Summer Analytics 2024 – Week 1 Assignment: Data Grand Prix!

Welcome to my submission for Week 1 of Summer Analytics 2024 by C&A. This assignment focused on applying basic data analysis skills using Python libraries such as Pandas, NumPy, Seaborn, and Matplotlib.

Dataset

The dataset used is Cars.csv, which contains specifications of cars from the 1970s and 1980s, including:

• mpg (Miles per gallon)
• cylinders, displacement, horsepower
• weight, acceleration, model_year, origin, name

Tasks Performed

• Imported the dataset and handled missing values (e.g., filled missing horsepower values with the median)
• Generated descriptive statistics
• Visualized relationships using:
  • Pairplot
  • Correlation heatmap
  • Line plot of MPG trend by model year
  • Bar plot of average MPG by origin
  • Count plot for car distribution by origin

Key Insights

• Japanese cars have the highest average fuel efficiency.
• MPG increased steadily from 1970 to 1982.
• Negative correlation found between MPG and horsepower, weight, and displacement.

Libraries Used

• Python 3
• pandas
• numpy
• seaborn
• matplotlib

How to Run

1. Clone this repo or download the notebook and dataset.
2. Open Summer_Analytics_Week1.ipynb in Jupyter Notebook.
3. Make sure Cars.csv is in the same directory.
4. Run all cells.

Submission Info

• Name: Shivangi Sharma
• Institute: JECRC University
• Program: Summer Analytics 2024
• Week: 1 – Data Analysis
• Assignment Title: Data Grand Prix!

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Thank you C&A for this opportunity!