Developed a Machine Learning model to predict the house prices on Bangalore Housing Prices Dataset.
- Used Pandas and Numpy to clean the data and handle missing values.
- Visualized the important features that impacted the price using matplotlib and used mean and one standard deviation to remove outliers in order to make the model efficient.
- Used methods like Dimensionality reduction to preprocess the data before one hot encoding.
- Used sklearn to split the data into train and test. Built a model Using Linear Regression.
- To test the model’s accuracy, used K Fold cross Validation.
- In order to test other algorithms to reach a better efficiency, used GridSearchCV and tested the model against, Lasso regression and Decision Tree Regressor.
- Exported the model to a pickle file using the pickle library.