Regression with a Tabular Paris Housing Price Dataset

1. Problem Statement

You are a newly appointed data scientist at a real estate investment firm with a keen interest in the vibrant Paris housing market. Your firm believes that by leveraging the power of data science, they can gain a competitive edge in this complex and dynamic market. Currently, housing price valuations rely heavily on traditional methods, such as appraisals and comparable sales analysis, but there's a growing recognition that these techniques are limited in their ability to capture all the nuances and factors that influence property values. Your primary task is to design and implement a data-driven solution that will transform the firm's approach to property valuation.

Using a comprehensive dataset that captures various characteristics of Paris housing, including size, location, amenities, and historical sales data, you will build a robust regression model to predict housing prices. This model will not only provide more accurate and reliable valuations but also uncover hidden relationships between property features and market values, allowing the firm to identify undervalued opportunities and optimize their investment strategies. Success in this endeavor will be critical for expanding the firm's portfolio and achieving their financial goals in the competitive Paris real estate landscape.

Goal: The goal of this project is to build a regression model that can accurately predict housing prices in Paris.

2. Data Description

The dataset contains information about various housing properties in Paris, including size, number of rooms, amenities, and location.

• Rows: 22731
• Columns: 18
• Variables:
  • id: Unique identifier
  • squareMeters: Square meters
  • numberOfRooms: Number of rooms
  • hasYard: Has yard
  • hasPool: Has pool
  • floors: Floors
  • cityCode: City code
  • cityPartRange: City part range
  • numPrevOwners: Number of previous owners
  • made: Year made
  • isNewBuilt: Is new built
  • hasStormProtector: Has storm protector
  • basement: Basement
  • attic: Attic
  • garage: Garage
  • hasStorageRoom: Has storage room
  • hasGuestRoom: Has guest room
  • price: Price (target variable)

Data Source: Kaggle Playground Series S3E6

3. Your Task

Your task is to build a regression model to predict housing prices in Paris.

1. Data Exploration and Preprocessing:
   • Load the dataset using Pandas.
   • Explore the data to understand the distribution of features.
   • Handle missing values, if any.
   • Convert categorical variables to numerical format using techniques like one-hot encoding.
   • Scale numerical features to ensure fair treatment in the model.
2. Feature Engineering (Optional):
   • Create new features that may improve model performance.
3. Model Building:
   • Split the data into training and validation sets.
   • Choose a suitable regression algorithm (e.g., Linear Regression, Random Forest, Gradient Boosting).
   • Train the model on the training data.
4. Model Evaluation:
   • Evaluate the model's performance on the validation data.
   • Use metrics such as RMSE, MAE, and R-squared.
   • Visualize the results using scatter plots.
5. Prediction and Submission:
   • Make predictions on the test data.

Python Libraries: Pandas, NumPy, scikit-learn, Matplotlib/Seaborn.