student placement package prediction by using regression analysis

The main aim of every academia enthusiast is placement in a reputed MNC’s and even the reputation and every year admission of Institute depends upon placement that it provides to their students. So, any system that will predict the placements of the students will be a positive impact on an institute and increase strength and decreases some workload of any institute’s training and placement office (TPO). With the help of Machine Learning techniques, the knowledge can be extracted from past placed students and placement of upcoming students can be predicted. Data used for training is taken from the same institute for which the placement prediction is done. Suitable data pre-processing methods are applied along with the feature selections. Some Domain expertise is used for pre-processing as well as for outliers that grab in the dataset. We have used various Machine Learning Algorithms like Logistic, SVM, KNN, Decision Tree, Random Forest and advance techniques like Bagging, Boosting and Voting Classifier Nowadays Placement plays an important role in this world full of unemployment. Even the ranking and rating of institutes depend upon the amount of average package and amount of placement they are providing. So basically, main objective of this model is to predict whether the student might get placement or not. Different kinds of classifiers were applied i.e., Logistic Regression, SVM, Decision Tree, Random Forest, KNN, AdaBoost, Gradient Boosting and XGBoost. For this all over academics of students are taken under consideration. As placements activity take place in last year of academics so last year semesters are not taken under consideration

No description available

This project demonstrates how to use Linear Regression to predict a student's placement package based on their CGPA. It involves data visualization, model training, and making predictions using the scikit-learn library in Python

This project demonstrates a Linear Regression model using Python to predict student placement packages based on CGPA. It uses Pandas and NumPy for data handling, Matplotlib for visualization, and scikit-learn for model training and prediction, making it a beginner-friendly ML project.