USA Real Estate

This project focuses on USA Real Estate Data Analysis, a comprehensive initiative to explore and model real estate trends across the United States. The project uses a multi-faceted approach, combining data cleaning, exploratory data analysis (EDA), and machine learning to understand the factors influencing real estate prices and to predict property values.

Data Analysis & Preprocessing

The initial phase of the project involved meticulous data cleaning and preparation. Key steps included:

Handling Missing Values: Missing data was addressed through imputation and removal of incomplete records to ensure data integrity.

Outlier Detection: Outliers were identified and managed using statistical methods to prevent them from skewing the analysis.

Feature Engineering: New features were created to enhance the dataset's predictive power. This included a "Total_Area" feature, which combines the basement and living areas, and a "Yearly_Tax_Rate" feature, derived from annual tax data.

Exploratory Data Analysis (EDA): A thorough EDA was performed to visualize the data and uncover key relationships between variables. The analysis utilized various charts—including histograms, box plots, and scatter plots—to examine the distribution of home prices, the impact of living area and lot size on price, and the relationship between tax rates and property values.

Machine Learning Model

The second phase of the project leveraged the cleaned and preprocessed data to build a machine learning model for real estate price prediction. The following steps were executed:

Model Selection: A Random Forest Regressor model was chosen for its robustness and accuracy in handling complex datasets.

Model Training: The dataset was split into training and testing sets to evaluate the model's performance on unseen data. The model was trained on the training set to learn the underlying patterns in real estate prices.

Hyperparameter Tuning: GridSearchCV was used to fine-tune the model's hyperparameters, such as n_estimators, max_depth, and min_samples_leaf, to optimize its performance.

Model Evaluation: The model's predictive accuracy was evaluated using the R-squared (R^2) score, which measures the proportion of variance in the dependent variable that is predictable from the independent variables. The project achieved an impressive R^2

score of 97.6%, indicating that the model can explain a significant portion of the price variation in the real estate market.