Added codes, datasets and Jupyter notebooks directory.

Codes/Code-A10 (Data Visualization-3).md

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+# A10 - Data Visualization-3
+
+✅ Tested and working as intended.
+
+---
+
+## Pre-requisites
+
+- Install required libraries: `pandas`, `seaborn` & `matplotlib`
+
+```shell
+pip install pandas matplotlib seaborn
+```
+
+---
+
+1. Import libraries:
+
+```python3
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+```
+
+2. Load dataset into Pandas DataFrame:
+
+```python3
+df = pd.read_csv('iris.csv')
+df.head()
+```
+
+3. Features & their datatypes:
+
+```python3
+print("Feature and their types:")
+df.dtypes
+```
+
+4. Histogram for each numerical feature:
+
+```python3
+plt.figure(figsize=(12, 6))
+
+for i, column in enumerate(df.columns[:-1]):  # Exclude 'species' column
+    plt.subplot(2, 2, i + 1)
+
+    ax = plt.hist(df[column], edgecolor="black")
+    plt.gca().bar_label(plt.gca().containers[0], fmt='%d')  # Add count labels
+    plt.title(f"Histogram of {column}")
+    plt.xlabel(column)
+    plt.ylabel("Frequency")
+
+plt.tight_layout()
+plt.show()
+```
+
+5. Boxplot (for identifying outliers in this case):
+
+```python3
+plt.figure(figsize=(12, 6))
+for i, column in enumerate(df.columns[:-1]):  # Exclude 'species' column
+    ax = plt.subplot(2, 2, i + 1)
+    # Create boxplot and store it in a container
+    box_container = sns.boxplot(x=df[column], ax=ax, color='salmon')
+    plt.title(f"Boxplot of {column}")
+
+plt.tight_layout()
+plt.show()
+```
+
+6. Detecting outliers:
+
+```python3
+for column in df.columns[:-1]:  # Exclude 'species' column
+    Q1 = df[column].quantile(0.25)
+    Q3 = df[column].quantile(0.75)
+    
+    IQR = Q3 - Q1
+    
+    lower_bound = Q1 - 1.5 * IQR
+    upper_bound = Q3 + 1.5 * IQR
+    
+    outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)][column]
+
+    print(f"\nFeature: {column}")
+    print(f"  Mean: {df[column].mean():.2f}, Median: {df[column].median():.2f}, Std Dev: {df[column].std():.2f}")
+    print(f"  Outliers Detected: {'Yes' if not outliers.empty else 'No'}","\n  " f"Outlier Values: {outliers.tolist()}" if not outliers.empty else "")
+    print("-" * 40)
+```
+
+7. Violin plot:
+
+```python3
+plt.figure(figsize=(12, 8))
+for i, column in enumerate(df.columns[:-1]):  # Exclude 'species' column
+    plt.subplot(2, 2, i + 1)
+    sns.violinplot(x=df["variety"], y=df[column], palette="Set2", hue=df['variety'])
+    plt.title(f"Violin Plot of {column} by variety")
+
+plt.tight_layout()
+plt.show()
+```
+
+---
+
+## References
+
+- [Dataset source-1](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data) *(not exactly, but yes, kinda)*
+- [Dataset source-2](https://archive.ics.uci.edu/dataset/53/iris) *(not exactly, but yes, kinda)*
+
+---