Data Analytics with R Programming

This repository contains implementations of various data analytics algorithms using R programming. The projects in this repository are part of a syllabus on data analytics and demonstrate the application of key machine learning algorithms on real-world datasets. The following algorithms are implemented:

• Decision Tree (using the Titanic dataset)
• Naive Bayes (using the Titanic dataset)
• K-Nearest Neighbors (KNN) (using the Iris dataset)
• K-Means Clustering (using the USArrests dataset)

The datasets used in these projects are:

• Titanic dataset: A dataset that contains information about passengers aboard the Titanic ship, including whether they survived.
• Iris dataset: A classic dataset used in machine learning for classification, containing measurements of different species of iris flowers.
• USArrests dataset: A dataset containing crime statistics for US states, used for clustering analysis.

Project Details

1. Decision Tree using Titanic dataset

• Description: A decision tree algorithm is used to predict the survival of passengers aboard the Titanic.
• Dataset: titanic.csv
• Steps:
  • Preprocess the data.
  • Split the data into training and testing sets.
  • Train a decision tree model using the rpart library.
  • Evaluate the model's performance.
• Algorithm: Decision Tree (using the rpart library in R).

2. Naive Bayes using Titanic dataset

• Description: The Naive Bayes algorithm is used to predict the survival of passengers based on different features (e.g., age, class, etc.).
• Dataset: titanic.csv
• Steps:
  • Preprocess the data.
  • Split the data into training and testing sets.
  • Train a Naive Bayes model using the e1071 library.
  • Evaluate the model's performance.
• Algorithm: Naive Bayes (using the e1071 library in R).

3. K-Nearest Neighbors (KNN) using Iris dataset

• Description: The K-Nearest Neighbors algorithm is used to classify the species of iris flowers based on their measurements.
• Dataset: iris_dataset.csv
• Steps:
  • Load and preprocess the data.
  • Split the data into training and testing sets.
  • Train a KNN model using the class library.
  • Evaluate the model's performance using accuracy for various values of k.
• Algorithm: K-Nearest Neighbors (using the class library in R).

4. K-Means Clustering using USArrests dataset

• Description: K-Means clustering is performed to group US states based on their crime statistics.
• Dataset: USArrests.csv
• Steps:
  • Load and scale the data (excluding the state names).
  • Perform K-Means clustering for different numbers of clusters (2, 3, 4, and 5).
  • Visualize the clustering results using fviz_cluster.
  • Use the silhouette method to determine the optimal number of clusters.
• Algorithm: K-Means Clustering (using the factoextra and ggplot2 libraries in R).

Files in the Repository

• titanic.csv: Titanic dataset used for the Decision Tree and Naive Bayes algorithms.
• iris.csv: Iris dataset used for the KNN algorithm.
• USArrests.csv: USArrests dataset used for the K-Means Clustering algorithm.
• decisionTree.R: R script for implementing the Decision Tree algorithm on the Titanic dataset.
• naivebayes.R: R script for implementing the Naive Bayes algorithm on the Titanic dataset.
• K nearest integer using iris database.R: R script for implementing the KNN algorithm on the Iris dataset.
• K-Cluster.R: R script for implementing the K-Means Clustering algorithm on the USArrests dataset.
• randomForest.R: R script for implementing the Random Forest algorithm on the Iris dataset with hyperparameter tuning.

Installation and Setup

To run the R scripts in this repository, make sure you have R installed on your system. You can download R from here.

Required Libraries:

To install the required libraries for each algorithm, you can use the following commands in R:

install.packages("dplyr")
install.packages("rpart")      
install.packages("e1071")      
install.packages("caTools")   
install.packages("class") 
install.packages("randomForest") 
install.packages("caret") 
install.packages("ggplot2")
install.packages("factoextra")

Running the Scripts:

1. Clone the repository:

   git clone https://github.com/divyakanth07/Data-Analytis-using-R-programming-SRMIST-MCA-I--F-

2. Navigate to the project directory:

   cd Data-Analytis-using-R-programming-SRMIST-MCA-I--F-

3. Run each R script in RStudio or R console:

   source("decisionTree.R")
   source("naivebayes.R")
   source("K nearest integer using iris database.R")
   source("randomForest.R")
   source("K Cluster.R")

Project Summary

This project demonstrates the application of four important machine learning algorithms using the R programming language:

• Decision Tree to predict Titanic survivors.
• Naive Bayes to predict Titanic survivors.
• K-Nearest Neighbors (KNN) to classify iris flower species.
• Random Forest to classify iris flower species with hyperparameter tuning.
• K-Means Clustering is performed to group US states based on their crime statistics.

Each algorithm provides a hands-on introduction to important machine learning concepts and their practical implementation in R.

License

This project is licensed under the MIT License - see the LICENSE file for details.