This python file uses housing price prediction example to explain various concepts of Machine Learning. It is an end to end machine learning project, which includes following steps:
- Getting Data
- Data Cleaning
- Splitting data into test set and training set 3.1 Random sampling vs Stratified sampling
- Data Visualization 4.1 Understanding Data: Looking for correlation 4.2 Experimenting with Attribute Combinations
- Handling Text and Categorical Attributes 5.1 Encoding and their types
- Custom Transformers
- Feature Scaling
- Train and compare models 8.1 Linear regression 8.2 Decision Tree Regressor 8.3 Random Forest
- Cross validation and fine-Tuning
- Evaluate our System on the Test Set
Some images from project:
For Dependencies/Environment information ==> requirement.txt (pip -r requirement.txt)
To setup virtual environment:
python -m venv env
source env\bin\activate
pip install -r requirement.txt
Source: Book-Handson machine learning with scikit learn and tensorflow (Chapter 2) (Must Read)