The aim of project is to predict whether the student will be recruited in campus placements
or not based on the available factors in the dataset.
Life Cycle of implementing machine learning application.
- Gathering the Data
- Data Preparation
- Data Preprocessing
- Create Model
- Evaluate Model
- Deploy the model
The Campus Recruitment Prediction (Course Project) Dataset BY QuantumAI has been used for this purpose, taken from the Kaggle*. link is below.
- Python (Programming Language version 3.7+)
- Flask (Python Backend Framework)
- sklearn (Machine Learning Library)
- pandas (Python Library for Data operations)
- NumPy (Python Library for Numerical operations)
- VS code (IDE)
- Azure (Cloud platform)
- Create virtual environment
conda create -n myenv python=3.9
- Activate the environment
conda activate myenv
- Install the packages
pip install -r requirements.txt
- Run the app
python app.py
- Navigate to URL http://127.0.0.1:5000/
- Enter valid values in all input boxes and hit Predict.
If everything goes well, you should be able to see the predcition on the HTML page!
Devansh Mistry - Linkedin
If you like this project, please do give the star. If you have any suggestions or issues, please drop me a message.
- Kaggle Dataset mentioned here has it's own permission of use, it has not any relationship with this project.