Placement Prediction using Machine Learning Model

Introduction

In this project, we have developed a placement prediction model using machine learning techniques. The goal of this model is to predict the likelihood of a student being placed in a job based on various features such as academic performance, skills, and other relevant attributes.

Model Development

We trained a Random Forest classifier using a dataset containing historical placement data of students. The dataset includes features such as:

• Academic performance (e.g., GPA, percentage)
• Internship experience
• Backlogs
• leetcode stats
• aptitude marks
• And more...

Steps Taken:

1. Data Preprocessing:

   • Handled missing values
   • Encoded categorical variables
   • Split the data into training and testing sets

2. Model Training:

   • Utilized Random Forest algorithm for classification
   • Tuned hyperparameters for optimal performance
   • Trained the model on the training data

3. Model Evaluation:

   • Ensured the model generalizes well on unseen data

4. Saving the Model:

   • Saved the trained Random Forest model to a file for future use

5. Saving the Scaler:

   • Saved the scaler used for feature scaling to ensure consistency during prediction

Deployment in a Website

To utilize the placement prediction model in a website for progress tracking and placement prediction, we can follow these steps:

1. Model Integration:

   • Load the saved Random Forest model and scaler in the web application backend.

2. Feature Collection:

   • Collect relevant information from the user such as academic performance, skills, internships, etc., through a user-friendly interface.

3. Data Preprocessing:

   • Preprocess the collected data by handling missing values, encoding categorical variables, and scaling features using the saved scaler.

4. Prediction:

   • Use the preprocessed data as input to the loaded Random Forest model to predict the probability of placement.

5. Display Results:

   • Display the prediction result (placement probability) to the user.
   • Provide additional insights or recommendations based on the prediction result to guide the user's progress tracking and career decisions.

Conclusion

By integrating the placement prediction model into a website, students can receive personalized guidance and insights regarding their placement prospects. This not only helps in progress tracking but also assists students in making informed decisions about their career paths.