House Price Prediction

Problem Statement:

• The main objective of this project was to build a model that predict the housing prices in California using the California census data.

Methodology:

• Utilized a regression-based machine learning approach to predict housing prices.
• Implemented data preprocessing techniques such as handling missing values, feature scaling,feature extraction and encoding categorical variables.
• Explored various regression algorithms including linear regression, decision trees, knn, svr, random forests, adaptive boosting, gradient boosting and XG boost.

Results:

• Achieved a root mean squared error (RMSE) of 40000 on the test dataset, indicating the model's ability to predict housing prices.

Skills Demonstrated:

• Data preprocessing, data visualization, regression modeling, hyperparameter tuning, model evaluation.

Technologies Used:

• Python, pandas, scikit-learn, matplotlib, seaborn, Jupyter Notebook.