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Student360

https://rrambhia22-student360-app-54gmno.streamlitapp.com/

Student 360 deals with analyzing the student performance based on the various external factors to determine the student dropout rate and predict the CGPA of the students.

The exploration and analysis of student dataset helps to predict the possible dropouts and determine the significant factors affecting the eligibility of students to complete the course.

The various data processing steps implemented are:

  1. Data Collection & Preparation
  2. Data Exploration (Exploratory Data Analysis)
  3. Data Cleaning
  4. Data Visualization
  5. Model Building (Predictive Analysis)
  6. Deployment

The machine learning models implemented are:

Classification models-

  1. Logistic Regression Model
  2. Decision Tree Classifier
  3. Random Forest Classifier
  4. SVM Linear Kernel
  5. KNN Classifier
  6. XGBoost Classifier

Regressor models-

  1. Linear Regression Model
  2. Decision Tree Regressor Model
  3. Random Forest Regressor Model

The developed system will be able to classify students into the eligible or not eligible category based on their overall performance and also be able to predict the CGPA of the BTech Course for the new students to understand whether the students will dropout from the course or not.