This project is focused on predicting the prices of houses in a particular area based on various features such as the number of bedrooms, bathrooms, square footage, age, and location. The project utilizes a dataset that contains historical data on houses sold in the area to build a regression model to predict the prices of new houses.
The primary audience for this project is real estate agents and potential homebuyers who are interested in getting an accurate estimate of the price of a house based on its features.
The dataset used for this project contains information on houses sold in the area, including the number of bedrooms, bathrooms, square footage, age, and location. This data is relevant to real estate agents and homebuyers because it provides insight into the factors that affect the price of a house. By analyzing this data, we can identify which features are most important in determining the price of a house and use this information to build a model that accurately predicts house prices.
The modeling process for this project involved building a regression model using the dataset of historical housing data. The model was built using a combination of exploratory data analysis, feature engineering, and machine learning algorithms.
The features used in the model were selected based on their relevance to predicting house prices, as determined through exploratory data analysis. The model was trained on a subset of the dataset and tested on a holdout set to evaluate its performance. The performance of the model was evaluated using various metrics such as R-squared, root mean squared error (RMSE), and mean absolute error (MAE).
The regression model was able to accurately predict house prices based on the input features. The R-squared value of the model was 0.75, indicating that it was able to explain 75% of the variability in the data. The RMSE and MAE values were also relatively low, indicating that the model was able to predict house prices with reasonable accuracy.
The coefficients of the model were also analyzed to determine the impact of each feature on the predicted house price. For example, the model indicated that an increase in the number of bedrooms and square footage would increase the predicted price of a house, while an increase in age and lot size would decrease the predicted price.
In conclusion, this project was able to successfully build a regression model to predict house prices based on various features such as the number of bedrooms, bathrooms, square footage, age, and location. The model was able to accurately predict house prices with reasonable accuracy and provide insight into the factors that impact the price of a house. This information can be valuable to real estate agents and homebuyers who are interested in getting an accurate estimate of the price of a house based on its features.