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.

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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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Engineering placements predictions using Random Forest Classifier.

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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.

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

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.

End-to-end student placement prediction pipeline — MICE imputation on 40K noisy records, soft-voting ensemble (94.9% accuracy), Flask REST API, and branch-wise analysis across engineering disciplines.

Job Acceptance Prediction project using Machine Learning to analyze candidate data and predict placement outcomes. Includes data preprocessing, EDA, feature engineering, model building (Logistic Regression, Decision Tree, Random Forest), SQL integration, and an interactive Streamlit dashboard.

Professional Portfolio Website - AI Solutions Architect & Educator specializing in RAG architecture, prompt engineering, and LLM integration. Features projects in educational technology, machine learning systems, and AI-powered solutions for certification exam preparation and music ensemble placement prediction.

Developed a machine learning system using Random Forest to predict student placement status and salary from a custom dataset. Built a Streamlit web app for real-time predictions with data preprocessing, feature engineering, model training, evaluation, and deployment readiness.

An end-to-end machine learning project that predicts candidate job placement outcomes using academic, skill, and interview data. The project includes data cleaning, EDA, feature engineering, model training, and a Streamlit-based KPI dashboard for real-time predictions and recruitment insights.

Designed and developed an end-to-end ML-driven web application to predict student placement probability using ATS score and structured resume features. Built a Python-based resume parsing and feature engineering pipeline to transform uploaded PDFs into model ready datasets. Trained and validated a supervised machine learning model for prediction.