📊 Data Analysis & Visualization Lab

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📊 Data Analysis & Visualization Lab

🏫 Chennai Institute of Technology — Semester 5

A comprehensive collection of hands-on laboratory experiments exploring data analysis,
manipulation, and visualization using Python's scientific computing ecosystem.

🗂️ Repository Structure

DAV_LAB/
│
├── 📁 exp1/
│   ├── 📓 exp1.ipynb        ← Jupyter Notebook
│   └── 🐍 exp1.py           ← Python Script
│
├── 📁 exp2/
│   ├── 📓 exp2.ipynb        ← Jupyter Notebook (with outputs)
│   └── 🐍 exp2.py           ← Python Script
│
├── 📁 exp3/
│   ├── 📊 data.csv          ← Sample Dataset
│   ├── 📓 exp3.ipynb        ← Jupyter Notebook (with outputs)
│   └── 🐍 exp3.py           ← Python Script
│
├── 📁 exp4/
│   ├── 📊 Google_data (2b.c1).csv  ← Text/CSV Dataset
│   ├── 📊 data (2c2).xlsx          ← Excel Spreadsheet Dataset
│   ├── 📓 exp4.ipynb               ← Jupyter Notebook (with outputs)
│   ├── 🐍 exp4.py                  ← Python Script
│   ├── 📄 processed_text.csv       ← Exported Processed CSV
│   └── 📄 processed_excel.xlsx     ← Exported Processed Excel
│
├── 📁 exp5/
│   ├── 📊 iris_dataset(2d).csv     ← Iris CSV Dataset
│   ├── 📓 exp5.ipynb               ← Jupyter Notebook (with outputs)
│   ├── 🐍 exp5.py                  ← Python Script
│   ├── 🖼️ histograms.png            ← Feature Distributions Plot
│   ├── 🖼️ sepal_length_boxplot.png  ← Sepal Length Boxplot
│   └── 🖼️ pairplot.png              ← Feature Pairwise Scatter/KDE Plot
│
├── 📁 exp6/
│   ├── 📊 uci_diabetes.csv         ← UCI Diabetes Dataset
│   ├── 📊 pima_diabetes.csv        ← Pima Indians Diabetes Dataset
│   ├── 📓 exp6.ipynb               ← Jupyter Notebook (with outputs)
│   └── 🐍 exp6.py                  ← Python Script
│
├── 📁 exp7/
│   ├── 📊 uci_diabetes.csv         ← UCI Diabetes Dataset
│   ├── 📊 pima_diabetes.csv        ← Pima Indians Diabetes Dataset
│   ├── 📓 exp7.ipynb               ← Jupyter Notebook (with outputs)
│   ├── 🐍 exp7.py                  ← Python Script
│   ├── 🖼️ uci_linear_regression.png ← UCI Linear Regression Scatter & Line Plot
│   └── 🖼️ pima_linear_regression.png ← Pima Linear Regression Scatter & Line Plot
│
└── 📄 README.md

🧪 Experiments at a Glance

#	Experiment	Description	Key Libraries
1	Environment Setup	Verify installation & versions of essential data science packages	`numpy` `pandas` `matplotlib` `jupyter`
2	NumPy Fundamentals	Core array operations — creation, indexing, slicing, math, reshaping	`numpy`
3	Pandas Data Analysis	DataFrame manipulation — loading, cleaning, filtering, grouping, exporting	`pandas`
4	Data Input/Output Operations	Reading data from CSV, Excel, and Web; missing value treatment; exporting	`pandas` `openpyxl`
5	Descriptive Analytics (Iris)	Exploring statistics, distributions, boxplots, and pairplots on Iris dataset	`pandas` `seaborn` `matplotlib`
6	Univariate Statistical Analysis	Calculating Mean, Median, Mode, Variance, Std, Skewness, Kurtosis on Diabetes datasets	`pandas` `numpy` `scipy`
7	Bivariate Analysis (Linear & Logistic Regression)	Linear Regression (Glucose vs BMI) & Logistic Regression (Predicting Diabetes)	`pandas` `numpy` `scikit-learn` `matplotlib`

📝 Detailed Experiment Breakdown

🔬 Experiment 1 — Environment Setup & Package Verification

📌 Objective

Verify the installation and versions of all essential data science libraries required for the lab.

