Machine Learning (ML) is a rapidly developing branch of Artificial Intelligence (A.I.) that helps computers learn from the past data and improve their performance without explicit programming. Machine learning has gained significant attention in recent years due to its ability to analyze large amounts of data and provide intelligent solutions for complex problems. The most significant application of machine learning is house price prediction, which is helpful in understanding the prices of houses based on different factors. The present study utilizes the machine learning technique to make accurate predictions in house price prediction using the given data set of houses, which consists of 1,460 records and 81 features. Data preprocessing is the initial step in which the data is made suitable for further analysis. The data preprocessing steps include missing value handling, data cleaning, and removal of outliers. After preprocessing the data, Exploratory Data Analysis (EDA) is performed to understand the data, identify the patterns, and analyze the relationships between different features and the target variable. The dataset is split into training and testing data sets using the train-test split. Various machine learning algorithms were implemented to build prediction models. The algorithms used were Linear Regression, Decision Tree Regressor, Random Forest Regressor, and K-Nearest Neighbors. The R² score was used to evaluate the models' performances. The results show that the Random Forest Regressor performs better than the other algorithms since it has the highest R² score. This proves that the machine learning algorithms can be used to make house price predictions. This study proves that machine learning algorithms can be used in predicting house prices. The process is simple and easy to follow even for a beginner who wants to learn the process of building a machine learning model.