In the dynamic landscape of the real estate industry, the accurate determination of rental prices stands as a pivotal factor impacting property owners, tenants, and property management companies alike. The quest for precise rent predictions is not merely a technical endeavor; it is a strategic imperative that empowers landlords to set competitive prices, enables tenants to make informed decisions, and assists property management companies in optimizing their portfolios.
This project utilizes the following key technologies and skills:
1.Python 2. Numpy: 3. Pandas: 4. Scikit-Learn: 5. Matplotlib: 6. Seaborn: 7. Pickle
This dataset encompasses a rich array of features, each playing a pivotal role in our pursuit of developing a predictive model for rental prices. Here's a succinct overview of the dataset's key elements, serving as the foundation for our modeling endeavor.
unique Identifiers : The dataset is meticulously organized with unique identifiers assigned to each property.
Property Types: Diverse property types contribute to the dataset's vibrancy, capturing the spectrum of residential offerings. From apartments to villas, each property type introduces a unique set of characteristics that influences rental pricing and shapes the market landscape.
Localities: Geographical diversity is a cornerstone of our dataset, featuring a comprehensive collection of localities. The inherent characteristics of each locality contribute nuances to property values, offering a localized perspective crucial for our predictive modeling efforts.
Property Characteristics: Fundamental attributes such as size, age, and floor details are meticulously documented. These characteristics form the building blocks for understanding how the physical attributes of a property contribute to its overall rental valuation.
Amenities: The dataset delves into the amenities offered by each property, providing insights into the additional features that contribute to a property's appeal. The presence or absence of amenities significantly influences rental values and is a crucial aspect of our predictive modeling considerations.
Rental Price (Target Variable): At the core of our predictive modeling lies the rental price, our key target variable.
Handling Null Values: To address missing values in the dataset, a systematic approach was adopted where most features with null values were replaced with Mean and Mode.
Encoding and Data Type Conversion: The process involves preparing categorical features for modeling by transforming them into numerical representations, considering their inherent nature and relationship with the target variable
Feature Improvement: Using Seaborn's Heatmap highlights key features positively correlated with rent, including type, property size, floor, balconies, bathroom, parking, and amenities count. Conversely, building type demonstrates a negative correlation with rent, providing valuable insights into the significance of each feature in predicting rental prices.
PCA, is a dimensionality reduction technique employed to transform a dataset with numerous correlated features into a set of linearly uncorrelated variables known as principal components.PCA allows us to condense the dataset's complexity while retaining the essential information that contributes most significantly to variance.
Model Bulidling : The first step in our algorithmic odyssey involves the division of the dataset into training and testing subsets. This partitioning ensures a robust evaluation of algorithmic performance, with the training set serving as the crucible for model learning and the testing set acting as a litmus test for predictive accuracy on unseen data.
Algorithm Selection: After thorough evaluation Linear Regression and XGB Regressor both are good testing accuracy.I choose the Xgb Regressor for its ability to strike a balance between interpretability and accuracy, ensuring robust performance on unseen data. Hyperparameter Tuning: grid search and cross-validation. Grid search helps us systematically test different hyperparameter combinations, while cross-validation ensures we evaluate our model's performance thoroughly, avoiding overfitting or underfitting. Best Hyperparameters: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 150}
Model Accuracy and Metrics: Assessing the performance of our predictive model involves a comprehensive examination of regression metrics. Key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R-squared (R²) are this holistic evaluation provides valuable insights into the model's ability to effectively capture and predict rental property prices, ensuring a nuanced understanding of its overall performance.
Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.
This project is licensed under the MIT License. Refer to the LICENSE file for more details.
📧 Email: [email protected]
🌐 LinkedIn:linkedin.com/in/thangarasu-m-
For any further questions or inquiries, feel free to reach out. We are happy to assist you with any queries.