Visualize distribution of car prices: Histograms, box plots, and density plots to understand

price variability.

Explore feature correlations: Heatmaps and scatter plots to identify relationships between

features and target variable $($price$).$

Geographic insights: Utilize maps to visualize the spatial distribution of car listings and

their prices.

Feature Engineering and Selection:

Transform features: Apply transformations such as scaling $($MinMaxScaler$,$ StandardScaler$)$

to ensure all features contribute equally to model training.

Select relevant features: Use techniques like correlation analysis or feature importance from

tree$-$based models to identify impactful predictors.

Model Selection and Training:

Evaluate regression models: Experiment with various algorithms including Linear

Regression, Random Forest, Gradient Boosting, and potentially neural networks.

Hyperparameter tuning: Utilize techniques such as GridSearchCV or RandomizedSearchCV

for optimizing model performance.

Validation: Employ cross$-$validation methods $($e$.$g$.,$ k$-$fold cross$-$validation$)$ to assess model

generalization ability.

Model Evaluation:

Performance metrics: Assess models based on metrics like Root Mean Squared Error

$($RMSE$),$ Mean Absolute Error $($MAE$),$ and R$-$squared.

Interpretation: Analyze residual plots and predicted vs$.$ actual plots to understand model

strengths and weaknesses.

GUI Development using Streamlit:

Interface design: Create an intuitive UI allowing users to input car details $($make$,$ model,

year, mileage, condition$)$ and obtain predicted prices.

Interactive elements: Implement sliders, dropdowns, and text inputs for user interaction.

Real$-$time predictions: Ensure seamless integration with the trained model to deliver instant

price estimates based on user inputs.

Visualizations: Display EDA insights and model predictions through charts and graphs

within the GUI.

Documentation:

Project overview: Describe objectives, dataset sources, and methodologies used.

Technical details: Provide step$-$by$-$step instructions for data preprocessing, model

development, and GUI implementation.

Results and analysis: Present findings from EDA, model performance evaluations, and

insights gained.

Future considerations: Discuss potential enhancements or extensions to improve model

accuracy and usability.

Presentation:

Slide deck preparation: Summarize project goals, methodology, and key findings.

Demonstration: Showcase the functionality of the Streamlit GUI, emphasizing its practical

applications for users.

Stakeholder engagement: Highlight the project's impact on decision$-$making in the used car

market and potential business opportunities.