This project focuses on analyzing real estate sales data to extract meaningful market insights and build predictive models for property prices. The goal is to create a structured dataset from raw real estate data, visualize trends using Tableau, and apply machine learning models to predict future prices.
- Data Collection & Cleaning: Processed and structured real estate data for analysis.
- Exploratory Data Analysis (EDA): Identified pricing trends, demand patterns, and suburb-wise distributions.
- Visualization & Dashboarding: Created interactive Tableau dashboards to display market insights.
- Machine Learning Predictions: Trained models to forecast property prices based on historical data.
- ✅ R (Tidyverse, Rvest, Lubridate) → Data collection, cleaning, and transformation.
- ✅ Tableau → Creating dashboards and visual reports.
- ✅ Python (Scikit-learn, XGBoost, Random Forest) → Building predictive models.