Project: House Price Prediction

House price prediction is a crucial task in the field of data science and machine learning. It involves building a model that can estimate the price of a house based on various features or attributes associated with the property. The prediction of house prices is valuable for both homebuyers and sellers

House Price Prediction Model

In this project, you will have the exciting opportunity to build a cool regression model that predicts house prices. As a data scientist, your task is to analyze a dataset containing various features of houses and their corresponding prices. By harnessing the power of machine learning, you will develop a model that can ACCURATELY estimate house prices based on the given features and EVALUATE the model.

Task: Build a regression model to predict house prices based on various features and evaluate the model using multiple evaluation metrics.

Dataset

The dataset contains information about houses and their prices. The features included are:

• Price: The price of the house.
• Area: The total area of the house in square feet.
• Bedrooms: The number of bedrooms in the house.
• athrooms: The number of bathrooms in the house.
• Stories: The number of stories in the house.
• Mainroad: Whether the house is connected to the main road (Yes/No).
• Guestroom: Whether the house has a guest room (Yes/No).
• Basement: Whether the house has a basement (Yes/No).
• Hot water heating: Whether the house has a hot water heating system (Yes/No).
• Airconditioning: Whether the house has an air conditioning system (Yes/No).
• Parking: The number of parking spaces available within the house.
• Prefarea: Whether the house is located in a preferred area (Yes/No).
• Furnishing status: The furnishing status of the house (Fully Furnished, Semi-Furnished, Unfurnished).

TODOs

• Load the dataset into a pandas DataFrame.
• Perform data exploration and preprocessing, including handling missing values and encoding categorical variables.
• Split the dataset into features (X) and target (y), where y is the 'Price' column.
• Split the dataset into training and testing sets using train-test split (e.g., 80% training and 20% testing).
• Build a regression model using scikit-learn (e.g., Linear Regression, Random Forest, or any other suitable model).
• Train the model on the training data using the fit method.
• Make predictions on the test data using the predict method.
• Evaluate the model using the following metrics:
  • Precision
  • Recall
  • F1 Score
  • Confusion Matrix
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R-squared (R²)
• Complete the assignment using the notebook in the repository.
  • Push your solution back to Github once completed.

HINTS

• Utilize pandas library for data manipulation and preprocessing.
• Use scikit-learn library to build and train the regression model.
• Since this is a regression tasks, focus on MAE, MSE, RMSE, and R-squared. You can use the mean_absolute_error, mean_squared_error, mean_squared_error, and r2_score functions from the sklearn.metrics module.
• Apply k-fold cross-validation by using scikit-learn's cross_val_score function. You can use the cross_val_score function from the sklearn.model_selection module.
• Use the pandas get_dummies function for one-hot encoding categorical variables.
• Consider feature scaling if necessary, using MinMaxScaler or StandardScaler from scikit-learn.

Note: Make sure to properly interpret the evaluation metrics to understand the model's performance. The goal is to build a model that accurately predicts house prices and minimizes the error between predicted and actual prices.