This repository contains two projects that utilize linear regression to analyze and predict data. The projects are based on two different datasets: Ecommerce Customers and USA_Housing.
This project focuses on a dataset of Ecommerce customer data for a company's online platform and in-store style advice sessions. The goal is to understand customer behavior and to maximize yearly spend per customer.
- Email: Customer's email id
- Address: Customer's home address
- Avatar: Avatar chosen by the customer
- Avg. Session Length: Average session time in minutes
- Time on App: Time spent on the app in minutes
- Time on Website: Time spent on the website in minutes
- Length of Membership: How many years the customer has been a member
The USA_Housing project involves a dataset of housing prices across the United States. The aim is to create a model that can predict housing prices based on several features.
- Avg. Area Income: Average income of residents in the city house is located
- Avg. Area House Age: Average age of houses in same city
- Avg. Area Number of Rooms: Average number of rooms in houses in same city
- Avg. Area Number of Bedrooms: Average number of bedrooms in houses in same city
- Area Population: Population of the city where the house is located
- Price: Price of the house
- Address: Address of the house
- Python
- Pandas
- Numpy
- Matplotlib
- Seaborn
- Scikit-Learn
- Clone the repository
- Install the necessary libraries and tools
- Run the Jupyter notebooks
The results of these projects provide valuable insights into customer behavior and housing prices. The linear regression models built were able to predict with a reasonable level of accuracy.
Please refer to the individual Jupyter notebooks for a detailed analysis and breakdown of the models.
Future work on these projects could involve refining the models with more data, trying different regression models, and tuning the parameters of the current models for better performance.
[CHIRAG AGRAWAL]