Cars24 Dataset Analysis

Project Overview

The Cars24 Dataset Analysis project aims to analyze a dataset from Cars24 to uncover insights about used car sales. The analysis is performed using Python, with data sourced from Kaggle. The project involves data cleaning, exploration, and visualization to present key findings and trends.

Tools Used

• Python: Programming language for data analysis.
• Kaggle: Source of the Cars24 dataset.
• Pandas: For data manipulation and cleaning.
• Matplotlib: For data visualization.
• Seaborn: For advanced data visualization.

Project Steps

1. Data Collection

• Obtained the Cars24 dataset from Kaggle.
• Loaded the dataset into a Pandas DataFrame for analysis.

2. Data Cleaning

• Inspected the dataset for missing values, duplicates, and incorrect data types.
• Performed data cleaning tasks including:
  • Removing duplicates.
  • Filling or dropping missing values.
  • Converting data types for consistency.
  • Renaming columns for better readability.

3. Exploratory Data Analysis (EDA)

• Conducted an initial exploration of the dataset to understand its structure and key characteristics.
• Used Pandas to generate summary statistics and identify patterns.

4. Data Visualization

• Utilized Matplotlib and Seaborn to create visual representations of the data.
• Developed various charts and graphs to highlight important trends and insights, including:
  • Bar charts to show the distribution of car brands, fuel types, transmission and models.
  • Scatter plots to analyze the relationship between car price and other numerical features.
  • Line chart to show the car price trends over year.
  • pie chart to show the distribution in fuel types and transmission types.
  • Ranked the car models on factors like sales price, mileage etc.

5. Insights and Findings

• Analyzed the visualizations to derive insights about the used car market.
• Identified key factors affecting car prices, such as brand, age, mileage, and location.
• Summarized findings in a clear and concise manner, highlighting actionable insights for stakeholders.

Repository Structure

• raw dataset/: Contains the raw and cleaned dataset files.
• Project files/: Includes Jupyter notebooks used for data cleaning, EDA, and visualization.
• ppt file: This is my presentation file in which i include all the key insights and findings.
• README.md: Project description and overview.

How to Use

1. Clone the repository to your local machine.
2. Install the required dependencies using the following command:

   pip install -r requirements.txt

3. Load the dataset and follow the analysis steps provided in the Jupyter notebooks Car dataset python file located inside the Project files/ folder.

Conclusion

This project provides a comprehensive analysis of the Cars24 dataset, utilizing Python, Pandas, Matplotlib, and Seaborn for data cleaning and visualization. The resulting insights offer a detailed understanding of the used car market, assisting in data-driven decision-making.

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Feel free to contribute to this project by suggesting improvements, reporting issues, or submitting pull requests. Happy analyzing!