2$, ..., $b_n$ are the coefficients for each independent variable.

Conclusion

In this project, we used linear regression to predict house prices based on various features. We first preprocessed the data by removing outliers, handling missing values, encoding categorical variables, and aligning columns. We then trained the linear regression model and evaluated its performance on a validation set. Finally, we used the model to make predictions on a test set and saved the results to a CSV file. The accuracy of the model can be further improved by tuning hyperparameters and using more advanced techniques such as regularization.

License

This project is licensed under the Salar Mokhtari Laleh Open-Source License. See the LICENSE file for details