Student Placement Prediction - Beginner ML Project This is a beginner-friendly machine learning project that analyzes and models a dataset related to student placements. The objective is to explore how factors like CGPA and IQ score influence placement outcomes and to build a predictive model using logistic regression.

What This Project Covers Exploratory Data Analysis (EDA): Understand the distribution and relationship between CGPA, IQ, and placement Visualize trends and correlations using plots

Data Preprocessing: Clean the dataset (removed index column) Ensure correct data types and handle missing values (none in this dataset)

Model Building (Logistic Regression): Built and trained a logistic regression model using scikit-learn Used CGPA and IQ score as predictors to estimate placement status

Dataset Summary The dataset placement.csv contains: cgpa: Student’s academic performance iq: Student’s IQ score placement: Binary value indicating placement status (1 = Placed, 0 = Not Placed)

Tools & Libraries Used Python Pandas, NumPy – for data manipulation Matplotlib, Seaborn – for visualization Scikit-learn – for modeling and evaluation

Goals Understand the basics of machine learning Learn how to clean and prepare a dataset Train and evaluate a simple classification model Gain insights from real-world educational data