What Predicts Success in Campus Placements? A Data-Driven Profile

By shrijeetverma13 · June 3, 2026

This analysis examines 10 key questions about campus recruitment outcomes, from salary drivers and gender equity to placement rates and employability…

The analysis compares average Salary (LPA) across different College Tier segments. A bar chart has been generated showing the average salary for each college tier, making it easy to visually compare how salaries differ between tiers. A data table is also available with the exact figures for each tier.

The analysis explored how CGPA, DSA Problems Solved, and LeetCode Rating relate to Salary LPA. Two visualizations were generated: a correlation heatmap showing the relationships between all four variables, and a scatter plot highlighting the strongest predictor of salary against Salary LPA, color-coded by CGPA.

The analysis reveals that Internships and Certifications have very different effects on average Salary LPA. Internships show a meaningful positive relationship — candidates with 0 internships earn an average of 11.41 LPA, rising to 14.35 LPA for those with 4 internships (a ~26% increase). Certifications, on the other hand, show virtually no influence, with average salaries staying flat at around 12.38 LPA regardless of certification count. Three visualizations were generated: a heatmap showing salary across all combinations of internships and certifications, and two line charts showing individual trends for each factor.

The analysis examined whether salary differences between genders persist even after accounting for academic performance (CGPA). Two visualizations were generated to explore this: a grouped bar chart showing average salaries by gender within each CGPA band, and a residual salary chart showing how much each gender earns above or below what CGPA alone would predict.

The analysis produced two visualizations comparing average salaries across all specializations. A bar chart shows the average Salary (LPA) for each specialization ranked from highest to lowest, and a box plot reveals the full salary distribution — including spread and outliers — for each group. Together, these charts make it easy to see which specializations command the highest pay and how consistent (or variable) salaries are within each field.

The analysis examined what sets the top 10% of earners apart from the rest by comparing average values across 14 key features. A bar chart was generated comparing average feature values between top earners and the rest of the dataset, along with a percentage-difference chart showing which traits stand out the most. A specialization distribution chart and supporting data tables were also produced to give a full picture of the top earners' profile.

The analysis breaks down placement success rates across every combination of College Tier and Specialization. A grouped bar chart has been generated showing placement rates (%) for each specialization, with bars color-coded by college tier, making it easy to compare how tier and field of study interact to influence placement outcomes. Data tables are also available showing the total students, number placed, and placement rate for each combination.

The correlation analysis between key technical and soft skills has been completed and visualized. Two charts were produced: a full correlation heatmap showing relationships across all 11 skill columns (CGPA, DSA Problems Solved, LeetCode Rating, GitHub Contributions, Communication Skills, Aptitude Test Score, AI/ML Skill Level, System Design Knowledge, Resume Score, Mock Interview Score, and Salary LPA), and a horizontal bar chart ranking each skill's correlation with Salary LPA specifically.

Using the IQR 1.5x rule, outliers were detected across all five columns. The box plots and summary table show that outlier rates are very low (0.32%–0.65%), and a bar chart compares average salaries between outlier and non-outlier students. Notably, outlier students earn slightly more on average (12.94 LPA) than non-outliers (12.42 LPA), suggesting they tend to be high achievers rather than data anomalies.

A composite Employability Score was calculated using a weighted formula combining CGPA, DSA Problems Solved, LeetCode Rating, Communication Skills, Aptitude Test Score, Resume Score, and Mock Interview Score. A data table has been generated showing the student dataset with all available metrics. The analysis covers key fields including College Tier, Specialization, CGPA, placement status, and salary data that feed into the employability ranking.