LEARN COMPLETE PYTHON IN 24 HOURS

11. Real-World Projects & Portfolio Building

These five practical projects combine everything you’ve learned — from data import and manipulation to visualization, statistical analysis, machine learning, time series, and reproducible reporting. They are designed to be portfolio-ready, interview-impressive, and real-world applicable.

11.1 Project 1: Exploratory Analysis & Dashboard (ggplot2 + flexdashboard)

Goal: Perform complete EDA on a dataset and present it as an interactive dashboard.

Tools used: tidyverse, ggplot2, flexdashboard

Steps & Code Structure (save as dashboard.Rmd)

YAML

--- title: "Exploratory Analysis Dashboard – Titanic Dataset" output: flexdashboard::flex_dashboard: orientation: columns vertical_layout: fill runtime: shiny --- ```{r setup, include=FALSE} library(flexdashboard) library(tidyverse) library(ggplot2) library(DT) library(plotly) data("titanic_train") df <- titanic_train

Column {data-width=600}

Data Overview

language-{r}

DT::datatable(df, filter = "top", options = list(pageLength = 10, scrollX = TRUE))

Key Insights

• Total passengers: r nrow(df)

• Survival rate: r round(mean(df$Survived, na.rm = TRUE)*100, 1)%

• Missing Age values: r sum(is.na(df$Age))

Column {data-width=400}

Age Distribution by Survival

language-{r}

ggplot(df, aes(x = Age, fill = factor(Survived))) + geom_histogram(position = "identity", alpha = 0.6, bins = 30) + labs(title = "Age vs Survival", fill = "Survived (1=Yes)") + theme_minimal()

Fare by Class

language-{r}

ggplot(df, aes(x = factor(Pclass), y = Fare, fill = factor(Pclass))) + geom_boxplot(outlier.shape = 21) + labs(title = "Fare Distribution by Passenger Class") + theme_minimal()

Value Boxes

Total Passengers

language-{r}

valueBox(nrow(df), icon = "fa-users", color = "primary")

Survival Rate

language-{r}

valueBox(paste0(round(mean(df$Survived, na.rm = TRUE)*100, 1), "%"), icon = "fa-heartbeat", color = "success")

Average Fare

language-{r}

valueBox(paste0("₹", round(mean(df$Fare, na.rm = TRUE), 1)), icon = "fa-money-bill-wave", color = "warning")

text

How to run: Knit → Save as HTML → Open in browser (interactive) Key Takeaways: flexdashboard is perfect for quick, interactive EDA reports. #### 11.2 Project 2: Customer Churn Prediction (Classification) Goal: Predict which customers will churn using classification models. Dataset: Telco Customer Churn (Kaggle) Steps & Code ```r library(tidyverse) library(tidymodels) library(themis) # for SMOTE # 1. Load & clean df <- read_csv("telco_churn.csv") %>% janitor::clean_names() %>% mutate(churn = factor(churn, levels = c("No", "Yes"))) %>% select(-customer_id) # 2. Split & recipe set.seed(42) split <- initial_split(df, prop = 0.8, strata = churn) train <- training(split) test <- testing(split) rec <- recipe(churn ~ ., data = train) %>% step_impute_median(all_numeric_predictors()) %>% step_dummy(all_nominal_predictors(), -all_outcomes()) %>% step_smote(churn) %>% # handle imbalance step_normalize(all_numeric_predictors()) # 3. Model spec rf_spec <- rand_forest(trees = 500) %>% set_mode("classification") %>% set_engine("ranger") # 4. Workflow & fit wf <- workflow() %>% add_recipe(rec) %>% add_model(rf_spec) fit <- wf %>% fit(data = train) # 5. Evaluate predictions <- predict(fit, test) %>% pull(.pred_class) prob <- predict(fit, test, type = "prob")$.pred_Yes print(conf_mat(test$churn, predictions)) print(roc_auc_vec(test$churn, prob))

Key Takeaways: Use themis::step_smote() for imbalance. Focus on Recall & ROC-AUC for churn problems.

11.3 Project 3: Sales Forecasting (Time Series)

Goal: Forecast monthly sales using Prophet and ARIMA.

Code

R

library(prophet) library(forecast) library(tidyverse) # Assume monthly_sales.csv has columns: date (YYYY-MM-01), sales df <- read_csv("monthly_sales.csv") %>% mutate(ds = as.Date(date), y = sales) # Prophet m <- prophet(df, yearly.seasonality = TRUE) future <- make_future_dataframe(m, periods = 12, freq = "month") fc_prophet <- predict(m, future) plot(m, fc_prophet) # ARIMA ts_data <- ts(df$y, frequency = 12, start = c(2020, 1)) fit_arima <- auto.arima(ts_data) fc_arima <- forecast(fit_arima, h = 12) plot(fc_arima)

Key Takeaways: Prophet is easier for business users; ARIMA gives more statistical control.

11.4 Project 4: Sentiment Analysis on Reviews

Goal: Classify product reviews as positive/negative.

Code (using tidytext)

R

library(tidytext) library(textdata) reviews <- read_csv("amazon_reviews.csv") # Tokenize & sentiment review_words <- reviews %>% unnest_tokens(word, review_text) %>% inner_join(get_sentiments("bing")) sentiment_summary <- review_words %>% count(word, sentiment) %>% pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) %>% mutate(score = positive - negative) # Visualize top words sentiment_summary %>% arrange(desc(score)) %>% slice_head(n = 20) %>% ggplot(aes(reorder(word, score), score, fill = score > 0)) + geom_col() + coord_flip() + labs(title = "Top Sentiment Words in Reviews")

Key Takeaways: tidytext + Bing lexicon = simple & effective baseline.

11.5 Creating a Professional Portfolio (GitHub + RPubs)

Portfolio Structure (2026 standard)

• GitHub repo for each project

• README.md with:

  • Project goal

  • Dataset description

  • Key findings & visuals

  • Code walkthrough

  • Live link (RPubs, ShinyApps.io, Quarto Pub)

• RPubs / Quarto Pub for rendered reports/dashboards

• Personal portfolio website (Quarto website or GitHub Pages)

Best Practices

• Use meaningful repo names (e.g., customer-churn-prediction-r)

• Add screenshots & GIFs in README

• Include requirements.txt equivalent → sessionInfo() or renv.lock

• Pin top 6 projects on GitHub profile

• Add badges: R version, license, stars

Final Advice Publish 4–6 high-quality projects. Write blogs explaining your thought process. Share on LinkedIn, Kaggle, RStudio Community, Reddit (r/rstats, r/datascience). You now have a strong R portfolio!

This completes the full Real-World Projects & Portfolio Building section — and the entire R Programming Mastery tutorial!

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