Uses a linear regression model to predict the future resale prices for public housing flats in Singapore based on the following factors:
- Floor Area (sqm)
- Storey Number
The dataset is collected from Kaggle.
From what I have learnt, the model uses one of the 2 sets of formula (both are minimum sum of squares) to calculate the output.
- For a model with 1 feature:
$$\min_{\beta_0, \beta_1} \sum_{i=1}^{n} \left( y_i - (\beta_0 + \beta_1 x_i) \right)^2$$ - For a model with multiple features:
$$\min_{\beta_0, \beta_1, \ldots, \beta_p} \sum_{i=1}^{n} \left( y_i - \left( \beta_0 + \sum_{j=1}^{p} \beta_j x_{ij} \right) \right)^2$$
Where:
-
$n$ is the number of observations. -
$y_i$ represents the observed value for$i$ -th observation -
$\beta_0$ is the y-intercept. -
$\beta_1,...,\beta_p$ are coefficients for the features -
$x_{ij}$ represents he$j$ -th feature of the$i$ -th observation
The model assumes a linear relationship between the variables, then draws a best-fit line on a graph to match the most accurate result using the formula
There are two notebooks, floor_area and storey_number that use the first formula to predict the resale prices.
On main.py, you can use the tool to predict the resale price of the public housing flats with both features while also being able to choose a location for the model to predict on.
