Brief overview of the project's focus: Exploring car market trends through EDA. Importance of understanding factors influencing car prices based on brand, model, and other attributes.
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Data Collection:
- Source of the dataset (e.g., website, database).
- Explanation of the features: price, brand_model, year, condition, body, fuel, location.
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Data Preprocessing:
- Cleaning the dataset by handling missing values and duplicates.
- Ensuring data consistency and accuracy.
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Initial Exploration:
- Overview of dataset size and structure.
- Basic statistics of numerical features (price, year) and categorical features (condition, fuel, etc.).
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Price Distribution:
- Visualizing the distribution of car prices using histograms or density plots.
- Identifying potential outliers and extreme values.
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Temporal Analysis:
- Analyzing car price trends over different years.
- Investigating how car prices have changed over time.
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Condition and Price:
- Exploring how car condition affects its price.
- Comparing prices for different condition categories.
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Body Type and Fuel:
- Examining the relationship between car body type and price.
- Analyzing price differences based on fuel type (petrol, diesel, etc.).
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Geographical Insights:
- Mapping car distribution across different locations.
- Discovering regional price variations and potential drivers.
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Brand and Model Analysis:
- Identifying popular car brands and models in the dataset.
- Investigating the price range for each brand and model.
- Correlation Exploration:
- Calculating correlations between numeric features (price, year, etc.).
- Uncovering relationships that impact car prices.
- Visualization:
- Creating insightful visual representations like scatter plots, bar charts, and heatmaps.
- Presenting findings in a clear and compelling manner.
This Car Model Based EDA project aims to provide valuable insights into how different factors contribute to variations in car prices, focusing on brand, model, and related attributes.