An end to end regression problem done on a real world data. The data is downloaded from Kaggle. The implementation is done step by step in the jupyter-notebook.

• Clone the repo using git clone https://github.com/mohdsaqibhbi/End_to_End_Regression.git.
• Go to this directory using cd End_to_End_Regression
• Create virtual environment.
• Install dependencies using pip install -r requirements.txt.
• Run everything step by step.

• Exploratory Data Analysis (EDA).
• Different kind of plots using matplotlib and seaborn.
• Data preprocessing pipline.
• Preparation training and validation data.
• Training model with Linear Regression, Decision Tree and Random Forest.
• Evaluating model.
• Hypertuning model.
• Grid Search.
• Feature importance.

Note: This is not a tutorial.