📦 Packages Checked

Package	Status	Version
NumPy	✅ Installed	`2.2.6`
Pandas	✅ Installed	`2.3.3`
Matplotlib	✅ Installed	`3.10.3`
JupyterLab	✅ Installed	`4.5.1`
Seaborn	❌ Not Installed	—
SciPy	❌ Not Installed	—
Plotly	❌ Not Installed	—
Bokeh	❌ Not Installed	—
Statsmodels	❌ Not Installed	—

📂 Files

exp1/exp1.ipynb — Jupyter Notebook
exp1/exp1.py — Python Script

🔬 Experiment 2 — Fundamentals of NumPy

📌 Objective

Learn and demonstrate core NumPy operations for numerical computing.

🧩 Topics Covered

Section	Topic	Key Functions
1	Version Verification	`np.__version__`
2	Array Creation	`np.array()`, `np.ones()`
3	Indexing & Slicing	`arr[i]`, `arr[start:end]`, `arr[row, col]`
4	Element-wise Operations	`+`, `-`, `*`, `/`, scalar math
5	Statistical Aggregations	`np.sum()`, `np.mean()`, `np.std()`
6	Comparison & Masking	`>`, boolean indexing, fancy indexing
7	Reshaping & Structured Arrays	`.reshape()`, structured `dtype`

💡 Sample Output

>>> arr_a = np.array([10, 20, 30])
>>> arr_b = np.array([1, 2, 3])
>>> print("Addition:", arr_a + arr_b)
Addition: [11 22 33]

📂 Files

exp2/exp2.ipynb — Jupyter Notebook (with cell outputs)
exp2/exp2.py — Python Script

🔬 Experiment 3 — Data Analysis & Manipulation using Pandas

📌 Objective

Perform real-world data analysis workflows using Pandas DataFrames.

🧩 Topics Covered

Section	Topic	Key Functions
1	Load & Preview	`pd.read_csv()`, `.head()`, `.tail()`
2	Inspection	`.info()`, `.describe()`
3	Missing Values & Column Ops	`.fillna()`, column arithmetic
4	Filtering & Groupby	Boolean conditions, `.groupby().mean()`
5	Sorting & Boolean Masking	`.sort_values()`, `.median()` masking
6	Export & Aggregations	`.to_csv()`, `.sum()`, `.mean()`, `.std()`

💡 Sample Output

>>> grouped = df.groupby('category_column')['numeric_column'].mean()
>>> print(grouped)
category_column
A    180.0
B    237.5
Name: numeric_column, dtype: float64

📂 Files

exp3/exp3.ipynb — Jupyter Notebook (with cell outputs)
exp3/exp3.py — Python Script
exp3/data.csv — Sample Dataset

🔬 Experiment 4 — Reading Data from Text Files, Excel, and the Web

📌 Objective

Read and process data from various sources, including CSV text files, Excel spreadsheets, and web-based URLs using Pandas.

🧩 Topics Covered

Section	Topic	Key Functions
1	Read CSV / Text Data	`pd.read_csv('Google_data (2b.c1).csv')`
2	Read Excel Data	`pd.read_excel('data (2c2).xlsx', sheet_name='Sheet1')`
3	Read Web-Based Data	`pd.read_csv('https://raw.githubusercontent.com/...')`
4	Preview Datasets	`df.head()`
5	Handle Missing Values	`.ffill()`, `.bfill()`, `.dropna()`
6	Export Processed Data	`.to_csv('processed_text.csv')`, `.to_excel('processed_excel.xlsx')`

💡 Sample Code & Output

import pandas as pd

text_df = pd.read_csv('Google_data (2b.c1).csv')
excel_df = pd.read_excel('data (2c2).xlsx', sheet_name='Sheet1')
web_df = pd.read_csv('https://raw.githubusercontent.com/cs109/2014_data/master/countries.csv')

print(text_df.head(), "\n", excel_df.head(), "\n", web_df.head())

📂 Files

exp4/exp4.ipynb — Jupyter Notebook (with cell outputs)
exp4/exp4.py — Python Script
exp4/Google_data (2b.c1).csv — CSV Dataset
exp4/data (2c2).xlsx — Excel Dataset
exp4/processed_text.csv — Exported CSV Data
exp4/processed_excel.xlsx — Exported Excel Data

🔬 Experiment 5 — Exploring Descriptive Analytics Using the Iris Dataset

📌 Objective

Perform descriptive analytics, summary statistics, univariate, and bivariate visualizations on the Iris dataset using Pandas, Seaborn, and Matplotlib.

