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https://github.com/szymon-budziak/real_estate_house_prices_prediction
Predicting real estate house prices using various machine learning algorithms, including data exploration, preprocessing, model training, and evaluation.
https://github.com/szymon-budziak/real_estate_house_prices_prediction
data-analysis data-preprocessing data-science eda jupyter-notebook machine-learning matplotlib numpy optuna pandas predictive-modeling price-prediction python random-forest regression scikit-learn seaborn
Last synced: 3 months ago
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Predicting real estate house prices using various machine learning algorithms, including data exploration, preprocessing, model training, and evaluation.
- Host: GitHub
- URL: https://github.com/szymon-budziak/real_estate_house_prices_prediction
- Owner: Szymon-Budziak
- License: mit
- Created: 2024-08-05T17:00:59.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-05T17:32:13.000Z (5 months ago)
- Last Synced: 2024-09-23T06:03:33.043Z (3 months ago)
- Topics: data-analysis, data-preprocessing, data-science, eda, jupyter-notebook, machine-learning, matplotlib, numpy, optuna, pandas, predictive-modeling, price-prediction, python, random-forest, regression, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.12 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Real Estate House Prices Prediction
---
This repository contains a Jupyter Notebook that demonstrates a comprehensive process for predicting real estate house prices using various machine learning techniques. The notebook walks through data exploration, preprocessing, model training, evaluation, and selection, ultimately culminating in a robust predictive model.
## Table of Contents
- [Introduction](#introduction)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Notebook Overview](#notebook-overview)
- [1. Data Loading and Initial Exploration](#1-data-loading-and-initial-exploration)
- [2. Exploratory Data Analysis (EDA)](#2-exploratory-data-analysis-eda)
- [3. Data Cleaning and Preprocessing](#3-data-cleaning-and-preprocessing)
- [4. Model Training and Evaluation](#4-model-training-and-evaluation)
- [5. Hyperparameter Tuning](#5-hyperparameter-tuning)
- [6. Model Comparison](#6-model-comparison)
- [7. Final Model and Pipeline](#7-final-model-and-pipeline)
- [8. Conclusion](#8-conclusion)
- [Contributing](#contributing)
- [License](#license)
## Introduction
This project aims to predict real estate hosue prices using a dataset containing various features such as area, furnishing status, and more. It employs multiple machine learning algorithms to find the best model for the task.
## Dataset
The dataset used in this project is a structured CSV file containing information about real estate properties. Key features include price, area, and furnishing status.
## Installation
The project uses Poetry to manage dependencies. To install the dependencies, run the following command:
```bash
poetry install
```
## Notebook Overview
### 1. Data Loading and Initial Exploration
- Load the dataset.
- Display basic statistics and the first few rows of the data.
- Check for unique values and missing data.
### 2. Exploratory Data Analysis (EDA)
- Visualize the distribution of the target variable (price) using histograms and KDE plots.
- Examine the relationship between price and other features using scatter plots and box plots.
- Generate a heatmap to understand feature correlations.
- Create pair plots to visualize relationships between multiple features.
### 3. Data Cleaning and Preprocessing
- Handle binary categorical variables and create dummy variables for other categorical data.
- Standardize or normalize numerical features as needed.
### 4. Model Training and Evaluation
- Train multiple regression models including Linear Regression, Lasso, Ridge, Polynomial Regression, and Random Forest.
- Evaluate models using metrics like R², MSE, and RMSE.
### 5. Hyperparameter Tuning
- Use Optuna for hyperparameter tuning of the Random Forest model to improve performance.
### 6. Model Comparison
- Compare the performance of various models on the training and test sets.
- Visualize and interpret model performance metrics.
### 7. Final Model and Pipeline
- Save the best model using pickle.
- Create a prediction pipeline with the best model and necessary preprocessing steps.
- Demonstrate making predictions with the final model.
### 8. Conclusion
- Summarize findings, model performance, and insights gained from the project.