Exploratory Data Analysis on Terrorism Dataset

Introduction: In this project, we will perform Exploratory Data Analysis (EDA) on a terrorism dataset. The dataset contains information about various terrorist attacks worldwide, including details such as attack type, target type, location, and casualties. Through visualizations and analysis, we aim to gain insights and understand patterns within the data.

Content:

1. Dataset Overview:

   • Introduce the terrorism dataset used in the project.

   • Highlight the significance of analyzing global terrorism to understand patterns and enhance security measures.

   • Discuss the key features or columns present in the dataset.

2. Data Loading and Preprocessing:

   • Load the terrorism dataset into the analysis environment using pandas.

   • Check the dataset's structure by displaying the first few rows.

   • Perform necessary preprocessing steps, such as handling missing values.

Code and Explanation:

import pandas as pd

# Load the terrorism dataset
terrorism_data = pd.read_csv('terrorism_dataset.csv')

# Display the first few rows of the dataset
print(terrorism_data.head())

Explanation:

• We use the pd.read_csv() function to load the terrorism dataset from a CSV file and store it in the terrorism_data variable.

• The print(terrorism_data.head()) function displays the first few rows of the dataset to get a glimpse of the data.

1. Summary Statistics and Data Types:

   • Obtain summary statistics to gain insights into the dataset's numerical columns.

   • Check the data types of each column to ensure they are correctly interpreted.

Code and Explanation:

# Check the summary statistics of the dataset
print(terrorism_data.describe())

# Check the data types of each column
print(terrorism_data.dtypes)

Explanation:

• print(terrorism_data.describe()) provides summary statistics such as count, mean, standard deviation, and quartiles for numerical columns in the dataset.

• print(terrorism_data.dtypes) displays the data types of each column, which helps identify any incorrect data types or potential issues with the dataset.

1. Handling Missing Values:

   • Identify missing values in the dataset to determine if any data cleaning is required.

Code and Explanation:

# Check for missing values in the dataset
print(terrorism_data.isnull().sum())

Explanation:

• print(terrorism_data.isnull().sum()) calculates the sum of missing values for each column in the dataset, helping identify columns with missing data.

1. Visualizing Target Types:

   • Visualize the distribution of different target types to understand the primary targets of terrorist attacks.

Code and Explanation:

import matplotlib.pyplot as plt
import seaborn as sns

# Visualize the distribution of target types
plt.figure(figsize=(10, 6))
sns.countplot(x='target_type', data=terrorism_data)
plt.xticks(rotation=90)
plt.title('Distribution of Target Types')
plt.show()

Explanation:

• We use sns.countplot() from the Seaborn library to create a bar plot of the count of each target type.

• plt.figure(), plt.xticks(), and plt.title() are used for customization, such as setting the figure size, rotating x-axis labels, and adding a title.

1. Analyzing Attacks by Year:

   • Analyze the number of terrorist attacks by year to identify any significant trends or patterns.

Code and Explanation:

# Visualize the number of terrorist attacks by year
plt.figure(figsize=(10, 6))
terrorism_data['year'].value_counts().sort_index().plot(kind='bar')
plt.xlabel('Year')
plt.ylabel('Number of Attacks')
plt.title('Number of Terrorist Attacks by Year')
plt.show()

1. Handling Missing Values:

   • Identify and handle missing values in the dataset to ensure data quality.

Code and Explanation:

pythonCopy code# Check for missing values in the dataset
print(terrorism_data.isnull().sum())

Explanation:

• The print(terrorism_data.isnull().sum()) function calculates the sum of missing values for each column, allowing us to identify columns with missing data.

1. Visualizing Target Types:

   • Visualize the distribution of different target types to understand the primary targets of terrorist attacks.

Code and Explanation:

pythonCopy codeimport matplotlib.pyplot as plt
import seaborn as sns

# Visualize the distribution of target types
plt.figure(figsize=(10, 6))
sns.countplot(x='target_type', data=terrorism_data)
plt.xticks(rotation=90)
plt.title('Distribution of Target Types')
plt.show()

Explanation:

• We use the sns.countplot() function from the Seaborn library to create a bar plot showing the count of each target type.

• Customization options, such as plt.figure(), plt.xticks(), and plt.title(), are used to set the figure size, rotate x-axis labels, and add a title.

1. Analyzing Attacks by Year:

   • Analyze the number of terrorist attacks by year to identify any significant trends or patterns.

Code and Explanation:

pythonCopy code# Visualize the number of terrorist attacks by year
plt.figure(figsize=(10, 6))
terrorism_data['year'].value_counts().sort_index().plot(kind='bar')
plt.xlabel('Year')
plt.ylabel('Number of Attacks')
plt.title('Number of Terrorist Attacks by