This data science project series walks through step by step process of how to build a real estate price prediction website. We will first build a model using sklearn and linear regression using banglore home prices dataset from kaggle.com. Second step would be to write a python flask server that uses the saved model to serve http requests. Third component is the website built in html, css and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price. During model building we will cover almost all data science concepts such as data load and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tunning, k fold cross validation etc. Technology and tools wise this project covers,
- Python
- Numpy and Pandas for data cleaning
- Matplotlib for data visualization
- Sklearn for model building
- Jupyter notebook, visual studio code and pycharm as IDE
- Python flask for http server
- HTML/CSS/Javascript for UI
The House Prediction Model leverages machine learning algorithms to analyze historical housing data and make predictions about future house prices. It utilizes a dataset containing information about various houses, including their attributes and corresponding sale prices. By training on this data, the model learns patterns and relationships, allowing it to make accurate predictions for new houses.
- Clean and preprocess the housing dataset to handle missing values, outliers, and categorical variables.
- Feature engineering: Extract relevant features and transform data for better model performance.
- Select appropriate machine learning algorithms such as linear regression, decision trees, or ensemble methods.
- Train the model using labeled data, optimizing hyperparameters to improve prediction accuracy.
- Utilize the trained model to predict house prices for new or unseen data.
- Evaluate model performance using metrics such as mean absolute error, mean squared error, and R-squared score.
To run the House Prediction Model locally:
- Clone or download the repository to your local machine.
- Install the required dependencies listed in the 'requirements.txt' file:
pip install -r requirments.txt - Run the Jupyter notebook or Python script to train the model and make predictions.
If you have any questions, feedback, or need assistance with the House Prediction Model, feel free to reach out to aryan.sinha2002@gmail.com.