# Housing Price Prediction Project

## Project Overview

This project focuses on predicting housing prices using supervised machine learning techniques. The dataset used contains various attributes such as longitude, latitude, housing median age, total rooms, total bedrooms, population, households, median income, and ocean proximity. The goal is to build a model that can accurately predict the median house value based on these attributes.

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## Table of Contents

- [Data Preparation](#data-preparation)
- [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)
- [Feature Engineering](#feature-engineering)
- [Model Selection and Training](#model-selection-and-training)
- [Model Evaluation](#model-evaluation)
- [Final Model and Testing](#final-model-and-testing)
- [Conclusion](#conclusion)

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## Data Preparation

The dataset is loaded and inspected to understand its structure and features. Missing values are handled using imputation techniques. Numerical and categorical features are processed separately using pipelines to ensure the data is ready for the machine learning models.

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## Exploratory Data Analysis (EDA)

Several visualizations are created to explore the relationships between different features and the target variable (median house value). Scatter plots, histograms, and correlation matrices are used to identify patterns and insights in the data.

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## Feature Engineering

New features are created from existing ones to potentially improve the model's performance. For example, ratios like rooms per household and bedrooms ratio are calculated. These new features can provide additional insights into the dataset.

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## Model Selection and Training

Several machine learning models are trained and evaluated using cross-validation. Models such as Linear Regression, Decision Tree Regressor, and Random Forest Regressor are used to predict housing prices. Hyperparameter tuning is performed using Grid Search and Randomized Search to find the best performing model.

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## Model Evaluation

The models are evaluated using the root mean squared error (RMSE) metric. This metric measures the difference between the predicted and actual values. The goal is to minimize this error to improve the model's accuracy.

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## Final Model and Testing

The best performing model is selected, and its performance is evaluated on the test set to ensure its generalization to unseen data. Confidence intervals are computed to understand the model's uncertainty in its predictions.

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## Conclusion

While the model may not have achieved a perfect score, it has shown decent performance and provided valuable insights into the dataset. Future work may involve further model refinement and feature engineering to improve the prediction accuracy.

This project has been a valuable learning experience, and I look forward to applying these skills to future projects. Thank you for taking the time to review my work!