🧩 Topics Covered

Section	Topic	Key Functions
1	Dataset Load & Preview	`pd.read_csv('iris_dataset(2d).csv')`
2	Basic Info & Statistics	`df.info()`, `df.describe()`
3	Univariate Analysis	`df['species'].value_counts()`
4	Distribution Plots	`df.hist(figsize=(8, 6), edgecolor='black')`
5	Boxplot Analysis	`sns.boxplot(data=df, x='species', y='sepal length (cm)')`
6	Pair Plot Visualizations	`sns.pairplot(df, hue='species')`

💡 Sample Code & Output

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv('iris_dataset(2d).csv')
print(df.info())
print(df.describe())

sns.boxplot(data=df, x='species', y='sepal length (cm)')
sns.pairplot(df, hue='species')

📂 Files

exp5/exp5.ipynb — Jupyter Notebook (with cell outputs)
exp5/exp5.py — Python Script
exp5/iris_dataset(2d).csv — Iris Dataset
exp5/histograms.png — Feature Distribution Plot
exp5/sepal_length_boxplot.png — Boxplot
exp5/pairplot.png — Pairwise Plot

🔬 Experiment 6 — Statistical Analysis Using Diabetes Datasets (Univariate Analysis)

📌 Objective

Perform univariate statistical analysis on the UCI Diabetes and Pima Indians Diabetes datasets to compute central tendency, dispersion, skewness, and kurtosis.

🧩 Topics Covered

Section	Topic	Key Functions / Metrics
1	Import Datasets	`pd.read_csv('uci_diabetes.csv')`, `pd.read_csv('pima_diabetes.csv')`
2	Central Tendency	`np.mean()`, `np.median()`, `df[col].mode()[0]`
3	Dispersion	`np.var(ddof=1)`, `np.std(ddof=1)`
4	Shape & Tail Metrics	`scipy.stats.skew()`, `scipy.stats.kurtosis()`
5	Automated Analysis Pipeline	Custom function `univariate_analysis(df, columns)`

💡 Sample Code & Output

import pandas as pd
import numpy as np
from scipy.stats import skew, kurtosis

def univariate_analysis(df, columns):
    stats = {}
    for col in columns:
        stats[col] = {
            "Mean": np.mean(df[col]),
            "Median": np.median(df[col]),
            "Mode": df[col].mode()[0],
            "Variance": np.var(df[col], ddof=1),
            "Standard Deviation": np.std(df[col], ddof=1),
            "Skewness": skew(df[col]),
            "Kurtosis": kurtosis(df[col])
        }
    return pd.DataFrame(stats).T

📂 Files

exp6/exp6.ipynb — Jupyter Notebook (with cell outputs)
exp6/exp6.py — Python Script
exp6/uci_diabetes.csv — UCI Diabetes Dataset
exp6/pima_diabetes.csv — Pima Indians Diabetes Dataset

🔬 Experiment 7 — Bivariate Analysis: Linear and Logistic Regression Modeling

📌 Objective

Perform bivariate analysis on the UCI Diabetes Dataset and Pima Indians Diabetes Dataset using Linear Regression (continuous vs. continuous) and Logistic Regression (binary classification of diabetes presence).

