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Exploratory Data Analysis and Linear Regression Analysis

Overview

This repository contains a Jupyter notebook where Exploratory Data Analysis (EDA) and Linear Regression analysis have been performed on a dataset. The primary goal of this analysis is to gain insights into the data and understand the relationships between variables using linear regression.

Getting Started

Prerequisites

Before running the Jupyter notebook, make sure you have the following dependencies installed:

  • Jupyter Notebook
  • Python 3.11
  • Required Python packages: pandas, numpy, matplotlib, seaborn, scikit-learn

You can install the required packages using the following command:

pip install pandas numpy matplotlib seaborn scikit-learn

Installation

  1. Clone the repository:
git clone https://github.com/analondhe/linear-regression-ecomm-dataset.git
cd linear-regression-ecomm-dataset
  1. Run Jupyter Notebook:
jupyter notebook
  1. Open the notebook in your browser and navigate to the analysis.ipynb file.

Notebook Contents

  1. Introduction:

    • Brief overview of the dataset and the problem statement.

  2. Exploratory Data Analysis (EDA):

    • Data loading and cleaning
    • Visualization of key features using plots and graphs.

  3. Linear Regression Analysis:

    • Data preprocessing.
    • Splitting the dataset into training and testing sets.
    • Building a linear regression model.
    • Model evaluation using metrics such as Mean Squared Error, R-squared, etc.
    • Visualizing the regression line.

Contact

Feel free to reach out for any questions or collaborations!

Happy analyzing!