Diamond Price Prediction Model:-

Technologies Used: Data Handling and Manipulation: Pandas and NumPy for data loading, cleaning, and transformation. Data Visualization: Matplotlib and Seaborn for creating visualizations like scatter plots, box plots, and heatmaps to gain insights into the data. Machine Learning: Scikit-learn for building and evaluating the regression model. Includes preprocessing tools like StandardScaler, OneHotEncoder, and LabelEncoder. Uses the Linear Regression model for prediction. Employs metrics like RMSE and R-squared for evaluating model performance.

Regression Model:A Linear Regression model was used to predict diamond prices. Numerical features were scaled using StandardScaler. Categorical features were encoded using OneHotEncoder/LabelEncoder. The model was trained on 75% of the data and tested on the remaining 25%.

Accuracy:The model achieved an R-squared (R2) value of approximately 0.86. This suggests that the model can explain around 92% of the variance in diamond prices. The Root Mean Squared Error (RMSE) is around 1478.32, suggesting an average prediction error of roughly 1478.

Outcome:The project successfully built a diamond price prediction model using linear regression. The model demonstrated reasonably good accuracy with an R-squared value of 0.86. Further improvements could be explored by experimenting with different regression models, feature engineering, and hyperparameter tuning.