The deployment files are in the 'master' branch :)

Title:

Placement Predictor Using Machine Learning

Description:

This project leverages advanced machine learning algorithms, specifically Random Forest Classifier and Decision Tree, to predict the likelihood of student placement based on critical factors such as age, gender, CGPA, number of internships, and number of backlogs. By analyzing these variables, the project aims to provide actionable insights into the employability of students, facilitating informed career planning and decision-making. The project includes developing a user-friendly web application using Flask, which will be deployed on the Render platform to ensure broad accessibility.

Objective:

The primary objective of this project is to develop an accurate and reliable placement prediction model to assist students in understanding their placement prospects. Detailed objectives include:

1. Data Collection and Preprocessing:

   • Gather relevant data on student demographics and academic performance.
   • Preprocess the data to handle missing values, normalize features, and encode categorical variables.

2. Feature Selection and Model Building:

   • Identify the most significant predictors of student placements using exploratory data analysis and feature selection techniques.
   • Build a robust machine learning model using Random Forest Classifier and Decision Tree algorithms.
   • Perform hyperparameter tuning using Grid Search to optimize model performance.

3. Model Evaluation:

   • Evaluate model performance using classification metrics such as accuracy, precision, recall, and F1 score.
   • Achieve an accuracy score of 87% with the Random Forest Classifier.
   • Generate detailed classification reports to analyze model strengths and weaknesses.

4. Web Application Development:

   • Develop a Flask web application that allows users to input their details and receive placement predictions.
   • Implement user-friendly interfaces and validation to ensure a smooth user experience.

5. Deployment:

   • Deploy the Flask web application as a web service on the Render platform.
   • Ensure the web application is scalable, secure, and accessible to students, educators, and career advisors.

By achieving these objectives, the project aims to provide a practical tool for predicting student placements, thereby aiding educational institutions and students in enhancing their career planning processes.