Predict_the_House_Prices_King_County

The objective of this notebook is to create a machine learning model that can predict the sales price of houses based on various input variables. The dataset used for training and evaluation contains information about houses sold in King County, Seattle, between May 2014 and May 2015. The notebook follows these steps: Load the dataset: The house data is loaded using the provided URL using the pd.read_csv() function from the pandas library. Data exploration: A description of the dataset is provided, including the target variable ('price') and various features such as the number of bedrooms, bathrooms, square footage, condition, and more. Splitting the data: The dataset is split into features (X) and the target variable (y). The 'price' column is separated from the feature data. Model training: A linear regression model is created using the LinearRegression() class from the sklearn library. The model is trained on the training dataset. Model evaluation: The trained model is evaluated using the validation dataset. Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics are calculated to assess the model's performance. Making predictions: The trained model is used to make predictions on the evaluation dataset, which doesn't include the target variable. The predictions are stored in a DataFrame. Saving predictions: The predictions are saved to a CSV file named 'house_price_predictions.csv' with the required column header 'prediction'. The notebook provides a complete pipeline for training a machine learning model, evaluating its performance, and generating predictions for new unseen data. It can be further extended by exploring different models, optimizing hyperparameters, or incorporating additional features to improve the model's accuracy.