House Price Prediction using Machine Learning

A simple Machine Learning project that predicts house prices using the Kaggle House Price dataset. The project includes data preprocessing, exploratory data analysis (EDA), feature engineering, model training, and performance evaluation using multiple regression algorithms.

Project Overview

The main objective of this project is to build a supervised machine learning model that can accurately predict house prices based on various property features such as living area, number of bedrooms, bathrooms, grade, condition, and more.

Features

• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Selection
• Data Scaling
• Training Multiple Machine Learning Models
• Model Performance Evaluation
• House Price Prediction

Machine Learning Models Used

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor
• K-Nearest Neighbors (KNN)
• Support Vector Regressor (SVR)

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

Dataset

Dataset: Kaggle House Price Prediction Dataset Dataset link: https://www.kaggle.com/datasets/harlfoxem/housesalesprediction

The dataset contains information about residential houses such as:

• Bedrooms
• Bathrooms
• Living Area
• Lot Size
• Floors
• Condition
• Grade
• Year Built
• Waterfront
• View
• Zipcode
• and other housing features.

Project Workflow

1. Import Dataset
2. Data Cleaning
3. Exploratory Data Analysis
4. Feature Engineering
5. Train-Test Split
6. Feature Scaling
7. Model Training
8. Model Evaluation
9. Price Prediction

Evaluation Metrics

The models are evaluated using:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R² Score

Future Improvements

• Hyperparameter Tuning
• Feature Engineering
• Model Deployment using Flask or Streamlit
• Interactive Web Application
• Cross Validation

Author

Pratham Sindkar

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