A model for predicting real estate prices
The following operations were performed:
- Create a Jupyter notebook file to carry out all the further actions.
- Open train.csv file as pandas Dataframe.
- Choose a metric to evaluate the model and justify your choice.
- Transform data to the input format of the model.
- Train the model, evaluate its work.
- Using the model, make predictions from data with test.csv. Save the result to a prediction.csv file with two columns: Id and SalePrice.
- Conduct basic EDA data. Describe the main features that you should pay attention to
- Conduct feature selection.
- python - version 3.7
Copyright [2021] [Vladyslav Petrenko]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.