The price of a house is based on several characteristics such as location, total area, number of rooms, various amenities available, etc. In this project, we will perform house price prediction for 200 apartments in Pune city. Different regression models such as Linear, Random Forest, XGBoost, etc., will be implemented. Also, multi-layer perceptron (MLP) models will be implemented using scikit-learn and TensorFlow. This house price prediction project will help you predict the price of houses based on various features and house properties.
We are given a real estate dataset with around 200 rows and 17 different variables that play an important role in predicting our target variable, i.e., price.
The goal is to predict sale prices for homes in Pune city.
⮚ Language - Python ⮚ Libraries - sklearn, pandas, NumPy, matplotlib, seaborn, xgboost
- Importing the required libraries and reading the dataset.
- Preliminary exploration
- Check for the outliers and remove outliers.
- Dropping of redundant feature columns
- Missing value handling
- Regularizing the categorical columns
- Save the cleaned data
- Import the required libraries and read the cleaned dataset.
- Converting binary columns to dummy variables
- Feature Engineering
- Univariate and Bivariate analysis
- Check for correlation
- Feature selection
- Data Scaling
- Saving the final updated dataset
- Data preparation
- Performing train test split
- Linear Regression
- Ridge Regression
- Lasso Regressor
- Elastic Net
- Random Forest Regressor
- XGBoost Regressor
- K-Nearest Neighbours Regressor
- Support Vector Regressor
- Mean Squared Error
- R2 score
- Plot for residuals
- MLP Regression with scikit-learn
- Regression with TensorFlow