Welcome to the Car Prediction Project repository! In this project, I explore and analyze a dataset related to cars, aiming to predict various aspects using data analysis and machine learning techniques. The project is implemented in Python within Jupyter Notebooks, utilizing popular libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.

Key Features:

• Data Exploration: Dive into the dataset to understand its structure, identify patterns, and gain insights into the characteristics of the car-related data.

• Data Preprocessing: Cleanse and preprocess the data to handle missing values, outliers, and ensure its suitability for machine learning algorithms.

• Visualization: Utilize Matplotlib and Seaborn for data visualization, creating insightful plots and charts to better understand the relationships between variables.

• Machine Learning Models: Implement predictive models using Scikit-learn, including Linear Regression and XGBoost, to forecast various aspects related to cars.

• Evaluation: Assess the performance of the models, considering metrics such as Mean Squared Error (MSE) and R-squared to gauge the accuracy and reliability of predictions.

Libraries Used:

• Pandas: Data manipulation and analysis.
• NumPy: Numerical operations and array handling.
• Matplotlib: Plotting and visualization.
• Seaborn: Enhanced data visualization.
• Scikit-learn: Machine learning algorithms and tools.
• XGBoost: Implementation of gradient-boosted decision trees.