nandinimanchanda/prodigy_ml_01

Predict house prices using Python libraries like Pandas and scikit-learn. Analyze features like Overall Quality and Living Area to estimate sale prices accurately.

PRODIGY_ML_01

Predict house prices using Python libraries like Pandas and scikit-learn. Analyze features like Overall Quality and Living Area to estimate sale prices accurately.

House Price Predictor using Linear Regression Model

This project aims to predict house prices based on various features using a Linear Regression model. The dataset used for this project contains information about different aspects of residential properties, such as their size, quality, and number of rooms.

Steps Involved:

1. Importing Necessary Libraries: The initial step involves importing the required libraries such as Pandas, NumPy, Seaborn, and Matplotlib for data manipulation and visualization, as well as scikit-learn for machine learning algorithms.

2. Data Loading and Exploration: The dataset is loaded using Pandas, and the features and target variable (SalePrice) are explored to understand their characteristics.

3. Feature Selection: Certain features are selected based on their correlation with the target variable. Features like OverallQual, GrLivArea, GarageArea, etc., are chosen as they exhibit a strong correlation with the sale price.

4. Data Preprocessing: Data preprocessing steps such as handling missing values and creating new features like 'TotalBath' and 'TotalSF' are performed to prepare the data for modeling.

5. Model Training: A Linear Regression model is trained using the selected features to learn the relationship between the independent variables and the target variable.

6. Model Evaluation: The trained model's performance is evaluated using metrics like Mean Squared Error (MSE) and R-squared score to assess its predictive accuracy.

7. Prediction: Finally, the trained model is used to make predictions on new data or the test set to estimate the house prices.

Conclusion:

This project demonstrates the application of a Linear Regression model to predict house prices based on various features. By analyzing and selecting relevant features, preprocessing the data, and training the model, accurate predictions can be made, aiding in informed decision-making in the real estate domain.