Welcome to the House Price Prediction project using the Boston House Dataset!. The aim of this project was to predict house prices based on various features using machine learning techniques.
The project involved data preprocessing, exploratory data analysis, feature selection, and building a regression model to make accurate predictions. The dataset used in this project is the famous Boston House Prices dataset, which contains various attributes like average number of rooms, crime rate, and accessibility to highways among others.
This Jupyter Notebook contains the complete code for the data preprocessing, analysis, and model building process.
The raw dataset used in the project.
This file provides an overview of the project and instructions to run the Jupyter Notebook.
The following libraries are required to run the Jupyter Notebook successfully:
Python 3.x Jupyter Pandas NumPy Scikit-learn Matplotlib Seaborn
You can install the required libraries using pip:
pip install jupyter pandas numpy scikit-learn matplotlib seaborn
Clone this repository to your local machine using:
git clone https://github.com/yourusername/house-price-prediction.git
Navigate to the project directory:
cd house-price-prediction
Launch the Jupyter Notebook:
jupyter notebook house_price_prediction.ipynb
Follow the step-by-step instructions in the Jupyter Notebook to explore the dataset, preprocess the data, build the regression model, and make predictions.
The trained model achieved impressive accuracy in predicting house prices based on the given features. The project provides insights into the factors that significantly impact house prices in the Boston area.
I completed this project as part of my internship at Bharat Intern and am currently not accepting contributions. However, feel free to explore the code, open issues, or fork the repository for your own experiments.
This project is licensed under the MIT License.