Regression with a Tabular California Housing Dataset

1. Problem Statement

Picture yourself as a data scientist working for a real estate investment firm that's looking to expand its portfolio in California. The firm wants to leverage data-driven insights to make informed investment decisions, but current methods rely heavily on outdated valuation techniques and subjective assessments. The management believes that a predictive model based on comprehensive data can provide a significant competitive advantage.

You are given various features such as the location, average income, property type, and size. Your task is to build a machine-learning model that accurately predicts housing prices. This predictive tool will not only aid in identifying undervalued properties but also assist in assessing market trends and understanding the factors that influence housing prices. By providing accurate and reliable price predictions, you will enable the firm to optimize its investment strategies, reduce risk, and maximize profitability in the competitive California real estate market.

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

2. Data Description

The dataset for this competition was generated from a deep learning model trained on the California Housing Dataset.

• Rows: 37138
• Columns: 10
• Variables:
  • id: Unique identifier
  • MedInc: Median income in block group
  • HouseAge: Median house age in block group
  • AveRooms: Average number of rooms per household
  • AveBedrms: Average number of bedrooms per household
  • Population: Block group population
  • AveOccup: Average household occupancy
  • Latitude: Block group latitude
  • Longitude: Block group longitude
  • MedHouseVal: Median house value (target variable)

Data Source: Kaggle Playground Series S3E1

3. Your Task

Your task is to build a regression model to predict the median house value.

1. Data Exploration and Preprocessing:
   • Load the dataset using Pandas.
   • Explore the data to understand the distribution of features.
   • Handle missing values, if any.
   • 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.