This project aims to predict house prices using a dataset containing various features of houses. The goal is to build a predictive model that can accurately estimate the price of a house given its characteristics. This README provides a detailed overview of the steps and methodologies used to achieve this objective.
The project is organized into the following sections:
- Data Exploration
- Exploratory Data Analysis (EDA)
- Data Preprocessing
- Model Building
- Model Evaluation
The initial phase of the project involves loading and exploring the dataset to understand its structure and contents. This includes:
- Loading the dataset using Pandas.
- Displaying the first few rows of the dataset.
- Checking the dimensions and types of features in the dataset.
- Identifying and handling missing values.
- Checking for and addressing duplicate rows.
EDA involves creating visualizations to understand the relationships between features and the target variable (house prices). Various plots are generated to uncover patterns and insights, including:
- Scatter plots to visualize relationships between numerical features (e.g., area and price).
- Count plots and bar plots to examine the distribution of categorical features (e.g., air conditioning, number of stories).
- Box plots to show the distribution of numerical features and identify outliers.
- Distribution plots of the target variable and categorical features.
Data preprocessing is crucial to prepare the dataset for modeling. Key preprocessing steps include:
- Handling missing values by filling them with the median or mean of the feature.
- Encoding categorical variables using techniques like one-hot encoding to convert them into numerical format.
- Scaling numerical features using StandardScaler to normalize the data and improve model performance.
With the data preprocessed, the next step involves building predictive models. The dataset is split into training and testing sets to evaluate the model's performance on unseen data. Various regression models are then trained on the training set and tested on the testing set.
- Linear Regression
- Used as a baseline model. It is a simple and interpretable model that establishes a relationship between the dependent variable and independent variables.
- Ridge Regression
- Introduces regularization to Linear Regression, adding a penalty to the size of coefficients to prevent overfitting and manage multicollinearity.
- XGBoost Regressor
- An efficient and scalable implementation of the gradient boosting framework. It handles various data types and captures complex patterns, making it a powerful tool for regression tasks.
- Random Forest Regressor
- An ensemble learning method that combines multiple decision trees to improve predictive performance and reduce overfitting. Each tree in the forest is trained on a different subset of the data, and their predictions are averaged to produce the final output.
- Mean Squared Error (MSE): MSE is the average of the squared differences between the predicted and actual values. It provides a measure of the accuracy of the predictions, with lower values indicating better performance.
- Root Mean Squared Error (RMSE): RMSE is the square root of the average of squared differences between the predicted and actual values. It provides a measure of the average error in the predictions, with lower values indicating better performance.
- R-squared (R²): R² measures the proportion of the variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with higher values indicating a better fit.
To replicate the results of this project:
- Clone the repository containing the Jupyter Notebook.
- Ensure all necessary libraries (pandas, numpy, seaborn, matplotlib, scikit-learn, xgboost) are installed.
- Run the notebook to see the entire process, from data loading and exploration to model evaluation.