import pandas as pd # Specify the file path file_path = 'Self_Master.csv' # Read the CSV file into a DataFrame df = pd.read_csv(file_path) df

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsRegressor from sklearn.metrics import mean_squared_error import numpy as np # Load the data into a DataFrame file_path = 'Self_Master.csv' df = pd.read_csv(file_path) # Assuming 'target_variable' is the column you want to predict target_variable = 'Total Single Unit Housing' # Replace with the actual column name # Extract features (X) and target variable (y) X = df.drop(columns=[target_variable]) y = df[target_variable] # Split the data into training and testing sets (90% training, 10% testing, random state = 51) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=51) # Create and train the KNN model k_value = 3 # Choose an appropriate value for k knn_model = KNeighborsRegressor(n_neighbors=k_value) knn_model.fit(X_train, y_train) # Make predictions on the test set y_pred = knn_model.predict(X_test) # Evaluate the model's performance using mean squared error mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) # Display the root mean squared error print(f'Root Mean Squared Error: {rmse}') knn_model