House Price Prediction with Python

Summary

The house price prediction is a machine learning project that aims to provide fast and accurate house price estimation. I used a data set consisting of 81 variables and 1460 observation units from Iowa State to predict the price of a house based on its features.

Project Content

1 - SETTING UP THE ESSENTIALS AND IMPORTING THE DATASET

2 - PREVIEW OF THE DATASET

3 - DATASET PREPARATION

• 3.1 CLASSIFICATION OF THE VARIABLE TYPES(CATEGORICAL AND NUMERICAL)
• 3.2 ADJUSTING MISSING VALUE COLUMNS
• 3.3 CATEGORICAL VARIABLE ANALYSIS
• 3.4 NUMERICAL VARIABLE ANALYSIS
• 3.5 TARGET VALUE ANALYSIS
• 3.6 OUTLIER ANALYSIS
• 3.7 MISSING VALUE ANALYSIS
• 3.8 RARE ENCODING
• 3.9 FEATURE ENGINEERING
• 3.10 LABEL ENCODING - ONE-HOT ENCODING
• 3.11 STANDARD SCALING

4 - MODELING

• 4.1 DIVIDING THE DATA SET(TRAIN - TEST)
• 4.2 INITIAL MODEL ALTERNATIVES
• 4.3 AUTOMATED HYPERPARAMETER OPTIMIZATION
• 4.4 MODEL TUNING AND FINALIZING THE MODEL
• 4.5 STACKING & ENSEMBLE LEARNING

5 - MODEL TESTING

Built With

Built with

• Numpy
• Pandas
• Sci-kit Learn
• Seaborn

Project Presentation

Presentation Link

Screenshots

License

Feedback

If you have any feedback, please reach out to me at [email protected]