Used Car Price Analysis and Prediction

This project involves analyzing a dataset of used cars to understand factors influencing their selling prices and preparing the data for predictive modeling. Visualizations and machine learning techniques are employed to derive insights and create a model to predict the selling prices of cars.

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Table of Contents

• Dataset Description
• Project Workflow
• Libraries Used
• Installation
• Exploratory Data Analysis (EDA)
• Data Preparation
• Modeling
• Visualization Examples

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Dataset Description

The dataset contains information about 301 used cars with the following features:

• Car_Name: Name of the car (removed during preprocessing).
• Year: Year of manufacturing.
• Selling_Price: Price the car was sold for (target variable).
• Present_Price: Current market price of the car.
• Driven_kms: Kilometers driven by the car.
• Fuel_Type: Type of fuel used (Petrol, Diesel, or CNG).
• Selling_type: Selling method (Dealer or Individual).
• Transmission: Type of transmission (Manual or Automatic).
• Owner: Number of previous owners.

Project Workflow

1. Load and explore the dataset.
2. Perform exploratory data analysis (EDA) to identify patterns and trends.
3. Preprocess the data:
   • Remove unnecessary columns.
   • Encode categorical variables.
   • Derive additional features.
4. Visualize relationships and distributions.
5. Split the data into training and testing sets.
6. Train and test machine learning models.

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Libraries Used

• pandas: Data manipulation and preprocessing.
• numpy: Mathematical operations.
• seaborn: Data visualization.
• matplotlib: Plotting graphs.
• scikit-learn: Splitting data and machine learning.

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Installation

1. Clone this repository:

   git clone https://github.com/your-repo/used-car-analysis.git

2. Navigate to the project directory:

   cd used-car-analysis

3. Install the required dependencies:

   pip install -r requirements.txt

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Exploratory Data Analysis (EDA)

Perform EDA to uncover patterns and relationships in the data. Key steps include:

1. Inspecting the dataset structure and summary statistics.
2. Visualizing distributions and correlations.
3. Identifying missing or inconsistent data.

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Data Preparation

Preprocess the data for analysis and modeling:

1. Remove the Car_Name column (irrelevant for prediction).
2. Convert the Year column into car age.
3. Encode categorical columns (Fuel_Type, Selling_type, Transmission) using one-hot encoding or label encoding.
4. Normalize or scale features if needed.
5. Split the dataset into training and test sets.

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Modeling

1. Train multiple regression models, such as:
   • Linear Regression
   • Random Forest Regressor
   • Gradient Boosting Regressor
2. Evaluate model performance using metrics like:
   • Mean Absolute Error (MAE)
   • Mean Squared Error (MSE)
   • R² Score

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Visualization Examples

• Price vs. Driven Kilometers: Understand how usage affects the price.
• Fuel Type Distribution: Analyze the popularity of different fuel types.
• Correlation Heatmap: Visualize relationships among numerical features.