Predicting House Prices
Developed a sophisticated house price prediction model incorporating data pre-processing, feature engineering, and Linear Regression, validated by Root Mean Squared Error (RMSE) evaluation.
Project Workflow:-
Exploratory Data Analysis (EDA): Understanding the dataset, identifying patterns, and visualizing relationships between variables.
Data Preprocessing: Cleaning the data, handling missing values, scaling numerical features, and encoding categorical variables.
Feature Engineering: Creating new features, transforming variables to improve model performance.
Model Building: Training and evaluating regression models (e.g., Linear Regression, Random Forest, Gradient Boosting) to predict house prices.
Model Evaluation: Assessing model performance using metrics like RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R-squared.
Deployment and Reporting: Saving the best-performing model, preparing reports and presentations summarizing findings, insights, and next steps.