Regression using data transformers

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The dataset commonly known as the California Housing dataset, is indeed well-known in the field of machine learning and data science. It’s often used for educational purposes, especially in regression analysis tasks to predict house values based on various features.

You can download the file, change rows or edit as you wish, keeping the format. Then upload it and see the regression results in google docs.

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Python Code Example
        
            import pandas as pd
            from sklearn.preprocessing import OneHotEncoder
            from sklearn.preprocessing import StandardScaler
            import numpy as np
            from sklearn.metrics import mean_squared_error
            from sklearn.linear_model import LinearRegression

            # Load the housing data from a CSV file into a DataFrame
            df = pd.read_csv('housing.csv')

            # Fill missing values in the 'total_bedrooms' column with the median
            median = df["total_bedrooms"].median()
            df["total_bedrooms"].fillna(median, inplace=True)

            # Split the data into categorical, numerical, and target columns
            df_num=df.drop(["ocean_proximity","longitude","latitude","median_house_value"],          
            axis=1)
            df_cat = df[["ocean_proximity"]]
            y = df["median_house_value"]

            # Handle Categorical Attributes by one-hot encoding
            onehot_encoder = OneHotEncoder()
            df_cat_encoded = onehot_encoder.fit_transform(df_cat).toarray()

            # Handle Numerical Attributes by standard scaling
            standar_scaler = StandardScaler()
            df_num_scaled = standar_scaler.fit_transform(df_num)

            # Concatenate the one-hot encoded categorical and scaled numerical data
            X = np.concatenate((df_num_scaled, df_cat_encoded), axis=1)

            # Create a Linear Regression model, fit it to the data, and make predictions
            lin_reg = LinearRegression()
            lin_reg.fit(X, y)
            predictions = lin_reg.predict(X)

            # Calculate Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
            lin_mse = mean_squared_error(y, predictions)
            lin_rmse = np.sqrt(lin_mse)

            # Print the MSE and RMSE for the linear regression model
            print("Mean Squared Error (MSE):", lin_mse)
            print("Root Mean Squared Error (RMSE):", lin_rmse)