Housing Price Prediction This project aims to predict house prices using a linear regression model based on a dataset of housing data. The project includes data preprocessing, analysis, feature selection, and model building to achieve an accuracy of 83%.

Dataset The dataset used in this project consists of various features related to houses, such as lot frontage, lotarea, Overall Cond, square footage, location, etc. The target variable is the house price.

Project Overview The project follows the following steps:

Data Preprocessing: The dataset is preprocessed to handle missing values, outliers, and any necessary data transformations. Feature scaling and normalization are performed as required.

Exploratory Data Analysis (EDA): EDA techniques are applied to gain insights into the dataset. Statistical summaries, data visualizations, and correlation analysis are used to understand the relationships between features and the target variable.

Feature Selection: Based on the EDA, feature selection techniques are employed to identify the most relevant features for the prediction task. This helps in reducing dimensionality and improving model performance.

Model Building: A linear regression model is trained on the selected features to predict house prices. The model is implemented using appropriate libraries or frameworks such as scikit-learn.

Model Evaluation: The trained model is evaluated using suitable metrics, such as mean squared error (MSE) or R-squared, to assess its performance and accuracy. The achieved accuracy of 83% is reported as the evaluation result.

Contributions Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, feel free to create a pull request or open an issue.

License This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for your own purposes.