HOUSE PRICE PREDICTION

Issue Identification: Develop a predictive model for house prices based on detailed house information.

Data Acquisition: Gather and import data into a Jupyter notebook as a CSV file using the Pandas library in Python. Thoroughly explore the dataset, identifying and addressing issues such as null values, irrelevant features, duplicate entries, and different feature types.

DATA CLEANSING: Manage and eliminate null and duplicate values. Detect and handle outliers, particularly in the number of bedrooms (BHK) and price per square foot, using Matplotlib charts and Python code.

EXPLORATORY DATA ANALYSIS: Enhance feature interpretability for machine comprehension through data mining. Convert certain string values to floats, separate numeric and string components, and create new columns as needed.

MODEL DEVELOPMENT: Implement a train-test split for dependent and independent variables. Construct and train a linear regression model on the training data and predict values on the test set, achieving an 84% accuracy rate.

DATA VISUALIZATION: Export the cleaned data from the Jupyter Notebook and import it into Tableau. Develop a user-friendly and visually informative dashboard for stakeholders by applying visualization techniques and filtering methods.