https://github.com/jatin-mehra119/paris_housing_price-kaggle-

Paris Housing Price Kaggle Competiton
https://github.com/jatin-mehra119/paris_housing_price-kaggle-

data data-visualization kaggle-competition machine-learning numpy pandas predictive-modeling scikit-learn

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Paris Housing Price Kaggle Competiton

• Host: GitHub
• URL: https://github.com/jatin-mehra119/paris_housing_price-kaggle-
• Owner: Jatin-Mehra119
• Created: 2024-05-30T16:55:02.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-10T09:04:58.000Z (4 months ago)
• Last Synced: 2024-07-10T10:54:59.126Z (4 months ago)
• Topics: data, data-visualization, kaggle-competition, machine-learning, numpy, pandas, predictive-modeling, scikit-learn
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/competitions/playground-series-s3e6
• Size: 21.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Playground Series S3E6: Predicting the Future

Welcome to the Kaggle Playground Series S3E6 project! This repository contains all the essential files and scripts to participate in the competition. For a detailed overview, visit the Kaggle competition page.

Link - https://www.kaggle.com/competitions/playground-series-s3e6/overview

# Contents-

## 1. train.csv: Training dataset

### All attributes are numeric variables and they are listed bellow:

All attributes are numeric variables and they are listed bellow:

1. squareMeters

2. numberOfRooms

3. hasYard

4. hasPool

5. floors - number of floors

6. cityCode - zip code

7. cityPartRange - the higher the range, the more exclusive the neighbourhood is

8. numPrevOwners - number of prevoious owners

9. made - year

10. isNewBuilt

11. hasStormProtector

12. basement - basement square meters

13. attic - attic square meteres

14. garage - garage size

15. hasStorageRoom

16. hasGuestRoom - number of guest rooms

17. price - predicted value

## 2. Notebook.ipynb:

A. Exploratory Data Analysis (EDA)

B. Feature Engineering

C. Data Preprocessing (custom transformers, scalers, imputer)

D. Model Training (Random Forest Regression)

E. Model Testing