href="#ulink_3f66ef47-2798-5aa6-8844-facf45d02293">Table 2.3. The desired libraries were imported and GridSearchCV used to locate the satisfactory model. It compares the model on multiple regresses and different parameters and offers the best score among them. With the assistance of GridSearchCV, we have compared the algorithms, i.e., linear regression, LASSO regression, decision tree, support vector machine, random forest regressor, and XGBoost.
To aid this challenge, various device mastering algorithms are checked. It has been clear that XGBoost acts better with 85% accuracy and with an awful lot less blunders values. While this test is compared to the result, once those algorithms predicts properly. This task has been finished with the number one aim to determine the prediction for prices, which we have got efficiently completed using specific system analyzing algorithms like a linear regression, LASSO regression, decision tree, random forest, more than one regression, guide vector gadget, gradient boosted trees, neural networks, and bagging.
Consequently, it is clear that the XGBoost gives more accuracy in prediction in comparison to the others, and additionally, our research offers to locate the attributes contribution in prediction. In addition, python flask can be used as an http server and CSS/Html for creating UI for internet site. Therefore, one might agree with this that studies may be useful for the people and governments, and the future works are stated under every system, and new software program technology can assist in the future to expect the costs. Price prediction can be advanced by way of including many attributes like surroundings, marketplaces, and many different related variables to the houses.
Table 2.3 Comparison of algorithm.
| Model | Best score | RSME score | Error score | Accuracy percent | |
|---|---|---|---|---|---|
| 0 | Linear regression | 0.790932 | 64.813703 | 0.209068 | 79% |
| 1 | LASSO regression | 0.803637 | 62.813241 | 0.196363 | 80% |
| 2 | Decision tree | 0.71606 | 70.813421 | 0.283936 | 72% |
| 3 | Support Vector Machine | 0.204336 | 126.440620 | 0.795664 | 20% |
| 4 | Random Forest Regressor | 0.884247 | 48.226644 | 0.115753 | 88% |
| 5 | XGBoost | 0.891979 | 46.588246 | 0.108021 | 89% |
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1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
3
Multi-Criteria–Based Entertainment Recommender System Using Clustering Approach
Chandramouli Das, Abhaya Kumar Sahoo* and Chittaranjan Pradhan
School of Computer Engineering, KIIT Deemed to be University, Odisha, India
Abstract
