House Price Prediction

Data Preprocessing for House Price Prediction:

• Performed Data Cleaning to identify and handle missing values, duplicated records, and inconsistent data.

• Conducted Data Analysis to understand the dataset structure, distributions, and relationships between features.

• Applied Feature Encoding to transform categorical variables into suitable numerical representations.

• Performed Feature Selection to identify the most relevant features for house price prediction.

• Applied Feature Engineering to create and transform features to improve their usefulness for predictive modeling.

• Used Data Visualization to explore feature distributions, relationships, and potential patterns within the dataset.

• Conducted Testing to verify the preprocessing steps and ensure the resulting dataset was

properly prepared for Machine Learning.

Tools & Technologies:

Python | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn