Predicting House Prices using Linear Regression and Gradient Boosting Regressor Project

Introduction: In this project, we aim to build a predictive model that can accurately predict house prices based on various features. We will use two different regression algorithms - Linear Regression and Gradient Boosting Regressor - to compare their performance in predicting house prices.

Dataset: We start by acquiring a dataset that contains information about different houses along with their corresponding selling prices. The dataset should include features like the number of bedrooms, bathrooms, square footage, lot size, location, and other relevant attributes that might influence the house prices.

Data Preprocessing: Before feeding the data to our models, we need to perform data preprocessing. This step involves handling missing values, encoding categorical variables, scaling numerical features, and splitting the dataset into training and testing sets.

Linear Regression: The Linear Regression algorithm is a simple and interpretable model that assumes a linear relationship between the independent variables and the target variable (house prices). We will train the Linear Regression model on the training data and then evaluate its performance on the test data. The evaluation metrics used can include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) to assess how well the model is performing.

Gradient Boosting Regressor: Gradient Boosting is an ensemble learning technique that combines multiple weak learners (decision trees) to create a strong predictive model. In this project, we will use the Gradient Boosting Regressor, a variant of gradient boosting designed for regression tasks. We will also train this model on the training data, evaluate its performance on the test data, and compare it with the Linear Regression model using the same evaluation metrics.

Hyperparameter Tuning: To improve the performance of the models, we may perform hyperparameter tuning for both Linear Regression and Gradient Boosting Regressor. This involves searching for the optimal set of hyperparameters that produce the best results. Techniques like Grid Search or Random Search can be used for this purpose.

Model Comparison: After training and fine-tuning both models, we will compare their performances using the evaluation metrics. We will analyze which model provides better predictions for house prices based on the dataset and problem requirements.

Model Interpretability: One of the advantages of Linear Regression is its interpretability. We can examine the coefficients of the linear model to understand the impact of each feature on the house prices. This information can provide valuable insights into the housing market.

Visualization: To gain better insights into the predictions and model behavior, we can create visualizations. These visualizations can include scatter plots to see how well the models' predictions align with the actual house prices, feature importance plots to identify the most influential features, and residual plots to check for patterns in the model's errors.

Conclusion: In the final section of the project, we will summarize our findings and discuss which model performed better in predicting house prices. We will also mention any challenges faced during the project and suggest possible improvements for future work.

Remember that the success of the project depends on the quality of the dataset, the data preprocessing steps, the selection of relevant features, and the fine-tuning of hyperparameters. Additionally, it is essential to consider factors like overfitting, generalization, and model complexity while developing and evaluating the models.

Thank You