Regression Models in Python for House Price Prediction

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

Data Cleaning

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

2. Data Pre-processing

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

3. Model Building

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

4. Model Validation

Mean Squared Error

R2 score

Plot for residuals

Performs the grid search and cross-validation for the given regressor

Fitting the model and making predictions on the test data

Checking for Feature Importance

Model comparisons.

MLP (Multi-Layer Perceptron) Models

MLP Regression with scikit-learn

Regression with TensorFlow