House Prices detection and analysis

The House Price Detection and Analysis project leverages machine learning techniques to predict and analyze house prices based on various features. As a machine learning engineer, my role involved developing and deploying predictive models to assist users in making informed decisions in the real estate market.

Data Preprocessing: Cleaned and preprocessed the dataset, handling missing values, encoding categorical variables, and scaling numerical features to prepare the data for modeling.

Feature Engineering: Engineered new features and transformed existing ones to enhance model performance and capture valuable information for predicting house prices.

Model Selection: Explored various machine learning algorithms such as linear regression, decision trees, random forests, and gradient boosting to identify the most suitable model for the task.

Hyperparameter Tuning: Optimized model hyperparameters using techniques like grid search and randomized search to improve predictive performance and generalization ability.

Model Evaluation: Evaluated model performance using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared to assess accuracy and reliability.

Deployment: Deployed the trained model into production using Django Framework, allowing users to interact with the system via a user-friendly web interface.

Prediction: Users can input details about a house, such as its location, size, number of rooms, amenities, etc., and receive an estimated price prediction based on the trained machine learning model.

Analysis: The system provides insights into factors influencing house prices, such as neighborhood demographics, market trends, economic indicators, and more, helping users make informed decisions.

Visualization: Interactive visualizations and dashboards display key metrics and trends, facilitating data exploration and interpretation for users.