数据集:https://www.kaggle.com/competitions/predict-student-performance-from-game-play/data score为0.678,300/600的rank 竞赛目标: You'll develop a model trained on one of the largest open datasets of game logs. 文件包括: train.csv - the training set ...
Explore and run machine learning code with Kaggle Notebooks | Using data from Students Performance in Exams
students_tests.csv(2.23 kB) get_app fullscreen chevron_right Unable to show preview Failed to load columns Data Explorer Version 1 (2.23 kB) calendar_view_week students_tests.csv Summary arrow_right folder 1 file arrow_right calendar_view_week 3 columns lightbulb See what others are saying ab...
1. Input: Pre-processed CSV data 2. Output: Student Dropout Prediction model 3. Model and Parameter Initialization: LR_model = LogisticRegression () ANN_model = Neural_Netwrok (layers) epochs = 300 batch_size = 48 val_split = 0.2 4. # HLRNN model implementation 5. for folds in k-fold...
file_downloadDownload Logs check_circle Successfully ran in 45.7s Accelerator None Environment Latest Container Image Output 0 B Time # Log Message 10.0s 1 /kaggle/input/students-performance-in-exams/StudentsPerformance.csv 10.4s 2 <class 'pandas.core.frame.DataFrame'> 10.4s 3 RangeIndex: ...
Explore and run machine learning code with Kaggle Notebooks | Using data from Student Feedback Survey Responses
We used the Python platform to process all the files and obtain a single .csv file containing student demographic information, daily interaction with the university’s VLE, student assessment results, and final results of the students. The final results of the students were categorized as ...
Firstly, the Kalboard dataset were named in the RM using operator “Read.CSV”, which is commonly used for reading data from the comma separated values file as shown in Figure 2. This figure illustrates the first step applied in this experiment in which the model was prepared by applying ML...