- Loading and Preparing the Dataset The dataset contains historical property prices with columns like Date, Year, and Sale Price. Here's what we do: Explanation: Loading Data: The dataset is loaded into a pandas DataFrame (df).
Date Conversion: The Date column is converted to a datetime format for easier manipulation.
Feature Extraction: New columns (Year, Month, Day) are extracted from the Date column. These will be used as features for the model.
- Preparing the Data for Training We need to separate the features (X) and the target variable (y). Explanation: Features (X): These are the input variables used to predict the target. Here, we use Year, Month, and Day.
Target (y): This is the variable we want to predict, which is Sale Price.
- Training the Model We use a Random Forest Regressor to train the model on the historical data. Explanation: Random Forest Regressor: A machine learning algorithm that works well for regression tasks like predicting prices.
Training: The model learns the relationship between the features (Year, Month, Day) and the target (Sale Price).
- Predicting for Future Years We create a function to predict prices for any future year. Explanation: Function Input: The year for which you want to predict prices.
Creating Future Data: For each month of the specified year, we create a row with Year, Month, and Day (default is 1).
Prediction: The trained model predicts the prices for each month.
Output: A DataFrame with the predicted prices for the specified year.
- Using the Function to Predict for a Specific Year You can call the function to predict prices for any future year (e.g., 2023). Explanation: Input: Specify the year you want to predict (e.g., 2023).
Output:
The predicted prices for each month of the specified year are displayed.
The results are saved to a CSV file (e.g., predicted_prices_2023.csv).
- Predicting for Multiple Future Years If you want to predict for multiple years (e.g., 2023, 2024, 2025), you can use a loop. Explanation: Input: A list of years you want to predict (e.g., [2023, 2024, 2025]).
Output:
Predicted prices for each month of all specified years.
Results are saved to a CSV file (predicted_prices_future_years.csv). io;u