Predicting house prices is a common problem in the real estate industry, as the value of a property can have significant financial and investment implications. Machine learning can be used to automate the process of house price prediction, making it faster, more efficient, and less prone to human error. This is an model inspired by the concept of predicting property prices based on the area, bedrooms, bathrooms and location.
THE STEPS INVOLVED:
1)Data loading
We will be using Banglore house price prediction dataset from kaggle
Data shape is 13320 rows and 9 columns.
2)Data Preprocessing
3)EDA [Handling missing values, one hot encoding,outlier detection and removal, Visualization]
4)Modeling TEST TRAIN SPLIT Predictors in X and Response in y dataframe Data split (X_train, X_test, y_train, y_test)
5)MODEL FITTING Applying Linear Regression
6)Grid SearchCV :for finding the best model
TECHNOLOGIES USED:
1.Python
2.Numpy and Pandas for data cleaning
3.Matplotlib for data visualization
4.Sklearn for model building
5.Jupyter notebook