🧩 Topics Covered

Section	Topic	Key Functions / Metrics
1	Load Datasets	`pd.read_csv('uci_diabetes.csv')`, `pd.read_csv('pima_diabetes.csv')`
2	Linear Regression	`LinearRegression()`, `.fit()`, `.predict()`, `r2_score()`
3	Regression Visualizations	`plt.scatter()`, `plt.plot()`, regression line plotting
4	Logistic Regression	`train_test_split()`, `LogisticRegression()`, `accuracy_score()`
5	Performance Comparison	Evaluating model performance across dataset variations

💡 Sample Code & Output

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.metrics import r2_score, accuracy_score

# 1. Linear Regression (Glucose vs. BMI)
model_lin = LinearRegression().fit(df[['Glucose']], df['BMI'])
y_pred = model_lin.predict(df[['Glucose']])
print("R2 Score:", r2_score(df['BMI'], y_pred))

# 2. Logistic Regression (Outcome prediction)
X_train, X_test, y_train, y_test = train_test_split(df[['Glucose', 'BloodPressure', 'BMI', 'Age']], df['Outcome'], test_size=0.2, random_state=42)
model_log = LogisticRegression().fit(X_train, y_train)
print("Accuracy Score:", accuracy_score(y_test, model_log.predict(X_test)))

📂 Files

exp7/exp7.ipynb — Jupyter Notebook (with cell outputs & plots)
exp7/exp7.py — Python Script
exp7/uci_diabetes.csv — UCI Diabetes Dataset
exp7/pima_diabetes.csv — Pima Indians Diabetes Dataset
exp7/uci_linear_regression.png — UCI Scatter Plot & Linear Fit
exp7/pima_linear_regression.png — Pima Scatter Plot & Linear Fit

🚀 Getting Started

Prerequisites

# Ensure Python 3.x is installed
python --version

# Install required packages
pip install numpy pandas matplotlib seaborn scipy scikit-learn jupyterlab

Running Notebooks

# Clone the repository
git clone https://github.com/NareeshKannaS/DAV-LAB-EXP.git
cd DAV-LAB-EXP

# Launch Jupyter Lab
jupyter lab

Running Python Scripts

# Example: Run Experiment 2
cd exp2
python exp3.py

🛠️ Tech Stack

Technology	Purpose
🐍 Python 3.x	Core programming language
🔢 NumPy	Numerical computing & array operations
🐼 Pandas	Data manipulation & analysis
📈 Matplotlib	Data visualization & plotting
📓 Jupyter Lab	Interactive notebook environment

📊 Learning Roadmap

 ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
 │   Exp 1      │     │   Exp 2      │     │   Exp 3      │
 │ Environment  │────▶│   NumPy      │────▶│   Pandas     │──▶ ...
 │   Setup      │     │ Fundamentals │     │  Analysis    │
 └──────────────┘     └──────────────┘     └──────────────┘

Note

Each experiment builds upon concepts from the previous one. It is recommended to follow the experiments in order.

Tip

All notebooks include pre-rendered cell outputs so you can review results without running the code.

⭐ Star this repo if you found it helpful!

Made with ❤️ for Data Analysis & Visualization Lab

======= # DAV-LAB-EXP >>>>>>> f4a60fb126ce4a50d61afe94c5995a90c36c8569

Name		Name	Last commit message	Last commit date
exp1		exp1
exp2		exp2
exp3		exp3
exp4		exp4
exp5		exp5
exp6		exp6
exp7		exp7
README.md		README.md

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📊 Data Analysis & Visualization Lab

🏫 Chennai Institute of Technology — Semester 5

🗂️ Repository Structure

🧪 Experiments at a Glance

📝 Detailed Experiment Breakdown

📌 Objective

📦 Packages Checked

📂 Files

📌 Objective

🧩 Topics Covered

💡 Sample Output

📂 Files

📌 Objective

🧩 Topics Covered

💡 Sample Output

📂 Files

📌 Objective

🧩 Topics Covered

💡 Sample Code & Output

📂 Files

📌 Objective

🧩 Topics Covered

💡 Sample Code & Output

📂 Files

📌 Objective

🧩 Topics Covered

💡 Sample Code & Output

📂 Files

📌 Objective

🧩 Topics Covered

💡 Sample Code & Output

📂 Files

🚀 Getting Started

Prerequisites

Running Notebooks

Running Python Scripts

🛠️ Tech Stack

📊 Learning Roadmap

⭐ Star this repo if you found it helpful!

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