This projects helps predicting the residential house prices in Ames (Iowa, US) provided the features mentioned in data_description.txt file along with their descriptions.
- The original dataset (train.csv and test.csv) are in data folder.
- Missing_values.ipynb file gives the walkthrough over the treatment of missing of missing values and then the preprocessed train and test datasets are stored in data folder under names train_processed.csv and test_processed.csv, respectively.
- EDA_and_Preprocessing.ipynb file gives the walkthrough over the Exlporatory Data Analysis and preprocessing of the data and then the preprocessed train and test datasets are stored in data folder under names train_final.csv and test_final.csv, respectively.
- model.ipynb file gives the walkthrough over the treatment of missing of missing values and predictions made on test dataset is stored in submission.csv file in data folder.
- Plots_model_before_hyperparameter_tuning folder contains all the plots of various model perfomances before hyperparameter tuning.
- Plots_model_after_hyperparameter_tuning folder contains all the plots of various model perfomances after hyperparameter tuning.
- Plots folder contains various plots like heatmaps and pairplots of all numerical features along with decision tree plot representing decision tree formed while using decision tree regressor.
This dataset is from Kaggle competetion and hence credit for the dataset used for training goes to https://www.kaggle.com/mohansacharya/graduate-admissions