Welcome to the House Price Prediction project, where we explore, clean, and model housing data to predict prices accurately. This project takes you through the journey of transforming raw data into actionable insights and building predictive models.
- Introduction
- Preprocessing
- Modeling
- Hyperparameter Tuning
- Choosing the Best Model
- Usage
- Contributions
- Contact
Real estate is all about location, location, location. This project dives into the world of housing data to predict prices based on various features, helping buyers and sellers make informed decisions.
We start by loading data from CSV files and meticulously cleaning it to ensure data quality. We remove mistakes and discrepancies that might affect our models' performance.
To work with statistical models, we encode categorical data and ensure our features are in a format that our algorithms can understand.
Missing data can be problematic. We fill in missing values where appropriate, so our models have all the information they need.
We don't stop at preprocessing. Our project includes the following models:
- Decision Tree
- Random Forest
- XGBoost
- Ridge Regression
- Lasso Regression
To squeeze out the best performance from our models, we fine-tune their hyperparameters. We're dedicated to finding the optimal configuration for each algorithm.
With a set of well-tuned models in hand, we embark on the journey to choose the ultimate house price predictor. We compare the models' performance and select the one that shines the brightest.
Got questions or ideas? Reach out to us at https://www.linkedin.com/in/m-jameel-40a977224/. Let's connect and discuss how we can make house price predictions more accurate and valuable.
Thank you for visiting our project! 🏡✨