Reach Top 10% on leaderboard with this notebook at kaggle competition here
If you wanna work on this Notebook then Fork my Notebook on Kaggle here. Do appreciate it, if it helped you learn. This will motivate me to make more kernals.
The goal is to predict the final price of each home with given many features of residential houses in Ames and Iowa.
- Download this repository in a zip file by clicking on this link or execute this from the terminal:
git clone https://github.com/vjgpt/Housing-Prices-Competition-Kaggle.git
- Download Jupyter Notebook
- Open the housing.ipynb file using Jupyter and start playing.
- NumPy
- IPython
- Pandas
- SciKit-Learn
- Matplotlib
- Seaborn
- SciPy
Data Handling
- Importing packages
- Loading and Inspecting data
Data Analysis
- Dealing with outliers
- Correlation Analysis
- Target Variable Transform
Feature Engineering
- Find Missing Data
- Handle Missing Data
- Label Encoding
- Box Cox Transformation
Modelling
- Lasso Regression
- Ridge Regression
- ElasticNet Regression
- Gradient Boosting
- Cross Validation
- Mean of all Model Prediction
With this competition, you will get familiar with Feature enginnering and Advanced Regression techniques like Gradient Boosting, Lasso,Ridge,etc.
Support me for more contents to push.