Objective: Analyze car sales data to understand factors influencing prices and provide insights for pricing strategies.
Key Activities:
Data Collection:
Gathered car sales data from various sources such as dealerships, online platforms, and industry reports. Collected information on car features, specifications, condition, and sales prices. Data Cleaning and Preprocessing:
Cleaned and standardized the data to ensure consistency and accuracy. Handled missing values, outliers, and inconsistencies in the dataset. Exploratory Data Analysis (EDA):
Conducted EDA to explore relationships between car features and sales prices. Visualized data using scatter plots, histograms, and box plots to identify patterns and outliers. Feature Engineering:
Created new features such as age of the car, mileage, and engine size from existing data. Used domain knowledge to engineer features that could impact sales prices. Model Building:
Developed predictive models to estimate car sales prices using regression techniques. Explored linear regression, decision trees, random forests, and gradient boosting algorithms. Split the data into training and testing sets for model evaluation. Model Evaluation:
Evaluated model performance using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. Used cross-validation to assess model generalization and robustness. Insights and Recommendations:
Provided insights into the most influential factors affecting car sales prices. Recommended pricing strategies based on model findings to optimize sales and profitability.