prediction_demo.py

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

def data_preparation(df: pd.DataFrame):
    # select target column and feature columns
    target = "price"
    features = ["furnishingstatus"]

    feature_df = pd.get_dummies(
        df.loc[:,features],
        columns=["furnishingstatus"])
        

    target_series = df.loc[:,target]

    return feature_df, target_series

def data_split(features: pd.DataFrame, target:pd.Series):
    X_train, X_test, y_train, y_test = \
        train_test_split(features, 
                         target, 
                         test_size=0.33, 
                         random_state=42)
    return X_train, X_test, y_train, y_test

def train_model(X_train: np.ndarray, 
                y_train: np.ndarray):
    reg = LinearRegression().fit(X_train, y_train)
    return reg

def eval_model(X_test: np.ndarray,
               y_test: np.ndarray,
               model: LinearRegression):
    return model.score(X_test,y_test)

if __name__ == "__main__":
    df_raw = pd.read_csv('Housing.csv')
    feature_df, target_series = data_preparation(df_raw)
    X_train, X_test, y_train, y_test = data_split(feature_df, target_series)
    reg=train_model(X_train,y_train)
    eval_score=eval_model(X_test,y_test,reg)
    print(f"Trained model score is: {eval_score}")