Build Marker Based AR Solution To build an AI-powered performance predictor, we started with a neural network, which is an algorithm designed to approximate the function of a human brain. A neural network lets you input many different inputs and predict one output. In this case, the inputs are performance data, and the output is how many wins a player would be expected to get. This is a performance predictor that uses AI to predict a team's performance. It uses complex algorithms to formulate and test the model. The data science team uses a range of tools, like Python, Numpy, Jupyter metapackage, pandas, pycaret, etc., to build this pipeline. kandi kit provides you with a fully deployable AI powered Performance Predictor. Source code included so that you can customize it for your requirement.

Kit Deployment Instructions

For Windows OS, Download, extract and double-click kit installer file to install the kit. Note: Do ensure to extract the zip file before running it. The installation may take from 2 to 10 minutes based on bandwidth. 1. When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from Menu bar to see how prediction works. It is loaded with sample files. 2. To run the app manually, press N when you're prompted and locate the zip file 3. Extract the zip file and navigate to the directory student-grade-analytics-main 4. Open command prompt in the extracted directory student-grade-analytics-main and run the command jupyter notebook For other OS, 1. Click here to install python 2. Click here to download the repo 3. Extract the zip file and navigate to dir student-grade-analytics-main 4. Open terminal in the extracted dir student-grade-analytics-main 5. Install dependencies by executing the command pip install -r requirements.txt 6. Run the command jupyter notebook

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.

Instruction to Run

Follow below instructions to run the solution. 1. Locate and open the Student Performance Prediction App.ipynb notebook from the Jupyter Notebook browser window. 2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar For running with your dataset, 1. Load input files train.csv and pred.csv with your dataset in the student-grade-analytics-main directory from kit_installer.bat location 2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar 3. The output file output.csv will be generated in the directory student-grade-analytics-main from the kit_installer.bat location Sample Input: student-grade-analytics-main/train.csv, student-grade-analytics-main/pred.csv - csv files containing dataset to train and predict Output: student-grade-analytics-main/output.csv - a csv file containing predicted output You can additionally build interfaces to the prediction app and other enhancements. For any support, you can direct message us at #help-with-kandi-kits


1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway 2. During kit installer, if you encounter Windows security alert, click Allow


For any support, you can direct message us at #help-with-kandi-kits