Introduction

The Capstone Project is designed to consolidate and apply the knowledge and skills you have acquired throughout the R Programming course. This project will involve a comprehensive data analysis task, where you will be required to:

Import and clean data
Perform exploratory data analysis (EDA)
Visualize data
Conduct statistical analysis
Build and evaluate a machine learning model
Present your findings

Project Overview

Objective

The objective of this project is to analyze a real-world dataset and derive meaningful insights. You will be expected to:

Identify and define the problem
Collect and preprocess the data
Perform exploratory data analysis
Visualize the data using various techniques
Apply statistical methods to test hypotheses
Build and evaluate predictive models
Summarize and present your findings

Dataset

You can choose a dataset from the following sources or any other dataset of your interest:

Ensure that the dataset you choose is rich enough to allow for comprehensive analysis and modeling.

Project Steps

Step 1: Define the Problem

Identify the problem: Clearly state the problem you aim to solve or the question you want to answer with your analysis.
Set objectives: Define the goals of your analysis and what you hope to achieve.

Step 2: Data Collection and Preprocessing

Import data: Use R to import your dataset.
Clean data: Handle missing values, outliers, and any inconsistencies in the data.
Transform data: Convert data types, create new variables, and normalize/standardize data if necessary.

# Example: Importing and cleaning data
library(readr)
data <- read_csv("path/to/your/dataset.csv")

# Handling missing values
data <- na.omit(data)

# Transforming data
data$NewVariable <- data$ExistingVariable * 2

Step 3: Exploratory Data Analysis (EDA)

Summary statistics: Calculate mean, median, standard deviation, etc.
Data visualization: Use histograms, boxplots, scatter plots, etc., to understand the data distribution and relationships.

# Example: Summary statistics and visualization
summary(data)
hist(data$Variable)
boxplot(data$Variable ~ data$Category)

Step 4: Data Visualization

Visualize key insights: Use ggplot2 or plotly to create informative and aesthetically pleasing visualizations.

# Example: Data visualization with ggplot2
library(ggplot2)
ggplot(data, aes(x=Variable1, y=Variable2)) +
  geom_point() +
  theme_minimal()

Step 5: Statistical Analysis

Hypothesis testing: Conduct t-tests, chi-square tests, ANOVA, etc., to test your hypotheses.
Correlation and regression: Analyze relationships between variables.

# Example: Hypothesis testing
t.test(data$Variable1, data$Variable2)

# Example: Correlation and regression
cor(data$Variable1, data$Variable2)
model <- lm(Variable2 ~ Variable1, data=data)
summary(model)

Step 6: Machine Learning

Data preprocessing: Split data into training and testing sets.
Model building: Build and train machine learning models (e.g., linear regression, decision trees, random forests).
Model evaluation: Evaluate model performance using metrics like accuracy, precision, recall, F1-score, etc.

# Example: Building and evaluating a machine learning model
library(caret)
set.seed(123)
trainIndex <- createDataPartition(data$Target, p = .8, 
                                  list = FALSE, 
                                  times = 1)
trainData <- data[ trainIndex,]
testData  <- data[-trainIndex,]

model <- train(Target ~ ., data = trainData, method = "rf")
predictions <- predict(model, testData)
confusionMatrix(predictions, testData$Target)

Step 7: Presentation of Findings

Summarize results: Provide a clear and concise summary of your findings.
Visualize results: Use charts and graphs to support your conclusions.
Report: Prepare a detailed report or presentation that includes your methodology, analysis, results, and conclusions.

Conclusion

The Capstone Project is an opportunity to demonstrate your proficiency in R programming and data analysis. By following the steps outlined above, you will be able to showcase your ability to handle real-world data, perform comprehensive analysis, and derive meaningful insights. Good luck!

Capstone Project

Introduction

Project Overview

Objective

Dataset

Project Steps

Step 1: Define the Problem

Step 2: Data Collection and Preprocessing

Step 3: Exploratory Data Analysis (EDA)

Step 4: Data Visualization

Step 5: Statistical Analysis

Step 6: Machine Learning

Step 7: Presentation of Findings

Conclusion

R Programming: From Beginner to Advanced

Module 1: Introduction to R

Module 2: Data Manipulation

Module 3: Data Visualization

Module 4: Statistical Analysis

Module 5: Advanced Data Handling

Module 6: Advanced Programming Concepts

Module 7: Machine Learning with R

Module 8: Specialized Topics

Module 9: Project and Case Studies