Predicting Housing Prices

Context:

The real estate market is highly dynamic and influenced by various factors such as location, economic conditions, interest rates, and more. Accurately predicting housing prices can benefit buyers, sellers, and investors by providing insights into market trends and helping make informed decisions.

My Contribution:

I developed a machine learning model to predict housing prices using a comprehensive dataset. The project involved several key steps:

Data Collection and Preprocessing:

Gathered data from various sources including online real estate listings and public databases.

Cleaned and preprocessed the dataset, handling missing values, encoding categorical variables, and scaling numerical features.

Exploratory Data Analysis (EDA):

Conducted EDA to identify important features and visualize trends and correlations using tools like pandas, seaborn, and matplotlib.

Model Training and Evaluation:

Experimented with multiple regression models including linear regression, decision trees, random forests, and gradient boosting.

Evaluated model performance using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Fine-tuned the models through hyperparameter optimization to improve accuracy.

User Interface Development:

Developed a user-friendly web interface using Flask, allowing users to input property details and receive price predictions.

This project provided valuable insights into the factors influencing housing prices and demonstrated the potential of machine learning in real estate market analysis.