📘 DSBDA — Data Science & Big Data Analytics

Complete practical programs for DSBDA (Data Science & Big Data Analytics) course.
Includes Python notebooks (Practical 1–10) with datasets, Hadoop MapReduce (Java), and Apache Spark (Scala).

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📂 Repository Structure

DSBDA/
│
├── pra1/
│   ├── pr 1.ipynb              ← Practical 1 Notebook
│   └── titanic.csv             ← Dataset
│
├── pra 2/
│   ├── pr 2.ipynb              ← Practical 2 Notebook
│   └── academic_performance.csv← Dataset
│
├── pra 3/
│   ├── pr 3.ipynb              ← Practical 3 Notebook
│   ├── loan_data_set.csv       ← Dataset
│   └── iris.csv                ← Dataset
│
├── pra 4/
│   ├── pr 4.ipynb              ← Practical 4 Notebook
│   └── BostonHousing.csv       ← Dataset
│
├── pra 5/
│   ├── pr 5.ipynb              ← Practical 5 Notebook
│   └── Social_Network_Ads.csv  ← Dataset
│
├── pra 6/
│   ├── pr 6.ipynb              ← Practical 6 Notebook
│   └── iris.csv                ← Dataset
│
├── pra 7/
│   └── pr 7.ipynb              ← Practical 7 Notebook (no dataset needed)
│
├── pra 8/
│   ├── pr 8.ipynb              ← Practical 8 Notebook
│   └── titanic.csv             ← Dataset
│
├── pra 9/
│   ├── pr 9.ipynb              ← Practical 9 Notebook
│   └── titanic.csv             ← Dataset
│
├── pra 10/
│   ├── pr 10.ipynb             ← Practical 10 Notebook
│   └── iris.csv                ← Dataset
│
├── hadoop/
│   ├── WordCount.java          ← Hadoop MapReduce Word Count
│   └── LogProcessor.java       ← Hadoop MapReduce Log Processor
│
├── scala/
│   └── SparkWordCount.scala    ← Apache Spark Word Count (Scala)
│
└── README.md

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📝 Practical List

#	Topic	Dataset	Folder
1	Data Wrangling — Import, missing values, outliers, normalization	titanic.csv	pra1/
2	Data Wrangling — Variable transformation, type conversion, z-score	academic_performance.csv	pra 2/
3	Descriptive Statistics — Summary stats, grouping, mean/median/mode	loan_data_set.csv, iris.csv	pra 3/
4	Linear Regression — Regression model, MSE, R² score	BostonHousing.csv	pra 4/
5	Logistic Regression — Confusion matrix, accuracy, precision, recall	Social_Network_Ads.csv	pra 5/
6	Naive Bayes Classifier — Classification report, accuracy metrics	iris.csv	pra 6/
7	Text Analytics & NLP — Tokenization, stemming, lemmatization, TF-IDF	No dataset (inline text)	pra 7/
8	Data Visualization I — Survival plots, histograms, heatmaps	titanic.csv	pra 8/
9	Data Visualization II — Box plots, scatter plots, survival analysis	titanic.csv	pra 9/
10	Data Visualization III — Species distribution, pairplots	iris.csv	pra 10/
11	Hadoop WordCount — Count word occurrences using MapReduce (Java)	Text input (create in terminal)	hadoop/
12	Hadoop Log Processor — Count log levels using MapReduce (Java)	Log input (create in terminal)	hadoop/
13	Spark WordCount — Count words using Apache Spark (Scala)	Text input (create in terminal)	scala/

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⚡ How to Run

Python Notebooks (Practical 1–10)

1. Open the .ipynb file in Jupyter Notebook or Google Colab
2. The dataset is already in the same folder — just run all cells
3. No extra setup needed

Hadoop Programs (Practical 11–12)

1. Open WordCount.java or LogProcessor.java
2. Scroll to the bottom — terminal commands are in the comments
3. Follow the steps one by one in your Ubuntu terminal
4. Commands start from hadoop version (Hadoop must be pre-installed)

Spark Program (Practical 13)

1. Open SparkWordCount.scala
2. Scroll to the bottom — install + run commands are in the comments
3. Follow the steps to install Spark and run the program

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🛠 Technologies Used

Technology	Practicals
Python, Pandas, NumPy	1–10
Scikit-learn	4, 5, 6
Matplotlib, Seaborn	2, 4, 8, 9, 10
NLTK	7
Java + Hadoop MapReduce	11, 12
Scala + Apache Spark	13

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📄 License

Free to use for educational purposes.