dav-pracs 1.0.0

DAV practicals package with all code files and datasets

📊 Data Analytics Practical README (Python + R)

This guide helps you quickly understand:

• How to use R and Python
• How to install libraries
• Which libraries are required
• What to replace in code during exam

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🐍 Python Setup

✅ Install Libraries

Run in terminal:

pip install pandas numpy matplotlib seaborn scikit-learn statsmodels textblob wordcloud

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📦 Python Libraries Used

Purpose	Library
Data handling	pandas
Numerical operations	numpy
Visualization	matplotlib, seaborn
ML models	scikit-learn
Time series	statsmodels
Text analysis	textblob
Word cloud	wordcloud

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📈 Running Python Code

• Use VS Code / Jupyter Notebook
• Run using ▶️ or python file.py

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🟦 R Setup

✅ Install R

Download from: https://cran.r-project.org/

✅ Install Libraries

Run in R console:

install.packages("ggplot2")
install.packages("dplyr")
install.packages("wordcloud")
install.packages("e1071")
install.packages("syuzhet")
install.packages("tm")

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📦 R Libraries Used

Purpose	Library
Visualization	ggplot2
Data manipulation	dplyr
Text mining	tm
Spam filter	e1071
Sentiment	syuzhet
Word cloud	wordcloud

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▶️ Running R Code

Option 1: VS Code

• Install "R" extension
• Press Ctrl + Enter

Option 2: RStudio (Recommended)

• Open RStudio
• Run code directly

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🔁 IMPORTANT: What to Replace in Codes

📊 Dataset Columns

Placeholder	Replace With
data.csv	Your dataset file
df / data	Keep same
x	Independent / input column
y	Dependent / numeric column
value	Main numeric column
date	Date/time column
text	Text column
label	Output column (spam/ham, sentiment)

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🧠 How to Identify Columns

✔ Independent (X)

• Input features
• Example: age, salary, experience

✔ Dependent (y)

• Output to predict
• Example: price, result, spam/ham

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⚡ Quick Tricks During Exam

Python

print(df.columns)

R

colnames(data)

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📌 Topic-wise Replacement Guide

🔹 Regression

• X → input columns
• y → output column

🔹 Time Series

• date → time column
• value → numeric column

🔹 Text Analytics

• text → text column
• label → spam/ham or sentiment

🔹 Visualization

• x → category column
• y → numeric column

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✅ Final Exam Strategy

1. Load dataset
2. Identify columns using head() or columns
3. Replace placeholders
4. Run code

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🚀 Final Tip

👉 Always keep code simple 👉 Don’t overthink dataset 👉 Replace column names correctly 👉 Write clean output

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You are now ready for your practical exam ✅🔥