CMT 429: Introduction to Data Science

Group Assignment

Welcome to the CMT 429 Group Assignment repository! This project aims to explore fundamental concepts in Data Science and apply them to real-world problems.

Table of Contents

• Project Overview
• Objectives
• Technologies Used
• Installation Instructions
• Data Analysis
• Visualizations
• Contributors
• License

Project Overview

This project involves analyzing datasets to extract meaningful insights and build predictive models. We will cover various aspects of Data Science, including data cleaning, visualization, and machine learning.

Objectives

• Understand the data science workflow.
• Gain hands-on experience with data analysis tools.
• Develop skills in statistical analysis and machine learning.

Technologies Used

• Programming Language: R
• Libraries:
  • tidyverse for data manipulation and visualization
  • ggplot2 for creating visualizations
  • randomForest for machine learning models
  • corrplot for correlation analysis

Installation Instructions

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/ismailanyi/Assignment.git

2. Navigate to the project directory:

   cd Assignment

3. Install the required packages:

   install.packages(c("tidyverse", "randomForest", "corrplot", "dplyr"))

Data Analysis

The project includes various scripts for data analysis, including:

• Data Loading: Loading datasets and performing initial checks.
• Data Cleaning: Handling missing values and duplicates.
• Correlation Analysis: Analyzing relationships between variables.

Visualizations

Visualizations are created using ggplot2 to illustrate key findings, including:

• Average sales volume by region.
• Trends in used BMW prices across regions.
• Correlation heatmaps.

Contributors

• Mailanyi Ismail - 1049453
• Renish Amondi - 1049526
• Victor Ochieng -1049246
• Maxwell Muguna -1049417
• Reagan Machuki - 1049420
• Enock Ikhavi - 1049071

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