https://github.com/mastermindromii/car-price-prediction-analysis

Welcome to the Car Price Prediction EDA project! On my third day of practicing Exploratory Data Analysis (EDA) in data science, I'm excited to explore the fascinating world of data analysis. After successfully navigating the complexities of the Diwali Sales Data on my first day, I'm now diving into the realm of car price prediction.
https://github.com/mastermindromii/car-price-prediction-analysis

Last synced: 9 days ago
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Welcome to the Car Price Prediction EDA project! On my third day of practicing Exploratory Data Analysis (EDA) in data science, I'm excited to explore the fascinating world of data analysis. After successfully navigating the complexities of the Diwali Sales Data on my first day, I'm now diving into the realm of car price prediction.

• Host: GitHub
• URL: https://github.com/mastermindromii/car-price-prediction-analysis
• Owner: MasterMindRomii
• Created: 2023-10-18T18:29:39.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-18T20:11:57.000Z (about 1 year ago)
• Last Synced: 2024-11-10T21:17:27.862Z (9 days ago)
• Language: Jupyter Notebook
• Size: 71.3 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CAR-PRICE-PREDICTION

Welcome to the Car Price Prediction EDA project! On my third day of practicing Exploratory Data Analysis (EDA) in data science, I'm excited to explore the fascinating world of data analysis. After successfully navigating the complexities of the Diwali Sales Data on my first day, I'm now diving into the realm of car price prediction.

Project Highlights

Challenges Faced

I encountered a crucial challenge during my analysis:

Data Cleaning Challenge: While cleaning the dataset, I aimed to process the 'Price' column effectively. However, I faced a significant roadblock. When I tried to apply the price_cleaner function to the 'Price' column, it raised a NameError. Upon further investigation, it became apparent that this error might be related to missing or unexpected values within the 'Price' column. This challenge highlighted the importance of robust data cleaning techniques.

Actions Taken

To address the 'Price' column issue, I undertook the following actions:

Data Cleaning: I performed data cleaning operations on the 'Price' column, including removing commas and converting 'Lakh' to its corresponding numerical value. These steps were vital to prepare the data for further analysis.

Function Troubleshooting: As I tried to apply the price_cleaner function, I encountered a NameError related to 'nan.' This indicates the presence of missing or NaN values in the 'Price' column. To ensure accurate analysis, I recognized the need to handle these values effectively.

Learning Points

Throughout this day's work, I gained valuable insights and knowledge:

Data Cleaning: Data cleaning is an indispensable part of data analysis. It involves handling missing values, transforming data types, and addressing inconsistencies to ensure accurate analysis.

Function Usage: The use of custom functions, such as price_cleaner, can streamline data cleaning processes. However, it's essential to anticipate and address potential issues, such as missing values.

Data Visualization: I also had the opportunity to create informative data visualizations using my index data. Visualizations are a powerful means of conveying data insights effectively.