This repository contains a Jupyter notebook where comprehensive machine learning approach is taken to predict the price of a house.

This project is a comprehensive study of a common problem in Machine Learning: regression analysis. The aim is to provide a step-by-step guide to understanding, implementing, and refining a regression model using a real-world dataset. The dataset selected for this exercise contains information pertaining to various aspects of houses, along with their respective prices, making it an ideal example to illustrate a regression problem.

The dataset encompasses a variety of feature types, both numerical and categorical, which are expected to influence a house's price. Numerical features include quantifiable characteristics like the area of the house, the number of rooms, age of the house, etc. On the other hand, categorical features comprise of qualitative characteristics such as neighborhood, house style, condition, and more.

• Initial Data Exploration: Familiarization with the dataset and its features.

• Exploratory Data Analysis (EDA): Detailed analysis and visualization of the data to understand patterns and relationships.

• Data Cleaning: Handling missing values and outliers.

• Categorical Variables Encoding: Using different encoding techniques as per the data requirements.

• Feature Selection: Identifying and selecting the most significant features.

• Model Training: Training various machine learning models including Logistic Regression, XGBoost Regressor, Random Forest Regressor, and CatBoost Regressor.

• Hyperparameter Tuning: Tuning parameters for the machine learning models to improve performance.

• Performance Evaluation: Evaluating the performance of all the models.

The project is written in Python and housed in a Jupyter notebook. To run the notebook, you need Jupyter Notebook installed, and you need to install the necessary Python libraries such as pandas, numpy, matplotlib, seaborn, sklearn, and catboost.

Once the environment is set up, clone this repository, navigate to the local directory where the repository is cloned, and run the cells in the notebook (.ipynd file).