Task 4: Data Cleaning and Insight Generation from Survey

This project is part of my Elevvo Internship (Data Analytics track).
The objective was to clean and analyze real-world survey data from the Kaggle Data Science Survey (2017–2021) to generate actionable insights about respondents' demographics, coding experience, and programming language preferences.

📂 Dataset

• Source: Kaggle Data Science Survey 2017–2021
• Contains survey responses from data science professionals worldwide (age, gender, country, experience, tools, languages, etc.)

🛠️ Tools & Libraries

• Python
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

🔎 Key Steps

1. Data Loading – Read the survey dataset into a Pandas DataFrame
2. Data Cleaning – Handle missing values, remove duplicates, format columns
3. Summary Statistics – Inspect distributions of age, gender, country, experience, and programming languages
4. Key Insights – Extract most common demographics, experience levels, and popular programming languages
5. Data Visualization – Create summary plots for age, gender, country, experience, and top languages
6. Insights & Conclusion – Interpret the findings

📊 Key Insights

• Age Group: Most respondents are 25–29 years old
• Gender: Majority identify as Male
• Country: India had the highest number of participants
• Coding Experience: Most respondents have 3–5 years of coding experience
• Programming Language: Python is the most popular language

📈 Visualizations

Age Group Distribution

Gender Distribution

Top 10 Countries

Coding Experience Distribution

Top 5 Programming Languages

▶️ How to Run

• Install dependencies: pip install -r requirements.txt
• Open the notebook: jupyter notebook Survey_Data_Insight_Generation.ipynb