🚀 Space Mission Launch Analysis

A full exploratory data analysis (EDA) project using Python, designed to uncover insights from a historical dataset of global space missions. From success rates to cost trends and country-wise contributions — this dashboard gives a 360° view of the evolution of space launches.

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📁 Project Title

Space Mission Launch Analysis

🎓 Course: INT 375 – Data Science Toolbox

🏫 Institution: Lovely Professional University

👨‍🎓 Submitted By: Ayush Kumar

👩‍🏫 Guide: Dr. Tanima Thakur

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📊 Dataset Overview

Source: Maven Analytics - Space Missions Dataset

This dataset includes detailed launch records from 1957 to 2022, covering space agencies and companies like NASA, SpaceX, ISRO, Roscosmos, and more.

🔑 Key Features:

• Company, Rocket, Location, Date, Time
• Price (USD), MissionStatus, RocketStatus
• Derived fields: LaunchDateTime, Country, Year, Month, Weekday, Price_Clean

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🧪 Technologies Used

Tool / Library	Use Case
Python 3	Programming language
Pandas	Data cleaning & manipulation
NumPy	Numerical operations
Matplotlib	Data visualization
Seaborn	Statistical plotting

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🔍 Project Objectives

1. Success Rate Analysis

   • Compare mission outcomes across top countries & companies

2. Price vs Outcome Analysis

   • Explore cost patterns for successful vs failed missions

3. Temporal Trends

   • Weekly and monthly launch frequency patterns

4. Mission Status Over Time

   • Analyze how launch outcomes have changed from 1957 to 2022

5. Country-wise Launch Distribution

   • Identify top countries by number of launches

6. Correlation Analysis

   • Study relationship between Price, Year, and Month

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📈 Visual Insights

All visualizations were created using matplotlib and seaborn, including:

• Bar plots of top companies and countries
• Stacked bar charts comparing success vs failure
• Line plots showing price trends over the years
• Box plots for price distribution by mission outcome
• Correlation heatmaps showing numeric relationships
• Count plots for launch frequency by month and weekday
• Year-wise breakdown of mission statuses

📂 Saved output plots:

• space_mission_overview.png
• success_by_country_company.png
• price_vs_status_boxplot.png
• temporal_trends.png
• price_trend.png
• country_distribution.png
• correlation_heatmap.png
• mission_status_over_time.png

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📘 Key Findings

• The US and Russia dominated space activity historically; China and India have seen recent growth.
• Companies like RVSN USSR and CASC have conducted the most missions.
• Private players like SpaceX have high success rates with frequent launches in recent years.
• Higher-cost missions tend to succeed more, but success is not solely dependent on budget.
• Clear patterns in launch timing: more missions in mid-year months and certain weekdays.

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🔮 Future Scope

• Add recent mission data (post-2022)
• Use machine learning to predict mission success
• Include mission payload, orbit type, and purpose for deeper analysis
• Build an interactive dashboard using Streamlit or Plotly Dash
• Explore the environmental impact of launches (fuel, emissions)

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📌 How to Run the Code

1. Clone this repo:

git clone https://github.com/yourusername/space-mission-analysis.git
cd space-mission-analysis