Project Overview

• Name: Prabhas Teja Penugonda
• Company: CODTECH IT Solutions
• ID: CT6WDS1908
• Domain: Data Science
• Duration: September to October 2024
• Mentor: Neela Santhosh Kumar

This project focuses on predictive modeling using linear regression to estimate car prices based on various features in the dataset. The goal is to develop a model that accurately predicts the selling price of used cars based on attributes such as fuel type, seller type, transmission, and other numeric values.

---

Objective

The main objective of this project is to:

• Develop a linear regression model to predict car prices.
• Understand how different factors influence car prices through data analysis and visualizations.
• Evaluate the performance of the predictive model using appropriate metrics.

---

Key Activities

1. Data Preparation:

   • Cleaning and preprocessing the dataset to handle missing values and categorical variables.
   • Splitting the dataset into training and testing sets for model evaluation.

2. Predictive Modeling:

   • Building a linear regression model to predict car prices based on the selected features.
   • Evaluating model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² Score.

3. Data Visualization:

   • Creating visualizations to understand the relationships between features and car prices.

---

Visualizations

The project includes the following visualizations that illustrate key relationships and insights:

1. Fuel Type, Seller Type, and Transmission vs. Selling Price

   • Description: This visualization combines the effects of different fuel types, seller types, and transmission types on the selling price of cars, providing a comprehensive view of how these factors influence pricing.

   

2. Correlation Between Numeric Columns:

   • A heatmap displaying correlations between numeric features in the dataset.

3. Correlation Between Selling Price and Present Price:

   • A scatter plot showing the relationship between actual selling prices and present prices.

4. Actual Price vs. Predicted Price:

   • A comparison plot illustrating the accuracy of the model predictions against actual selling prices.

---

Technologies Used

• Python: Primary language for data analysis and modeling.
• pandas: For data manipulation and preprocessing.
• matplotlib: For generating static and interactive visualizations.
• seaborn: To create appealing statistical graphics.
• scikit-learn: For building and evaluating the linear regression model.

---

How to Run the Project

1. Clone the Repository:

   git clone <https://github.com/prabhasteja007/CODTECH-Task2>
   cd your-repository-name