This project aims to develop a machine learning model that accurately predicts housing prices using the Boston Housing dataset. By analyzing various features of houses, such as crime rate, number of rooms, and accessibility to highways, the model provides valuable insights for potential buyers or sellers in estimating housing prices. The project utilizes the powerful CatBoostRegressor algorithm for optimal performance and incorporates techniques like data preprocessing, exploratory data analysis, and model training. The trained model can be used as a tool to make informed decisions in the real estate market.

• Flask
• pandas
• numpy
• matplotlib
• scikit-learn
• catboost
• gunicorn

This is make you understand how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

1. Clone the repo

git clone https://github.com/KalyanMurapaka45/Spam-Email-Detection.git

2. Install the required libraries

pip install -r requirements.txt

3. Open and execute .ipynb file (After complete Execution you will get a .pkl file for project Deployment)

The Boston Housing dataset is imported from the sklearn.datasets module in Python. It consists of a total of 506 instances, each representing a house in the Boston area. The dataset contains 13 numerical features that describe various aspects of the houses, such as crime rate, average number of rooms, and proximity to employment centers. The target variable is the median value of owner-occupied homes in thousands of dollars.

1. CRIM: Per capita crime rate by town
2. ZN: Proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS: Proportion of non-retail business acres per town
4. CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise)
5. NOX: Nitric oxide concentration (parts per 10 million)
6. RM: Average number of rooms per dwelling
7. AGE: Proportion of owner-occupied units built prior to 1940
8. DIS: Weighted distances to five Boston employment centers
9. RAD: Index of accessibility to radial highways
10. TAX: Full-value property tax rate per $10,000
11. PTRATIO: Pupil-teacher ratio by town
12. B: 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
13. LSTAT: Percentage of lower status of the population

• MEDV: Median value of owner-occupied homes in $1000s

The Boston Housing dataset is preprocessed before training the machine learning model. The dataset is imported and split into input features (X) and the target variable (y). The input features are then standardized using the StandardScaler from the sklearn.preprocessing module to ensure that all features have a similar scale. The preprocessed dataset is further divided into training and testing sets using a 80:20 train-test split ratio.

A CatBoostRegressor model is trained using the preprocessed dataset. The model is built to predict housing prices based on the given features. Hyperparameter tuning is performed using a RandomizedSearchCV approach from the sklearn.model_selection module. The best set of hyperparameters is selected based on 5-fold cross-validation. The model is trained on the training set using the optimized hyperparameters. The trained CatBoostRegressor model is evaluated using the testing set. The predicted housing prices are compared to the actual prices, and the performance of the model is assessed using the R-squared metric.

• Algorithm Used: Catboost Algorithm

This project includes a Flask-based web application for deploying the house price prediction model. The model is loaded from the saved pickle file ('housepred.pkl'), and the scaler object is loaded from 'scaler.pkl' for preprocessing the input data. The web application allows users to input the necessary features of a house through a form or API request, and it returns the predicted house price.

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch
3. Commit your Changes
4. Push to the Branch
5. Open a Pull Request

Distributed under the GNU General Public License v3.0. See LICENSE.txt for more information.

This project was inspired by the Kaggle dataset on Boston House Price Prediction and the corresponding competition. We also acknowledge the open-source Python libraries used in this project and their contributors.