Overview: The House Price Prediction project leverages the power of machine learning to forecast the future prices of residential properties. This project is part of the GirlScript Summer of Code, aimed at providing hands-on experience in applying machine learning techniques to real-world problems. Participants will gain valuable insights into data preprocessing, feature engineering, model selection, and evaluation.
Objective: The primary goal of this project is to create an accurate and robust model that can predict house prices based on various features such as location, size, number of rooms, and other relevant factors. By the end of the project, participants will have a comprehensive understanding of the end-to-end process involved in building a predictive model.
Key Features of the Project:
-
Data Collection and Preprocessing:
- Gather and clean the dataset containing historical house prices and their attributes.
- Handle missing values, outliers, and ensure the data is in a suitable format for analysis.
-
Exploratory Data Analysis (EDA):
- Perform EDA to uncover patterns, correlations, and insights within the data.
- Visualize the data using graphs and charts to better understand the relationships between features.
-
Feature Engineering:
- Select and create meaningful features that improve the model's predictive power.
- Transform categorical variables into numerical ones using techniques like one-hot encoding.
-
Model Selection:
- Experiment with various machine learning algorithms such as Linear Regression, Decision Trees, Random Forest, and Gradient Boosting.
- Compare the performance of different models using appropriate evaluation metrics.
-
Model Training and Evaluation:
- Split the data into training and testing sets to evaluate the model's performance.
- Use metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared to assess accuracy.
-
Hyperparameter Tuning:
- Optimize the model's parameters using techniques such as Grid Search and Random Search to improve performance.