Appartment-Complex-Reccomendation-System

Using data mining to make a recommendation system for choosing an apartment complex to stay in for a new resident given a city

Used this dataset for our data analysis

Requirements

• Python 3.8
• required PIP packages (can be installed by python3 -m pip install numpy pandas sklearn tensorflow torch)

Data Cleaning and Transformation

KNN Clustering and recommendation engine

KMeans clustering and recommendation engine

Linear Regressor Pricer

Used the cleaned housing_dataset.csv (the output of the cleaning script) as the input to our script

• Keep the housing_dataset.csv in the same folder as the script / Upload it to the Google Colab Python Notebook
• Run the script to get the R Squared score of the actual and predicted price of the test dataset.
• Price any arbitrary vector having the following attributes: ['availability', 'size', 'total_sqft', 'bath', 'balcony']
• Eg: Add the following code at the end, after adding a test_housing_data.csv with vectors having the above attributes:

test_dataset = pd.read_csv('test_housing_data.csv')
print(model.predict(test_dataset))