1)House Price Prediction Machine Learning Project This project explores the task of predicting house prices using machine learning techniques. It provides a comprehensive solution for data preprocessing, model training, evaluation, and visualization, focusing on a user-friendly and informative experience.
Key Features:
Data Preprocessing: Loads data from CSV files. Handles missing values using appropriate strategies (e.g., imputation, removal). Performs feature engineering techniques (e.g., one-hot encoding for categorical features). Splits data into training and testing sets for model evaluation. Optionally scales numerical features using techniques like StandardScaler. Machine Learning Models: Implements and trains various regression models, including: Linear Regression Decision Tree Regressor Random Forest Regressor Allows for easy experimentation with different models through a modular design. Model Evaluation: Calculates common performance metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Provides visualizations of predicted vs. actual values to assess model performance. Visualization: Generates informative plots and charts to explore data distribution, feature relationships, and model performance. Helps in understanding the impact of features on house prices. Benefits:
Easy to Use: The project is well-structured and documented, enabling quick setup and execution. Modular Design: Experiment with different models and preprocessing techniques with minimal code changes. Educational Value: Gain practical experience with machine learning for real-world regression tasks. Clear Insights: Visualization tools aid in understanding the factors influencing house prices. Potential Applications:
Real estate market analysis Automated property valuation Predictive modeling for investment decisions Getting Started:
Clone this repository. Install required libraries (refer to the requirements.txt file). Load your CSV data containing house price information. Run the provided scripts to perform data preprocessing, model training, and evaluation. This project provides a valuable foundation for exploring house price prediction using machine learning. Feel free to customize it further by adding more advanced models, visualizations, or data sources!