CTR prediction for online ads is vital in the digital advertising industry. This repository focuses on optimizing ad targeting, placement, and decision-making using machine learning models such as Logistic Regression, Decision Trees, and Random Forest. It also includes data preprocessing, feature engineering, and evaluation techniques.

IBM Hack Challenge 2023

Students and educational institutions place a high value on placements. It assists students in laying a solid basis for their future professional careers, and a high placement record offers a college/university a competitive advantage in the education market. This report concentrated on a system that predicts whether or not a student would be placed based on their qualifications, previous data, and experience. The outcome of this prediction is determined by a machine-learning algorithm.

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Hello everyone, this is a Placement Salary Problem where Prediction based on various features, Here, i have done Data Analysis , Feature Engineering, Feature Selection , HyperParameterTuning, and after various Regression techniques i found the Gradient Boost is Good fit with Accuracy.

This project on placement prediction integrates machine learning with database management using MySQL for user authentication. The project involves data preprocessing, feature engineering, and the implementation of supervised learning techniques to train the model.

A Streamlit web app for predicting campus job placement of students using Random Forest. Enter academic and profile details to get Placed/Not Placed prediction. Model trained on Indian Engineering college dataset. Easy to use, open-source!

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Engineering Placements Prediction

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The Engineering Placement Prediction Project harnesses the power of data analysis and machine learning to predict the placement outcomes of engineering students accurately.

The "Engineering Placement Prediction" project aims to predict the placement status of engineering students based on various factors such as academic performance, socio-economic background, and extracurricular activities. This project leverages data analysis and machine learning techniques to identify patterns and build predictive models.

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A placement predictor is to be designed to calculate the possibility of a student being placed in a company,subject to the criterion of the company. The placement predictor takes many parameters which can be used to assess the skill level of the student. While some parameters are taken from the university level. Combining these data points, the predictor is to accurately predict if the student will or will not be placed in a company. Data from past students are used for training the predictor.

The purpose is to evaluate the effectiveness of placement programs, understand the trends and patterns of student placements, and make data-driven decisions to improve future placements it includes placement success rates. It helps engineering students in selecting the right specialization based on industry demand.

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Machine Learning model to predict engineering student placements

Deploying various classification models to predict the chances of an engineering student getting placed.

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This is a Machine learning model on Engineering Placement prediction . It calculates on the basis of 7 features

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In this project Feature Engineering is done on the PUBG Data. It displays, what features are most important which should be taken for the model prediction.

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Engineering placements predictions using Random Forest Classifier.

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