Exploratory Data Analysis (EDA & Cleaning) on Iris Dataset 🌸

📌 Project Overview

This project performs data cleaning, preprocessing, and exploratory data analysis (EDA) on the Iris dataset.
The analysis focuses on understanding the dataset structure, ensuring data quality, visualizing feature distributions, and identifying relationships between numerical variables.

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📋 Project Structure

The project is organized into the following sections:

1. Introduction
2. Data Loading
3. Data Cleaning & Preprocessing
4. Exploratory Data Analysis (EDA)
5. Insights & Conclusion

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🎯 Objective

The objective of this project is to perform data cleaning and exploratory data analysis (EDA) on a given dataset in order to identify:

• Patterns and trends
• Summary statistics
• Relationships between numerical features

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🧹 Task 1: Data Cleaning and Preprocessing

Dataset: Iris Dataset

This section demonstrates fundamental data cleaning steps using Pandas, including:

• Loading the dataset
• Checking for missing values
• Identifying and removing duplicate records
• Standardizing categorical variables

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1️⃣ Import Libraries and Load Dataset

• Imported required libraries
• Loaded the Iris dataset into a Pandas DataFrame for analysis

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2️⃣ Understand Dataset Structure

• Displayed the first few rows of the dataset
• Inspected dataset structure and data types using .info()

This step helped confirm column names, data types, and overall dataset size.

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3️⃣ Identify Missing Values

• Checked for missing values across all columns

Result:
No missing values were found; therefore, no imputation or removal was required.

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4️⃣ Identify and Remove Duplicate Rows

• Checked for duplicate records
• Removed duplicate rows to ensure data integrity

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5️⃣ Standardize Categorical Variables

• Standardized the species column by:
  • Converting all values to lowercase
  • Removing extra whitespace

This ensures consistency and prevents categorical mismatches during analysis.

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6️⃣ Final Validation

• Verified dataset shape after cleaning
• Reconfirmed that no missing values remain

At this stage, the dataset was fully cleaned and ready for analysis.

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🧠 Conclusion (Data Cleaning)

The dataset has been successfully cleaned and prepared.

• No missing values were present
• Duplicate rows were removed
• Categorical data was standardized for consistency

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📊 Task 2: Exploratory Data Analysis (EDA)

Description

This task focuses on exploring the dataset to identify patterns, trends, and summary statistics through statistical analysis and visualization.

Objectives

• Calculate summary statistics (mean, median, mode, standard deviation)
• Visualize data distributions using histograms and boxplots
• Analyze relationships using scatter plots
• Examine correlations between numerical features

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📈 Summary Statistics

Calculated:

• Mean
• Median
• Mode
• Standard Deviation

These statistics provide insight into the central tendency and variability of numerical features.

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🎨 Data Visualization

Visualization libraries used:

• Matplotlib
• Seaborn

Custom plotting parameters were applied to ensure clear and readable visuals.

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🖼 Figure 1: Histograms of Numerical Features

Histograms were generated for all numerical features to examine:

• Distribution shape
• Spread
• Skewness

Insight:
This helps understand feature behavior and detect unusual distributions.

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🖼 Figure 2: Boxplot of Numerical Features

Boxplots were used to:

• Observe variability
• Identify potential outliers

This visualization highlights differences in feature ranges.

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🖼 Figure 3: Scatter Plot (Feature Relationship)

A scatter plot was created to analyze the relationship between:

• Sepal Length
• Petal Length
  (with species-based color encoding)

Insight:
Clear clustering indicates strong feature separability among species.

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🔗 Correlation Analysis

• Computed correlation matrix for numerical features
• Visualized correlations using a heatmap

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🖼 Figure 4: Correlation Heatmap

The heatmap highlights:

• Strong positive correlations
• Relationships useful for future predictive modeling

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🔑 Key Insights

• Petal length and petal width show a strong positive correlation
• Sepal features exhibit weaker correlations
• Species are clearly separable, making the dataset suitable for classification tasks

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📌 Conclusion (EDA)

This exploratory data analysis revealed meaningful relationships and patterns within the dataset.
The findings support feature selection and provide a strong foundation for future machine learning models.

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📄 Exporting Visualizations

All EDA visualizations were exported into a PDF report, including:

• A cover page with project details
• Histograms
• Boxplots
• Scatter plots
• Correlation heatmap

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⭐ Acknowledgements

This project was completed as part of a task from my ongoing internship at Codveda Technologies, where I gained hands-on experience in data analytics and machine learning through practical, real-world projects.

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🔗 Author

Alex Mathias
🌐 LinkedIn: https://linkedin.com/in/mathias-alex-2a1873111
💻 GitHub: https://github.com/alexmatins234