Project Overview

This project demonstrates the application of logistic regression to predict placement outcomes based on features such as CGPA and IQ scores. The goal is to build a predictive model that can accurately determine whether a student will be placed or not based on their attributes. 🎯

Steps

1. Preprocessing + EDA + Feature Selection 🧹

Load Data: Load the dataset and perform exploratory data analysis (EDA) to understand the data and select relevant features. 📊
Feature Selection: Choose input and output columns for the model. 🔍

2. Extract Input and Output Columns 🔄

Extract the features (X) and target variable (Y) from the dataset. 📋

3. Scale the Values 📏

Normalize feature values to ensure better performance and convergence of the model. 🚀

4. Train-Test Split 🔀

Split the data into training and testing sets to evaluate model performance. 🧪

5. Train the Model 🛠️

Implement and train the logistic regression model on the training data. 🎓

6. Evaluate the Model 📈

Assess the model's performance using metrics like accuracy. 🏅
Visualize the decision boundaries to understand model behavior. 🖼️

Day 9 - Placement Project: Logistic Regression📈

Table of contents